From 329b859b0c4d0c40b8197e595e0072f5043f1db0 Mon Sep 17 00:00:00 2001 From: SHAHROKH DAIJAVAD Date: Fri, 10 Jan 2025 14:44:58 -0800 Subject: [PATCH] Deleting obsolete notebooks Deleting the archive folder in examples and also the language folder under fine-tuning examples --- examples/notebooks/archive/README.md | 4 - examples/notebooks/archive/code/README.md | 43 - .../archive/code/demo_with_apis.ipynb | 2345 ----------------- .../archive/code/demo_with_launcher.ipynb | 1338 ---------- .../archive/code/sample-notebook.ipynb | 869 ------ .../archive/code_developers/Makefile | 14 - .../archive/code_developers/README.md | 20 - .../code_developers/demo_with_launcher.ipynb | 1334 ---------- .../archive/code_developers/launch.sh | 25 - .../archive/code_developers/prepare_env.sh | 22 - .../archive/code_developers/requirements.txt | 0 .../notebooks/archive/language/.gitignore | 1 - examples/notebooks/archive/language/Makefile | 14 - examples/notebooks/archive/language/README.md | 20 - .../archive/language/demo_with_launcher.ipynb | 1274 --------- .../Granite%20Foundation%20Models.pdf | Bin 5661156 -> 0 bytes examples/notebooks/archive/language/launch.sh | 7 - .../notebooks/archive/language/output.zip | Bin 963223 -> 0 bytes .../notebooks/archive/language/prepare_env.sh | 38 - examples/notebooks/archive/language/utils.py | 4 - .../notebooks/fine tuning/language/.gitignore | 1 - .../notebooks/fine tuning/language/Makefile | 14 - .../notebooks/fine tuning/language/README.md | 20 - .../language/demo_with_launcher.ipynb | 1274 --------- .../Granite%20Foundation%20Models.pdf | Bin 5661156 -> 0 bytes .../notebooks/fine tuning/language/launch.sh | 7 - .../notebooks/fine tuning/language/output.zip | Bin 963223 -> 0 bytes .../fine tuning/language/prepare_env.sh | 38 - .../notebooks/fine tuning/language/utils.py | 4 - 29 files changed, 8730 deletions(-) delete mode 100644 examples/notebooks/archive/README.md delete mode 100644 examples/notebooks/archive/code/README.md delete mode 100644 examples/notebooks/archive/code/demo_with_apis.ipynb delete mode 100644 examples/notebooks/archive/code/demo_with_launcher.ipynb delete mode 100644 examples/notebooks/archive/code/sample-notebook.ipynb delete mode 100644 examples/notebooks/archive/code_developers/Makefile delete mode 100644 examples/notebooks/archive/code_developers/README.md delete mode 100644 examples/notebooks/archive/code_developers/demo_with_launcher.ipynb delete mode 100644 examples/notebooks/archive/code_developers/launch.sh delete mode 100644 examples/notebooks/archive/code_developers/prepare_env.sh delete mode 100644 examples/notebooks/archive/code_developers/requirements.txt delete mode 100644 examples/notebooks/archive/language/.gitignore delete mode 100644 examples/notebooks/archive/language/Makefile delete mode 100644 examples/notebooks/archive/language/README.md delete mode 100644 examples/notebooks/archive/language/demo_with_launcher.ipynb delete mode 100644 examples/notebooks/archive/language/input_data/Granite%20Foundation%20Models.pdf delete mode 100644 examples/notebooks/archive/language/launch.sh delete mode 100644 examples/notebooks/archive/language/output.zip delete mode 100644 examples/notebooks/archive/language/prepare_env.sh delete mode 100644 examples/notebooks/archive/language/utils.py delete mode 100644 examples/notebooks/fine tuning/language/.gitignore delete mode 100644 examples/notebooks/fine tuning/language/Makefile delete mode 100644 examples/notebooks/fine tuning/language/README.md delete mode 100644 examples/notebooks/fine tuning/language/demo_with_launcher.ipynb delete mode 100644 examples/notebooks/fine tuning/language/input_data/Granite%20Foundation%20Models.pdf delete mode 100644 examples/notebooks/fine tuning/language/launch.sh delete mode 100644 examples/notebooks/fine tuning/language/output.zip delete mode 100644 examples/notebooks/fine tuning/language/prepare_env.sh delete mode 100644 examples/notebooks/fine tuning/language/utils.py diff --git a/examples/notebooks/archive/README.md b/examples/notebooks/archive/README.md deleted file mode 100644 index 6510994fe..000000000 --- a/examples/notebooks/archive/README.md +++ /dev/null @@ -1,4 +0,0 @@ -# Data Prep Kit Examples - -* [Code](code) -* [Language](language) - coming soon. diff --git a/examples/notebooks/archive/code/README.md b/examples/notebooks/archive/code/README.md deleted file mode 100644 index 314b8c85d..000000000 --- a/examples/notebooks/archive/code/README.md +++ /dev/null @@ -1,43 +0,0 @@ -## Demo Notebook - -We can launch a jupyter notebook to try out the library or transforms or to build a usecase. - - -## Setup simple environment with Jupyter notebook - -1. Prepare virtual env - - -``` -python3 -m venv venv -source venv/bin/activate -python3 -m pip install jupyter -deactivate -``` - -2. After jupyter dependancies are installed, we can launch the jupyter notebook - - -``` -source venv/bin/activate -jupyter notebook -``` - - -It will launch browser with the jupyter notebook where we can try out the transforms. - -## Demo API notebook - -**Prepare virtualenv** - -`make venv` - -**Activate virtualenv** - -`. ./venv/bin/activate` - -**Run notebook** - -`jupyter notebook` - - diff --git a/examples/notebooks/archive/code/demo_with_apis.ipynb b/examples/notebooks/archive/code/demo_with_apis.ipynb deleted file mode 100644 index 499d9e373..000000000 --- a/examples/notebooks/archive/code/demo_with_apis.ipynb +++ /dev/null @@ -1,2345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "841e533d-ebb3-406d-9da7-b19e2c5f5866", - "metadata": {}, - "source": [ - "
\n", - "

Demo: Data Prep Kit

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "00be1755-e999-40c1-927f-7b662591ae6d", - "metadata": {}, - "source": [ - "## Overview\n", - "Welcome to the demo notebook! Inside, you will find an end-to-end sample data pipeline designed for processing code datasets, beginning with GitHub repositories (.zip files) and culminating in processed data. This notebook provides the following transforms for processing the data. \n", - "\n", - "- [Ingest2parquet](#item1)\n", - "- [Exact Dedup](#item2)\n", - "- [Doc_ID generation](#item3)\n", - "- [Fuzzy Dedup](#item4)\n", - "- [Programming Language Select](#item5)\n", - "- [Code quality](#item6)\n", - "- [Filtering](#item7)\n", - "- [Tokenization](#item8)\n", - "\n", - "### Getting started\n", - "\n", - "If you want to try this pipeline on your data, you need to download your github repositories, as .zip files. Please refer to steps below for the same. One can also try it on sample data by downloading a few repos of interest.\n", - "\n", - "Here's how to download a GitHub repository in ZIP format:\n", - "\n", - "1. Go to the desired repository on GitHub.\n", - "2. Click the \"Code\" button near the top right corner of the repository.\n", - "3. Click the \"Download ZIP\" button.\n", - "\n", - "This will download a ZIP archive of the entire repository to your computer.\n", - "\n", - "Follow these steps and download some repositories from github into a folder. Now your data is ready.\n", - "\n", - "The folder containing this data would serve as the input to the pipeline. Assign the path of this data folder to the variable `zip_input_folder` in the below cell. \n" - ] - }, - { - "cell_type": "markdown", - "id": "03c3570f-fe8b-466a-bb12-4e16640292f5", - "metadata": {}, - "source": [ - "### Import Common python modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "66178913-42b8-426b-a2e9-9587268fd05b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import ast\n", - "\n", - "from data_processing_ray.runtime.ray import execute_ray_transform\n", - "from data_processing.runtime.pure_python import execute_python_transform\n", - "from data_processing.utils import TransformsConfiguration, ParamsUtils, GB" - ] - }, - { - "cell_type": "markdown", - "id": "81fbc537-663a-4ca2-9b7d-6f6a16c9c8f4", - "metadata": {}, - "source": [ - "### To make a code smaller we will be using the same Ray environments for all transforms" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "15ef8914-f9f3-4994-88cf-79f24345062e", - "metadata": {}, - "outputs": [], - "source": [ - "worker_options = {\"num_cpus\": 0.8, \"memory\": 2 * GB}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "\n", - "runtime_ray_params = {\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - "}\n", - "runtime_python_params = {\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "72510ae6-48b0-4b88-9e13-a623281c3a63", - "metadata": {}, - "source": [ - "### Set input/output path variables for the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "60ac8bee-0960-4309-b225-d7a211b14262", - "metadata": {}, - "outputs": [], - "source": [ - "# Example\n", - "# We can set input paths here\n", - "zip_input_folder = \"input_data\"\n", - "\n", - "if not os.path.exists(zip_input_folder):\n", - " print (\"NO INPUT DATA\")\n", - " print (\"Please set `zip_input_folder` variable to path containing data\")\n", - "\n", - "# make sure the paths are correct\n", - "data_base_path = \"test-data\"\n", - "\n", - "parquet_data_output = os.path.join(data_base_path, \"parquet_input\")\n", - "\n", - "ededup_out = os.path.join(data_base_path, \"ededup_out\")\n", - "\n", - "doc_id_out = os.path.join(data_base_path, \"doc_id_out\")\n", - "fdedup_out = os.path.join(data_base_path, \"fdedup_out\")\n", - "\n", - "lang_out = os.path.join(data_base_path,\"lang_out\")\n", - "cq_out = os.path.join(data_base_path,\"cq_out\")\n", - "\n", - "filter_out = os.path.join(data_base_path ,\"filter_out\")\n", - "tokensization_out = os.path.join(data_base_path ,\"tokenization_out\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "d591278b-699f-418e-87ac-2e718b5b281e", - "metadata": {}, - "source": [ - "### Finally lets print the list of available transforms" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "34773843-4209-42d2-b390-71286eb76d11", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:50:34 INFO - loading from transforms configuration from /Users/borisl/Projects/data-prep-kit/data-processing-lib/python/src/data_processing/utils/transform_configuration.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['code2parquet', 'code_quality', 'malware', 'proglang_select', 'lang_id', 'doc_id', 'ededup', 'fdedup', 'filter', 'noop', 'profiler', 'resize', 'tokenization']\n" - ] - } - ], - "source": [ - "t_configuration = TransformsConfiguration()\n", - "transforms = t_configuration.get_available_transforms()\n", - "print(transforms)" - ] - }, - { - "cell_type": "markdown", - "id": "2e76725b-a5c3-400b-ad14-85c41594849e", - "metadata": {}, - "source": [ - "## 1. Convert data to parquet using ingest2parquet [<-](#top)_zip_ to _parquet_ Python transformer \n", - "\n", - "Raw code data files which are in zip format are converted to parquet files, where each row of the parquet file corresponds to a separate code file. Apart from the contents of the code file, every row also contains a unique document id, file URL, name of the repository, source of the data, date of acquisition and license of the repository. For every code file, a language field is also added, which is detected using the filename\n", - "extensions.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0c574c4-9dc4-4dab-9ad6-b5338207e67a", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "482605b2-d814-456d-9195-49a2ec454ef0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/input_data\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/parquet_input\n", - "/Users/borisl/Projects/data-prep-kit/transforms/code/code2parquet/python/test-data/languages/lang_extensions.json\n" - ] - } - ], - "source": [ - "# For this stage input folder contains the zip files, each zip file contains a github repo.\n", - "\n", - "input_folder = os.path.abspath(zip_input_folder)\n", - "output_folder = os.path.abspath(parquet_data_output)\n", - "supported_languages_file = os.path.abspath(\"../../../transforms/code/code2parquet/python/test-data/languages/lang_extensions.json\")\n", - "print(input_folder)\n", - "print(output_folder)\n", - "print(supported_languages_file)" - ] - }, - { - "cell_type": "markdown", - "id": "9bb15f02-ab5c-4525-a536-cfa1fd2ba70b", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b0cd8ebd-bf71-42d6-a397-8df0c7b66a26", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-zsc1hsbw/dpk-code2parquet-transform-python_b4ece9cd00d5447dae0162c1b5265d6b\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - 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"08:50:49 INFO - Completed 1 files (100.0%) in 0.10142393112182617 min\n", - "08:50:49 INFO - done flushing in 7.867813110351562e-06 sec\n", - "08:50:49 INFO - Completed execution in 0.10149399836858114 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_code2parquet_transform_python 0.2.1.dev0\n", - "Uninstalling dpk_code2parquet_transform_python-0.2.1.dev0:\n", - " Successfully uninstalled dpk_code2parquet_transform_python-0.2.1.dev0\n", - "Found existing installation: parameterized 0.9.0\n", - "Uninstalling parameterized-0.9.0:\n", - " Successfully uninstalled parameterized-0.9.0\n", - "Found existing installation: pandas 2.2.2\n", - "Uninstalling pandas-2.2.2:\n", - " Successfully uninstalled pandas-2.2.2\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create parameters\n", - "ingest_config = {\n", - " \"data_files_to_use\": ast.literal_eval(\"['.zip']\"), \n", - " \"code2parquet_supported_langs_file\": supported_languages_file,\n", - " \"code2parquet_detect_programming_lang\": True,\n", - "}\n", - "\n", - "execute_python_transform(\n", - " configuration = t_configuration,\n", - " name=\"code2parquet\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_python_params | ingest_config\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "3802fa4e-3eaa-45ff-b9eb-1be7daac1631", - "metadata": {}, - "source": [ - "## 2. Exact Dedup [<-](#top) Using Ray transform\n", - "\n", - "Remove documents having identical code to remove bias in the training data. On the content of each document, a SHA256 hash is computed,\n", - "followed by de-duplication of record having identical hashes." - ] - }, - { - "cell_type": "markdown", - "id": "5acfd3a2-a236-4143-bcfc-15804f1da7fe", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "58a0e1f6-ff53-40aa-96b1-096ade4bd1c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/parquet_input\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/ededup_out\n" - ] - } - ], - "source": [ - "## For this stage the input is the folder containing parquet data which is output from the ingest2parquet tool\n", - "\n", - "input_folder = output_folder\n", - "output_folder = os.path.abspath(ededup_out)\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "3661cb37-39c7-4b09-a784-925bfa9eaf1e", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a624b2b2-faad-4325-ac7d-53a840f564ef", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:50:50 WARNING - transform ededup for Python is not defined\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_ededup_transform_ray\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-tl1vq06t/dpk-ededup-transform-ray_5347ed0a09a24a2cbf307f1026352492\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-tl1vq06t/dpk-ededup-transform-ray_5347ed0a09a24a2cbf307f1026352492\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit-ray==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_ededup_transform_ray) (0.2.1.dev0)\n", - "Requirement already satisfied: mmh3==4.1.0 in ./venv/lib/python3.10/site-packages (from dpk_ededup_transform_ray) (4.1.0)\n", - "Collecting xxhash==3.4.1 (from dpk_ededup_transform_ray)\n", - " Using cached xxhash-3.4.1-cp310-cp310-macosx_11_0_arm64.whl.metadata (12 kB)\n", - "Collecting tqdm==4.66.3 (from dpk_ededup_transform_ray)\n", - 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"Building wheels for collected packages: dpk_ededup_transform_ray\n", - " Building wheel for dpk_ededup_transform_ray (pyproject.toml): started\n", - " Building wheel for dpk_ededup_transform_ray (pyproject.toml): finished with status 'done'\n", - " Created wheel for dpk_ededup_transform_ray: filename=dpk_ededup_transform_ray-0.2.1.dev0-py3-none-any.whl size=9782 sha256=57039d9e526c3d104ebed3909cab5fdf017086aeb573feb6cff3e2df9299f7d0\n", - " Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-dvgm1app/wheels/ad/18/1e/ec9ab33194e8e23b842d0fbf93d11695ce89eddc5a1d8bdeae\n", - "Successfully built dpk_ededup_transform_ray\n", - "Installing collected packages: argparse, xxhash, tqdm, dpk_ededup_transform_ray\n", - " Attempting uninstall: tqdm\n", - " Found existing installation: tqdm 4.66.4\n", - " Uninstalling tqdm-4.66.4:\n", - " Successfully uninstalled tqdm-4.66.4\n", - "Successfully installed argparse-1.4.0 dpk_ededup_transform_ray-0.2.1.dev0 tqdm-4.66.3 xxhash-3.4.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:50:59 INFO - Using local data\n", - "08:50:59 INFO - Running locally\n", - "08:50:59 INFO - exact dedup params are {'hash_cpu': 0.5, 'num_hashes': 2, 'doc_column': 'contents'}\n", - "08:50:59 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/parquet_input output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/ededup_out\n", - "08:50:59 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:50:59 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:50:59 INFO - pipeline id pipeline_id\n", - "08:50:59 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:50:59 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1}\n", - "08:50:59 INFO - actor creation delay 0\n", - "08:50:59 INFO - job details {'job category': 'preprocessing', 'job name': 'ededup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-22 08:51:04,368\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:05 INFO - orchestrator started at 2024-07-22 08:51:05\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:05 INFO - Number of files is 1, source profile {'max_file_size': 0.7287473678588867, 'min_file_size': 0.7287473678588867, 'total_file_size': 0.7287473678588867}\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:05 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 14.355607605539262, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:05 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:05 INFO - Completed 0 files (0.0%) in 2.368291219075521e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:06 INFO - Completed processing 1 files in 0.028407434622446697 min\n", - "\u001b[36m(orchestrate pid=88132)\u001b[0m 08:51:06 INFO - done flushing in 0.0011510848999023438 sec\n", - "08:51:16 INFO - Completed execution in 0.29611429770787556 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_ededup_transform_ray 0.2.1.dev0\n", - "Uninstalling dpk_ededup_transform_ray-0.2.1.dev0:\n", - " Successfully uninstalled dpk_ededup_transform_ray-0.2.1.dev0\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Prepare the commandline params\n", - "ededup_config = {\n", - " \"ededup_hash_cpu\": 0.5,\n", - " \"ededup_num_hashes\": 2,\n", - " \"ededup_doc_column\": \"contents\",\n", - "}\n", - "\n", - "execute_ray_transform(\n", - " configuration = t_configuration,\n", - " name=\"ededup\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_ray_params | ededup_config\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "4234b150-5f9b-4b89-961e-7097400353d6", - "metadata": {}, - "source": [ - "## 3. DOC ID generation [<-](#top) Ray Transform\n", - "\n", - "This transform annotates documents with document \"ids\". It supports the following transformations of the original data:\n", - "\n", - " - Adding document hash: this enables the addition of a document hash-based id to the data. The hash is calculated with `hashlib.sha256(doc.encode(\"utf-8\")).hexdigest()`. To enable this annotation, set hash_column to the name of the column, where you want to store it.\n", - " - Adding integer document id: this allows the addition of an integer document id to the data that is unique across all rows in all tables provided to the transform() method. To enable this annotation, set int_id_column to the name of the column, where you want to store it. **This is a pre-requisite for fuzzy dedup** in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e6f62394-fbde-495c-bbbb-83161b006bed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/ededup_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/doc_id_out\n" - ] - } - ], - "source": [ - "# Input for this stage is the output of exact dedeup component\n", - "# output of this component makes it possible for fdedup component to run on data.\n", - "\n", - "input_folder = output_folder\n", - "output_folder = os.path.abspath(doc_id_out)\n", - "\n", - "print(input_folder)\n", - "print(output_folder)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a6daf36d-686c-4e0a-aabf-ce55f999bb2d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:51:18 WARNING - transform doc_id for Python is not defined\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_docid_transform_ray\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-8i2caoqz/dpk-docid-transform-ray_895e34ab2a6d46ceb5980d4b48fa8525\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-8i2caoqz/dpk-docid-transform-ray_895e34ab2a6d46ceb5980d4b48fa8525\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit-ray==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_docid_transform_ray) (0.2.1.dev0)\n", - "Requirement already satisfied: data-prep-toolkit==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit-ray==0.2.1.dev0->dpk_docid_transform_ray) (0.2.1.dev0)\n", - "Requirement already satisfied: ray==2.24.0 in ./venv/lib/python3.10/site-packages (from ray[default]==2.24.0->data-prep-toolkit-ray==0.2.1.dev0->dpk_docid_transform_ray) (2.24.0)\n", - 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" Building wheel for dpk_docid_transform_ray (pyproject.toml): finished with status 'done'\n", - " Created wheel for dpk_docid_transform_ray: filename=dpk_docid_transform_ray-0.2.1.dev0-py3-none-any.whl size=9817 sha256=a30797aea220b6508b0c22f4da7f321bba7174448588cec0b45cc3ce54184570\n", - " Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-5nupdmkg/wheels/5b/80/4c/f9b2ad453cd0af87fcdf24b82ed3070834d121ff5ac86ce316\n", - "Successfully built dpk_docid_transform_ray\n", - "Installing collected packages: argparse, dpk_docid_transform_ray\n", - "Successfully installed argparse-1.4.0 dpk_docid_transform_ray-0.2.1.dev0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:51:27 INFO - Using local data\n", - "08:51:27 INFO - Running locally\n", - "08:51:27 INFO - Doc id parameters are : {'doc_column': 'contents', 'hash_column': 'hash_column', 'int_column': 'int_id_column'}\n", - "08:51:27 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/ededup_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/doc_id_out\n", - "08:51:27 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:51:27 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:51:27 INFO - pipeline id pipeline_id\n", - "08:51:27 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:51:27 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1}\n", - "08:51:27 INFO - actor creation delay 0\n", - "08:51:27 INFO - job details {'job category': 'preprocessing', 'job name': 'doc_id', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-22 08:51:28,647\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - orchestrator started at 2024-07-22 08:51:29\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - Number of files is 1, source profile {'max_file_size': 0.7212724685668945, 'min_file_size': 0.7212724685668945, 'total_file_size': 0.7212724685668945}\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 14.992045593447983, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - Completed 0 files (0.0%) in 1.903374989827474e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - Completed processing 1 files in 0.007911535104115804 min\n", - "\u001b[36m(orchestrate pid=88192)\u001b[0m 08:51:29 INFO - done flushing in 0.001026153564453125 sec\n", - "08:51:39 INFO - Completed execution in 0.21493083238601685 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_docid_transform_ray 0.2.1.dev0\n", - "Uninstalling dpk_docid_transform_ray-0.2.1.dev0:\n", - " Successfully uninstalled dpk_docid_transform_ray-0.2.1.dev0\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "docid_config = {\n", - " \"doc_id_doc_column\": \"contents\",\n", - " \"doc_id_hash_column\": \"hash_column\",\n", - " \"doc_id_int_column\": \"int_id_column\",\n", - "}\n", - "execute_ray_transform(\n", - " configuration = t_configuration,\n", - " name=\"doc_id\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_ray_params | docid_config\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b995d859-9bfd-4693-b50d-9901582629c9", - "metadata": {}, - "source": [ - "## 4. Fuzzy Dedup [<-](#top)Ray transform \n", - "\n", - "Post exact deduplication, fuzzy deduplication is applied with\n", - "the goal of removing code files that may have slight variations and thereby unbiasing\n", - "the data further. Small variations are quite commonly seen in code data in the form\n", - "of variations in the values of variables, addittion of logging statements etc. Find near-\n", - "duplicate." - ] - }, - { - "cell_type": "markdown", - "id": "fcf574a3-b287-419c-9c86-07b828b41ca6", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9e431c8c-c7c7-48de-ba5f-2c4649c35399", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/doc_id_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/fdedup_out\n" - ] - } - ], - "source": [ - "## Input to this component is the output of doc_id generator component. \n", - "\n", - "input_folder = output_folder\n", - "output_folder = os.path.abspath(fdedup_out)\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "f4c82a8f-b513-4fe5-b172-d41b104b54f3", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3864ff77-e9a8-48f7-973b-c3b3aef1a94f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:51:41 WARNING - transform fdedup for Python is not defined\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_fdedup_transform_ray\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-4sze1ap_/dpk-fdedup-transform-ray_64bce67154264ce894ca948e5265255d\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-4sze1ap_/dpk-fdedup-transform-ray_64bce67154264ce894ca948e5265255d\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit-ray==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_fdedup_transform_ray) (0.2.1.dev0)\n", - "Requirement already satisfied: mmh3==4.1.0 in ./venv/lib/python3.10/site-packages (from dpk_fdedup_transform_ray) (4.1.0)\n", - "Requirement already satisfied: xxhash==3.4.1 in ./venv/lib/python3.10/site-packages (from dpk_fdedup_transform_ray) (3.4.1)\n", - "Requirement already satisfied: tqdm==4.66.3 in ./venv/lib/python3.10/site-packages (from dpk_fdedup_transform_ray) (4.66.3)\n", - 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" Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-au4mnb5e/wheels/10/fd/9c/2dc669bfbbb56322ecc31a537af07251168787edd1fc03dc60\n", - "Successfully built dpk_fdedup_transform_ray\n", - "Installing collected packages: argparse, scipy, dpk_fdedup_transform_ray\n", - " Attempting uninstall: scipy\n", - " Found existing installation: scipy 1.14.0\n", - " Uninstalling scipy-1.14.0:\n", - " Successfully uninstalled scipy-1.14.0\n", - "Successfully installed argparse-1.4.0 dpk_fdedup_transform_ray-0.2.1.dev0 scipy-1.12.