From 24efec51e09db72a70d31c6c7743c55933c19041 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20H=C3=B6lzer?= Date: Fri, 11 Oct 2024 17:24:44 +0200 Subject: [PATCH] Update docs to post-workshop status --- README.md | 270 --------------- docs/assets/darts_nextgen_architecture.png | Bin 928984 -> 3168809 bytes docs/assets/darts_nextgen_pipeline-steps.png | Bin 0 -> 671628 bytes docs/contribute.md | 295 +++------------- docs/darts-acquisition/quickstart.md | 9 - docs/darts-ensemble/quickstart.md | 9 - docs/darts-export/quickstart.md | 9 - docs/darts-postprocessing/quickstart.md | 9 - docs/darts-preprocessing/quickstart.md | 9 - docs/darts-segmentation/quickstart.md | 9 - docs/darts-superresolution/quickstart.md | 9 - docs/darts_nextgen_architecture.png | Bin 901474 -> 0 bytes docs/dev/arch.md | 322 ++++++++++++++++++ docs/index.md | 75 ---- .../ref.md => ref/acquisition.md} | 0 .../ref.md => ref/ensemble.md} | 0 docs/{darts-export/ref.md => ref/export.md} | 0 .../ref.md => ref/postprocessing.md} | 0 .../ref.md => ref/preprocessing.md} | 0 .../ref.md => ref/segmentation.md} | 0 .../ref.md => ref/superresolution.md} | 0 docs/xarray_ray_overhead.png | Bin 59431 -> 0 bytes mkdocs.yml | 31 +- 23 files changed, 380 insertions(+), 676 deletions(-) create mode 100644 docs/assets/darts_nextgen_pipeline-steps.png delete mode 100644 docs/darts-acquisition/quickstart.md delete mode 100644 docs/darts-ensemble/quickstart.md delete mode 100644 docs/darts-export/quickstart.md delete mode 100644 docs/darts-postprocessing/quickstart.md delete mode 100644 docs/darts-preprocessing/quickstart.md delete mode 100644 docs/darts-segmentation/quickstart.md delete mode 100644 docs/darts-superresolution/quickstart.md delete mode 100644 docs/darts_nextgen_architecture.png create mode 100644 docs/dev/arch.md rename docs/{darts-acquisition/ref.md => ref/acquisition.md} (100%) rename docs/{darts-ensemble/ref.md => ref/ensemble.md} (100%) rename docs/{darts-export/ref.md => ref/export.md} (100%) rename docs/{darts-postprocessing/ref.md => ref/postprocessing.md} (100%) rename docs/{darts-preprocessing/ref.md => ref/preprocessing.md} (100%) rename docs/{darts-segmentation/ref.md => ref/segmentation.md} (100%) rename docs/{darts-superresolution/ref.md => ref/superresolution.md} (100%) delete mode 100644 docs/xarray_ray_overhead.png diff --git a/README.md b/README.md index 7b8da13..09b4fa6 100644 --- a/README.md +++ b/README.md @@ -89,273 +89,3 @@ Please see these issues: - [Rye: Can't specify per-dependency package index / can't specify uv behavior in config file](https://github.com/astral-sh/rye/issues/1210#issuecomment-2263761535) - [UV: Add support for pinning a package to a specific index](https://github.com/astral-sh/uv/issues/171) - -## Architecture describtion - -This repository is a workspace repository, managed by [Rye](https://rye.astral.sh/). -Read more about workspaces at the [Rye docs](https://rye.astral.sh/guide/workspaces/). -Each workspace-member starts with `darts-*` and can be seen as an own package, exexpt the `darts-pipeline` member. -Each package has it's own internal functions and it's public facing API. -The public facing API of each package MUST follow the following section [API paradigms](#api-paradigms). -The `darts-pipeline` is a [`virtual` project](https://rye.astral.sh/guide/virtual/), hence it can't be installed as a package and has no public facing API. - -| Package Name | Type | Description | (Major) Dependencies - all need Xarray | Public Methods* | -| ----------------------- | -------- | ------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ----------------------------------------- | -| `darts-ensemble` | Ensemble | Ensembles the different models and run the multi-stage inference pipeline. | PyTorch, `darts-superresolution`, `darts-detection`, `darts-segmentation` | `Ensemble.infer` | -| `darts-preprocessing` | Data | Loads data and combines the features to a Xarray Dataset | GDAL | `scene_to_tiles`, `preprocess` | -| `darts-acquisition` | Data | Fetches data from the data sources | GEE, rasterio, ? | `fetch_tcvis`, `fetch_s2`, `fetch_planet` | -| `darts-export` | Data | Saves the results from inference and combines the result to the final DARTS dataset | GeoPandas, Scipy, Cucim | `export_tile` | -| `darts-superresolution` | Train | Trains a supper resolution model to scale Sentinel 2 images from 10m to 3m resolution | PyTorch | TBD | -| `darts-detection` | Train | Trains an object detection model | PyTorch | TBD | -| `darts-segmentation` | Train | Trains an segmentation model | PyTorch, segmentation_models_pytorch | TBD | -| `darts-?` | Train | Trains a ? | ? | TBD | -| `darts-evaluation` | Test | Evaluates the end-to-end process on a test dataset and external dataset | GeoPandas | `test_lewkowicz`, `test_ensemble` | -| `darts-utils` | Data | Shared utilities for data processing | Scipy, Cucim, GeoPandas | TBD | -| `darts-train-utils` | Train | Shared utilities for training | PyTorch | TBD | - -\* : These public facing function (-names) are no hard requirements, they should rather help getting an idea of what and where needs to be implemented. - -The `darts-pipeline` utilized [Ray](https://docs.ray.io/en/latest/index.html) to automaticly parallize the different computations. -However, each package should be designed so that one could build their own pipeline without Ray. -Hence, all Ray-related functions / transformations etc. should be defined in the `darts-pipeline` project. - -The packages can decide to wrap their public functions into a CLI with typer. - -The `Train` packages should also hold the code for training specific data preparation, model training and model evaluation. -These packages should get their data from (already processed) data from the `darts-preprocessing` package. -They should expose a statefull Model class with an `inference` function, which can be used by the `darts-ensemble` package. - -> TBD: Would it be better if all "inference" functions are part of the `darts-ensemble` package? -> This would remove the dependency to each of the training repositories and with that potential dependencies to unused training-specific libraries (e.g. pytorch lightning) -> TBD: Should the `darts-ensemble` package be split into `darts-inference` and `darts-ensemble`? - No -> TBD: Should the `darts-evaluation` package be merged into the `darts-ensemble` package? - No - -The packages should follow this architecture: -![DARTS nextgen architecture](./docs/assets/darts_nextgen_architecture.png) - -### Conceptual migration from thaw-slump-segmentation - -- The `darts-ensemble` package is the successor of the `process-02-inference` and `process-03-ensemble` scripts. -- The `darts-preprocessing` and `darts-acquisition` packages are the successors of the `setup-raw-data` script and manual work of obtaining data. -- The `darts-export` package is splitted from the `inference` script, should include the previous manual works of combining everything into the final dataset. -- The `darts-superresolution` package is the successor of the `superresolution` repository. -- The `darts-detection` package is a new package. -- The `darts-segmentation` package is the successor of the `train` and `prepare_data` script. -- The `darts-evaluation` package is the successor of the different manual evaluations. - -### Inference pipeline steps - -> TODO: This is a draft and should be discussed - -### API paradigms - -The packages should pass the data as Xarray Datasets between each other. Datasets can hold coordinate information aswell as other metadata (like CRS) in a single self-describing object. -Since different `tiles` do not share the same coordinates or metadata, each `tile` should be represented by a single Xarray `Dataset`. - -- Each public facing API function which in some way **transforms** data should accept a Xarray Dataset as input and return an Xarray Dataset. - - Data can also be accepted as a list of Xarray Dataset as input and returned as a list of Xarray Datasets for batched processing. - In this case, concattenation should happend internally and on `numpy` or `pytorch` level, NOT on `xarray` abstraction level. - The reason behind this it that the tiles don't share their coordinates, resulting in a lot of empty spaces between the tiles and high memory usage. - The name of the function should then be `function_batched`. -- Each public facing API function which **loads** data should return a single Xarray Dataset for each `tile`. -- Data