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:51:52 INFO - Using local data\n", - "08:52:00 INFO - Running locally\n", - "08:52:00 INFO - fuzzy dedup params are {'doc_column': 'contents', 'id_column': 'int_id_column', 'cluster_column': 'hash_column', 'bucket_cpu': 0.5, 'mhash_cpu': 0.5, 'doc_cpu': 0.5, 'num_doc_actors': 2, 'num_minhash_actors': 1, 'num_bucket_actors': 1, 'num_preprocessors': 2, 'num_permutations': 64, 'threshold': 0.8, 'shingles_size': 5, 'delimiters': ' ', 'snapshot_delay': 1, 'use_bucket_snapshot': False, 'use_doc_snapshot': False, 'random_delay_limit': 10, 'worker_options': {'num_cpus': 0.8, 'memory': 2147483648}}\n", - "08:52:00 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/doc_id_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/fdedup_out\n", - "08:52:00 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:52:00 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:52:00 INFO - pipeline id pipeline_id\n", - "08:52:00 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:52:00 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1}\n", - "08:52:00 INFO - actor creation delay 0\n", - "08:52:00 INFO - job details {'job category': 'preprocessing', 'job name': 'fdedup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-22 08:52:01,728\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - orchestrator started at 2024-07-22 08:52:02\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Number of files is 1, source profile {'max_file_size': 0.7627429962158203, 'min_file_size': 0.7627429962158203, 'total_file_size': 0.7627429962158203}\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 14.878721619024873, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - starting run from the beginning\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - continuing from the very beginning\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Fuzzy: num buckets 5, bucket length 11\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - created 1 bucket actors\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - created 1 minhash actors\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Table preprocessing uses 2 readers\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - created 2 table processor actors\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:02 INFO - Completed 0 files (0.0%) in 2.2371610005696613e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:11 INFO - Completed processing 1 files in 0.147070050239563 min\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:11 INFO - creating minhash snapshots\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:12 INFO - minhash snapshots created\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:12 INFO - creating bucket snapshots\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - bucket snapshots created\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - created 2 document actors\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - created 2 bucket processor actors\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - created bucket processor invoker\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - added invoker to bucket collectors\n", - "\u001b[36m(BucketsHash pid=88332)\u001b[0m 08:52:13 INFO - processing buckets 0 long, 2654 short\n", - "\u001b[36m(BucketsHash pid=88332)\u001b[0m 08:52:13 INFO - Done submitting long buckets\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - Done processing buckets in 0.008832867940266926 min\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:13 INFO - creating document snapshots\n", - "\u001b[36m(BucketsHashProcessorInvoker pid=88348)\u001b[0m 08:52:13 INFO - Waiting bucket processing completion. Submitted requests 27\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:15 INFO - document snapshots created\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:15 INFO - Completed 0 files (0.0%) in 4.613399505615235e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:24 INFO - Completed processing 1 files in 0.1418306310971578 min\n", - "\u001b[36m(orchestrate pid=88322)\u001b[0m 08:52:24 INFO - done flushing in 0.0031850337982177734 sec\n", - "08:52:34 INFO - Completed execution in 0.5729915340741475 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_fdedup_transform_ray 0.2.1.dev0\n", - "Uninstalling dpk_fdedup_transform_ray-0.2.1.dev0:\n", - " Successfully uninstalled dpk_fdedup_transform_ray-0.2.1.dev0\n", - "Found existing installation: xxhash 3.4.1\n", - "Uninstalling xxhash-3.4.1:\n", - " Successfully uninstalled xxhash-3.4.1\n", - "Found existing installation: tqdm 4.66.3\n", - "Uninstalling tqdm-4.66.3:\n", - " Successfully uninstalled tqdm-4.66.3\n", - "Found existing installation: scipy 1.12.0\n", - "Uninstalling scipy-1.12.0:\n", - " Successfully uninstalled scipy-1.12.0\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create parameters\n", - "fuzzy_config = {\n", - " # columns used\n", - " \"fdedup_doc_column\": \"contents\",\n", - " \"fdedup_id_column\": \"int_id_column\",\n", - " \"fdedup_cluster_column\": \"hash_column\",\n", - " # infrastructure\n", - " \"fdedup_bucket_cpu\": 0.5,\n", - " \"fdedup_doc_cpu\": 0.5,\n", - " \"fdedup_mhash_cpu\": 0.5,\n", - " \"fdedup_num_doc_actors\": 2,\n", - " \"fdedup_num_bucket_actors\": 1,\n", - " \"fdedup_num_minhash_actors\": 1,\n", - " \"fdedup_num_preprocessors\": 2,\n", - " # fuzzy parameters\n", - " \"fdedup_num_permutations\": 64,\n", - " \"fdedup_threshold\": 0.8,\n", - " \"fdedup_shingles_size\": 5,\n", - " \"fdedup_delimiters\": \" \"\n", - "}\n", - "\n", - "execute_ray_transform(\n", - " configuration = t_configuration,\n", - " name=\"fdedup\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_ray_params | fuzzy_config\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "3f87c8ec-a0fc-422a-8d35-96f2e52a47cb", - "metadata": {}, - "source": [ - "## 5. Programming language annotation [<-](#top) Python transform \n", - "\n", - "The raw data may contains many programming languages. Of this, we would wish to retain a prioritised list of selected programming languages. This component takes a file which has new line separated names of languages we need to select. It annotates the data a new column with boolean values. This column can be used by filter component to select the required languages." - ] - }, - { - "cell_type": "markdown", - "id": "3db05e1e-4c62-4367-93ca-b2ddff95e4b4", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a8ec4fb6-fa62-45d1-9aa1-596d7182b2c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/fdedup_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/lang_out\n", - "/Users/borisl/Projects/data-prep-kit/transforms/code/proglang_select/python/test-data/languages/allowed-code-languages.txt\n" - ] - } - ], - "source": [ - "\n", - "input_folder = output_folder\n", - "output_folder = os.path.abspath(lang_out) \n", - "selected_languages_file = os.path.abspath(\"../../../transforms/code/proglang_select/python/test-data/languages/allowed-code-languages.txt\")\n", - "print(input_folder)\n", - "print(output_folder)\n", - "print(selected_languages_file)" - ] - }, - { - "cell_type": "markdown", - "id": "c07e7e5a-064f-4dca-a017-4211f7a3e980", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "48dbb2a3-a6f4-4a3d-bb2f-8491fd063611", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_proglang_select_transform_python\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-bbswfpzr/dpk-proglang-select-transform-python_7bd5e7f6636e4b44b04628ca9e316f03\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-bbswfpzr/dpk-proglang-select-transform-python_7bd5e7f6636e4b44b04628ca9e316f03\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_proglang_select_transform_python) (0.2.1.dev0)\n", - "Requirement already satisfied: numpy<1.29.0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (1.26.4)\n", - "Requirement already satisfied: pyarrow==16.1.0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (16.1.0)\n", - "Requirement already satisfied: boto3==1.34.69 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (1.34.69)\n", - "Collecting argparse (from data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python)\n", - " Using cached argparse-1.4.0-py2.py3-none-any.whl.metadata (2.8 kB)\n", - "Requirement already satisfied: mmh3 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (4.1.0)\n", - "Requirement already satisfied: botocore<1.35.0,>=1.34.69 in ./venv/lib/python3.10/site-packages (from boto3==1.34.69->data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (1.34.145)\n", - "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in ./venv/lib/python3.10/site-packages (from boto3==1.34.69->data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (1.0.1)\n", - "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in ./venv/lib/python3.10/site-packages (from boto3==1.34.69->data-prep-toolkit==0.2.1.dev0->dpk_proglang_select_transform_python) (0.10.2)\n", - 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" Created wheel for dpk_proglang_select_transform_python: filename=dpk_proglang_select_transform_python-0.2.1.dev0-py3-none-any.whl size=8470 sha256=7f5b1ef0876440c08ec62e73d71539cb660b5ae46f0b10d8061e09585bbfe51a\n", - " Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-12csut66/wheels/c1/26/79/c962d5ecc219b42e8fd695aec8b0183f81191f3f99104fc1df\n", - "Successfully built dpk_proglang_select_transform_python\n", - "Installing collected packages: argparse, dpk_proglang_select_transform_python\n", - "Successfully installed argparse-1.4.0 dpk_proglang_select_transform_python-0.2.1.dev0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:52:44 INFO - Using local data\n", - "08:52:44 INFO - data factory proglang_select_ is using local configuration without input/output path\n", - "08:52:44 INFO - data factory proglang_select_ max_files -1, n_sample -1\n", - "08:52:44 INFO - data factory proglang_select_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:52:44 INFO - pipeline id pipeline_id\n", - "08:52:44 INFO - job details {'job category': 'preprocessing', 'job name': 'proglang_select', 'job type': 'pure python', 'job id': 'job_id'}\n", - "08:52:44 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:52:44 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/fdedup_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/lang_out\n", - "08:52:44 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:52:44 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:52:44 INFO - orchestrator proglang_select started at 2024-07-22 08:52:44\n", - "08:52:44 INFO - Number of files is 1, source profile {'max_file_size': 0.6529102325439453, 'min_file_size': 0.6529102325439453, 'total_file_size': 0.6529102325439453}\n", - "08:52:44 INFO - Getting supported languages from file /Users/borisl/Projects/data-prep-kit/transforms/code/proglang_select/python/test-data/languages/allowed-code-languages.txt\n", - "08:52:44 INFO - Supported languages b'Java\\nC\\nGo\\nABAP\\n'\n", - "08:52:44 INFO - Completed 1 files (100.0%) in 0.0002715468406677246 min\n", - "08:52:44 INFO - done flushing in 9.059906005859375e-06 sec\n", - "08:52:44 INFO - Completed execution in 0.0003596345583597819 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_proglang_select_transform_python 0.2.1.dev0\n", - "Uninstalling dpk_proglang_select_transform_python-0.2.1.dev0:\n", - " Successfully uninstalled dpk_proglang_select_transform_python-0.2.1.dev0\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create parameters\n", - "langselect_config = {\n", - " \"proglang_select_allowed_langs_file\": selected_languages_file,\n", - " \"proglang_select_language_column\": \"programming_language\",\n", - " \"proglang_select_output_column\": \"lang_selected\",\n", - "}\n", - "\n", - "execute_python_transform(\n", - " configuration = t_configuration,\n", - " name=\"proglang_select\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_python_params | langselect_config\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "7b9cee43-0533-4201-ba5b-418d47da258d", - "metadata": {}, - "source": [ - "## 6. Code Quality [<-](#top) Python Transform\n", - "\n", - "We experiment with various code quality metrics but finally retain\n", - "the four code quality metrics used by (Li et al., 2023) to balance the tradeoff between\n", - "code quality versus data volume. \n" - ] - }, - { - "cell_type": "markdown", - "id": "2e985668-848b-4633-b0d8-9fe70ada0c91", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9f080011-c9fe-430e-9ecc-f2220d2c8d18", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/lang_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/cq_out\n" - ] - } - ], - "source": [ - "input_folder = output_folder\n", - "output_folder = os.path.abspath(cq_out)\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "c02982c5-f398-4a1a-a9fe-42d7ae748c7c", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "29319fb9-b0d8-4f86-9bc5-b92960ad8ae5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_code_quality_transform_python\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-_jfhfvkg/dpk-code-quality-transform-python_134fff8b71ce48bab5517892c8cfbdde\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-_jfhfvkg/dpk-code-quality-transform-python_134fff8b71ce48bab5517892c8cfbdde\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_code_quality_transform_python) (0.2.1.dev0)\n", - "Collecting bs4==0.0.2 (from dpk_code_quality_transform_python)\n", - " Using cached bs4-0.0.2-py2.py3-none-any.whl.metadata (411 bytes)\n", - "Collecting transformers==4.38.2 (from dpk_code_quality_transform_python)\n", - " Using cached transformers-4.38.2-py3-none-any.whl.metadata (130 kB)\n", - 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" Building wheel for dpk_code_quality_transform_python (pyproject.toml): started\n", - " Building wheel for dpk_code_quality_transform_python (pyproject.toml): finished with status 'done'\n", - " Created wheel for dpk_code_quality_transform_python: filename=dpk_code_quality_transform_python-0.2.1.dev0-py3-none-any.whl size=8993 sha256=e947e292544424cc1ce0df9f5f56524ea4f311f39c2361a4eadecefb5b54166b\n", - " Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-gdqng__2/wheels/a2/4c/fa/87cebb0cf43cbfcefaab615d241431b731ddd3bc66f33c4843\n", - "Successfully built dpk_code_quality_transform_python\n", - "Installing collected packages: argparse, tqdm, bs4, tokenizers, transformers, dpk_code_quality_transform_python\n", - " Attempting uninstall: tokenizers\n", - " Found existing installation: tokenizers 0.19.1\n", - " Uninstalling tokenizers-0.19.1:\n", - " Successfully uninstalled tokenizers-0.19.1\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.42.4\n", - " Uninstalling transformers-4.42.4:\n", - " Successfully uninstalled transformers-4.42.4\n", - "Successfully installed argparse-1.4.0 bs4-0.0.2 dpk_code_quality_transform_python-0.2.1.dev0 tokenizers-0.15.2 tqdm-4.66.4 transformers-4.38.2\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:52:57 INFO - Using local data\n", - "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n", - "08:52:57 INFO - pipeline id pipeline_id\n", - "08:52:57 INFO - job details {'job category': 'preprocessing', 'job name': 'code_quality', 'job type': 'pure python', 'job id': 'job_id'}\n", - "08:52:57 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:52:57 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/lang_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/cq_out\n", - "08:52:57 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:52:57 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:52:57 INFO - orchestrator code_quality started at 2024-07-22 08:52:57\n", - "08:52:57 INFO - Number of files is 1, source profile {'max_file_size': 0.6531686782836914, 'min_file_size': 0.6531686782836914, 'total_file_size': 0.6531686782836914}\n", - "Token indices sequence length is longer than the specified maximum sequence length for this model (1676 > 1024). Running this sequence through the model will result in indexing errors\n", - "08:52:58 INFO - Completed 1 files (100.0%) in 0.01030890146891276 min\n", - "08:52:58 INFO - done flushing in 2.002716064453125e-05 sec\n", - "08:52:58 INFO - Completed execution in 0.016517980893452962 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_code_quality_transform_python 0.2.1.dev0\n", - "Uninstalling dpk_code_quality_transform_python-0.2.1.dev0:\n", - " Successfully uninstalled dpk_code_quality_transform_python-0.2.1.dev0\n", - "Found existing installation: bs4 0.0.2\n", - "Uninstalling bs4-0.0.2:\n", - " Successfully uninstalled bs4-0.0.2\n", - "Found existing installation: transformers 4.38.2\n", - "Uninstalling transformers-4.38.2:\n", - " Successfully uninstalled transformers-4.38.2\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cq_config = {\n", - " \"cq_contents_column_name\": \"contents\",\n", - " \"cq_language_column_name\": \"programming_language\",\n", - "}\n", - "\n", - "execute_python_transform(\n", - " configuration = t_configuration,\n", - " name=\"code_quality\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_python_params | cq_config\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "ccf468ee-524f-4d54-a207-d0674e880f85", - "metadata": {}, - "source": [ - "## 7. Filtering [<-](#top) Python Transform\n", - "\n", - "Filter out documents that do not meet the quality threshold for each annotation. The thresholds are computed based on a distributional\n", - "analysis as well as manual inspection of samples maintaining the balance between data quality and data volume" - ] - }, - { - "cell_type": "markdown", - "id": "78c54d69-8aee-4f0f-b74c-35dc0609270f", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a7991811-b19e-43b5-89ac-b24060c0ccfa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/cq_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/filter_out\n" - ] - } - ], - "source": [ - "input_folder = output_folder\n", - "output_folder = os.path.abspath(filter_out)\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "c460e05c-aeee-4b53-9dd5-8dfa1afc0ece", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "61dea2b0-0e54-4912-8620-886e2b8420ef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_filter_transform_python\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-jtqmffzs/dpk-filter-transform-python_c0b9e4d76e594c6aa2ef1b109d701350\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-jtqmffzs/dpk-filter-transform-python_c0b9e4d76e594c6aa2ef1b109d701350\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_filter_transform_python) (0.2.1.dev0)\n", - "Collecting duckdb==0.10.1 (from dpk_filter_transform_python)\n", - " Using cached duckdb-0.10.1-cp310-cp310-macosx_11_0_arm64.whl.metadata (763 bytes)\n", - "Requirement already satisfied: numpy<1.29.0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_filter_transform_python) (1.26.4)\n", - "Requirement already satisfied: pyarrow==16.1.0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_filter_transform_python) (16.1.0)\n", - 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" Created wheel for dpk_filter_transform_python: filename=dpk_filter_transform_python-0.2.1.dev0-py3-none-any.whl size=14352 sha256=d4203dda068d9c2139e8a0d82f517da64aa3a2d997080d0c76a953397131c2c7\n", - " Stored in directory: /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-ephem-wheel-cache-9prwhlvd/wheels/2a/dc/88/f8aeac7d6ad2fc5b5aa5eecc611943408746ad39e681c7636e\n", - "Successfully built dpk_filter_transform_python\n", - "Installing collected packages: argparse, duckdb, dpk_filter_transform_python\n", - " Attempting uninstall: duckdb\n", - " Found existing installation: duckdb 1.0.0\n", - " Uninstalling duckdb-1.0.0:\n", - " Successfully uninstalled duckdb-1.0.0\n", - "Successfully installed argparse-1.4.0 dpk_filter_transform_python-0.2.1.dev0 duckdb-0.10.1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:53:08 INFO - Using local data\n", - "08:53:09 INFO - pipeline id pipeline_id\n", - "08:53:09 INFO - job details {'job category': 'preprocessing', 'job name': 'filter', 'job type': 'pure python', 'job id': 'job_id'}\n", - "08:53:09 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:53:09 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/cq_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/filter_out\n", - "08:53:09 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:53:09 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:53:09 INFO - orchestrator filter started at 2024-07-22 08:53:09\n", - "08:53:09 INFO - Number of files is 1, source profile {'max_file_size': 0.6735477447509766, 'min_file_size': 0.6735477447509766, 'total_file_size': 0.6735477447509766}\n", - "08:53:09 INFO - Completed 1 files (100.0%) in 0.00015051762262980144 min\n", - "08:53:09 INFO - done flushing in 7.867813110351562e-06 sec\n", - "08:53:09 INFO - Completed execution in 0.0001826167106628418 min, execution result 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: dpk_filter_transform_python 0.2.1.dev0\n", - "Uninstalling dpk_filter_transform_python-0.2.1.dev0:\n", - " Successfully uninstalled dpk_filter_transform_python-0.2.1.dev0\n", - "Found existing installation: duckdb 0.10.1\n", - "Uninstalling duckdb-0.10.1:\n", - " Successfully uninstalled duckdb-0.10.1\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# This is just an example criteria to filter\n", - "filter_criteria = [\n", - " \"total_num_lines > 10 AND total_num_lines < 90\",\n", - " \"lang_selected = 1\",\n", - "]\n", - "filter_logical_operator = \"AND\"\n", - "filter_columns_to_drop = [\"lang_selected\", \"hash_column\"]\n", - "\n", - "filter_config = {\n", - " \"filter_criteria_list\": filter_criteria,\n", - " \"filter_columns_to_drop\": filter_columns_to_drop,\n", - " \"filter_logical_operator\": filter_logical_operator,\n", - "}\n", - "\n", - "execute_python_transform(\n", - " configuration = t_configuration,\n", - " name=\"filter\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_python_params | filter_config\n", - ") " - ] - }, - { - "cell_type": "markdown", - "id": "7256aebf-57fb-4d31-ae0c-2c880f113157", - "metadata": {}, - "source": [ - "## 8. Tokenization [<-](#top) Python transform\n", - "\n", - "The data tokenization transform maps a (non-empty) input table to an output table using a pre-trained tokenizer. The input table must contain at least two columns, by default named document_id and contents. The tokenization transform utilizes the pre-trained tokenizer to tokenize each row (assuming a document) in the input table to each row in the output folder.\n", - "\n", - "A pre-trained tokenizer must be specified through the --tkn_tokenizer parameter, which can be the name of a ready-for-download tokenizer from HuggingFace such as hf-internal-testing/llama-tokenizer, bigcode/starcoder or any others that can loaded by the Huggingface AutoTokenizer library. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "20a153fa-fd56-401e-86be-4f7617affcc8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/filter_out\n", - "/Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/tokenization_out\n" - ] - } - ], - "source": [ - "input_folder = output_folder\n", - "output_folder = os.path.abspath(tokensization_out)\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "228df6b2-bc62-494b-9697-03ece98d7853", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://blublinsky%40ibm.com:****@na.artifactory.swg-devops.com/artifactory/api/pypi/res-data-engineering-team-pypi-local/simple\n", - "Collecting dpk_tokenization_transform_python\n", - " Cloning https://github.com/IBM/data-prep-kit.git to /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-ph8qsu3y/dpk-tokenization-transform-python_46ce6bf92cfe45a3b64ac11a29af82db\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " Running command git clone --filter=blob:none --quiet https://github.com/IBM/data-prep-kit.git /private/var/folders/7l/54q_29q57dv5vwgqw0h3btlm0000gn/T/pip-install-ph8qsu3y/dpk-tokenization-transform-python_46ce6bf92cfe45a3b64ac11a29af82db\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Resolved https://github.com/IBM/data-prep-kit.git to commit 29e83ed88c942317bdf17f4934d2847b6fc8d1fc\n", - " Installing build dependencies: started\n", - " Installing build dependencies: finished with status 'done'\n", - " Getting requirements to build wheel: started\n", - " Getting requirements to build wheel: finished with status 'done'\n", - " Preparing metadata (pyproject.toml): started\n", - " Preparing metadata (pyproject.toml): finished with status 'done'\n", - "Requirement already satisfied: data-prep-toolkit==0.2.1.dev0 in ./venv/lib/python3.10/site-packages (from dpk_tokenization_transform_python) (0.2.1.dev0)\n", - "Collecting transformers==4.38.0 (from dpk_tokenization_transform_python)\n", - " Using cached transformers-4.38.0-py3-none-any.whl.metadata (131 kB)\n", - "Requirement already satisfied: numpy<1.29.0 in ./venv/lib/python3.10/site-packages (from data-prep-toolkit==0.2.1.dev0->dpk_tokenization_transform_python) (1.26.4)\n", - 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] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "08:53:20 INFO - Using local data\n", - "08:53:20 INFO - pipeline id pipeline_id\n", - "08:53:20 INFO - job details {'job category': 'preprocessing', 'job name': 'Tokenization', 'job type': 'pure python', 'job id': 'job_id'}\n", - "08:53:20 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "08:53:20 INFO - data factory data_ is using local data access: input_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/filter_out output_folder - /Users/borisl/Projects/data-prep-kit/examples/notebooks/code/test-data/tokenization_out\n", - "08:53:20 INFO - data factory data_ max_files -1, n_sample -1\n", - "08:53:20 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "08:53:20 INFO - orchestrator Tokenization started at 2024-07-22 08:53:20\n", - "08:53:20 INFO - 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"True" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "execute_python_transform(\n", - " configuration = t_configuration,\n", - " name=\"tokenization\",\n", - " input_folder=input_folder,\n", - " output_folder=output_folder,\n", - " params=runtime_python_params\n", - ") " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "372aafad-9dc1-4201-afd5-b03d2db015c2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/archive/code/demo_with_launcher.ipynb b/examples/notebooks/archive/code/demo_with_launcher.ipynb deleted file mode 100644 index 827b723cc..000000000 --- a/examples/notebooks/archive/code/demo_with_launcher.ipynb +++ /dev/null @@ -1,1338 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "841e533d-ebb3-406d-9da7-b19e2c5f5866", - "metadata": {}, - "source": [ - "
\n", - "

Demo: Data Prep Kit

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "053ecf08-5f62-4b99-9347-8a0955843d21", - "metadata": {}, - "source": [ - "## Overview\n", - "Welcome to the demo notebook! Inside, you will find an end-to-end sample data pipeline designed for processing code datasets, beginning with GitHub repositories (.zip files) and culminating in processed data. This notebook provides the following transforms for processing the data. \n", - "\n", - "- [Ingest2parquet](#item1)\n", - "- [Exact Dedup](#item2)\n", - "- [Doc_ID generation](#item3)\n", - "- [Fuzzy Dedup](#item4)\n", - "- [Programming Language Select](#item5)\n", - "- [Code quality](#item6)\n", - "- [Filtering](#item7)\n", - "- [Tokenization](#item8)\n", - "\n", - "### Getting started\n", - "\n", - "If you want to try this pipeline on your data, you need to download your github repositories, as .zip files. Please refer to steps below for the same. One can also try it on sample data by downloading a few repos of interest.\n", - "\n", - "Here's how to download a GitHub repository in ZIP format:\n", - "\n", - "1. Go to the desired repository on GitHub.\n", - "2. Click the \"Code\" button near the top right corner of the repository.\n", - "3. Click the \"Download ZIP\" button.\n", - "\n", - "This will download a ZIP archive of the entire repository to your computer.\n", - "\n", - "Follow these steps and download some repositories from github into a folder. Now your data is ready.\n", - "\n", - "The folder containing this data would serve as the input to the pipeline. Assign the path of this data folder to the variable `zip_input_folder` in the below cell. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d11e5ce1-23cc-445a-b1fa-4b8129aa645e", - "metadata": {}, - "outputs": [], - "source": [ - "%%capture logpip --no-stderr\n", - "!pip install data-prep-toolkit-transforms-ray==0.2.1.dev1" - ] - }, - { - "cell_type": "markdown", - "id": "bd5d976e-cb4c-4469-af39-4b7ea507e9d8", - "metadata": {}, - "source": [ - "### Import Common python modules" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "66178913-42b8-426b-a2e9-9587268fd05b", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import os\n", - "import sys\n", - "\n", - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "from data_processing.utils import ParamsUtils" - ] - }, - { - "cell_type": "markdown", - "id": "72510ae6-48b0-4b88-9e13-a623281c3a63", - "metadata": {}, - "source": [ - "### Set input/output path variables for the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "60ac8bee-0960-4309-b225-d7a211b14262", - "metadata": {}, - "outputs": [], - "source": [ - "# Example\n", - "# We can set input paths here\n", - "zip_input_folder = \"input_data\"\n", - "\n", - "if not os.path.exists(zip_input_folder):\n", - " print (\"NO INPUT DATA\")\n", - " print (\"Please set `zip_input_folder` variable to path containing data\")\n", - "\n", - "# make sure the paths are correct\n", - "data_base_path = \"test-data\"\n", - "\n", - "parquet_data_output = os.path.join(data_base_path, \"parquet_input\")\n", - "\n", - "ededup_out = os.path.join(data_base_path, \"ededup_out\")\n", - "\n", - "doc_id_out = os.path.join(data_base_path, \"doc_id_out\")\n", - "fdedup_out = os.path.join(data_base_path, \"fdedup_out\")\n", - "\n", - "lang_out = os.path.join(data_base_path,\"lang_out\")\n", - "cq_out = os.path.join(data_base_path,\"cq_out\")\n", - "\n", - "filter_out = os.path.join(data_base_path ,\"filter_out\")\n", - "tokensization_out = os.path.join(data_base_path ,\"tokenization_out\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2449e5c7-078c-4ad6-a2f6-21d39d4da3fb", - "metadata": {}, - "source": [ - "## 1. Convert data to parquet using ingest2parquet [<-](#top)\n", - "_zip_ to _parquet_ \n", - "\n", - "Raw code data files which are in zip format are converted to parquet files, where each row of the parquet file corresponds to a separate code file. Apart from the contents of the code file, every row also contains a unique document id, file URL, name of the repository, source of the data, date of acquisition and license of the repository. For every code file, a language field is also added, which is detected using the filename\n", - "extensions.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0c574c4-9dc4-4dab-9ad6-b5338207e67a", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "482605b2-d814-456d-9195-49a2ec454ef0", - "metadata": {}, - "outputs": [], - "source": [ - "# For this stage input folder contains the zip files, each zip file contains a github repo.