should NOT be saved to file internally, with `darts-export` as the only exception. Instead, data should returned in-memory as a Xarray Dataset, so the user / pipeline can decide what to save and when. -- Function names should be verbs, e.g. `process`, `ensemble`, `do_inference`. -- If a function is stateless it should NOT be part of a class or wrapper -- If a function is stateful it should be part of a class or wrapper, this is important for Ray - -Here are some examples, how these API paradigms should look like. - -1. Single transformation - - ```py - import darts-package - import xarray as xr - - # User loads / creates the dataset (a single tile) by themself - ds = xr.open_dataset("...") - - # User calls the function to transform the dataset - ds = darts-package.transform(ds, **kwargs) - - # User can decide by themself what to do next, e.g. save - ds.to_netcdf("...") - ``` - -2. Batched transformation - - ```py - import darts_package - import xarray as xr - - # User loads / creates multiple datasets (hence, multiple tiles) by themself - data = [xr.open_dataset("..."), xr.open_dataset("..."), ...] - - # User calls the function to transform the dataset - data = darts_package.transform_batched(data, **kwargs) - - # User can decide by themself what to do next - data[0].whatever() - ``` - -3. Load & preprocess some data - - ```py - import darts_package - - # User calls the function to transform the dataset - ds = darts_package.load("path/to/data", **kwargs) - - # User can decide by themself what to do next - ds.whatever() - ``` - -4. Custom pipeline example - - ```py - from pathlib import Path - import darts_preprocess - import darts_inference - - DATA_DIR = Path("./data/") - MODEL_DIR = Path("./models/") - OUT_DIR = Path("./out/") - - # Inference is a stateful transformation, because it needs to load the model - # Hence, the - ensemble = darts_inference.Ensemble.load(MODEL_DIR) - - # The data directory contains subfolders which then hold the input data - for dir in DATA_DIR: - name = dir.name - - # Load the files from the processing directory - ds = darts_preprocess.load_and_preprocess(dir) - - # Do the inferencce - ds = ensemble.inference(ds) - - # Save the results - ds.to_netcdf(OUT_DIR / f"{name}-result.nc") - ``` - -5. Pipeline with Ray - - ```py - from dataclasses import dataclass - from pathlib import Path - import ray - import darts_preprocess - import darts_inference - import darts_export - - DATA_DIR = Path("./data/") - MODEL_DIR = Path("./models/") - OUT_DIR = Path("./out/") - - ray.init() - - # We need to wrap the Xarray dataset in a class, so that Ray can serialize it - @dataclass - class Tile: - ds: xr.Dataset - - # Wrapper for ray - def open_dataset_ray(row: dict[str, Any]) -> dict[str, Any]: - data = xr.open_dataset(row["path"]) - tile = Tile(data) - return { - "input": tile, - } - - # Wrapper for the preprocessing -> Stateless - def preprocess_tile_ray(row: dict[str, Tile]) -> dict[str, Tile]: - ds = darts_preprocess.preprocess(row["input"].ds) - return { - "preprocessed": Tile(ds), - "input": row["input"] - } - - # Wrapper for the inference -> Statefull - class EnsembleRay: - def __init__(self): - self.ensemble = darts_inference.Ensemble.load(MODEL_DIR) - - def __call__(self, row: dict[str, Tile]) -> dict[str, Tile]: - ds = self.ensemble.inference(row["preprocessed"].ds) - return { - "output": Tile(ds), - "preprocessed": row["preprocessed"], - "input": row["input"], - } - - # We need to add 'local:///' to tell ray that we want to use the local filesystem - files = data.glob("*.nc") - file_list = [f"local:////{file.resolve().absolute()}" for file in files] - - ds = ray.data.read_binary_files(file_list, include_paths=True) - ds = ds.map(open_dataset_ray) # Lazy open - ds = ds.map(preprocess_tile_ray) # Lazy preprocess - ds = ds.map(EnsembleRay) # Lazy inference - - # Save the results - for row in ds.iter_rows(): - darts_export.save(row["output"].ds, OUT_DIR / f"{row['input'].ds.name}-result.nc") - - ``` - -#### About the Xarray overhead with Ray - -Ray expects batched data to be in either numpy or pandas format and can't work with Xarray datasets directly. -Hence, a wrapper with custom stacking functions is needed. -This tradeoff is not small, however, the