\n", - "\n", - "input_folder = zip_input_folder\n", - "output_folder = parquet_data_output" - ] - }, - { - "cell_type": "markdown", - "id": "9bb15f02-ab5c-4525-a536-cfa1fd2ba70b", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b0cd8ebd-bf71-42d6-a397-8df0c7b66a26", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:52:23 INFO - Running locally\n", - "10:52:23 INFO - data factory code2parquet_ is using local configuration without input/output path\n", - "10:52:23 INFO - data factory code2parquet_ max_files -1, n_sample -1\n", - "10:52:23 INFO - data factory code2parquet_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:52:23 INFO - data factory data_ is using local data access: input_folder - input_data output_folder - test-data/parquet_input\n", - "10:52:23 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:52:23 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.zip'], files to checkpoint ['.parquet']\n", - "10:52:23 INFO - pipeline id pipeline_id\n", - "10:52:23 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "10:52:23 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1}\n", - "10:52:23 INFO - actor creation delay 0\n", - "10:52:23 INFO - job details {'job category': 'preprocessing', 'job name': 'code2parquet', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:52:25,864\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:26 INFO - orchestrator started at 2024-08-22 10:52:26\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:26 INFO - Number of files is 2, source profile {'max_file_size': 4.850915908813477, 'min_file_size': 0.2978849411010742, 'total_file_size': 5.148800849914551}\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:26 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 13.397023010998964, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:26 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:26 INFO - Completed 0 files (0.0%) in 3.45309575398763e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=88515)\u001b[0m 10:52:27 WARNING - file environments-master/cfortunes/diebenkorn_notes.dat is empty. content , skipping\n", - "\u001b[36m(RayTransformFileProcessor pid=88515)\u001b[0m 10:52:27 WARNING - file environments-master/cfortunes/obliquestrategies.dat is empty. content , skipping\n", - "\u001b[36m(RayTransformFileProcessor pid=88515)\u001b[0m 10:52:27 WARNING - file environments-master/commands/grel is empty. content , skipping\n", - "\u001b[36m(RayTransformFileProcessor pid=88515)\u001b[0m 10:52:27 WARNING - file environments-master/commands/ldid is empty. content , skipping\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m 10:52:27 WARNING - Exception File is not a zip file processing file /Users/touma/data-prep-lab/examples/notebooks/code/input_data/.ipynb_checkpoints/test-checkpoint.zip: Traceback (most recent call last):\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing/runtime/transform_file_processor.py\", line 62, in process_file\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m out_files, stats = self.transform.transform_binary(file_name=f_name, byte_array=filedata)\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/code2parquet_transform.py\", line 118, in transform_binary\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m with zipfile.ZipFile(io.BytesIO(bytes(byte_array))) as opened_zip:\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/zipfile.py\", line 1302, in __init__\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m self._RealGetContents()\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m File \"/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/zipfile.py\", line 1369, in _RealGetContents\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m raise BadZipFile(\"File is not a zip file\")\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m zipfile.BadZipFile: File is not a zip file\n", - "\u001b[36m(RayTransformFileProcessor pid=88516)\u001b[0m \n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:27 INFO - Completed processing 2 files in 0.010891234874725342 min\n", - "\u001b[36m(orchestrate pid=88509)\u001b[0m 10:52:27 INFO - done flushing in 0.0013649463653564453 sec\n", - "10:52:37 INFO - Completed execution in 0.22322853803634643 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import ast\n", - "import os\n", - "import sys\n", - "\n", - "from code2parquet_transform import (\n", - " detect_programming_lang_cli_key,\n", - " supported_langs_file_cli_key,\n", - ")\n", - "from code2parquet_transform_ray import CodeToParquetRayConfiguration\n", - "from data_processing.utils import GB, ParamsUtils\n", - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "\n", - "\n", - "# create parameters\n", - "supported_languages_file = os.path.abspath(\n", - " \"../../../transforms/code/code2parquet/python/test-data/languages/lang_extensions.json\"\n", - ")\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8, \"memory\": 2 * GB}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "ingest_config = {\n", - " supported_langs_file_cli_key: supported_languages_file,\n", - " detect_programming_lang_cli_key: True,\n", - "}\n", - "\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " \"data_files_to_use\": ast.literal_eval(\"['.zip']\"),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=(params | ingest_config))\n", - "# create launcher\n", - "launcher = RayTransformLauncher(CodeToParquetRayConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "4692975c-49ff-41ae-810e-0f5bc0bbdc53", - "metadata": {}, - "source": [ - "## 2. Exact Dedup [<-](#top) \n", - "\n", - "Remove documents having identical code to remove bias in the training data. On the content of each document, a SHA256 hash is computed,\n", - "followed by de-duplication of record having identical hashes." - ] - }, - { - "cell_type": "markdown", - "id": "5acfd3a2-a236-4143-bcfc-15804f1da7fe", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "58a0e1f6-ff53-40aa-96b1-096ade4bd1c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/parquet_input\n", - "test-data/ededup_out\n" - ] - } - ], - "source": [ - "## For this stage the input is the folder containing parquet data which is output from the ingest2parquet tool\n", - "\n", - "input_folder = parquet_data_output\n", - "output_folder = ededup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "3661cb37-39c7-4b09-a784-925bfa9eaf1e", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a624b2b2-faad-4325-ac7d-53a840f564ef", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:52:38 INFO - Running locally\n", - "10:52:38 INFO - exact dedup params are {'doc_column': 'contents', 'hash_cpu': 0.5, 'num_hashes': 2}\n", - "10:52:38 INFO - data factory data_ is using local data access: input_folder - test-data/parquet_input output_folder - test-data/ededup_out\n", - "10:52:38 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:52:38 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:52:38 INFO - pipeline id pipeline_id\n", - "10:52:38 INFO - code location None\n", - "10:52:38 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:52:38 INFO - actor creation delay 0\n", - "10:52:38 INFO - job details {'job category': 'preprocessing', 'job name': 'ededup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:52:40,592\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:41 INFO - orchestrator started at 2024-08-22 10:52:41\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:41 INFO - Number of files is 1, source profile {'max_file_size': 0.04346275329589844, 'min_file_size': 0.04346275329589844, 'total_file_size': 0.04346275329589844}\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:41 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 13.453863525763154, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:41 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:41 INFO - Completed 0 files (0.0%) in 2.5351842244466145e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:43 INFO - Completed processing 1 files in 0.03372826973597209 min\n", - "\u001b[36m(orchestrate pid=88563)\u001b[0m 10:52:43 INFO - done flushing in 0.0025000572204589844 sec\n", - "10:52:53 INFO - Completed execution in 0.2433228333791097 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Import ededup transform configuration\n", - "from ededup_transform_ray import EdedupRayTransformConfiguration\n", - "\n", - "\n", - "# Prepare the commandline params\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # ededup parameters\n", - " \"ededup_hash_cpu\": 0.5,\n", - " \"ededup_num_hashes\": 2,\n", - " \"ededup_doc_column\": \"contents\",\n", - "}\n", - "\n", - "# Pass the commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "ededup_launcher = RayTransformLauncher(EdedupRayTransformConfiguration())\n", - "# launch\n", - "ededup_launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "f15f4d00-33bb-4d9a-9f34-4d7f3ee0b7bc", - "metadata": {}, - "source": [ - "## 3. DOC ID generation [<-](#top) \n", - "\n", - "This transform annotates documents with document \"ids\". It supports the following transformations of the original data:\n", - "\n", - " - Adding document hash: this enables the addition of a document hash-based id to the data. The hash is calculated with `hashlib.sha256(doc.encode(\"utf-8\")).hexdigest()`. To enable this annotation, set hash_column to the name of the column, where you want to store it.\n", - " - Adding integer document id: this allows the addition of an integer document id to the data that is unique across all rows in all tables provided to the transform() method. To enable this annotation, set int_id_column to the name of the column, where you want to store it. **This is a pre-requisite for fuzzy dedup** in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "e6f62394-fbde-495c-bbbb-83161b006bed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/ededup_out\n", - "test-data/doc_id_out\n" - ] - } - ], - "source": [ - "# Input for this stage is the output of exact dedeup component\n", - "# output of this component makes it possible for fdedup component to run on data.\n", - "\n", - "input_folder = ededup_out\n", - "output_folder = doc_id_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a6daf36d-686c-4e0a-aabf-ce55f999bb2d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:52:54 INFO - Running locally\n", - "10:52:54 INFO - Doc id parameters are : {'doc_column': 'contents', 'hash_column': 'hash_column', 'int_column': 'int_id_column'}\n", - "10:52:54 INFO - data factory data_ is using local data access: input_folder - test-data/ededup_out output_folder - test-data/doc_id_out\n", - "10:52:54 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:52:54 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:52:54 INFO - pipeline id pipeline_id\n", - "10:52:54 INFO - code location None\n", - "10:52:54 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:52:54 INFO - actor creation delay 0\n", - "10:52:54 INFO - job details {'job category': 'preprocessing', 'job name': 'doc_id', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:52:56,551\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:57 INFO - orchestrator started at 2024-08-22 10:52:57\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:57 INFO - Number of files is 1, source profile {'max_file_size': 0.04342079162597656, 'min_file_size': 0.04342079162597656, 'total_file_size': 0.04342079162597656}\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:57 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 13.392010498791933, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:57 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:57 INFO - Completed 0 files (0.0%) in 2.102057139078776e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:58 INFO - Completed processing 1 files in 0.013987716039021809 min\n", - "\u001b[36m(orchestrate pid=88632)\u001b[0m 10:52:58 INFO - done flushing in 0.0014891624450683594 sec\n", - "10:53:08 INFO - Completed execution in 0.22342359622319538 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from doc_id_transform_ray import DocIDRayTransformConfiguration\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # doc id configuration\n", - " \"doc_id_doc_column\": \"contents\",\n", - " \"doc_id_hash_column\": \"hash_column\",\n", - " \"doc_id_int_column\": \"int_id_column\",\n", - "}\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "launcher = RayTransformLauncher(DocIDRayTransformConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "85309751-8556-41c6-ac32-84acc941bc8d", - "metadata": {}, - "source": [ - "## 4. Fuzzy Dedup [<-](#top) \n", - "\n", - "Post exact deduplication, fuzzy deduplication is applied with\n", - "the goal of removing code files that may have slight variations and thereby unbiasing\n", - "the data further. Small variations are quite commonly seen in code data in the form\n", - "of variations in the values of variables, addittion of logging statements etc. Find near-\n", - "duplicate." - ] - }, - { - "cell_type": "markdown", - "id": "fcf574a3-b287-419c-9c86-07b828b41ca6", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9e431c8c-c7c7-48de-ba5f-2c4649c35399", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/doc_id_out\n", - "test-data/fdedup_out\n" - ] - } - ], - "source": [ - "## Input to this component is the output of doc_id generator component. \n", - "\n", - "input_folder = doc_id_out\n", - "output_folder = fdedup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "f4c82a8f-b513-4fe5-b172-d41b104b54f3", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3864ff77-e9a8-48f7-973b-c3b3aef1a94f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:53:20 INFO - Running locally\n", - "10:53:20 INFO - fuzzy dedup params are {'doc_column': 'contents', 'id_column': 'int_id_column', 'cluster_column': 'hash_column', 'bucket_cpu': 0.5, 'mhash_cpu': 0.5, 'doc_cpu': 0.5, 'num_doc_actors': 2, 'num_minhash_actors': 1, 'num_bucket_actors': 1, 'num_preprocessors': 2, 'num_permutations': 64, 'threshold': 0.8, 'shingles_size': 5, 'delimiters': ' ', 'snapshot_delay': 1, 'use_bucket_snapshot': False, 'use_doc_snapshot': False, 'random_delay_limit': 10, 'worker_options': {'num_cpus': 0.8}}\n", - "10:53:20 INFO - data factory data_ is using local data access: input_folder - test-data/doc_id_out output_folder - test-data/fdedup_out\n", - "10:53:20 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:53:20 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:53:20 INFO - pipeline id pipeline_id\n", - "10:53:20 INFO - code location None\n", - "10:53:20 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:53:20 INFO - actor creation delay 0\n", - "10:53:20 INFO - job details {'job category': 'preprocessing', 'job name': 'fdedup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:53:21,947\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - orchestrator started at 2024-08-22 10:53:22\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Number of files is 1, source profile {'max_file_size': 0.04804039001464844, 'min_file_size': 0.04804039001464844, 'total_file_size': 0.04804039001464844}\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 13.416908264160156, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - starting run from the beginning\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - continuing from the very beginning\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Fuzzy: num buckets 5, bucket length 11\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - created 1 bucket actors\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - created 1 minhash actors\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Table preprocessing uses 2 readers\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - created 2 table processor actors\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:22 INFO - Completed 0 files (0.0%) in 3.135204315185547e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:26 INFO - Completed processing 1 files in 0.0659868836402893 min\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:26 INFO - creating minhash snapshots\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:27 INFO - minhash snapshots created\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:27 INFO - creating bucket snapshots\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:28 INFO - bucket snapshots created\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:28 INFO - created 2 document actors\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:28 INFO - created 2 bucket processor actors\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:28 INFO - created bucket processor invoker\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:28 INFO - added invoker to bucket collectors\n", - "\u001b[36m(BucketsHash pid=88740)\u001b[0m 10:53:28 INFO - processing buckets 0 long, 255 short\n", - "\u001b[36m(BucketsHash pid=88740)\u001b[0m 10:53:28 INFO - Done submitting long buckets\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:29 INFO - Done processing buckets in 0.01056810220082601 min\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:29 INFO - creating document snapshots\n", - "\u001b[36m(BucketsHashProcessorInvoker pid=88777)\u001b[0m 10:53:29 INFO - Waiting bucket processing completion. Submitted requests 3\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:31 INFO - document snapshots created\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:31 INFO - Completed 0 files (0.0%) in 3.449122111002604e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:34 INFO - Completed processing 1 files in 0.04709231456120809 min\n", - "\u001b[36m(orchestrate pid=88730)\u001b[0m 10:53:34 INFO - done flushing in 0.003609895706176758 sec\n", - "10:53:44 INFO - Completed execution in 0.4046771128972371 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from fdedup_transform_ray import FdedupRayTransformConfiguration\n", - "\n", - "# create parameters\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # Orchestration parameters\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # columns used\n", - " \"fdedup_doc_column\": \"contents\",\n", - " \"fdedup_id_column\": \"int_id_column\",\n", - " \"fdedup_cluster_column\": \"hash_column\",\n", - " # infrastructure\n", - " \"fdedup_bucket_cpu\": 0.5,\n", - " \"fdedup_doc_cpu\": 0.5,\n", - " \"fdedup_mhash_cpu\": 0.5,\n", - " \"fdedup_num_doc_actors\": 2,\n", - " \"fdedup_num_bucket_actors\": 1,\n", - " \"fdedup_num_minhash_actors\": 1,\n", - " \"fdedup_num_preprocessors\": 2,\n", - " # fuzzy parameters\n", - " \"fdedup_num_permutations\": 64,\n", - " \"fdedup_threshold\": 0.8,\n", - " \"fdedup_shingles_size\": 5,\n", - " \"fdedup_delimiters\": \" \"\n", - "}\n", - "\n", - "# Pass commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "fdedup_launcher = RayTransformLauncher(FdedupRayTransformConfiguration())\n", - "fdedup_launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "49d1d761-62a7-4a12-ad23-b2c268ad8ed2", - "metadata": {}, - "source": [ - "## 5. Programming language annotation [<-](#top) \n", - "\n", - "The raw data may contains many programming languages. Of this, we would wish to retain a prioritised list of selected programming languages. This component takes a file which has new line separated names of languages we need to select. It annotates the data a new column with boolean values. This column can be used by filter component to select the required languages." - ] - }, - { - "cell_type": "markdown", - "id": "3db05e1e-4c62-4367-93ca-b2ddff95e4b4", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a8ec4fb6-fa62-45d1-9aa1-596d7182b2c9", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "input_folder = fdedup_out\n", - "output_folder = lang_out \n", - "selected_languages_file = \"./test-data/allowed-code-languages.txt\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "c07e7e5a-064f-4dca-a017-4211f7a3e980", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "48dbb2a3-a6f4-4a3d-bb2f-8491fd063611", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:53:45 INFO - Running locally\n", - "10:53:45 INFO - data factory proglang_select_ is using local configuration without input/output path\n", - "10:53:45 INFO - data factory proglang_select_ max_files -1, n_sample -1\n", - "10:53:45 INFO - data factory proglang_select_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:53:45 INFO - data factory data_ is using local data access: input_folder - test-data/fdedup_out output_folder - test-data/lang_out\n", - "10:53:45 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:53:45 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:53:45 INFO - pipeline id pipeline_id\n", - "10:53:45 INFO - code location None\n", - "10:53:45 INFO - number of workers 1 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:53:45 INFO - actor creation delay 0\n", - "10:53:45 INFO - job details {'job category': 'preprocessing', 'job name': 'proglang_select', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:53:47,639\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 INFO - orchestrator started at 2024-08-22 10:53:48\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 INFO - Number of files is 1, source profile {'max_file_size': 0.044445037841796875, 'min_file_size': 0.044445037841796875, 'total_file_size': 0.044445037841796875}\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 INFO - Cluster resources: {'cpus': 12, 'gpus': 0, 'memory': 13.360301208682358, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 INFO - Number of workers - 1 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 INFO - Getting supported languages from file ./test-data/allowed-code-languages.txt\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 ERROR - Error reading file ./test-data/allowed-code-languages.txt: [Errno 2] No such file or directory: './test-data/allowed-code-languages.txt'\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m Traceback (most recent call last):\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing_ray/runtime/ray/transform_orchestrator.py\", line 83, in orchestrate\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m \"transform_params\": runtime.get_transform_config(\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/proglang_select_transform_ray.py\", line 70, in get_transform_config\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m lang_list = _get_supported_languages(\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m ^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/proglang_select_transform.py\", line 33, in _get_supported_languages\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m lang_list, _ = data_access.get_file(lang_file)\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing/data_access/data_access_local.py\", line 367, in get_file\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m raise e\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m File \"/Users/touma/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing/data_access/data_access_local.py\", line 361, in get_file\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m with open(path, \"rb\") as f:\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m ^^^^^^^^^^^^^^^^\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m FileNotFoundError: [Errno 2] No such file or directory: './test-data/allowed-code-languages.txt'\n", - "\u001b[36m(orchestrate pid=88862)\u001b[0m 10:53:48 ERROR - Exception during execution [Errno 2] No such file or directory: './test-data/allowed-code-languages.txt': None\n", - "10:53:58 INFO - Completed execution in 0.21568763256072998 min, execution result 1\n" - ] - }, - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from proglang_select_transform_ray import ProgLangSelectRayConfiguration\n", - "from proglang_select_transform import (\n", - " lang_allowed_langs_file_key,\n", - " lang_lang_column_key,\n", - " lang_output_column_key,\n", - ")\n", - "\n", - "# create parameters\n", - "language_column_name = \"programming_language\"\n", - "annotated_column_name = \"lang_selected\"\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "langselect_config = {\n", - " lang_allowed_langs_file_key: selected_languages_file,\n", - " lang_lang_column_key: language_column_name,\n", - " lang_output_column_key: annotated_column_name,\n", - "}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 1,\n", - " # language selection specific parameters\n", - " **langselect_config,\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(ProgLangSelectRayConfiguration())\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "markdown", - "id": "0646cbb7-3046-44c0-827d-d102d3ff7cb8", - "metadata": {}, - "source": [ - "## 6. Code Quality [<-](#top) \n", - "\n", - "We experiment with various code quality metrics but finally retain\n", - "the four code quality metrics used by (Li et al., 2023) to balance the tradeoff between\n", - "code quality versus data volume. \n" - ] - }, - { - "cell_type": "markdown", - "id": "2e985668-848b-4633-b0d8-9fe70ada0c91", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9f080011-c9fe-430e-9ecc-f2220d2c8d18", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/lang_out\n", - "test-data/cq_out\n" - ] - } - ], - "source": [ - "input_folder = lang_out\n", - "output_folder = cq_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "c02982c5-f398-4a1a-a9fe-42d7ae748c7c", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "29319fb9-b0d8-4f86-9bc5-b92960ad8ae5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:54:05 INFO - Running locally\n", - "10:54:05 INFO - data factory data_ is using local data access: input_folder - test-data/lang_out output_folder - test-data/cq_out\n", - "10:54:05 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:54:05 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:54:05 INFO - pipeline id pipeline_id\n", - "10:54:05 INFO - code location None\n", - "10:54:05 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:54:05 INFO - actor creation delay 0\n", - "10:54:05 INFO - job details {'job category': 'preprocessing', 'job name': 'code_quality', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:54:07,325\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=88945)\u001b[0m 10:54:09 INFO - orchestrator started at 2024-08-22 10:54:09\n", - "\u001b[36m(orchestrate pid=88945)\u001b[0m 10:54:09 ERROR - No input files to process - exiting\n", - "10:54:19 INFO - Completed execution in 0.22560758193333943 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "from pathlib import Path\n", - "\n", - "from code_quality_transform_ray import CodeQualityRayTransformConfiguration\n", - "from data_processing.utils import ParamsUtils\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "language_column_name = \"programming_language\"\n", - "\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " # code quality configuration\n", - " \"cq_contents_column_name\": \"contents\",\n", - " \"cq_language_column_name\": language_column_name,\n", - "}\n", - "\n", - "\n", - "Path(output_folder).mkdir(parents=True, exist_ok=True)\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(CodeQualityRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5cec9839-13b4-4d32-89c5-38f59c5a89f0", - "metadata": {}, - "source": [ - "## 7. Filtering [<-](#top) \n", - "\n", - "Filter out documents that do not meet the quality threshold for each annotation. The thresholds are computed based on a distributional\n", - "analysis as well as manual inspection of samples maintaining the balance between data quality and data volume" - ] - }, - { - "cell_type": "markdown", - "id": "78c54d69-8aee-4f0f-b74c-35dc0609270f", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a7991811-b19e-43b5-89ac-b24060c0ccfa", - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = cq_out\n", - "output_folder = filter_out" - ] - }, - { - "cell_type": "markdown", - "id": "c460e05c-aeee-4b53-9dd5-8dfa1afc0ece", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "61dea2b0-0e54-4912-8620-886e2b8420ef", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:54:21 INFO - Running locally\n", - "10:54:21 INFO - data factory data_ is using local data access: input_folder - test-data/cq_out output_folder - test-data/filter_out\n", - "10:54:21 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:54:21 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:54:21 INFO - pipeline id pipeline_id\n", - "10:54:21 INFO - code location None\n", - "10:54:21 INFO - number of workers 5 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:54:21 INFO - actor creation delay 0\n", - "10:54:21 INFO - job details {'job category': 'preprocessing', 'job name': 'filter', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:54:23,149\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=89002)\u001b[0m 10:54:23 INFO - orchestrator started at 2024-08-22 10:54:23\n", - "\u001b[36m(orchestrate pid=89002)\u001b[0m 10:54:23 ERROR - No input files to process - exiting\n", - "10:54:33 INFO - Completed execution in 0.20987151861190795 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "from data_processing.data_access import DataAccessLocal\n", - "from filter_transform import (\n", - " filter_columns_to_drop_cli_param,\n", - " filter_criteria_cli_param,\n", - " filter_logical_operator_cli_param,\n", - ")\n", - "from filter_transform_ray import FilterRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "# This is just an example criteria to filter\n", - "filter_criteria = [\n", - " \"total_num_lines > 10 AND total_num_lines < 90\",\n", - " \"lang_selected = 1\",\n", - "]\n", - "filter_logical_operator = \"AND\"\n", - "filter_columns_to_drop = [\"lang_selected\", \"hash_column\"]\n", - "\n", - "filter_params = {\n", - " filter_criteria_cli_param: filter_criteria,\n", - " filter_columns_to_drop_cli_param: filter_columns_to_drop,\n", - " filter_logical_operator_cli_param: filter_logical_operator,\n", - "}\n", - "\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "launcher_params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 5,\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(launcher_params | filter_params)\n", - "# Create the longer to launch with the blocklist transform.