benefits in terms of maintainability and readability are worth it. - -![Xarray overhead with Ray](./docs/assets/xarray_ray_overhead.png) - -### Package dependencies - -Each package should define it's own dependencies in it's respective `pyproject.toml` file. -Also, the dependencies should be as minimal as possible. -Especially with `gdal` and `torch`, they should only be required where needed, since their setup is more complicated. - -### New package - -A new package can easily created with: - -```py -rye init darts-packagename -``` - -Rye creates a minimal project structure for us. - -The following things needs to be updates: - -1. The `pyproject.toml` file inside the new package. - - Add to the `pyproject.toml` file inside the new package is the following to enable Ruff: - - ```toml - [tool.ruff] - # Extend the `pyproject.toml` file in the parent directory... - extend = "../pyproject.toml" - ``` - - Please also provide a description and a list of authors to the file. - -2. The `.github/workflows/update_version.yml` file, to include the package in the workflow. - - Under `package` and under step `Update version in pyproject.toml`. - -3. The docs by creating a new directory with a `quickstart.md` and a `ref.md` (and optionally more) and add them to the nav inside the `mkdocs.yml`. - - To enable code detection, also add the package directory under `plugins` in the `mkdocs.yml`. - Please also add the refs to the top-level `ref.md`. - -4. The Readme of the package - -## Documentation - -The documentation is made with [Material for Mkdocs](https://squidfunk.github.io/mkdocs-material/). 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yFU0=KV*h2a|FYPBS?s?o_Ftg@5DFUh&sbu2$)Qhc15N<{?t(OL7u>RX@_zuLEXET6 literal 0 HcmV?d00001 diff --git a/docs/contribute.md b/docs/contribute.md index 3961814..d9e1b1b 100644 --- a/docs/contribute.md +++ b/docs/contribute.md @@ -10,272 +10,75 @@ At the first start, VSCode should ask you if you want to install the recommended The settings should be automaticly used by VSCode. Both should provide the developers with a better experience and enforce code-style. -## Architecture describtion +## Environment setup -This repository is a workspace repository, managed by [Rye](https://rye.astral.sh/). -Read more about workspaces at the [Rye docs](https://rye.astral.sh/guide/workspaces/). -Each workspace-member starts with `darts-*` and can be seen as an own package, exexpt the `darts-pipeline` member. -Each package has it's own internal functions and it's public facing API. -The public facing API of each package MUST follow the following section [API paradigms](#api-paradigms). -The `darts-pipeline` is a [`virtual` project](https://rye.astral.sh/guide/virtual/), hence it can't be installed as a package and has no public facing API. +Prereq: -| Package Name | Type | Description | (Major) Dependencies - all need Xarray | Public Methods* | -| ----------------------- | -------- | ------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ----------------------------------------- | -| `darts-ensemble` | Ensemble | Ensembles the different models and run the multi-stage inference pipeline. | PyTorch, `darts-superresolution`, `darts-detection`, `darts-segmentation` | `Ensemble.infer` | -| `darts-preprocessing` | Data | Loads data and combines the features to a Xarray Dataset | GDAL | `scene_to_tiles`, `preprocess` | -| `darts-acquisition` | Data | Fetches data from the data sources | GEE, rasterio, ? | `fetch_tcvis`, `fetch_s2`, `fetch_planet` | -| `darts-export` | Data | Saves the results from inference and combines the result to the final DARTS dataset | GeoPandas, Scipy, Cucim | `export_tile` | -| `darts-superresolution` | Train | Trains a supper resolution model to scale Sentinel 2 images from 10m to 3m resolution | PyTorch | TBD | -| `darts-detection` | Train | Trains an object detection model | PyTorch | TBD | -| `darts-segmentation` | Train | Trains an segmentation model | PyTorch, segmentation_models_pytorch | TBD | -| `darts-?` | Train | Trains a ? | ? | TBD | -| `darts-evaluation` | Test | Evaluates the end-to-end process on a test dataset and external dataset | GeoPandas | `test_lewkowicz`, `test_ensemble` | -| `darts-utils` | Data | Shared utilities for data processing | Scipy, Cucim, GeoPandas | TBD | -| `darts-train-utils` | Train | Shared utilities for training | PyTorch | TBD | +- [Rye](https://rye.astral.sh/): `curl -sSf https://rye.astral.sh/get | bash` +- [GDAL](https://gdal.org/en/latest/index.html): `sudo apt update && sudo apt install libpq-dev gdal-bin libgdal-dev` or for HPC `conda install conda-forge::gdal` +- Clang: `sudo apt update && sudo apt install clang` or for HPC `conda install conda-forge::clang_linux-64` -\* : These public facing function (-names) are no hard requirements, they should rather help getting an idea of what and where needs to be implemented. +> If you install GDAL via apt for linux you can view the supported versions here: . For a finer controll over the versions please use conda. -The `darts-pipeline` utilized [Ray](https://docs.ray.io/en/latest/index.html) to automaticly parallize the different computations. -However, each package should be designed so that one could build their own pipeline without Ray. -Hence, all Ray-related functions / transformations etc. should be defined in the `darts-pipeline` project. +Now first check your gdal-version: -The packages can decide to wrap their public functions into a CLI with typer. - -The `Train` packages should also hold the code for training specific data preparation, model training and model evaluation. -These packages should get their data from (already processed) data from the `darts-preprocessing` package. -They should expose a statefull Model class with an `inference` function, which can be used by the `darts-ensemble` package. - -> TBD: Would it be better if all "inference" functions are part of the `darts-ensemble` package? -> This would remove the dependency to each of the training repositories and with that potential dependencies to unused training-specific libraries (e.g. pytorch lightning) -> TBD: Should the `darts-ensemble` package be split into `darts-inference` and `darts-ensemble`? - No -> TBD: Should the `darts-evaluation` package be merged into the `darts-ensemble` package? - No - -The packages should follow this architecture: -![DARTS nextgen architecture](./assets/darts_nextgen_architecture.png) - -### Conceptual migration from thaw-slump-segmentation - -- The `darts-ensemble` package is the successor of the `process-02-inference` and `process-03-ensemble` scripts. -- The `darts-preprocessing` and `darts-acquisition` packages are the successors of the `setup-raw-data` script and manual work of obtaining data. -- The `darts-export` package is splitted from the `inference` script, should include the previous manual works of combining everything into the final dataset. -- The `darts-superresolution` package is the successor of the `superresolution` repository. -- The `darts-detection` package is a new package. -- The `darts-segmentation` package is the successor of the `train` and `prepare_data` script. -- The `darts-evaluation` package is the successor of the different manual evaluations. - -### Inference pipeline steps - -> TODO: This is a draft and should be discussed - -### API paradigms - -The packages should pass the data as Xarray Datasets between each other. Datasets can hold coordinate information aswell as other metadata (like CRS) in a single self-describing object. -Since different `tiles` do not share the same coordinates or metadata, each `tile` should be represented by a single Xarray `Dataset`. - -- Each public facing API function which in some way **transforms** data should accept a Xarray Dataset as input and return an Xarray Dataset. - - Data can also be accepted as a list of Xarray Dataset as input and returned as a list of Xarray Datasets for batched processing. - In this case, concattenation should happend internally and on `numpy` or `pytorch` level, NOT on `xarray` abstraction level. - The reason behind this it that the tiles don't share their coordinates, resulting in a lot of empty spaces between the tiles and high memory usage. - The name of the function should then be `function_batched`. -- Each public facing API function which **loads** data should return a single Xarray Dataset for each `tile`. -- Data should NOT be saved to file internally, with `darts-export` as the only