\n", - "launcher = RayTransformLauncher(FilterRayTransformConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5370950a-2a3a-4143-8218-f9b4808099ba", - "metadata": {}, - "source": [ - "## 8. Tokenization [<-](#top) \n", - "\n", - "The data tokenization transform maps a (non-empty) input table to an output table using a pre-trained tokenizer. The input table must contain at least two columns, by default named document_id and contents. The tokenization transform utilizes the pre-trained tokenizer to tokenize each row (assuming a document) in the input table to each row in the output folder.\n", - "\n", - "A pre-trained tokenizer must be specified through the --tkn_tokenizer parameter, which can be the name of a ready-for-download tokenizer from HuggingFace such as hf-internal-testing/llama-tokenizer, bigcode/starcoder or any others that can loaded by the Huggingface AutoTokenizer library. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "20a153fa-fd56-401e-86be-4f7617affcc8", - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = filter_out\n", - "output_folder = tokensization_out" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "228df6b2-bc62-494b-9697-03ece98d7853", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "10:54:35 INFO - Running locally\n", - "10:54:35 INFO - data factory data_ is using local data access: input_folder - test-data/filter_out output_folder - test-data/tokenization_out\n", - "10:54:35 INFO - data factory data_ max_files -1, n_sample -1\n", - "10:54:35 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "10:54:35 INFO - pipeline id pipeline_id\n", - "10:54:35 INFO - code location None\n", - "10:54:35 INFO - number of workers 5 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "10:54:35 INFO - actor creation delay 0\n", - "10:54:35 INFO - job details {'job category': 'preprocessing', 'job name': 'Tokenization', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-08-22 10:54:37,293\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=89059)\u001b[0m 10:54:38 INFO - orchestrator started at 2024-08-22 10:54:38\n", - "\u001b[36m(orchestrate pid=89059)\u001b[0m 10:54:38 ERROR - No input files to process - exiting\n", - "10:54:48 INFO - Completed execution in 0.22238094806671144 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from tokenization_transform_ray import TokenizationRayConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 5,\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "# create launcher\n", - "launcher = RayTransformLauncher(TokenizationRayConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a6e86b0-4d1e-455a-a44a-d09107a9dae4", - "metadata": {}, - "source": [ - "## Repo Level Ordering Transform" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4c54cc37-7c60-43f7-9193-ce919995dddf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "usage: python [-h] [--run_locally RUN_LOCALLY] [--repo_lvl_stage_one_only]\n", - " [--repo_lvl_grouping_column REPO_LVL_GROUPING_COLUMN]\n", - " [--repo_lvl_language_column REPO_LVL_LANGUAGE_COLUMN]\n", - " [--repo_lvl_store_type REPO_LVL_STORE_TYPE]\n", - " [--repo_lvl_store_backend_dir REPO_LVL_STORE_BACKEND_DIR]\n", - " [--repo_lvl_store_ray_cpus REPO_LVL_STORE_RAY_CPUS]\n", - " [--repo_lvl_store_ray_nworkers REPO_LVL_STORE_RAY_NWORKERS]\n", - " [--repo_lvl_sorting_enabled REPO_LVL_SORTING_ENABLED]\n", - " [--repo_lvl_sorting_algo REPO_LVL_SORTING_ALGO]\n", - " [--repo_lvl_output_by_langs REPO_LVL_OUTPUT_BY_LANGS]\n", - " [--repo_lvl_combine_rows REPO_LVL_COMBINE_ROWS]\n", - " [--data_s3_cred DATA_S3_CRED] [--data_s3_config DATA_S3_CONFIG]\n", - " [--data_local_config DATA_LOCAL_CONFIG]\n", - " [--data_max_files DATA_MAX_FILES]\n", - " [--data_checkpointing DATA_CHECKPOINTING]\n", - " [--data_files_to_checkpoint DATA_FILES_TO_CHECKPOINT]\n", - " [--data_data_sets DATA_DATA_SETS]\n", - " [--data_files_to_use DATA_FILES_TO_USE]\n", - " [--data_num_samples DATA_NUM_SAMPLES]\n", - " [--runtime_num_workers RUNTIME_NUM_WORKERS]\n", - " [--runtime_worker_options RUNTIME_WORKER_OPTIONS]\n", - " [--runtime_creation_delay RUNTIME_CREATION_DELAY]\n", - " [--runtime_pipeline_id RUNTIME_PIPELINE_ID]\n", - " [--runtime_job_id RUNTIME_JOB_ID]\n", - " [--runtime_code_location RUNTIME_CODE_LOCATION]\n", - "python : error: argument --repo_lvl_output_by_langs: expected one argument\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'tuple' object has no attribute 'tb_frame'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mArgumentError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:1902\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m 1901\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1902\u001b[0m namespace, args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1903\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ArgumentError \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:2114\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args\u001b[0;34m(self, arg_strings, namespace)\u001b[0m\n\u001b[1;32m 2113\u001b[0m \u001b[38;5;66;03m# consume the next optional and any arguments for it\u001b[39;00m\n\u001b[0;32m-> 2114\u001b[0m start_index \u001b[38;5;241m=\u001b[39m \u001b[43mconsume_optional\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2116\u001b[0m \u001b[38;5;66;03m# consume any positionals following the last Optional\u001b[39;00m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:2044\u001b[0m, in \u001b[0;36mArgumentParser._parse_known_args..consume_optional\u001b[0;34m(start_index)\u001b[0m\n\u001b[1;32m 2043\u001b[0m selected_patterns \u001b[38;5;241m=\u001b[39m arg_strings_pattern[start:]\n\u001b[0;32m-> 2044\u001b[0m arg_count \u001b[38;5;241m=\u001b[39m \u001b[43mmatch_argument\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mselected_patterns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2045\u001b[0m stop \u001b[38;5;241m=\u001b[39m start \u001b[38;5;241m+\u001b[39m arg_count\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:2208\u001b[0m, in \u001b[0;36mArgumentParser._match_argument\u001b[0;34m(self, action, arg_strings_pattern)\u001b[0m\n\u001b[1;32m 2205\u001b[0m msg \u001b[38;5;241m=\u001b[39m ngettext(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexpected \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m argument\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 2206\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexpected \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m arguments\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 2207\u001b[0m action\u001b[38;5;241m.\u001b[39mnargs) \u001b[38;5;241m%\u001b[39m action\u001b[38;5;241m.\u001b[39mnargs\n\u001b[0;32m-> 2208\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ArgumentError(action, msg)\n\u001b[1;32m 2210\u001b[0m \u001b[38;5;66;03m# return the number of arguments matched\u001b[39;00m\n", - "\u001b[0;31mArgumentError\u001b[0m: argument --repo_lvl_output_by_langs: expected one argument", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mSystemExit\u001b[0m Traceback (most recent call last)", - " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", - "Cell \u001b[0;32mIn[20], line 50\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;66;03m# Launch the ray actor(s) to process the input\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m \u001b[43mlauncher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlaunch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing_ray/runtime/ray/transform_launcher.py:119\u001b[0m, in \u001b[0;36mRayTransformLauncher.launch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;124;03mExecute method orchestrates driver invocation\u001b[39;00m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;124;03m:return: launch result\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_parameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 120\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_submit_for_execution()\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/data_processing_ray/runtime/ray/transform_launcher.py:68\u001b[0m, in \u001b[0;36mRayTransformLauncher.__get_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecution_config\u001b[38;5;241m.\u001b[39madd_input_params(parser\u001b[38;5;241m=\u001b[39mparser)\n\u001b[0;32m---> 68\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_locally \u001b[38;5;241m=\u001b[39m args\u001b[38;5;241m.\u001b[39mrun_locally\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:1869\u001b[0m, in \u001b[0;36mArgumentParser.parse_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m 1868\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mparse_args\u001b[39m(\u001b[38;5;28mself\u001b[39m, args\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, namespace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1869\u001b[0m args, argv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_known_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnamespace\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1870\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m argv:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:1904\u001b[0m, in \u001b[0;36mArgumentParser.parse_known_args\u001b[0;34m(self, args, namespace)\u001b[0m\n\u001b[1;32m 1903\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ArgumentError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 1904\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merror\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43merr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1905\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:2630\u001b[0m, in \u001b[0;36mArgumentParser.error\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m 2629\u001b[0m args \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprog\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprog, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m: message}\n\u001b[0;32m-> 2630\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%(prog)s\u001b[39;49;00m\u001b[38;5;124;43m: error: \u001b[39;49m\u001b[38;5;132;43;01m%(message)s\u001b[39;49;00m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/argparse.py:2617\u001b[0m, in \u001b[0;36mArgumentParser.exit\u001b[0;34m(self, status, message)\u001b[0m\n\u001b[1;32m 2616\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_print_message(message, _sys\u001b[38;5;241m.\u001b[39mstderr)\n\u001b[0;32m-> 2617\u001b[0m \u001b[43m_sys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mSystemExit\u001b[0m: 2", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2145\u001b[0m, in \u001b[0;36mInteractiveShell.showtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m 2142\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exception_only:\n\u001b[1;32m 2143\u001b[0m stb \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAn exception has occurred, use \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mtb to see \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2144\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe full traceback.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m-> 2145\u001b[0m stb\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInteractiveTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_exception_only\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2146\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 2147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2149\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcontains_exceptiongroup\u001b[39m(val):\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:710\u001b[0m, in \u001b[0;36mListTB.get_exception_only\u001b[0;34m(self, etype, value)\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_exception_only\u001b[39m(\u001b[38;5;28mself\u001b[39m, etype, value):\n\u001b[1;32m 703\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Only print the exception type and message, without a traceback.\u001b[39;00m\n\u001b[1;32m 704\u001b[0m \n\u001b[1;32m 705\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[38;5;124;03m value : exception value\u001b[39;00m\n\u001b[1;32m 709\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 710\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mListTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:568\u001b[0m, in \u001b[0;36mListTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, context)\u001b[0m\n\u001b[1;32m 565\u001b[0m chained_exc_ids\u001b[38;5;241m.\u001b[39madd(\u001b[38;5;28mid\u001b[39m(exception[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m 566\u001b[0m chained_exceptions_tb_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 567\u001b[0m out_list \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 568\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 569\u001b[0m \u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 570\u001b[0m \u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 571\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchained_exc_ids\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m 572\u001b[0m \u001b[43m \u001b[49m\u001b[43mchained_exceptions_tb_offset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 573\u001b[0m \u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 574\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;241m+\u001b[39m chained_exception_message\n\u001b[1;32m 576\u001b[0m \u001b[38;5;241m+\u001b[39m out_list)\n\u001b[1;32m 578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out_list\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:1454\u001b[0m, in \u001b[0;36mAutoFormattedTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1452\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtb \u001b[38;5;241m=\u001b[39m etb\n\u001b[0;32m-> 1454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mFormattedTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1455\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m 1456\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:1345\u001b[0m, in \u001b[0;36mFormattedTB.structured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1342\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode\n\u001b[1;32m 1343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose_modes:\n\u001b[1;32m 1344\u001b[0m \u001b[38;5;66;03m# Verbose modes need a full traceback\u001b[39;00m\n\u001b[0;32m-> 1345\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVerboseTB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstructured_traceback\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1346\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\n\u001b[1;32m 1347\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMinimal\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 1349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ListTB\u001b[38;5;241m.\u001b[39mget_exception_only(\u001b[38;5;28mself\u001b[39m, etype, value)\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:1192\u001b[0m, in \u001b[0;36mVerboseTB.structured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstructured_traceback\u001b[39m(\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1185\u001b[0m etype: \u001b[38;5;28mtype\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1189\u001b[0m number_of_lines_of_context: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m,\n\u001b[1;32m 1190\u001b[0m ):\n\u001b[1;32m 1191\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1192\u001b[0m formatted_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat_exception_as_a_whole\u001b[49m\u001b[43m(\u001b[49m\u001b[43metype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1193\u001b[0m \u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1195\u001b[0m colors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mColors \u001b[38;5;66;03m# just a shorthand + quicker name lookup\u001b[39;00m\n\u001b[1;32m 1196\u001b[0m colorsnormal \u001b[38;5;241m=\u001b[39m colors\u001b[38;5;241m.\u001b[39mNormal \u001b[38;5;66;03m# used a lot\u001b[39;00m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:1082\u001b[0m, in \u001b[0;36mVerboseTB.format_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tb_offset, \u001b[38;5;28mint\u001b[39m)\n\u001b[1;32m 1080\u001b[0m head \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_header(\u001b[38;5;28mstr\u001b[39m(etype), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlong_header)\n\u001b[1;32m 1081\u001b[0m records \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m-> 1082\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_records\u001b[49m\u001b[43m(\u001b[49m\u001b[43metb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber_of_lines_of_context\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtb_offset\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m etb \u001b[38;5;28;01melse\u001b[39;00m []\n\u001b[1;32m 1083\u001b[0m )\n\u001b[1;32m 1085\u001b[0m frames \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 1086\u001b[0m skipped \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m~/data-prep-lab/examples/notebooks/code/venv/lib/python3.11/site-packages/IPython/core/ultratb.py:1150\u001b[0m, in \u001b[0;36mVerboseTB.get_records\u001b[0;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m 1148\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m cf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1150\u001b[0m mod \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetmodule(\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtb_frame\u001b[49m)\n\u001b[1;32m 1151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mod \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1152\u001b[0m mod_name \u001b[38;5;241m=\u001b[39m mod\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'tb_frame'" - ] - } - ], - "source": [ - "from repo_level_order_transform import RepoLevelOrderRayTransformConfiguration\n", - "\n", - "input_folder = \"../../../transforms/code/repo_level_ordering/ray/test-data/input\"\n", - "output_folder = \"./output\"\n", - "\n", - "import tempfile\n", - "\n", - "with tempfile.TemporaryDirectory() as tmpdirname:\n", - "\n", - " # create parameters\n", - " local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - " }\n", - "\n", - " worker_options = {\"num_cpus\": 0.8}\n", - " code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - " params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 2,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - " }\n", - "\n", - "\n", - " repo_level_params = {\n", - " \"repo_lvl_sorting_algo\": \"SORT_SEMANTIC_NORMALISED\",\n", - " \"repo_lvl_store_type\": \"local\",\n", - " \"repo_lvl_store_backend_dir\": tmpdirname,\n", - " }\n", - "\n", - " repo_level_flags = [\n", - " \"repo_lvl_output_by_langs\",\n", - " \"repo_lvl_combine_rows\",\n", - " \"repo_lvl_sorting_enabled\",\n", - " ]\n", - "\n", - " d = ParamsUtils.dict_to_req(d=params | repo_level_params)\n", - " sys.argv = d + [f\"--{flag}\" for flag in repo_level_flags]\n", - " # create launcher\n", - " launcher = RayTransformLauncher(RepoLevelOrderRayTransformConfiguration())\n", - " # Launch the ray actor(s) to process the input\n", - " launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdaee7b6-40a5-4422-9dee-e9421138c005", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/archive/code/sample-notebook.ipynb b/examples/notebooks/archive/code/sample-notebook.ipynb deleted file mode 100644 index 49f4a400a..000000000 --- a/examples/notebooks/archive/code/sample-notebook.ipynb +++ /dev/null @@ -1,869 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "NbF_Zw3KBazf" - }, - "source": [ - "# **Demo on building data prep pipeline for model fine tuning** " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - " \"Open\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_-NOkuTxiP7r", - "outputId": "043f32fc-c476-433e-86b6-d7e9abd4d285" - }, - "source": [ - "This demo notebook shows how to use [data-prep-kit](https://github.com/IBM/data-prep-kit) to build a data preparation pipeline that can be used for fine tuning or extended pre-training. We will discuss the various data preparation steps to process raw data (code repositories), tokenise it that can then be fine tuned using any popular code models. We will also discuss a novel recipe for semantic ordering of files in a repository which has shown to enhance model training. Please see our [paper](https://arxiv.org/abs/2407.13739) here for more details. For this demo, we will use the [codeparrot/github-code](https://huggingface.co/datasets/codeparrot/github-code) dataset hosted on Hugging Face datasets. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Install data-prep-toolkit and datasets library. This notebook requires atleast 8 cpus. \n", - "To run on google colab, it is recommended to change the runtime to TPUs to get the required number of cpus.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%capture logpip --no-stderr\n", - "!pip install data-prep-toolkit-transforms-ray==0.2.1.dev1\n", - "!pip install datasets\n", - "!pip install pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8VhIsZViaU2i" - }, - "source": [ - "We use parallel processing capability using Ray, so that beyond the demo, a user can also use this for actual production runs on larger datasets, with minor code changes. Please read [here](https://github.com/IBM/data-prep-kit?tab=readme-ov-file#-about-) on various features of data-prep-kit that includes flexibility of compute to run from laptop to cluster. There are three parameters, that the user can change, as per usecase:\n", - "\n", - "`runtime_num_worker`: number of parallel workers to be used\n", - "\n", - "`num_cpus`: number of cpus to be used per worker\n", - "\n", - "`run_locally: True` start a ray cluster for parallel computation\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "J_UbnF9wbj95" - }, - "outputs": [], - "source": [ - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "from data_processing.utils import ParamsUtils\n", - "import sys\n", - "import json\n", - "import pandas as pd\n", - "#Default parameters for computation\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "common_config_params = {\n", - " \"run_locally\": True,\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 2,\n", - " }\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "We will do all the processing in `sample_data` folder. This concludes our setup section. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf sample_data\n", - "!mkdir -p sample_data\n", - "!mkdir -p sample_data/hf_2_parquet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preparation Steps\n", - "\n", - "We now discuss the various data preparation steps to transform the raw data to a tokenised format post cleaning and transforming the data. We use the [parquet data format](https://parquet.apache.org/) for all our operations. This helps to efficiently scale the data for actual production runs, beyond the demo. \n", - "\n", - "1. HuggingFace2Parquet: Read the dataset from HF and convert into parquet format. \n", - "2. Exact Deduplication: Remove exact duplicates. \n", - "3. Fuzzy Deduplication: Remove near duplicates. \n", - "4. Programming Lang Selection: Select the programming languages to be used for the analysis.\n", - "5. Code Quality Annotations: Annotate whether a given code file is of high quality or not using various rules.\n", - "6. Filtering: Filter dataset to retain only programming language of interest. \n", - "7. Semantic Ordering: Organise code files by their semantic dependencies. \n", - "8. Tokenization: Tokenise the data for model fine tuning.\n", - "\n", - "The data processing pipeline is organised such that the output of the previous transform is used as input to the next one. Refer to the papers [here](https://arxiv.org/pdf/2405.04324) and [here](https://arxiv.org/abs/2407.13739) for complete details for each of the above steps. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xliMSdQEEwYx" - }, - "source": [ - "## 1. Huggingface datasets to Parquet\n", - "\n", - "This is the first component of this pipeline. It ingests a dataset `codeparrot/github-code` from huggingface and converts it into\n", - "parquet files for consumption by the next steps in this data processing pipeline.\n", - "\n", - "For this demo we are trying to process a few records. The following fields can be updated in case you want to use more data.\n", - "_total_files_ = 10
\n", - "_rows_per_file_ = 10\n", - "\n", - "The output of this stage of the pipeline would be written to `sample_data/hf_2_parquet`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wit7ic1GauWN", - "outputId": "cc9ee442-ea65-446c-d495-e5ac83bd5f1c" - }, - "outputs": [], - "source": [ - "import os\n", - "import pyarrow as pa\n", - "import pyarrow.parquet as pq\n", - "\n", - "from datasets import load_dataset\n", - "\n", - "import uuid\n", - "from data_processing.utils import TransformUtils\n", - "from collections import defaultdict\n", - "\n", - "DATASET_NAME='codeparrot/github-code'\n", - "\n", - "ds = load_dataset(DATASET_NAME, \n", - " streaming=True, \n", - " split=\"train\",\n", - " trust_remote_code=True)\n", - "\n", - "def row_mapper(row):\n", - " return {\n", - " 'ext': TransformUtils.get_file_extension(row['path'])[1],\n", - " 'document_id': str(uuid.uuid4())\n", - " }\n", - "\n", - "parquet_data_output = \"sample_data/hf_2_parquet\"\n", - "\n", - "## Converts a subset of a Hugging Face dataset to a Parquet file, optionally mapping and renaming columns.\n", - "def hf_dataset_to_parquet(ds, skip, nrows, file_name, mapper=None, renamed_columns=[]):\n", - " dst_ = ds.skip(skip).take(nrows)\n", - " \n", - " data_dict = defaultdict(list)\n", - "\n", - " dst = dst_.map(mapper)\n", - "\n", - " for data in dst:\n", - " for k, v in data.items():\n", - " data_dict[k].append(v)\n", - "\n", - " for old, new in renamed_columns:\n", - " data_dict[new] = data_dict[old]\n", - " del data_dict[old]\n", - "\n", - " table = pa.Table.from_pydict(data_dict)\n", - " pq.write_table(table, file_name)\n", - "\n", - "\n", - "## Create parquet files \n", - "\n", - "total_files = 20\n", - "rows_per_file = 20\n", - "for num in range(total_files):\n", - " file_name = os.path.join(\n", - " f\"{parquet_data_output}\",\n", - " f\"data_{num}.parquet\"\n", - " )\n", - " print (f\"Writing {file_name}\")\n", - " hf_dataset_to_parquet(ds, \n", - " 1 * rows_per_file,\n", - " rows_per_file,\n", - " file_name=file_name,\n", - " mapper=row_mapper,\n", - " renamed_columns=[(\"code\", \"contents\"),\n", - " (\"path\", \"title\")])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Function to read parquet files in a directory as pandas dataframe\n", - "from pathlib import Path\n", - "def read_parquet_bulk(dir_path):\n", - " data_dir = Path(dir_path)\n", - " # Get the list of all Parquet files in the directory\n", - " parquet_files = list(data_dir.glob('*.parquet'))\n", - " # Check if the directory contains any Parquet files\n", - " if not parquet_files:\n", - " raise ValueError(f\"No Parquet files found in directory: {dir_path}\") \n", - " # Concatenate all Parquet files into a single DataFrame\n", - " full_df = pd.concat(\n", - " pd.read_parquet(parquet_file)\n", - " for parquet_file in parquet_files\n", - " ).reset_index(drop=True)\n", - " \n", - " return full_df\n", - "\n", - "\n", - "input_df=read_parquet_bulk(parquet_data_output)\n", - "\n", - "print(\"No of rows, No of columns\",input_df.shape)\n", - "print(\"Sample data \\n \")\n", - "input_df.head(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Exact deduplication\n", - "\n", - "This step will find exact duplicates in the 'content' column and remove them. This is done by computing SHA256 hash on the code files and remove records having identical hashes.\n", - "\n", - "The transform specific params for exact deduplication are:
\n", - " _ededup_hash_cpu_ - Number of cpus per worker
\n", - " _ededup_num_hashes_ - Number of workers used to store hashes
\n", - " _ededup_doc_column_ - Name of column which has to be checked for deduplication
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bRUfjHExbd1g", - "outputId": "39459ec3-491a-4a6d-c80d-8b9bf1333a15" - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "from ededup_transform_ray import EdedupRayTransformConfiguration\n", - "\n", - "input_folder = parquet_data_output # Output of previous stage is used as input.\n", - "output_folder = \"sample_data/ededup_out\"\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "ededup_params = {\n", - " # ededup parameters\n", - " \"ededup_hash_cpu\": 0.5,\n", - " \"ededup_num_hashes\": 2,\n", - " \"ededup_doc_column\": \"contents\",\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "params = common_config_params | ededup_params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "ededup_launcher = RayTransformLauncher(EdedupRayTransformConfiguration()) \n", - "ededup_launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pprint\n", - "def read_metadata(path):\n", - " with open(path, 'r') as file:\n", - " metadata = json.load(file)\n", - " pprint.pp(metadata)\n", - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Fuzzy Deduplication\n", - "\n", - "This step will find near duplicates and remove them. The code is broken into two code cells, one for adding document ids to the parquet file and then running fuzzy dedup. Document id addition is a prerequisite for fuzzy dedup. \n", - "\n", - "We first add the document ids as an additional column to the parquet files.