exception. Instead, data should returned in-memory as a Xarray Dataset, so the user / pipeline can decide what to save and when. -- Function names should be verbs, e.g. `process`, `ensemble`, `do_inference`. -- If a function is stateless it should NOT be part of a class or wrapper -- If a function is stateful it should be part of a class or wrapper, this is important for Ray - -Here are some examples, how these API paradigms should look like. - -1. Single transformation - - ```py - import darts-package - import xarray as xr - - # User loads / creates the dataset (a single tile) by themself - ds = xr.open_dataset("...") - - # User calls the function to transform the dataset - ds = darts-package.transform(ds, **kwargs) - - # User can decide by themself what to do next, e.g. save - ds.to_netcdf("...") - ``` - -2. Batched transformation - - ```py - import darts_package - import xarray as xr - - # User loads / creates multiple datasets (hence, multiple tiles) by themself - data = [xr.open_dataset("..."), xr.open_dataset("..."), ...] - - # User calls the function to transform the dataset - data = darts_package.transform_batched(data, **kwargs) - - # User can decide by themself what to do next - data[0].whatever() - ``` - -3. Load & preprocess some data - - ```py - import darts_package - - # User calls the function to transform the dataset - ds = darts_package.load("path/to/data", **kwargs) - - # User can decide by themself what to do next - ds.whatever() - ``` - -4. Custom pipeline example - - ```py - from pathlib import Path - import darts_preprocess - import darts_inference - - DATA_DIR = Path("./data/") - MODEL_DIR = Path("./models/") - OUT_DIR = Path("./out/") - - # Inference is a stateful transformation, because it needs to load the model - # Hence, the - ensemble = darts_inference.Ensemble.load(MODEL_DIR) - - # The data directory contains subfolders which then hold the input data - for dir in DATA_DIR: - name = dir.name - - # Load the files from the processing directory - ds = darts_preprocess.load_and_preprocess(dir) - - # Do the inferencce - ds = ensemble.inference(ds) - - # Save the results - ds.to_netcdf(OUT_DIR / f"{name}-result.nc") - ``` - -5. Pipeline with Ray - - ```py - from dataclasses import dataclass - from pathlib import Path - import ray - import darts_preprocess - import darts_inference - import darts_export - - DATA_DIR = Path("./data/") - MODEL_DIR = Path("./models/") - OUT_DIR = Path("./out/") - - ray.init() - - # We need to wrap the Xarray dataset in a class, so that Ray can serialize it - @dataclass - class Tile: - ds: xr.Dataset - - # Wrapper for ray - def open_dataset_ray(row: dict[str, Any]) -> dict[str, Any]: - data = xr.open_dataset(row["path"]) - tile = Tile(data) - return { - "input": tile, - } - - # Wrapper for the preprocessing -> Stateless - def preprocess_tile_ray(row: dict[str, Tile]) -> dict[str, Tile]: - ds = darts_preprocess.preprocess(row["input"].ds) - return { - "preprocessed": Tile(ds), - "input": row["input"] - } - - # Wrapper for the inference -> Statefull - class EnsembleRay: - def __init__(self): - self.ensemble = darts_inference.Ensemble.load(MODEL_DIR) - - def __call__(self, row: dict[str, Tile]) -> dict[str, Tile]: - ds = self.ensemble.inference(row["preprocessed"].ds) - return { - "output": Tile(ds), - "preprocessed": row["preprocessed"], - "input": row["input"], - } - - # We need to add 'local:///' to tell ray that we want to use the local filesystem - files = data.glob("*.nc") - file_list = [f"local:////{file.resolve().absolute()}" for file in files] - - ds = ray.data.read_binary_files(file_list, include_paths=True) - ds = ds.map(open_dataset_ray) # Lazy open - ds = ds.map(preprocess_tile_ray) # Lazy preprocess - ds = ds.map(EnsembleRay) # Lazy inference - - # Save the results - for row in ds.iter_rows(): - darts_export.save(row["output"].ds, OUT_DIR / f"{row['input'].ds.name}-result.nc") - - ``` - -#### About the Xarray overhead with Ray - -Ray expects batched data to be in either numpy or pandas format and can't work with Xarray datasets directly. -Hence, a wrapper with custom stacking functions is needed. -This tradeoff is not small, however, the benefits in terms of maintainability and readability are worth it. - -![Xarray overhead with Ray](./assets/xarray_ray_overhead.png) - -### Package dependencies - -Each package should define it's own dependencies in it's respective `pyproject.toml` file. -Also, the dependencies should be as minimal as possible. -Especially with `gdal` and `torch`, they should only be required where needed, since their setup is more complicated. - -### New package +```sh +$ gdal-config --version +3.9.2 +``` -A new package can easily created with: +And your CUDA version (if you want to use CUDA): -```py -rye init darts-packagename +```sh +$ nvidia-smi +# Now look on the top right of the table ``` -Rye creates a minimal project structure for us. +> The GDAL version is relevant, since the version of the python bindings needs to match the installed GDAL version -The following things needs to be updated: +Now, to sync with a specific `gdal` version, add `darts-preprocessing/gdalXX` to the `--features` flag. +To sync with a specific `cuda` version, add `darts-ensemble/cuda1X` or without cuda `darts-ensemble/cpu`. +E.g.: -1. The `pyproject.toml` file inside the new package. - - Add to the `pyproject.toml` file inside the new package is the following to enable Ruff: +```sh +rye sync -f --features darts-preprocessing/gdal39,darts-ensemble/cuda12 # For CUDA 12 and GDAL 3.9.2 +``` - ```toml - [tool.ruff] - # Extend the `pyproject.toml` file in the parent directory... - extend = "../pyproject.toml" - ``` +As of right now, the supported `gdal` versions are: 3.9.2 (`gdal39`), 3.8.5 (`gdal38`), 3.8.4 (`gdal384`), 3.7.3 (`gdal37`) and 3.6.4 (`gdal36`). +If your GDAL version is not supported (yet) please sync without GDAL and then install GDAL to an new optional group. For example, if your GDAL version is 3.8.4: - Please also provide a description and a list of authors to the file. +```sh +rye sync -f +rye add --optional=gdal384 "gdal==3.8.4" +``` -2. The `.github/workflows/update_version.yml` file, to include the package in the workflow. +> IMPORTANT! If you installed any of clang or gdal with conda, please ensure that while installing dependencies and working on the project to have the conda environment activated in which you installed clang and or gdal. - Under step `Update version in pyproject.toml`. +### Troubleshoot: Rye can't find the right versions -3. The docs by creating a new directory with a `quickstart.md` and a `ref.md` (and optionally more) and add them to the nav inside the `mkdocs.yml`. +Because the `pyproject.toml` specifies additional sources, e.g. `https://download.pytorch.org/whl/cpu`, it can happen that the a package with an older version is found in these package-indexes. +If such a version is found, `uv` (the installer behind `Rye`) currently stops searching other sources for the right version and stops with an `Version not found` error. +This can look something like this: - To enable code detection, also add the package directory under `plugins` in the `mkdocs.yml`. - Please also add the refs to the top-level `ref.md`. +```sh +No solution found when resolving dependencies: + ╰─▶ Because only torchmetrics==1.0.3 is available and you require torchmetrics>=1.4.1, we can conclude that your requirements are unsatisfiable. +``` -4. The Readme of the package +To fix this you can set an environment variable to tell `uv` to search all package-indicies: -## Documentation +```sh +UV_INDEX_STRATEGY="unsafe-best-match" rye sync ... +``` -The documentation is made with [Material for Mkdocs](https://squidfunk.github.io/mkdocs-material/). To build and serve locally the docs you need run with rye: +I recommend adding the following to your `.zshrc` or `.bashrc`: ```sh -rye run mkdocs serve +# Change the behaviour of uv package resolution to enable additional sources without breaking existing version-requirements +export UV_INDEX_STRATEGY="unsafe-best-match" ``` + +Please see these issues: + +- [Rye: Can't specify per-dependency package index / can't specify uv behavior in config file](https://github.com/astral-sh/rye/issues/1210#issuecomment-2263761535) +- [UV: Add support for pinning a package to a specific index](https://github.com/astral-sh/uv/issues/171) diff --git a/docs/darts-acquisition/quickstart.md b/docs/darts-acquisition/quickstart.md deleted file mode 100644 