\n", - "_doc_column_ - specifies name of the column containing the document (required for ID generation)
\n", - "_hash_column_ - specifies name of the column created to hold the string document id, if None, id is not generated
\n", - "_int_id_column_ - specifies name of the column created to hold the integer document id, if None, id is not generated
\n", - "At least one of hash_column or int_id_column must be specified.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "H4cYttNlbgf0", - "outputId": "72790550-fac1-4dba-a332-fb36e4dcf483" - }, - "outputs": [], - "source": [ - "input_folder = \"sample_data/ededup_out\"\n", - "output_folder = \"sample_data/docid_out\"\n", - "\n", - "\n", - "from doc_id_transform_ray import DocIDRayTransformConfiguration\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "doc_id_params = {\n", - " # doc id configuration\n", - " \"doc_id_doc_column\": \"contents\",\n", - " \"doc_id_hash_column\": \"hash_column\",\n", - " \"doc_id_int_column\": \"int_id_column\",\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "params = doc_id_params | common_config_params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "launcher = RayTransformLauncher(DocIDRayTransformConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_df=read_parquet_bulk(output_folder)\n", - "input_df.head(1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Post adding the document ids, the next step is to run fuzzy deduplication. We apply a two-step method for this: (1) compute MinHashes of all the documents and then utilize Locally Sensitive Hashing (LSH) to group documents based on their MinHash fingerprints, (2) measure Jaccard similarity between each pair of documents\n", - "in the same bucket and annotate documents except one as duplicates based on a similarity\n", - "threshold. \n", - "\n", - "Some important transform specific params are:
\n", - "_fdedup_doc_column_ - Column to be used for deduplication
\n", - "_fdedup_threshold_ - specifies the Jaccard similarity threshold (default is 0.7)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b11MMQEheO6q", - "outputId": "4e6f4d73-4e60-4a28-b3c5-392c8c220111" - }, - "outputs": [], - "source": [ - "input_folder = \"sample_data/docid_out\"\n", - "output_folder = \"sample_data/fdedup_out\"\n", - "\n", - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from fdedup_transform_ray import FdedupRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "fdedup_params = {\n", - " # columns used\n", - " \"fdedup_doc_column\": \"contents\",\n", - " \"fdedup_id_column\": \"int_id_column\",\n", - " \"fdedup_cluster_column\": \"hash_column\",\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "params = common_config_params| fdedup_params\n", - "\n", - "# Pass commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "fdedup_launcher = RayTransformLauncher(FdedupRayTransformConfiguration())\n", - "fdedup_launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Programming Language Selection\n", - "\n", - "This module helps retain the code files for language of interest which can be specified using selected_languages_file. Post this step, a new column is added, that contains the programming language name. One can use the code in the Filtering step to do analytics on how many files are found for which languages and thereby selectively filter. \n", - "\n", - "The important parameters used by this transform are:
\n", - "_lang_allowed_langs_file_key_ - A file with a list of allowed languages.
\n", - "_lang_lang_column_key_ - The name of column which has programming language.
\n", - "_lang_output_column_key_ - The name of annotation column.
\n", - "\n", - "For this demo, we will use this [file](https://github.com/IBM/data-prep-kit/blob/dev/transforms/code/proglang_select/python/test-data/languages/allowed-code-languages.txt) to specify languages of interest and the module will add a new column called \"language_of_interest\" which can have two values 0/1. 1 is added for all rows that have code files belonging to programming language specified in the list." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QGaG8NWUAbAu", - "outputId": "ac40800f-d48a-4e64-c488-da8a16b7f6d5" - }, - "outputs": [], - "source": [ - "input_folder = \"sample_data/fdedup_out\"\n", - "output_folder = \"sample_data/ps_out\"\n", - "\n", - "# download allowed-code-languages.txt\n", - "# !wget https://raw.githubusercontent.com/IBM/data-prep-kit/dev/transforms/code/proglang_select/python/test-data/languages/allowed-code-languages.txt\n", - "\n", - "# Create a file with language of interest\n", - "! echo \"JavaScript\\nC++\\nC\\nGo\\nJava\" >> allowed-code-languages.txt\n", - "\n", - "selected_languages_file = \"./allowed-code-languages.txt\"\n", - "\n", - "from proglang_select_transform_ray import ProgLangSelectRayConfiguration\n", - "from proglang_select_transform import (\n", - " lang_allowed_langs_file_key,\n", - " lang_lang_column_key,\n", - " lang_output_column_key,\n", - ")\n", - "\n", - "# create parameters\n", - "language_column_name = \"language\"\n", - "annotated_column_name = \"language_of_interest\"\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "langselect_config = {\n", - " lang_allowed_langs_file_key: selected_languages_file,\n", - " lang_lang_column_key: language_column_name,\n", - " lang_output_column_key: annotated_column_name,\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "params = common_config_params| langselect_config\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(ProgLangSelectRayConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_parquet_bulk(output_folder).head(10)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Code Quality\n", - "\n", - "We experiment with various code quality metrics but finally retain the four code quality metrics used by (Li et al., 2023) to balance the tradeoff between code quality versus data volume.\n", - "\n", - "Quality metrics\n", - "\n", - "'line_mean': Average of the total line lengths.\n", - "'line_max': Maximum line length present .\n", - "'total_num_lines': Total number of lines present\n", - "'avg_longest_lines': Average of the first n longest lines, where n can be any number you choose.\n", - "'alphanum_frac': Calculates average of alpha numeric with respect to total data\n", - "'char_token_ratio': Computes character/token ratio of the file with tokenizer\n", - "'autogenerated': Check if file is autogenerated by looking for keywords in the first few lines of the file.\n", - "'config_or_test': Check if file is a configuration file or a unit test \n", - "'has_no_keywords': Check if a python file has none of the keywords - for funcion, class, for loop, while loop.\n", - "'has_few_assignments': Check if file uses symbol '=' less than 'minimum' times\n", - "'is_xml': Check if input data is xml content\n", - "'is_html': Check if input data is HTML files based on displayed text VS code ratio" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = \"sample_data/ps_out\"\n", - "output_folder = \"sample_data/cq_out\"\n", - "\n", - "from code_quality_transform_ray import CodeQualityRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "# ??\n", - "\n", - "\n", - "language_column_name = \"language\"\n", - "params = {\n", - " \"cq_contents_column_name\": \"contents\",\n", - " \"cq_language_column_name\": language_column_name,\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "params = common_config_params| params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(CodeQualityRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_parquet_bulk(output_folder).head(10)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oXu_i9jLAo9H" - }, - "source": [ - "## 6. Filtering\n", - "\n", - "This step can be used to filter the code files based on our chosen conditions. In this demo example, we have only used one annotation of adding programming language names for each code file. To demonstrate the utility, we will use this module to retain only code files of interest." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OAl7B58oAyZQ", - "outputId": "5fc229ef-bb87-4e34-9302-1670b8832d97" - }, - "outputs": [], - "source": [ - "input_folder = \"sample_data/cq_out\"\n", - "output_folder = \"sample_data/filter_out\"\n", - "\n", - "\n", - "from filter_transform import (\n", - " filter_columns_to_drop_cli_param,\n", - " filter_criteria_cli_param,\n", - " filter_logical_operator_cli_param,\n", - ")\n", - "from filter_transform_ray import FilterRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "# This is just an example criteria to filter\n", - "filter_criteria = [\n", - " \"language_of_interest = 1\",\n", - " \"total_num_lines > 10 AND total_num_lines < 90\"\n", - "]\n", - "filter_logical_operator = \"AND\"\n", - "filter_columns_to_drop = [\"language_of_interest\", \"hash_column\"]\n", - "\n", - "filter_params = {\n", - " filter_criteria_cli_param: filter_criteria,\n", - " filter_columns_to_drop_cli_param: filter_columns_to_drop,\n", - " filter_logical_operator_cli_param: filter_logical_operator,\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(common_config_params| filter_params)\n", - "launcher = RayTransformLauncher(FilterRayTransformConfiguration())\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Semantic Ordering of Code Files\n", - "\n", - "In this step, we order the code files such that we pack files from the same repository together, arranging them to prioritize semantic dependencies. We identify these dependencies by analyzing file imports and create a directed acyclic graph, where each file is a node and edges represent API imports between files. After breaking any cycles in the graph, we perform a topological sort to establish an ordering of files based on their semantic dependencies. We then organize the files in a repository by placing documentation and build files first, followed by the ordered set of files with semantic dependencies, and finally the remaining non-connected files. These non-connected files are arranged according to their folder structure, using a depth-first search to traverse the repository. Finally, we determine the dominant programming language of a repository based on file extensions and presence of build files, to organise repo-ordered files by programming languages.\n", - "\n", - "\n", - "This transform has following parameters:
\n", - " _repo_lvl_sorting_enabled_ - If True, the repo level output is sorted using _repo_lvl_sorting_algo_
\n", - " _repo_lvl_sorting_algo_ - Select the sorting algorithm to be used for repo level sorting. Use SORT_SEMANTIC_NORMALISED to organise by semantic dependencies or SORT_BY_PATH to arrange files based on folder structure in a repository.
\n", - " _repo_lvl_store_backend_dir_ - Directory to use for local store. Needed only when repo_lvl_store_type=local
\n", - " _repo_lvl_output_by_langs_ - If True, it organises output into folders of programming language.
\n", - " _repo_lvl_combine_rows_ - If True, it combines the contents of repo into a single row.
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = \"sample_data/filter_out\"\n", - "output_folder = \"sample_data/rlo_out\"\n", - "\n", - "import tempfile\n", - "from repo_level_order_transform import RepoLevelOrderRayTransformConfiguration\n", - "with tempfile.TemporaryDirectory() as tmpdirname:\n", - "\n", - " # create parameters\n", - " local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - " }\n", - "\n", - " worker_options = {\"num_cpus\": 0.8}\n", - " code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "\n", - " repo_level_params = {\n", - " \"repo_lvl_sorting_algo\": \"SORT_SEMANTIC_NORMALISED\",\n", - " \"repo_lvl_store_type\": \"local\",\n", - " \"repo_lvl_store_backend_dir\": tmpdirname,\n", - " \"repo_lvl_output_by_langs\": True,\n", - " \"repo_lvl_combine_rows\": True,\n", - " \"repo_lvl_sorting_enabled\": True,\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - " }\n", - "\n", - " \n", - " sys.argv = ParamsUtils.dict_to_req(d= common_config_params| repo_level_params)\n", - " launcher = RayTransformLauncher(RepoLevelOrderRayTransformConfiguration())\n", - " launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "byK75Kb1A3E7" - }, - "source": [ - "## 8. Tokenization\n", - "\n", - "Next, we tokenize the data to be used for fine tuning. \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kBYg93WMBBq6", - "outputId": "b3e0541e-4a3d-46f4-8809-ccc8778a53fc" - }, - "outputs": [], - "source": [ - "input_folder = \"sample_data/rlo_out\"\n", - "output_folder = \"sample_data/tokenize_out\"\n", - "\n", - "from tokenization_transform_ray import TokenizationRayConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "tf_params= {\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - "}\n", - "sys.argv = ParamsUtils.dict_to_req(d=common_config_params| tf_params)\n", - "# create launcher\n", - "launcher = RayTransformLauncher(TokenizationRayConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_metadata(f\"{output_folder}/metadata.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "read_parquet_bulk(f\"{output_folder}/C\").head(5)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xFUrzzjeBFfJ" - }, - "source": [ - "**The data is now ready for extended pretraining or fine tuning using any open source code models.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "TPU", - "colab": { - "gpuType": "V28", - "provenance": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/notebooks/archive/code_developers/Makefile b/examples/notebooks/archive/code_developers/Makefile deleted file mode 100644 index dcc6302f7..000000000 --- a/examples/notebooks/archive/code_developers/Makefile +++ /dev/null @@ -1,14 +0,0 @@ -REPOROOT=../../.. -# Use make help, to see the available rules - -include $(REPOROOT)/.make.defaults - -clean:: .defaults.clean - -venv:: .defaults.ray-lib-src-venv - @# Help: Install requirements from all transforms into a venv - . ./venv/bin/activate && bash ./prepare_env.sh - -jupyter:: - @# Help: Activate the venv and start the example notebook - . ./venv/bin/activate && . ./launch.sh diff --git a/examples/notebooks/archive/code_developers/README.md b/examples/notebooks/archive/code_developers/README.md deleted file mode 100644 index 9ef438a9f..000000000 --- a/examples/notebooks/archive/code_developers/README.md +++ /dev/null @@ -1,20 +0,0 @@ -## Demo Notebook - -We can launch a jupyter notebook to try out the library or transforms or to build a usecase. - - -## One time Step - -1. Run this step install the dependancies. - -**Prepare virtualenv** - -`make venv` - -## Launch - -2. After dependancies are installed, we can launch the jupyter notebook - -`make jupyter` - -It will launch browser with the jupyter notebook where we can try out the transforms. diff --git a/examples/notebooks/archive/code_developers/demo_with_launcher.ipynb b/examples/notebooks/archive/code_developers/demo_with_launcher.ipynb deleted file mode 100644 index 5f2b8a1b5..000000000 --- a/examples/notebooks/archive/code_developers/demo_with_launcher.ipynb +++ /dev/null @@ -1,1334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "841e533d-ebb3-406d-9da7-b19e2c5f5866", - "metadata": {}, - "source": [ - "
\n", - "

Demo: Data Prep Kit

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "053ecf08-5f62-4b99-9347-8a0955843d21", - "metadata": {}, - "source": [ - "## Overview\n", - "Welcome to the demo notebook! Inside, you will find an end-to-end sample data pipeline designed for processing code datasets, beginning with GitHub repositories (.zip files) and culminating in processed data. This notebook provides the following transforms for processing the data. \n", - "\n", - "- [Ingest2parquet](#item1)\n", - "- [Exact Dedup](#item2)\n", - "- [Doc_ID generation](#item3)\n", - "- [Fuzzy Dedup](#item4)\n", - "- [Programming Language Select](#item5)\n", - "- [Code quality](#item6)\n", - "- [Filtering](#item7)\n", - "- [Tokenization](#item8)\n", - "\n", - "### Getting started\n", - "\n", - "If you want to try this pipeline on your data, you need to download your github repositories, as .zip files. Please refer to steps below for the same. One can also try it on sample data by downloading a few repos of interest.\n", - "\n", - "Here's how to download a GitHub repository in ZIP format:\n", - "\n", - "1. Go to the desired repository on GitHub.\n", - "2. Click the \"Code\" button near the top right corner of the repository.\n", - "3. Click the \"Download ZIP\" button.\n", - "\n", - "This will download a ZIP archive of the entire repository to your computer.\n", - "\n", - "Follow these steps and download some repositories from github into a folder. Now your data is ready.\n", - "\n", - "The folder containing this data would serve as the input to the pipeline. Assign the path of this data folder to the variable `zip_input_folder` in the below cell. \n" - ] - }, - { - "cell_type": "markdown", - "id": "bd5d976e-cb4c-4469-af39-4b7ea507e9d8", - "metadata": {}, - "source": [ - "### Import Common python modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "66178913-42b8-426b-a2e9-9587268fd05b", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import os\n", - "import sys\n", - "\n", - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "from data_processing.utils import ParamsUtils" - ] - }, - { - "cell_type": "markdown", - "id": "72510ae6-48b0-4b88-9e13-a623281c3a63", - "metadata": {}, - "source": [ - "### Set input/output path variables for the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "60ac8bee-0960-4309-b225-d7a211b14262", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NO INPUT DATA\n", - "Please set `zip_input_folder` variable to path containing data\n" - ] - } - ], - "source": [ - "# Example\n", - "# We can set input paths here\n", - "zip_input_folder = \"input_data\"\n", - "\n", - "if not os.path.exists(zip_input_folder):\n", - " print (\"NO INPUT DATA\")\n", - " print (\"Please set `zip_input_folder` variable to path containing data\")\n", - "\n", - "# make sure the paths are correct\n", - "data_base_path = \"test-data\"\n", - "\n", - "parquet_data_output = os.path.join(data_base_path, \"parquet_input\")\n", - "\n", - "ededup_out = os.path.join(data_base_path, \"ededup_out\")\n", - "\n", - "doc_id_out = os.path.join(data_base_path, \"doc_id_out\")\n", - "fdedup_out = os.path.join(data_base_path, \"fdedup_out\")\n", - "\n", - "lang_out = os.path.join(data_base_path,\"lang_out\")\n", - "cq_out = os.path.join(data_base_path,\"cq_out\")\n", - "\n", - "filter_out = os.path.join(data_base_path ,\"filter_out\")\n", - "tokensization_out = os.path.join(data_base_path ,\"tokenization_out\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2449e5c7-078c-4ad6-a2f6-21d39d4da3fb", - "metadata": {}, - "source": [ - "## 1. Convert data to parquet using ingest2parquet [<-](#top)\n", - "_zip_ to _parquet_ \n", - "\n", - "Raw code data files which are in zip format are converted to parquet files, where each row of the parquet file corresponds to a separate code file. Apart from the contents of the code file, every row also contains a unique document id, file URL, name of the repository, source of the data, date of acquisition and license of the repository. For every code file, a language field is also added, which is detected using the filename\n", - "extensions.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0c574c4-9dc4-4dab-9ad6-b5338207e67a", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "482605b2-d814-456d-9195-49a2ec454ef0", - "metadata": {}, - "outputs": [], - "source": [ - "# For this stage input folder contains the zip files, each zip file contains a github repo.\n", - "\n", - "input_folder = zip_input_folder\n", - "output_folder = parquet_data_output" - ] - }, - { - "cell_type": "markdown", - "id": "9bb15f02-ab5c-4525-a536-cfa1fd2ba70b", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0cd8ebd-bf71-42d6-a397-8df0c7b66a26", - "metadata": {}, - "outputs": [], - "source": [ - "import ast\n", - "import os\n", - "import sys\n", - "\n", - "from code2parquet_transform import (\n", - " detect_programming_lang_cli_key,\n", - " supported_langs_file_cli_key,\n", - ")\n", - "from code2parquet_transform_ray import CodeToParquetRayConfiguration\n", - "from data_processing.utils import GB, ParamsUtils\n", - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "\n", - "\n", - "# create parameters\n", - "supported_languages_file = os.path.abspath(\n", - " \"../../../transforms/code/code2parquet/python/test-data/languages/lang_extensions.json\"\n", - ")\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8, \"memory\": 2 * GB}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "ingest_config = {\n", - " supported_langs_file_cli_key: supported_languages_file,\n", - " detect_programming_lang_cli_key: True,\n", - "}\n", - "\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " \"data_files_to_use\": ast.literal_eval(\"['.zip']\"),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=(params | ingest_config))\n", - "# create launcher\n", - "launcher = RayTransformLauncher(CodeToParquetRayConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "4692975c-49ff-41ae-810e-0f5bc0bbdc53", - "metadata": {}, - "source": [ - "## 2. Exact Dedup [<-](#top) \n", - "\n", - "Remove documents having identical code to remove bias in the training data. On the content of each document, a SHA256 hash is computed,\n", - "followed by de-duplication of record having identical hashes." - ] - }, - { - "cell_type": "markdown", - "id": "5acfd3a2-a236-4143-bcfc-15804f1da7fe", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "58a0e1f6-ff53-40aa-96b1-096ade4bd1c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/parquet_input\n", - "test-data/ededup_out\n" - ] - } - ], - "source": [ - "## For this stage the input is the folder containing parquet data which is output from the ingest2parquet tool\n", - "\n", - "input_folder = parquet_data_output\n", - "output_folder = ededup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "3661cb37-39c7-4b09-a784-925bfa9eaf1e", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a624b2b2-faad-4325-ac7d-53a840f564ef", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:31:45 INFO - Running locally\n", - "23:31:45 INFO - exact dedup params are {'hash_cpu': 0.5, 'num_hashes': 2, 'doc_column': 'contents'}\n", - "23:31:45 INFO - data factory data_ is using local data access: input_folder - test-data/parquet_input output_folder - test-data/ededup_out\n", - "23:31:45 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:31:45 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:31:45 INFO - pipeline id pipeline_id\n", - "23:31:45 INFO - code location None\n", - "23:31:45 INFO - number of workers 3 worker options {'num_cpus': 0.8}\n", - "23:31:45 INFO - actor creation delay 0\n", - "23:31:45 INFO - job details {'job category': 'preprocessing', 'job name': 'ededup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:31:47,958\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:48 INFO - orchestrator started at 2024-06-19 23:31:48\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:48 INFO - Number of files is 5, source profile {'max_file_size': 7.679162979125977, 'min_file_size': 0.010193824768066406, 'total_file_size': 8.804475784301758}\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:48 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 30.716969300061464, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:48 INFO - Number of workers - 3 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:49 INFO - Completed 1 files in 0.011938178539276123 min\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:49 INFO - Completed 2 files in 0.011993948618570964 min\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:49 INFO - Completed 2 files (40.0%) in 0.011996277173360189 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:49 INFO - Completed processing 5 files in 0.01667089859644572 min\n", - "\u001b[36m(orchestrate pid=52070)\u001b[0m 23:31:49 INFO - done flushing in 0.0008389949798583984 sec\n", - "23:31:59 INFO - Completed execution in 0.2331868847211202 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Import ededup transform configuration\n", - "from ededup_transform_ray import EdedupRayTransformConfiguration\n", - "\n", - "\n", - "# Prepare the commandline params\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # ededup parameters\n", - " \"ededup_hash_cpu\": 0.5,\n", - " \"ededup_num_hashes\": 2,\n", - " \"ededup_doc_column\": \"contents\",\n", - "}\n", - "\n", - "# Pass the commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "ededup_launcher = RayTransformLauncher(EdedupRayTransformConfiguration())\n", - "# launch\n", - "ededup_launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "f15f4d00-33bb-4d9a-9f34-4d7f3ee0b7bc", - "metadata": {}, - "source": [ - "## 3. DOC ID generation [<-](#top) \n", - "\n", - "This transform annotates documents with document \"ids\". It supports the following transformations of the original data:\n", - "\n", - " - Adding document hash: this enables the addition of a document hash-based id to the data. The hash is calculated with `hashlib.sha256(doc.encode(\"utf-8\")).hexdigest()`. To enable this annotation, set hash_column to the name of the column, where you want to store it.\n", - " - Adding integer document id: this allows the addition of an integer document id to the data that is unique across all rows in all tables provided to the transform() method. To enable this annotation, set int_id_column to the name of the column, where you want to store it. **This is a pre-requisite for fuzzy dedup** in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e6f62394-fbde-495c-bbbb-83161b006bed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/ededup_out\n", - "test-data/doc_id_out\n" - ] - } - ], - "source": [ - "# Input for this stage is the output of exact dedeup component\n", - "# output of this component makes it possible for fdedup component to run on data.\n", - "\n", - "input_folder = ededup_out\n", - "output_folder = doc_id_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a6daf36d-686c-4e0a-aabf-ce55f999bb2d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:32:01 INFO - Running locally\n", - "23:32:01 INFO - Doc id parameters are : {'doc_column': 'contents', 'hash_column': 'hash_column', 'int_column': 'int_id_column'}\n", - "23:32:01 INFO - data factory data_ is using local data access: input_folder - test-data/ededup_out output_folder - test-data/doc_id_out\n", - "23:32:01 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:32:01 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:32:01 INFO - pipeline id pipeline_id\n", - "23:32:01 INFO - code location None\n", - "23:32:01 INFO - number of workers 3 worker options {'num_cpus': 0.8}\n", - "23:32:01 INFO - actor creation delay 0\n", - "23:32:01 INFO - job details {'job category': 'preprocessing', 'job name': 'doc_id', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:32:03,187\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:03 INFO - orchestrator started at 2024-06-19 23:32:03\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:03 INFO - Number of files is 5, source profile {'max_file_size': 7.648163795471191, 'min_file_size': 0.004547119140625, 'total_file_size': 8.755243301391602}\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:03 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 31.14116516150534, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:03 INFO - Number of workers - 3 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:04 INFO - Completed 1 files in 0.012701082229614257 min\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:04 INFO - Completed 2 files in 0.012730999787648519 min\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:04 INFO - Completed 2 files (40.0%) in 0.012731997172037761 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:04 INFO - Completed processing 5 files in 0.013421499729156494 min\n", - "\u001b[36m(orchestrate pid=52123)\u001b[0m 23:32:04 INFO - done flushing in 0.0007081031799316406 sec\n", - "23:32:14 INFO - Completed execution in 0.22250189781188964 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from doc_id_transform_ray import DocIDRayTransformConfiguration\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # doc id configuration\n", - " \"doc_id_doc_column\": \"contents\",\n", - " \"doc_id_hash_column\": \"hash_column\",\n", - " \"doc_id_int_column\": \"int_id_column\",\n", - "}\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "launcher = RayTransformLauncher(DocIDRayTransformConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "85309751-8556-41c6-ac32-84acc941bc8d", - "metadata": {}, - "source": [ - "## 4. Fuzzy Dedup [<-](#top) \n", - "\n", - "Post exact deduplication, fuzzy deduplication is applied with\n", - "the goal of removing code files that may have slight variations and thereby unbiasing\n", - "the data further. Small variations are quite commonly seen in code data in the form\n", - "of variations in the values of variables, addittion of logging statements etc. Find near-\n", - "duplicate." - ] - }, - { - "cell_type": "markdown", - "id": "fcf574a3-b287-419c-9c86-07b828b41ca6", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9e431c8c-c7c7-48de-ba5f-2c4649c35399", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/doc_id_out\n", - "test-data/fdedup_out\n" - ] - } - ], - "source": [ - "## Input to this component is the output of doc_id generator component. \n", - "\n", - "input_folder = doc_id_out\n", - "output_folder = fdedup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "f4c82a8f-b513-4fe5-b172-d41b104b54f3", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3864ff77-e9a8-48f7-973b-c3b3aef1a94f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:32:16 INFO - Running locally\n", - "23:32:16 INFO - fuzzy dedup params are {'doc_column': 'contents', 'id_column': 'int_id_column', 'cluster_column': 'hash_column', 'bucket_cpu': 0.5, 'mhash_cpu': 0.5, 'doc_cpu': 0.5, 'num_doc_actors': 2, 'num_minhash_actors': 1, 'num_bucket_actors': 1, 'num_preprocessors': 2, 'num_permutations': 64, 'threshold': 0.8, 'shingles_size': 5, 'delimiters': ' ', 'snapshot_delay': 1, 'use_bucket_snapshot': False, 'use_doc_snapshot': False, 'random_delay_limit': 10, 'worker_options': {'num_cpus': 0.8}}\n", - "23:32:16 INFO - data factory data_ is using local data access: input_folder - test-data/doc_id_out output_folder - test-data/fdedup_out\n", - "23:32:16 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:32:16 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:32:16 INFO - pipeline id pipeline_id\n", - "23:32:16 INFO - code location None\n", - "23:32:16 INFO - number of workers 3 worker options {'num_cpus': 0.8}\n", - "23:32:16 INFO - actor creation delay 0\n", - "23:32:16 INFO - job details {'job category': 'preprocessing', 'job name': 'fdedup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:32:18,484\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - orchestrator started at 2024-06-19 23:32:19\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - Number of files is 5, source profile {'max_file_size': 7.7561540603637695, 'min_file_size': 0.005639076232910156, 'total_file_size': 8.945060729980469}\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 31.387203980237246, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - Number of workers - 3 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - starting run from the beginning\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - continuing from the very beginning\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - Fuzzy: num buckets 5, bucket length 11\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - created 1 bucket actors\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - created 1 minhash actors\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - Table preprocessing uses 2 readers\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:19 INFO - created 2 table processor actors\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:25 INFO - Completed 1 files in 0.09585129817326864 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:26 INFO - Completed 2 files in 0.11462793350219727 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:29 INFO - Completed 3 files in 0.1648825526237488 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:29 INFO - Completed 3 files (60.0%) in 0.16489086548487344 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:35 INFO - Completed processing 5 files in 0.2676188151041667 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:35 INFO - creating minhash snapshots\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:36 INFO - minhash snapshots created\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:36 INFO - creating bucket snapshots\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:37 INFO - bucket snapshots created\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:37 INFO - created 2 document actors\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:37 INFO - created 2 bucket processor actors\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:37 INFO - created bucket processor invoker\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:37 INFO - added invoker to bucket collectors\n", - "\u001b[36m(BucketsHash pid=52174)\u001b[0m 23:32:37 INFO - processing buckets 0 long, 13235 short\n", - "\u001b[36m(BucketsHash pid=52174)\u001b[0m 23:32:37 INFO - Done submitting long buckets\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:38 INFO - Done processing buckets in 0.01374209721883138 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:38 INFO - creating document snapshots\n", - "\u001b[36m(BucketsHashProcessorInvoker pid=52204)\u001b[0m 23:32:38 INFO - Waiting bucket processing completion\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:40 INFO - document snapshots created\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:43 INFO - Completed 1 files in 0.04649122953414917 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:45 INFO - Completed 2 files in 0.0795912504196167 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:45 INFO - Completed 2 files (40.0%) in 0.0795974850654602 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:54 INFO - Completed processing 5 files in 0.23002409934997559 min\n", - "\u001b[36m(orchestrate pid=52167)\u001b[0m 23:32:54 INFO - done flushing in 0.004540920257568359 sec\n", - "23:33:04 INFO - Completed execution in 0.791690719127655 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from fdedup_transform_ray import FdedupRayTransformConfiguration\n", - "\n", - "# create parameters\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # Orchestration parameters\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # columns used\n", - " \"fdedup_doc_column\": \"contents\",\n", - " \"fdedup_id_column\": \"int_id_column\",\n", - " \"fdedup_cluster_column\": \"hash_column\",\n", - " # infrastructure\n", - " \"fdedup_bucket_cpu\": 0.5,\n", - " \"fdedup_doc_cpu\": 0.5,\n", - " \"fdedup_mhash_cpu\": 0.5,\n", - " \"fdedup_num_doc_actors\": 2,\n", - " \"fdedup_num_bucket_actors\": 1,\n", - " \"fdedup_num_minhash_actors\": 1,\n", - " \"fdedup_num_preprocessors\": 2,\n", - " # fuzzy parameters\n", - " \"fdedup_num_permutations\": 64,\n", - " \"fdedup_threshold\": 0.8,\n", - " \"fdedup_shingles_size\": 5,\n", - " \"fdedup_delimiters\": \" \"\n", - "}\n", - "\n", - "# Pass commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "fdedup_launcher = RayTransformLauncher(FdedupRayTransformConfiguration())\n", - "fdedup_launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "49d1d761-62a7-4a12-ad23-b2c268ad8ed2", - "metadata": {}, - "source": [ - "## 5. Programming language annotation [<-](#top) \n", - "\n", - "The raw data may contains many programming languages. Of this, we would wish to retain a prioritised list of selected programming languages. This component takes a file which has new line separated names of languages we need to select. It annotates the data a new column with boolean values. This column can be used by filter component to select the required languages." - ] - }, - { - "cell_type": "markdown", - "id": "3db05e1e-4c62-4367-93ca-b2ddff95e4b4", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a8ec4fb6-fa62-45d1-9aa1-596d7182b2c9", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "input_folder = fdedup_out\n", - "output_folder = lang_out \n", - "selected_languages_file = \"./test-data/allowed-code-languages.txt\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "c07e7e5a-064f-4dca-a017-4211f7a3e980", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "48dbb2a3-a6f4-4a3d-bb2f-8491fd063611", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:33:05 INFO - Running locally\n", - "23:33:05 INFO - data factory proglang_select_ is using local configuration without input/output path\n", - "23:33:05 INFO - data factory proglang_select_ max_files -1, n_sample -1\n", - "23:33:05 INFO - data factory proglang_select_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:33:05 INFO - data factory data_ is using local data access: input_folder - test-data/fdedup_out output_folder - test-data/lang_out\n", - "23:33:05 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:33:05 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:33:05 INFO - pipeline id pipeline_id\n", - "23:33:05 INFO - code location None\n", - "23:33:05 INFO - number of workers 1 worker options {'num_cpus': 0.8}\n", - "23:33:05 INFO - actor creation delay 0\n", - "23:33:05 INFO - job details {'job category': 'preprocessing', 'job name': 'proglang_select', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:33:07,489\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - orchestrator started at 2024-06-19 23:33:08\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Number of files is 5, source profile {'max_file_size': 7.656813621520996, 'min_file_size': 0.005321502685546875, 'total_file_size': 8.772536277770996}\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 31.42457122821361, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Number of workers - 1 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Getting supported languages from file ./test-data/allowed-code-languages.txt\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Supported languages b'Java\\nC\\nGo\\nText\\nPython\\nMarkdown'\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Placed language list into Ray object storage under referenceObjectRef(00d950ec0ccf9d2affffffffffffffffffffffff0100000002e1f505)\n", - "\u001b[36m(RayTransformFileProcessor pid=52252)\u001b[0m 23:33:08 INFO - Loading languages to include from Ray storage under reference ObjectRef(00d950ec0ccf9d2affffffffffffffffffffffff0100000002e1f505)\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed 1 files in 0.009732401371002198 min\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed 2 files in 0.009788183371225993 min\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed 3 files in 0.010748851299285888 min\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed 4 files in 0.010778764883677164 min\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed 4 files (80.0%) in 0.010779603322347005 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - Completed processing 5 files in 0.010830299059549967 min\n", - "\u001b[36m(orchestrate pid=52238)\u001b[0m 23:33:08 INFO - done flushing in 0.0004150867462158203 sec\n", - "23:33:18 INFO - Completed execution in 0.22255444924036663 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from proglang_select_transform_ray import ProgLangSelectRayConfiguration\n", - "from proglang_select_transform import (\n", - " lang_allowed_langs_file_key,\n", - " lang_lang_column_key,\n", - " lang_output_column_key,\n", - ")\n", - "\n", - "# create parameters\n", - "language_column_name = \"programming_language\"\n", - "annotated_column_name = \"lang_selected\"\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "langselect_config = {\n", - " lang_allowed_langs_file_key: selected_languages_file,\n", - " lang_lang_column_key: language_column_name,\n", - " lang_output_column_key: annotated_column_name,\n", - "}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 1,\n", - " # language selection specific parameters\n", - " **langselect_config,\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(ProgLangSelectRayConfiguration())\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "markdown", - "id": "0646cbb7-3046-44c0-827d-d102d3ff7cb8", - "metadata": {}, - "source": [ - "## 6. Code Quality [<-](#top) \n", - "\n", - "We experiment with various code quality metrics but finally retain\n", - "the four code quality metrics used by (Li et al., 2023) to balance the tradeoff between\n", - "code quality versus data volume. \n" - ] - }, - { - "cell_type": "markdown", - "id": "2e985668-848b-4633-b0d8-9fe70ada0c91", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9f080011-c9fe-430e-9ecc-f2220d2c8d18", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test-data/lang_out\n", - "test-data/cq_out\n" - ] - } - ], - "source": [ - "input_folder = lang_out\n", - "output_folder = cq_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "c02982c5-f398-4a1a-a9fe-42d7ae748c7c", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "29319fb9-b0d8-4f86-9bc5-b92960ad8ae5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n", - "23:33:20 INFO - Running locally\n", - "23:33:20 INFO - data factory data_ is using local data access: input_folder - test-data/lang_out output_folder - test-data/cq_out\n", - "23:33:20 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:33:20 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:33:20 INFO - pipeline id pipeline_id\n", - "23:33:20 INFO - code location None\n", - "23:33:20 INFO - number of workers 3 worker options {'num_cpus': 0.8}\n", - "23:33:20 INFO - actor creation delay 0\n", - "23:33:20 INFO - job details {'job category': 'preprocessing', 'job name': 'code_quality', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:33:22,278\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:23 INFO - orchestrator started at 2024-06-19 23:33:23\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:23 INFO - Number of files is 5, source profile {'max_file_size': 7.65715217590332, 'min_file_size': 0.005566596984863281, 'total_file_size': 8.77397632598877}\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:23 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 30.955674744211137, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:23 INFO - Number of workers - 3 with {'num_cpus': 0.8} each\n", - "\u001b[36m(RayTransformFileProcessor pid=52295)\u001b[0m /Users/shivdeep/workspace/projects/current/oss-data-prep/test-ingest2parquet/data-prep-kit/examples/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", - "\u001b[36m(RayTransformFileProcessor pid=52295)\u001b[0m warnings.warn(\n", - "\u001b[36m(RayTransformFileProcessor pid=52295)\u001b[0m Token indices sequence length is longer than the specified maximum sequence length for this model (1676 > 1024). Running this sequence through the model will result in indexing errors\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:24 INFO - Completed 1 files in 0.02450234889984131 min\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:24 INFO - Completed 2 files in 0.024543317159016927 min\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:24 INFO - Completed 2 files (40.0%) in 0.024544183413187662 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:36 INFO - Completed processing 5 files in 0.2136624813079834 min\n", - "\u001b[36m(orchestrate pid=52278)\u001b[0m 23:33:36 INFO - done flushing in 0.005311012268066406 sec\n", - "\u001b[36m(RayTransformFileProcessor pid=52294)\u001b[0m None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\u001b[32m [repeated 3x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)\u001b[0m\n", - "\u001b[36m(RayTransformFileProcessor pid=52294)\u001b[0m /Users/shivdeep/workspace/projects/current/oss-data-prep/test-ingest2parquet/data-prep-kit/examples/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(RayTransformFileProcessor pid=52294)\u001b[0m warnings.warn(\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(RayTransformFileProcessor pid=52294)\u001b[0m Token indices sequence length is longer than the specified maximum sequence length for this model (2389 > 1024). Running this sequence through the model will result in indexing errors\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "23:33:46 INFO - Completed execution in 0.4255295991897583 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "from pathlib import Path\n", - "\n", - "from code_quality_transform_ray import CodeQualityRayTransformConfiguration\n", - "from data_processing.utils import ParamsUtils\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "language_column_name = \"programming_language\"\n", - "\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " # code quality configuration\n", - " \"cq_contents_column_name\": \"contents\",\n", - " \"cq_language_column_name\": language_column_name,\n", - "}\n", - "\n", - "\n", - "Path(output_folder).mkdir(parents=True, exist_ok=True)\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(CodeQualityRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5cec9839-13b4-4d32-89c5-38f59c5a89f0", - "metadata": {}, - "source": [ - "## 7. Filtering [<-](#top) \n", - "\n", - "Filter out documents that do not meet the quality threshold for each annotation. The thresholds are computed based on a distributional\n", - "analysis as well as manual inspection of samples maintaining the balance between data quality and data volume" - ] - }, - { - "cell_type": "markdown", - "id": "78c54d69-8aee-4f0f-b74c-35dc0609270f", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a7991811-b19e-43b5-89ac-b24060c0ccfa", - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = cq_out\n", - "output_folder = filter_out" - ] - }, - { - "cell_type": "markdown", - "id": "c460e05c-aeee-4b53-9dd5-8dfa1afc0ece", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "61dea2b0-0e54-4912-8620-886e2b8420ef", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:33:47 INFO - Running locally\n", - "23:33:47 INFO - data factory data_ is using local data access: input_folder - test-data/cq_out output_folder - test-data/filter_out\n", - "23:33:47 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:33:47 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:33:47 INFO - pipeline id pipeline_id\n", - "23:33:47 INFO - code location None\n", - "23:33:47 INFO - number of workers 5 worker options {'num_cpus': 0.8}\n", - "23:33:47 INFO - actor creation delay 0\n", - "23:33:47 INFO - job details {'job category': 'preprocessing', 'job name': 'filter', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:33:49,730\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:50 INFO - orchestrator started at 2024-06-19 23:33:50\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:50 INFO - Number of files is 5, source profile {'max_file_size': 7.711193084716797, 'min_file_size': 0.009368896484375, 'total_file_size': 8.878622055053711}\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:50 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 31.283493041992188, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:50 INFO - Number of workers - 5 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:50 INFO - Completed 0 files (0.0%) in 4.8319498697916664e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:51 INFO - Completed processing 5 files in 0.013345913092295329 min\n", - "\u001b[36m(orchestrate pid=52332)\u001b[0m 23:33:51 INFO - done flushing in 0.0010788440704345703 sec\n", - "23:34:01 INFO - Completed execution in 0.22851296265920004 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "from data_processing.data_access import DataAccessLocal\n", - "from filter_transform import (\n", - " filter_columns_to_drop_cli_param,\n", - " filter_criteria_cli_param,\n", - " filter_logical_operator_cli_param,\n", - ")\n", - "from filter_transform_ray import FilterRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "# This is just an example criteria to filter\n", - "filter_criteria = [\n", - " \"total_num_lines > 10 AND total_num_lines < 90\",\n", - " \"lang_selected = 1\",\n", - "]\n", - "filter_logical_operator = \"AND\"\n", - "filter_columns_to_drop = [\"lang_selected\", \"hash_column\"]\n", - "\n", - "filter_params = {\n", - " filter_criteria_cli_param: filter_criteria,\n", - " filter_columns_to_drop_cli_param: filter_columns_to_drop,\n", - " filter_logical_operator_cli_param: filter_logical_operator,\n", - "}\n", - "\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "launcher_params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 5,\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(launcher_params | filter_params)\n", - "# Create the longer to launch with the blocklist transform.\n", - "launcher = RayTransformLauncher(FilterRayTransformConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5370950a-2a3a-4143-8218-f9b4808099ba", - "metadata": {}, - "source": [ - "## 8. Tokenization [<-](#top) \n", - "\n", - "The data tokenization transform maps a (non-empty) input table to an output table using a pre-trained tokenizer. The input table must contain at least two columns, by default named document_id and contents. The tokenization transform utilizes the pre-trained tokenizer to tokenize each row (assuming a document) in the input table to each row in the output folder.\n", - "\n", - "A pre-trained tokenizer must be specified through the --tkn_tokenizer parameter, which can be the name of a ready-for-download tokenizer from HuggingFace such as hf-internal-testing/llama-tokenizer, bigcode/starcoder or any others that can loaded by the Huggingface AutoTokenizer library. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "20a153fa-fd56-401e-86be-4f7617affcc8", - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = filter_out\n", - "output_folder = tokensization_out" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "228df6b2-bc62-494b-9697-03ece98d7853", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "23:34:02 INFO - Running locally\n", - "23:34:02 INFO - data factory data_ is using local data access: input_folder - test-data/filter_out output_folder - test-data/tokenization_out\n", - "23:34:02 INFO - data factory data_ max_files -1, n_sample -1\n", - "23:34:02 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "23:34:02 INFO - pipeline id pipeline_id\n", - "23:34:02 INFO - code location None\n", - "23:34:02 INFO - number of workers 5 worker options {'num_cpus': 0.8}\n", - "23:34:02 INFO - actor creation delay 0\n", - "23:34:02 INFO - job details {'job category': 'preprocessing', 'job name': 'Tokenization', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-06-19 23:34:04,667\tINFO worker.py:1715 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:05 INFO - orchestrator started at 2024-06-19 23:34:05\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:05 INFO - Number of files is 5, source profile {'max_file_size': 0.3004436492919922, 'min_file_size': 0.004254341125488281, 'total_file_size': 0.48981380462646484}\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:05 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 31.70830688532442, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:05 INFO - Number of workers - 5 with {'num_cpus': 0.8} each\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:05 INFO - Completed 0 files (0.0%) in 4.2994817097981775e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=52394)\u001b[0m /Users/shivdeep/workspace/projects/current/oss-data-prep/test-ingest2parquet/data-prep-kit/examples/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", - "\u001b[36m(RayTransformFileProcessor pid=52394)\u001b[0m warnings.warn(\n", - "\u001b[36m(RayTransformFileProcessor pid=52396)\u001b[0m Token indices sequence length is longer than the specified maximum sequence length for this model (2344 > 2048). Running this sequence through the model will result in indexing errors\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:07 INFO - Completed processing 5 files in 0.02799654801686605 min\n", - "\u001b[36m(orchestrate pid=52380)\u001b[0m 23:34:07 INFO - done flushing in 0.0008699893951416016 sec\n", - "\u001b[36m(RayTransformFileProcessor pid=52395)\u001b[0m 23:34:07 WARNING - table is empty, skipping processing\n", - "23:34:17 INFO - Completed execution in 0.23911206324895223 min, execution result 0\n", - "\u001b[36m(RayTransformFileProcessor pid=52396)\u001b[0m None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\u001b[32m [repeated 5x across cluster]\u001b[0m\n", - "\u001b[36m(RayTransformFileProcessor pid=52396)\u001b[0m /Users/shivdeep/workspace/projects/current/oss-data-prep/test-ingest2parquet/data-prep-kit/examples/venv/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\u001b[32m [repeated 4x across cluster]\u001b[0m\n", - "\u001b[36m(RayTransformFileProcessor pid=52396)\u001b[0m warnings.warn(\u001b[32m [repeated 4x across cluster]\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from tokenization_transform_ray import TokenizationRayConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 5,\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "# create launcher\n", - "launcher = RayTransformLauncher(TokenizationRayConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "markdown", - "id": "4a6e86b0-4d1e-455a-a44a-d09107a9dae4", - "metadata": {}, - "source": [ - "## Repo Level Ordering Transform" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4c54cc37-7c60-43f7-9193-ce919995dddf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "12:08:28 INFO - Running locally\n", - "12:08:28 INFO - data factory data_ is using local data access: input_folder - ../../../transforms/code/repo_level_ordering/ray/test-data/input output_folder - ./output\n", - "12:08:28 INFO - data factory data_ max_files -1, n_sample -1\n", - "12:08:28 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "12:08:28 INFO - pipeline id pipeline_id\n", - "12:08:28 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "12:08:28 INFO - number of workers 2 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "12:08:28 INFO - actor creation delay 0\n", - "12:08:28 INFO - job details {'job category': 'preprocessing', 'job name': 'repo_lvl', 'job type': 'ray', 'job id': 'job_id'}\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating Store Params\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-02 12:08:29,866\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(orchestrate pid=8886)\u001b[0m Init Store params\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - orchestrator started at 2024-09-02 12:08:30\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - Number of files is 2, source profile {'max_file_size': 0.043808937072753906, 'min_file_size': 0.04120159149169922, 'total_file_size': 0.08501052856445312}\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 28.01360473688692, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - Number of workers - 2 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - => get_transform_config started\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - dict_keys(['store_backend_dir', 'store_type', 's3_creds'])\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - <= get_transform_config\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:30 INFO - Completed 0 files (0.0%) in 0.0 min. Waiting for completion\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(RayTransformFileProcessor pid=8895)\u001b[0m Creating local store.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 INFO - Completed processing 2 files in 0.01 min\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 INFO - done flushing in 0.001 sec\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 INFO - Store Backend is None\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 INFO - Stage 1 Finished in 0:00:00.604581.\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 I - Repo level sorting is enabled. Algo: SORT_SEMANTIC_NORMALISED\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 I - normalised semantic sort enabled\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 I - Output by language enabled.\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 I - Combine rows enabled.\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:31 I - Processing 2 repos with 2 workers\n", - "\u001b[36m(orchestrate pid=8886)\u001b[0m 12:08:33 I - Finished the transform in 0:00:02.707978 \n", - "\u001b[36m(GroupByRepoActor pid=8898)\u001b[0m 12:08:33 I - Write unknown/repo2, tables: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(GroupByRepoActor pid=8898)\u001b[0m Most promiment languages: [unknown ,Tex]\n", - "\u001b[36m(GroupByRepoActor pid=8898)\u001b[0m returning from the end of function. chosen language: unknown\n", - "\u001b[36m(GroupByRepoActor pid=8899)\u001b[0m Most promiment languages: [unknown ,Markdown]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "12:08:43 INFO - Completed execution in 0.253 min, execution result 0\n", - "\u001b[36m(GroupByRepoActor pid=8899)\u001b[0m 12:08:33 I - Write unknown/SchapplM%2Frobotics-paper_ark2022_3T1R, tables: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36m(orchestrate pid=8886)\u001b[0m Creating local store.\u001b[32m [repeated 2x across cluster]\u001b[0m\n", - "\u001b[36m(GroupByRepoActor pid=8899)\u001b[0m returning from the end of function. chosen language: unknown\n" - ] - } - ], - "source": [ - "from repo_level_order_transform import RepoLevelOrderRayTransformConfiguration\n", - "\n", - "input_folder = \"../../../transforms/code/repo_level_ordering/ray/test-data/input\"\n", - "output_folder = \"./output\"\n", - "\n", - "import tempfile\n", - "\n", - "with tempfile.TemporaryDirectory() as tmpdirname:\n", - "\n", - " # create parameters\n", - " local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - " }\n", - "\n", - " worker_options = {\"num_cpus\": 0.8}\n", - " code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - " params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 2,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - " }\n", - "\n", - "\n", - " repo_level_params = {\n", - " \"repo_lvl_sorting_algo\": \"SORT_SEMANTIC_NORMALISED\",\n", - " \"repo_lvl_store_type\": \"local\",\n", - " \"repo_lvl_store_backend_dir\": tmpdirname,\n", - " \"repo_lvl_output_by_langs\": True,\n", - " \"repo_lvl_combine_rows\": True,\n", - " \"repo_lvl_sorting_enabled\": True,\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf)\n", - " }\n", - "\n", - " sys.argv= ParamsUtils.dict_to_req(d=params | repo_level_params)\n", - " launcher = RayTransformLauncher(RepoLevelOrderRayTransformConfiguration())\n", - " # Launch the ray actor(s) to process the input\n", - " launcher.launch()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/notebooks/archive/code_developers/launch.sh b/examples/notebooks/archive/code_developers/launch.sh deleted file mode 100644 index ce7aa4490..000000000 --- a/examples/notebooks/archive/code_developers/launch.sh +++ /dev/null @@ -1,25 +0,0 @@ -# Set REPOROOÞ -REPO_ROOT=$(cd ../../../ && pwd && cd - > /dev/null ) -echo $REPO_ROOT - - -# Set PYTHONPATH for `data_processing_ray` library -export PYTHONPATH=$REPO_ROOT/data-processing-lib/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/data-processing-lib/ray/src:$REPO_ROOT/data-processing-lib/python/src - -# Set PYTHONAPATH for transforms -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/malware/ray/src:$REPO_ROOT/transforms/code/malware/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/code_quality/ray/src:$REPO_ROOT/transforms/code/code_quality/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/code2parquet/ray/src:$REPO_ROOT/transforms/code/code2parquet/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/proglang_select/ray/src:$REPO_ROOT/transforms/code/proglang_select/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/universal/ededup/ray/src:$REPO_ROOT/transforms/universal/ededup/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/universal/fdedup/ray/src:$REPO_ROOT/transforms/universal/fdedup/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/universal/filter/ray/src:$REPO_ROOT/transforms/universal/filter/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/universal/doc_id/ray/src:$REPO_ROOT/transforms/universal/doc_id/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/universal/tokenization/ray/src:$REPO_ROOT/transforms/universal/tokenization/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/tools/ingest2parquet/src/ -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/ingest_2_parquet/ray/src:$REPO_ROOT/transforms/code/ingest_2_parquet/python/src -export PYTHONPATH=$PYTHONPATH:$REPO_ROOT/transforms/code/repo_level_ordering/ray/src/ - -. ./venv/bin/activate -jupyter notebook demo_with_launcher.ipynb diff --git a/examples/notebooks/archive/code_developers/prepare_env.sh b/examples/notebooks/archive/code_developers/prepare_env.sh deleted file mode 100644 index cfe1b8f9b..000000000 --- a/examples/notebooks/archive/code_developers/prepare_env.sh +++ /dev/null @@ -1,22 +0,0 @@ - - -# Set REPOROOT -REPO_ROOT=$(cd ../../../ && pwd && cd - > /dev/null ) -echo $REPO_ROOT - -pip install jupyter - -requirement_files=( -"$REPO_ROOT/tools/ingest2parquet/requirements.txt" -) - -# Iterate through the list and install requirements from each file -for requirements_file in "${requirement_files[@]}" -do - echo "Install packages from $requirements_file" - pip install -r "$requirements_file" -done - - -pip install duckdb pandas parameterized scipy transformers -pip install "networkx==3.3" "colorlog==6.8.2" "func-timeout==4.3.5" "pandas==2.2.2" "emerge-viz==2.0.0" diff --git a/examples/notebooks/archive/code_developers/requirements.txt b/examples/notebooks/archive/code_developers/requirements.txt deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/notebooks/archive/language/.gitignore b/examples/notebooks/archive/language/.gitignore deleted file mode 100644 index ea1472ec1..000000000 --- a/examples/notebooks/archive/language/.gitignore +++ /dev/null @@ -1 +0,0 @@ -output/ diff --git a/examples/notebooks/archive/language/Makefile b/examples/notebooks/archive/language/Makefile deleted file mode 100644 index dcc6302f7..000000000 --- a/examples/notebooks/archive/language/Makefile +++ /dev/null @@ -1,14 +0,0 @@ -REPOROOT=../../.. -# Use make help, to see the available rules - -include $(REPOROOT)/.make.defaults - -clean:: .defaults.clean - -venv:: .defaults.ray-lib-src-venv - @# Help: Install requirements from all transforms into a venv - . ./venv/bin/activate && bash ./prepare_env.sh - -jupyter:: - @# Help: Activate the venv and start the example notebook - . ./venv/bin/activate && . ./launch.sh diff --git a/examples/notebooks/archive/language/README.md b/examples/notebooks/archive/language/README.md deleted file mode 100644 index 9ef438a9f..000000000 --- a/examples/notebooks/archive/language/README.md +++ /dev/null @@ -1,20 +0,0 @@ -## Demo Notebook - -We can launch a jupyter notebook to try out the library or transforms or to build a usecase. - - -## One time Step - -1. Run this step install the dependancies. - -**Prepare virtualenv** - -`make venv` - -## Launch - -2. After dependancies are installed, we can launch the jupyter notebook - -`make jupyter` - -It will launch browser with the jupyter notebook where we can try out the transforms. diff --git a/examples/notebooks/archive/language/demo_with_launcher.ipynb b/examples/notebooks/archive/language/demo_with_launcher.ipynb deleted file mode 100644 index a623a8110..000000000 --- a/examples/notebooks/archive/language/demo_with_launcher.ipynb +++ /dev/null @@ -1,1274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "841e533d-ebb3-406d-9da7-b19e2c5f5866", - "metadata": {}, - "source": [ - "
\n", - "

Demo: Data Prep Kit

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "053ecf08-5f62-4b99-9347-8a0955843d21", - "metadata": {}, - "source": [ - "## Overview\n", - "Welcome to the demo notebook! Inside, you will find an end-to-end sample data pipeline designed for processing language datasets, beginning with a folder of PDF documents and culminating in a working Retrieval-Augmented Generation (RAG) system. This notebook provides the following transforms for processing the data. \n", - "\n", - "- [pdf2parquet](#item1)\n", - "- [Chunk documents](#item2)\n", - "- [Exact Dedup](#item3)\n", - "- [Doc_ID generation](#item4)\n", - "- [Fuzzy Dedup](#item5)\n", - "- [Language detection](#item6)\n", - "- [Doc quality](#item7)\n", - "- [Filtering](#item8)\n", - "- [Text encoder](#item9)\n", - "\n", - "### Getting started\n", - "\n", - "TBA" - ] - }, - { - "cell_type": "markdown", - "id": "bd5d976e-cb4c-4469-af39-4b7ea507e9d8", - "metadata": {}, - "source": [ - "### Import Common python modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "66178913-42b8-426b-a2e9-9587268fd05b", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "import os\n", - "import sys\n", - "\n", - "from data_processing_ray.runtime.ray import RayTransformLauncher\n", - "from data_processing.runtime.pure_python import PythonTransformLauncher\n", - "from data_processing.utils import ParamsUtils" - ] - }, - { - "cell_type": "markdown", - "id": "72510ae6-48b0-4b88-9e13-a623281c3a63", - "metadata": {}, - "source": [ - "### Set input/output path variables for the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "60ac8bee-0960-4309-b225-d7a211b14262", - "metadata": {}, - "outputs": [], - "source": [ - "# Example\n", - "# We can set input paths here\n", - "pdf_input_folder = \"input_data\"\n", - "\n", - "if not os.path.exists(pdf_input_folder):\n", - " print (\"NO INPUT DATA\")\n", - " print (\"Please set `pdf_input_folder` variable to path containing data\")\n", - "\n", - "# make sure the paths are correct\n", - "data_base_path = \"output\"\n", - "\n", - "parquet_data_output = os.path.join(data_base_path, \"01_parquet_input\")\n", - "\n", - "chunk_out = os.path.join(data_base_path, \"02_chunk_out\")\n", - "ededup_out = os.path.join(data_base_path, \"03_ededup_out\")\n", - "doc_id_out = os.path.join(data_base_path, \"04_doc_id_out\")\n", - "fdedup_out = os.path.join(data_base_path, \"05_fdedup_out\")\n", - "lang_out = os.path.join(data_base_path,\"06_lang_out\")\n", - "dq_out = os.path.join(data_base_path,\"07_dq_out\")\n", - "\n", - "filter_out = os.path.join(data_base_path ,\"08_filter_out\")\n", - "encoder_out = os.path.join(data_base_path ,\"09_encoder_out\")\n", - "\n", - "# Main repo root\n", - "from utils import rootdir" - ] - }, - { - "cell_type": "markdown", - "id": "2449e5c7-078c-4ad6-a2f6-21d39d4da3fb", - "metadata": {}, - "source": [ - "## 1. Convert data to parquet using pdf2parquet [<-](#top)\n", - "_pdf_ to _parquet_ \n", - "\n", - "This step is reading the input folder containing all PDF files and ingest them in a parquet table using the [Docling package](https://github.com/DS4SD/docling).\n", - "The documents are converted into a JSON format which allows to easily chunk it in the later steps.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c0c574c4-9dc4-4dab-9ad6-b5338207e67a", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "482605b2-d814-456d-9195-49a2ec454ef0", - "metadata": {}, - "outputs": [], - "source": [ - "# For this stage input folder contains the zip files, each zip file contains a github repo.\n", - "\n", - "input_folder = pdf_input_folder\n", - "output_folder = parquet_data_output" - ] - }, - { - "cell_type": "markdown", - "id": "9bb15f02-ab5c-4525-a536-cfa1fd2ba70b", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b0cd8ebd-bf71-42d6-a397-8df0c7b66a26", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:16:00 INFO - Running locally\n", - "17:16:00 INFO - pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", - "17:16:00 INFO - data factory data_ is using local data access: input_folder - input_data output_folder - output/parquet_input\n", - "17:16:00 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:16:00 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", - "17:16:00 INFO - pipeline id pipeline_id\n", - "17:16:00 INFO - code location {'github': 'github', 'commit_hash': '12345', 'path': 'path'}\n", - "17:16:00 INFO - number of workers 1 worker options {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1}\n", - "17:16:00 INFO - actor creation delay 0\n", - "17:16:00 INFO - job details {'job category': 'preprocessing', 'job name': 'pdf2parquet', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:16:04,745\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:16:07 INFO - orchestrator started at 2024-07-31 17:16:07\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:16:07 INFO - Number of files is 1, source profile {'max_file_size': 5.398899078369141, 'min_file_size': 5.398899078369141, 'total_file_size': 5.398899078369141}\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:16:07 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 33.14529113844037, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:16:07 INFO - Number of workers - 1 with {'num_cpus': 0.8, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:16:07 INFO - Completed 0 files (0.0%) in 1.8199284871419272e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m 17:16:09 INFO - Initializing models\n", - "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 145347.17it/s]\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m /Users/dol/scratch/dpk-dev/data-prep-kit/examples/notebooks/language/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m warnings.warn(\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m /Users/dol/scratch/dpk-dev/data-prep-kit/examples/notebooks/language/venv/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m warnings.warn(msg)\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m /Users/dol/scratch/dpk-dev/data-prep-kit/examples/notebooks/language/venv/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:17:16 INFO - Completed processing 1 files in 1.1512847383817038 min\n", - "\u001b[36m(orchestrate pid=98398)\u001b[0m 17:17:16 INFO - done flushing in 0.0004470348358154297 sec\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m /opt/homebrew/Cellar/python@3.10/3.10.14_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\n", - "\u001b[36m(RayTransformFileProcessor pid=98418)\u001b[0m warnings.warn('resource_tracker: There appear to be %d '\n", - "17:17:26 INFO - Completed execution in 1.4422833681106568 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import ast\n", - "import os\n", - "import sys\n", - "\n", - "from pdf2parquet_transform import (\n", - " pdf2parquet_contents_type_cli_param,\n", - " pdf2parquet_contents_types,\n", - ")\n", - "from pdf2parquet_transform_python import Pdf2ParquetPythonTransformConfiguration\n", - "from pdf2parquet_transform_ray import Pdf2ParquetRayTransformConfiguration\n", - "\n", - "from data_processing.utils import GB, ParamsUtils\n", - "\n", - "\n", - "# create parameters\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8, \"memory\": 2 * GB}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "ingest_config = {\n", - " pdf2parquet_contents_type_cli_param: pdf2parquet_contents_types.JSON,\n", - "}\n", - "\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " \"data_files_to_use\": ast.literal_eval(\"['.pdf']\"),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " # \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_code_location\": ParamsUtils.convert_to_ast(code_location),\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=(params | ingest_config))\n", - "# create launcher\n", - "launcher = RayTransformLauncher(Pdf2ParquetRayTransformConfiguration())\n", - "# launcher = PythonTransformLauncher(Pdf2ParquetPythonTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "addd5d13", - "metadata": {}, - "source": [ - "## 2. Doc chunks [<-](#top) \n", - "\n", - "Split the documents in chunks, according to their layout segmentation." - ] - }, - { - "cell_type": "markdown", - "id": "7d86f84e", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "81843d06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/parquet_input\n", - "output/chunk_out\n" - ] - } - ], - "source": [ - "## For this stage the input is the folder containing parquet data which is output from the ingest2parquet tool\n", - "\n", - "input_folder = parquet_data_output\n", - "output_folder = chunk_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "f35af440", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d34705d9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:17:48 INFO - Running locally\n", - "17:17:48 INFO - doc_chunk parameters are : {'chunking_type': , 'content_column_name': 'contents', 'output_chunk_column_name': 'contents', 'output_jsonpath_column_name': 'doc_jsonpath', 'output_pageno_column_name': 'page_number', 'output_bbox_column_name': 'bbox'}\n", - "17:17:48 INFO - data factory data_ is using local data access: input_folder - output/parquet_input output_folder - output/chunk_out\n", - "17:17:48 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:17:48 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:17:48 INFO - pipeline id pipeline_id\n", - "17:17:48 INFO - code location None\n", - "17:17:48 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:17:48 INFO - actor creation delay 0\n", - "17:17:48 INFO - job details {'job category': 'preprocessing', 'job name': 'doc_chunk', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:17:50,021\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:51 INFO - orchestrator started at 2024-07-31 17:17:51\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:51 INFO - Number of files is 1, source profile {'max_file_size': 0.0999746322631836, 'min_file_size': 0.0999746322631836, 'total_file_size': 0.0999746322631836}\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:51 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 35.02506561297923, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:51 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:51 INFO - Completed 0 files (0.0%) in 2.102057139078776e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:54 INFO - Completed processing 1 files in 0.04179106553395589 min\n", - "\u001b[36m(orchestrate pid=98829)\u001b[0m 17:17:54 INFO - done flushing in 0.0014500617980957031 sec\n", - "17:18:04 INFO - Completed execution in 0.26520586808522545 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Import doc_json_chunk transform configuration\n", - "from doc_chunk_transform_ray import DocChunkRayTransformConfiguration\n", - "\n", - "\n", - "# Prepare the commandline params\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # doc_chunk arguments\n", - " # ...\n", - "}\n", - "\n", - "# Pass the commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(DocChunkRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "4692975c-49ff-41ae-810e-0f5bc0bbdc53", - "metadata": {}, - "source": [ - "## 3. Exact Dedup [<-](#top) \n", - "\n", - "Remove documents having identical code to remove bias in the training data. On the content of each document, a SHA256 hash is computed,\n", - "followed by de-duplication of record having identical hashes." - ] - }, - { - "cell_type": "markdown", - "id": "5acfd3a2-a236-4143-bcfc-15804f1da7fe", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "58a0e1f6-ff53-40aa-96b1-096ade4bd1c0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/chunk_out\n", - "output/ededup_out\n" - ] - } - ], - "source": [ - "## For this stage the input is the folder containing parquet data which is output from the ingest2parquet tool\n", - "\n", - "input_folder = chunk_out\n", - "output_folder = ededup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "3661cb37-39c7-4b09-a784-925bfa9eaf1e", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a624b2b2-faad-4325-ac7d-53a840f564ef", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:18:05 INFO - Running locally\n", - "17:18:05 INFO - exact dedup params are {'doc_column': 'contents', 'hash_cpu': 0.5, 'num_hashes': 2}\n", - "17:18:05 INFO - data factory data_ is using local data access: input_folder - output/chunk_out output_folder - output/ededup_out\n", - "17:18:05 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:18:05 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:18:05 INFO - pipeline id pipeline_id\n", - "17:18:05 INFO - code location None\n", - "17:18:05 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:18:05 INFO - actor creation delay 0\n", - "17:18:05 INFO - job details {'job category': 'preprocessing', 'job name': 'ededup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:18:07,184\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:07 INFO - orchestrator started at 2024-07-31 17:18:07\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:07 INFO - Number of files is 1, source profile {'max_file_size': 0.04512310028076172, 'min_file_size': 0.04512310028076172, 'total_file_size': 0.04512310028076172}\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:07 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 34.54946746863425, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:07 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:07 INFO - Completed 0 files (0.0%) in 3.047784169514974e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:08 INFO - Completed processing 1 files in 0.008303900559743246 min\n", - "\u001b[36m(orchestrate pid=98943)\u001b[0m 17:18:08 INFO - done flushing in 0.0012729167938232422 sec\n", - "17:18:18 INFO - Completed execution in 0.2139238715171814 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Import ededup transform configuration\n", - "from ededup_transform_ray import EdedupRayTransformConfiguration\n", - "\n", - "\n", - "# Prepare the commandline params\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # ededup parameters\n", - " \"ededup_hash_cpu\": 0.5,\n", - " \"ededup_num_hashes\": 2,\n", - " \"ededup_doc_column\": \"contents\",\n", - "}\n", - "\n", - "# Pass the commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(EdedupRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "f15f4d00-33bb-4d9a-9f34-4d7f3ee0b7bc", - "metadata": {}, - "source": [ - "## 4. DOC ID generation [<-](#top) \n", - "\n", - "This transform annotates documents with document \"ids\". It supports the following transformations of the original data:\n", - "\n", - " - Adding document hash: this enables the addition of a document hash-based id to the data. The hash is calculated with `hashlib.sha256(doc.encode(\"utf-8\")).hexdigest()`. To enable this annotation, set hash_column to the name of the column, where you want to store it.\n", - " - Adding integer document id: this allows the addition of an integer document id to the data that is unique across all rows in all tables provided to the transform() method. To enable this annotation, set int_id_column to the name of the column, where you want to store it. **This is a pre-requisite for fuzzy dedup** in the pipeline." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e6f62394-fbde-495c-bbbb-83161b006bed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/ededup_out\n", - "output/doc_id_out\n" - ] - } - ], - "source": [ - "# Input for this stage is the output of exact dedeup component\n", - "# output of this component makes it possible for fdedup component to run on data.\n", - "\n", - "input_folder = ededup_out\n", - "output_folder = doc_id_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a6daf36d-686c-4e0a-aabf-ce55f999bb2d", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:18:19 INFO - Running locally\n", - "17:18:19 INFO - Doc id parameters are : {'doc_column': 'contents', 'hash_column': 'hash_column', 'int_column': 'int_id_column'}\n", - "17:18:19 INFO - data factory data_ is using local data access: input_folder - output/ededup_out output_folder - output/doc_id_out\n", - "17:18:19 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:18:19 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:18:19 INFO - pipeline id pipeline_id\n", - "17:18:19 INFO - code location None\n", - "17:18:19 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:18:19 INFO - actor creation delay 0\n", - "17:18:19 INFO - job details {'job category': 'preprocessing', 'job name': 'doc_id', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:18:21,364\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:21 INFO - orchestrator started at 2024-07-31 17:18:21\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:21 INFO - Number of files is 1, source profile {'max_file_size': 0.04512310028076172, 'min_file_size': 0.04512310028076172, 'total_file_size': 0.04512310028076172}\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:21 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 34.7070663459599, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:21 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:21 INFO - Completed 0 files (0.0%) in 2.1497408548990887e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:22 INFO - Completed processing 1 files in 0.009000436464945475 min\n", - "\u001b[36m(orchestrate pid=99027)\u001b[0m 17:18:22 INFO - done flushing in 0.0012788772583007812 sec\n", - "17:18:32 INFO - Completed execution in 0.21371768315633138 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from doc_id_transform_ray import DocIDRayTransformConfiguration\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # doc id configuration\n", - " \"doc_id_doc_column\": \"contents\",\n", - " \"doc_id_hash_column\": \"hash_column\",\n", - " \"doc_id_int_column\": \"int_id_column\",\n", - "}\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "launcher = RayTransformLauncher(DocIDRayTransformConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "85309751-8556-41c6-ac32-84acc941bc8d", - "metadata": {}, - "source": [ - "## 5. Fuzzy Dedup [<-](#top) \n", - "\n", - "Post exact deduplication, fuzzy deduplication is applied with\n", - "the goal of removing code files that may have slight variations and thereby unbiasing\n", - "the data further. Small variations are quite commonly seen in code data in the form\n", - "of variations in the values of variables, addittion of logging statements etc. Find near-\n", - "duplicate." - ] - }, - { - "cell_type": "markdown", - "id": "fcf574a3-b287-419c-9c86-07b828b41ca6", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "9e431c8c-c7c7-48de-ba5f-2c4649c35399", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/doc_id_out\n", - "output/fdedup_out\n" - ] - } - ], - "source": [ - "## Input to this component is the output of doc_id generator component. \n", - "\n", - "input_folder = doc_id_out\n", - "output_folder = fdedup_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "f4c82a8f-b513-4fe5-b172-d41b104b54f3", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3864ff77-e9a8-48f7-973b-c3b3aef1a94f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:18:33 INFO - Running locally\n", - "17:18:33 INFO - fuzzy dedup params are {'doc_column': 'contents', 'id_column': 'int_id_column', 'cluster_column': 'hash_column', 'bucket_cpu': 0.5, 'mhash_cpu': 0.5, 'doc_cpu': 0.5, 'num_doc_actors': 2, 'num_minhash_actors': 1, 'num_bucket_actors': 1, 'num_preprocessors': 2, 'num_permutations': 64, 'threshold': 0.8, 'shingles_size': 5, 'delimiters': ' ', 'snapshot_delay': 1, 'use_bucket_snapshot': False, 'use_doc_snapshot': False, 'random_delay_limit': 10, 'worker_options': {'num_cpus': 0.8}}\n", - "17:18:33 INFO - data factory data_ is using local data access: input_folder - output/doc_id_out output_folder - output/fdedup_out\n", - "17:18:33 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:18:33 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:18:33 INFO - pipeline id pipeline_id\n", - "17:18:33 INFO - code location None\n", - "17:18:33 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:18:33 INFO - actor creation delay 0\n", - "17:18:33 INFO - job details {'job category': 'preprocessing', 'job name': 'fdedup', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:18:35,563\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - orchestrator started at 2024-07-31 17:18:36\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Number of files is 1, source profile {'max_file_size': 0.05397510528564453, 'min_file_size': 0.05397510528564453, 'total_file_size': 0.05397510528564453}\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 34.67255554255098, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - starting run from the beginning\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - continuing from the very beginning\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Fuzzy: num buckets 5, bucket length 11\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - created 1 bucket actors\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - created 1 minhash actors\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Table preprocessing uses 2 readers\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - created 2 table processor actors\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:36 INFO - Completed 0 files (0.0%) in 2.666314442952474e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:44 INFO - Completed processing 1 files in 0.1439379135767619 min\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:44 INFO - creating minhash snapshots\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:45 INFO - minhash snapshots created\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:45 INFO - creating bucket snapshots\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:46 INFO - bucket snapshots created\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:46 INFO - created 2 document actors\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:46 INFO - created 2 bucket processor actors\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:46 INFO - created bucket processor invoker\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:46 INFO - added invoker to bucket collectors\n", - "\u001b[36m(BucketsHash pid=99121)\u001b[0m 17:18:46 INFO - processing buckets 0 long, 1080 short\n", - "\u001b[36m(BucketsHash pid=99121)\u001b[0m 17:18:46 INFO - Done submitting long buckets\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:47 INFO - Done processing buckets in 0.007944051424662273 min\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:47 INFO - creating document snapshots\n", - "\u001b[36m(BucketsHashProcessorInvoker pid=99168)\u001b[0m 17:18:47 INFO - Waiting bucket processing completion. Submitted requests 11\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:49 INFO - document snapshots created\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:49 INFO - Completed 0 files (0.0%) in 2.197424570719401e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:58 INFO - Completed processing 1 files in 0.14431485335032146 min\n", - "\u001b[36m(orchestrate pid=99116)\u001b[0m 17:18:58 INFO - done flushing in 0.002215862274169922 sec\n", - "17:19:08 INFO - Completed execution in 0.570045781135559 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from fdedup_transform_ray import FdedupRayTransformConfiguration\n", - "\n", - "# create parameters\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "code_location = {\"github\": \"github\", \"commit_hash\": \"12345\", \"path\": \"path\"}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # Orchestration parameters\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " # columns used\n", - " \"fdedup_doc_column\": \"contents\",\n", - " \"fdedup_id_column\": \"int_id_column\",\n", - " \"fdedup_cluster_column\": \"hash_column\",\n", - " # infrastructure\n", - " \"fdedup_bucket_cpu\": 0.5,\n", - " \"fdedup_doc_cpu\": 0.5,\n", - " \"fdedup_mhash_cpu\": 0.5,\n", - " \"fdedup_num_doc_actors\": 2,\n", - " \"fdedup_num_bucket_actors\": 1,\n", - " \"fdedup_num_minhash_actors\": 1,\n", - " \"fdedup_num_preprocessors\": 2,\n", - " # fuzzy parameters\n", - " \"fdedup_num_permutations\": 64,\n", - " \"fdedup_threshold\": 0.8,\n", - " \"fdedup_shingles_size\": 5,\n", - " \"fdedup_delimiters\": \" \"\n", - "}\n", - "\n", - "# Pass commandline params\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# launch\n", - "\n", - "launcher = RayTransformLauncher(FdedupRayTransformConfiguration())\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "49d1d761-62a7-4a12-ad23-b2c268ad8ed2", - "metadata": {}, - "source": [ - "## 6. Language identification [<-](#top) \n", - "\n", - "This transform identifies the language of the document components." - ] - }, - { - "cell_type": "markdown", - "id": "3db05e1e-4c62-4367-93ca-b2ddff95e4b4", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "a8ec4fb6-fa62-45d1-9aa1-596d7182b2c9", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "input_folder = fdedup_out\n", - "output_folder = lang_out \n" - ] - }, - { - "cell_type": "markdown", - "id": "c07e7e5a-064f-4dca-a017-4211f7a3e980", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "48dbb2a3-a6f4-4a3d-bb2f-8491fd063611", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:19:09 INFO - Running locally\n", - "17:19:09 INFO - lang_id parameters are : {'model_credential': 'None', 'model_kind': 'fasttext', 'model_url': 'facebook/fasttext-language-identification', 'content_column_name': 'contents', 'output_lang_column_name': 'lang', 'output_score_column_name': 'score'}\n", - "17:19:09 INFO - data factory data_ is using local data access: input_folder - output/fdedup_out output_folder - output/lang_out\n", - "17:19:09 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:19:09 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:19:09 INFO - pipeline id pipeline_id\n", - "17:19:09 INFO - code location None\n", - "17:19:09 INFO - number of workers 1 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:19:09 INFO - actor creation delay 0\n", - "17:19:09 INFO - job details {'job category': 'preprocessing', 'job name': 'lang_id', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:19:11,487\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:12 INFO - orchestrator started at 2024-07-31 17:19:12\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:12 INFO - Number of files is 1, source profile {'max_file_size': 0.04643535614013672, 'min_file_size': 0.04643535614013672, 'total_file_size': 0.04643535614013672}\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:12 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 34.089291382580996, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:12 INFO - Number of workers - 1 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:12 INFO - Completed 0 files (0.0%) in 2.1497408548990887e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=99305)\u001b[0m Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:13 INFO - Completed processing 1 files in 0.026285235087076822 min\n", - "\u001b[36m(orchestrate pid=99302)\u001b[0m 17:19:13 INFO - done flushing in 0.0004470348358154297 sec\n", - "17:19:23 INFO - Completed execution in 0.2331423838933309 min, execution result 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "from data_processing.utils import ParamsUtils\n", - "from lang_id_transform import (\n", - " content_column_name_cli_param,\n", - " model_credential_cli_param,\n", - " model_kind_cli_param,\n", - " model_url_cli_param,\n", - ")\n", - "from lang_models import KIND_FASTTEXT\n", - "from lang_id_transform_ray import LangIdentificationRayTransformConfiguration\n", - "\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "langid_config = {\n", - " model_credential_cli_param: None, #\"PUT YOUR OWN HUGGINGFACE CREDENTIAL\",\n", - " model_kind_cli_param: KIND_FASTTEXT,\n", - " model_url_cli_param: \"facebook/fasttext-language-identification\",\n", - " # content_column_name_cli_param: \"text\",\n", - "}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 1,\n", - " # language selection specific parameters\n", - " **langid_config,\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(LangIdentificationRayTransformConfiguration())\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "markdown", - "id": "0646cbb7-3046-44c0-827d-d102d3ff7cb8", - "metadata": {}, - "source": [ - "## 7. Document Quality [<-](#top) \n", - "\n", - "TBA" - ] - }, - { - "cell_type": "markdown", - "id": "2e985668-848b-4633-b0d8-9fe70ada0c91", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "9f080011-c9fe-430e-9ecc-f2220d2c8d18", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/lang_out\n", - "output/dq_out\n" - ] - } - ], - "source": [ - "input_folder = lang_out\n", - "output_folder = dq_out\n", - "\n", - "print(input_folder)\n", - "print(output_folder)" - ] - }, - { - "cell_type": "markdown", - "id": "c02982c5-f398-4a1a-a9fe-42d7ae748c7c", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "29319fb9-b0d8-4f86-9bc5-b92960ad8ae5", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:19:25 INFO - Running locally\n", - "17:19:25 INFO - doc_quality parameters are : {'text_lang': 'en', 'doc_content_column': 'contents', 'bad_word_filepath': '/Users/dol/scratch/dpk-dev/data-prep-kit/transforms/language/doc_quality/python/ldnoobw/en', 's3_cred': None, 'docq_data_factory': }\n", - "17:19:25 INFO - data factory docq_ is using local configuration without input/output path\n", - "17:19:25 INFO - data factory docq_ max_files -1, n_sample -1\n", - "17:19:25 INFO - data factory docq_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:19:25 INFO - data factory data_ is using local data access: input_folder - output/lang_out output_folder - output/dq_out\n", - "17:19:25 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:19:25 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:19:25 INFO - pipeline id pipeline_id\n", - "17:19:25 INFO - code location None\n", - "17:19:25 INFO - number of workers 3 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:19:25 INFO - actor creation delay 0\n", - "17:19:25 INFO - job details {'job category': 'preprocessing', 'job name': 'docq', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:19:26,814\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:27 INFO - orchestrator started at 2024-07-31 17:19:27\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:27 INFO - Number of files is 1, source profile {'max_file_size': 0.04783439636230469, 'min_file_size': 0.04783439636230469, 'total_file_size': 0.04783439636230469}\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:27 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 35.6348892217502, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:27 INFO - Number of workers - 3 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:27 INFO - Completed 0 files (0.0%) in 1.8517176310221354e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=99400)\u001b[0m 17:19:27 INFO - Load badwords found locally from /Users/dol/scratch/dpk-dev/data-prep-kit/transforms/language/doc_quality/python/ldnoobw/en\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:28 INFO - Completed processing 1 files in 0.009857388337453206 min\n", - "\u001b[36m(orchestrate pid=99393)\u001b[0m 17:19:28 INFO - done flushing in 0.0012810230255126953 sec\n", - "17:19:38 INFO - Completed execution in 0.2157872200012207 min, execution result 0\n", - "\u001b[36m(RayTransformFileProcessor pid=99399)\u001b[0m 17:19:27 INFO - Load badwords found locally from /Users/dol/scratch/dpk-dev/data-prep-kit/transforms/language/doc_quality/python/ldnoobw/en\u001b[32m [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "import sys\n", - "from pathlib import Path\n", - "\n", - "from doc_quality_transform import (\n", - " text_lang_cli_param,\n", - " doc_content_column_cli_param,\n", - " bad_word_filepath_cli_param,\n", - ")\n", - "from doc_quality_transform_ray import DocQualityRayTransformConfiguration\n", - "from data_processing.utils import ParamsUtils\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "doc_quality_basedir = os.path.join(rootdir, \"transforms\", \"language\", \"doc_quality\", \"python\")\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 3,\n", - " \"runtime_pipeline_id\": \"pipeline_id\",\n", - " \"runtime_job_id\": \"job_id\",\n", - " \"runtime_creation_delay\": 0,\n", - " # doc quality configuration\n", - " text_lang_cli_param: \"en\",\n", - " doc_content_column_cli_param: \"contents\",\n", - " bad_word_filepath_cli_param: os.path.join(doc_quality_basedir, \"ldnoobw\", \"en\"),\n", - "}\n", - "\n", - "\n", - "Path(output_folder).mkdir(parents=True, exist_ok=True)\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "\n", - "# create launcher\n", - "launcher = RayTransformLauncher(DocQualityRayTransformConfiguration())\n", - "# launch\n", - "launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5cec9839-13b4-4d32-89c5-38f59c5a89f0", - "metadata": {}, - "source": [ - "## 8. Filtering [<-](#top) \n", - "\n", - "Filter out documents that do not meet the quality threshold for each annotation. The thresholds are computed based on a distributional\n", - "analysis as well as manual inspection of samples maintaining the balance between data quality and data volume" - ] - }, - { - "cell_type": "markdown", - "id": "78c54d69-8aee-4f0f-b74c-35dc0609270f", - "metadata": {}, - "source": [ - "### Set Input/output Folder" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a7991811-b19e-43b5-89ac-b24060c0ccfa", - "metadata": {}, - "outputs": [], - "source": [ - "input_folder = dq_out\n", - "output_folder = filter_out" - ] - }, - { - "cell_type": "markdown", - "id": "c460e05c-aeee-4b53-9dd5-8dfa1afc0ece", - "metadata": {}, - "source": [ - "### Execute " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "61dea2b0-0e54-4912-8620-886e2b8420ef", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "from data_processing.data_access import DataAccessLocal\n", - "from filter_transform import (\n", - " filter_columns_to_drop_cli_param,\n", - " filter_criteria_cli_param,\n", - " filter_logical_operator_cli_param,\n", - ")\n", - "from filter_transform_ray import FilterRayTransformConfiguration\n", - "\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "\n", - "# TODO\n", - "# - decide which rules to apply for filtering\n", - "\n", - "\n", - "# This is just an example criteria to filter\n", - "filter_criteria = [\n", - " \"total_num_lines > 10 AND total_num_lines < 90\",\n", - " \"lang_selected = 1\",\n", - "]\n", - "filter_logical_operator = \"AND\"\n", - "filter_columns_to_drop = [\"lang_selected\", \"hash_column\"]\n", - "\n", - "filter_params = {\n", - " filter_criteria_cli_param: filter_criteria,\n", - " filter_columns_to_drop_cli_param: filter_columns_to_drop,\n", - " filter_logical_operator_cli_param: filter_logical_operator,\n", - "}\n", - "\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "launcher_params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 5,\n", - "}\n", - "\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(launcher_params | filter_params)\n", - "# Create the longer to launch with the blocklist transform.\n", - "launcher = RayTransformLauncher(FilterRayTransformConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "# launcher.launch()" - ] - }, - { - "cell_type": "markdown", - "id": "5370950a-2a3a-4143-8218-f9b4808099ba", - "metadata": {}, - "source": [ - "## 9. Text encoding [<-](#top) \n", - "\n", - "Encode text for the vector storage." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "20a153fa-fd56-401e-86be-4f7617affcc8", - "metadata": {}, - "outputs": [], - "source": [ - "# input_folder = filter_out\n", - "input_folder = dq_out\n", - "output_folder = encoder_out" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "228df6b2-bc62-494b-9697-03ece98d7853", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "17:19:40 INFO - Running locally\n", - "17:19:40 INFO - text_encoder parameters are : {'content_column_name': 'contents', 'output_embeddings_column_name': 'embeddings', 'model_name': 'BAAI/bge-small-en-v1.5'}\n", - "17:19:40 INFO - data factory data_ is using local data access: input_folder - output/dq_out output_folder - output/encoder_out\n", - "17:19:40 INFO - data factory data_ max_files -1, n_sample -1\n", - "17:19:40 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.parquet'], files to checkpoint ['.parquet']\n", - "17:19:40 INFO - pipeline id pipeline_id\n", - "17:19:40 INFO - code location None\n", - "17:19:40 INFO - number of workers 2 worker options {'num_cpus': 0.8, 'max_restarts': -1}\n", - "17:19:40 INFO - actor creation delay 0\n", - "17:19:40 INFO - job details {'job category': 'preprocessing', 'job name': 'text_encoder', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-07-31 17:19:42,211\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:44 INFO - orchestrator started at 2024-07-31 17:19:44\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:44 INFO - Number of files is 1, source profile {'max_file_size': 0.05544853210449219, 'min_file_size': 0.05544853210449219, 'total_file_size': 0.05544853210449219}\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:44 INFO - Cluster resources: {'cpus': 16, 'gpus': 0, 'memory': 34.719618225470185, 'object_store': 2.0}\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:44 INFO - Number of workers - 2 with {'num_cpus': 0.8, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:44 INFO - Completed 0 files (0.0%) in 2.968311309814453e-06 min. Waiting for completion\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:56 INFO - Completed processing 1 files in 0.20590876738230388 min\n", - "\u001b[36m(orchestrate pid=99483)\u001b[0m 17:19:56 INFO - done flushing in 0.001220703125 sec\n", - "\u001b[36m(RayTransformFileProcessor pid=99495)\u001b[0m /opt/homebrew/Cellar/python@3.10/3.10.14_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\n", - "\u001b[36m(RayTransformFileProcessor pid=99495)\u001b[0m warnings.warn('resource_tracker: There appear to be %d '\n", - "17:20:06 INFO - Completed execution in 0.4356376846631368 min, execution result 0\n", - "\u001b[36m(RayTransformFileProcessor pid=99496)\u001b[0m /opt/homebrew/Cellar/python@3.10/3.10.14_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown\n", - "\u001b[36m(RayTransformFileProcessor pid=99496)\u001b[0m warnings.warn('resource_tracker: There appear to be %d '\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from text_encoder_transform_ray import TextEncoderRayTransformConfiguration\n", - "\n", - "local_conf = {\n", - " \"input_folder\": input_folder,\n", - " \"output_folder\": output_folder,\n", - "}\n", - "worker_options = {\"num_cpus\": 0.8}\n", - "params = {\n", - " # where to run\n", - " \"run_locally\": True,\n", - " # Data access. Only required parameters are specified\n", - " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", - " # orchestrator\n", - " \"runtime_worker_options\": ParamsUtils.convert_to_ast(worker_options),\n", - " \"runtime_num_workers\": 2,\n", - " # text_encoder\n", - " \"text_encoder_model_name\": \"BAAI/bge-small-en-v1.5\",\n", - "}\n", - "\n", - "sys.argv = ParamsUtils.dict_to_req(d=params)\n", - "# create launcher\n", - "launcher = RayTransformLauncher(TextEncoderRayTransformConfiguration())\n", - "# Launch the ray actor(s) to process the input\n", - "launcher.launch()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aaca103c-b5a6-48aa-adbb-2f77b4af9299", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - 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