index 86ed912..0000000 --- a/docs/darts-acquisition/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS acquisition - -Acquisition of data from various sources for the DARTS dataset. - -## Usage - -```py -import darts_acquisition -``` diff --git a/docs/darts-ensemble/quickstart.md b/docs/darts-ensemble/quickstart.md deleted file mode 100644 index 2302e44..0000000 --- a/docs/darts-ensemble/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS ensemble - -Inference and model ensembling for the DARTS dataset. - -## Usage - -```py -import darts_ensemble -``` diff --git a/docs/darts-export/quickstart.md b/docs/darts-export/quickstart.md deleted file mode 100644 index 2232543..0000000 --- a/docs/darts-export/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS export - -Dataset export for the DARTS dataset. - -## Usage - -```py -import darts_export -``` diff --git a/docs/darts-postprocessing/quickstart.md b/docs/darts-postprocessing/quickstart.md deleted file mode 100644 index 8698651..0000000 --- a/docs/darts-postprocessing/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS postprocessing - -Postprocessing steps for the DARTS dataset. - -## Usage - -```py -import darts_postprocessing -``` diff --git a/docs/darts-preprocessing/quickstart.md b/docs/darts-preprocessing/quickstart.md deleted file mode 100644 index 9f1030e..0000000 --- a/docs/darts-preprocessing/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS preprocessing - -Data preprocessing and feature engineering for the DARTS dataset. - -## Usage - -```py -import darts_preprocessing -``` diff --git a/docs/darts-segmentation/quickstart.md b/docs/darts-segmentation/quickstart.md deleted file mode 100644 index 5329667..0000000 --- a/docs/darts-segmentation/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS segmentation - -Image segmentation of thaw-slumps for the DARTS dataset. - -## Usage - -```py -import darts_segmentation -``` diff --git a/docs/darts-superresolution/quickstart.md b/docs/darts-superresolution/quickstart.md deleted file mode 100644 index f58fbee..0000000 --- a/docs/darts-superresolution/quickstart.md +++ /dev/null @@ -1,9 +0,0 @@ -# DARTS superresolution - -Image superresolution of Sentinel 2 imagery for the DARTS dataset. - -## Usage - -```py -import darts_superresolution -``` diff --git a/docs/darts_nextgen_architecture.png b/docs/darts_nextgen_architecture.png deleted file mode 100644 index a11de092b6525b1e1b6d9cb34c6969515376e693..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 901474 zcmeEuby$>b_P>IFfCx%TBLX7*(ml$HfTSSOp(sd$G{b-+f*@Isb322cFdh%B)x?OjgwC-}gRKq)&9qL1 zu^rZ>p_!p#hfLAZ#i=N>2fjZsljrmN2huPK7pHZI1I&+qU z5%T2Je|Q1Ee3451cN0Fc|I+hLV=<}8T=;LS46`w^cTXSzAH!9=nf~*g{uC5RN*wNg zMQ-3}uWmtNZ{Ai;{`BA4?b&WM?Ej8dPh-ilFsj9;By}18w|0BRBIQ4X+R^Rsj~7W& zZr|xL=lyT(7E7@EKQ)BI)j@9KpY6VyHX}gw-?9iewZ(sIrhi>sY|5E47Gyn+)aMSD z`yX!m34}=SzhqjBYIm@(1YcQ*5&8ayi#og=66;I-UvP0hMRoPsX*jj4U=*&*e@PdN zYBEIsEyMqxoBMa+{69DMZ^HS1ZtmYT-v7C|e>ZCX&&~bM&HXFb`5&A6H=X+b*xbMC z)c?ok{uRUgd!PAV()#Z@_5aV@+&N=7J}K+xoO^WwxE2til+%K-xZO{v)v*7Mf4nNe z%_^M?FPVylPLxhX`|0GH*839Ce5|a5ojM?tH()!;M?2O)Lg_x*f9cm61|$eS(xFY1 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z>bLOKj>2xn^X9>=#Z6)!P_*Ow5TEa9b+o|x!1owN8}GiEl&dSb)i=0=7@1p|eXEt+!0tA-TkAI5_E4EO*7yh>ifhuH59cHv{PCDT8KN%~c(`3# zK3Xt}`BWjbg8hxI>vJN@RB&MOvG3E;^&Wk;M~}8nlfJ{tS}CGhBPhb*rrXb8PvdK5 zMV*(M;(EGyKkO9D7d*Z>`Xe53GDUk3?zQWGCVpHu3Xz)Y?p}j0s99D*%v`q)S)$`z z1NRiwpX>ETUIv|>uy;Frb7UpoQO7vgF+RRSaj;9aU%wF10=%6k8+#ab!1<*%fX@ba z8TnK%q4~WgoPHifFcD2zBZsZ7Sg;^-QUu_o?UowsE0({0A-o9yG(kZJJ{5jJbjePD zmrOO6p6@kb0su6^+6TU@{(@+qqW~|*TpFK#uL&anpw*v@z&?5K)!;@R0=#55-ah+Z z0$dX%{+9sZG2wp+@ZTu+pE22g3Gh#8^DmWwzwJ){OMq+Qy+8FD>c0f|{~ZBVGT9nF Xsn|RC%b|V%|Los;cu%&GL-_vz;PoTI diff --git a/docs/dev/arch.md b/docs/dev/arch.md new file mode 100644 index 0000000..59efff6 --- /dev/null +++ b/docs/dev/arch.md @@ -0,0 +1,322 @@ + +# Architecture describtion + +This repository is a workspace repository, managed by [Rye](https://rye.astral.sh/). +Read more about workspaces at the [Rye docs](https://rye.astral.sh/guide/workspaces/). +Each workspace-member starts with `darts-*` and can be seen as an own package or module, exexpt the `darts-pipeline` member. +Each package has it's own internal functions and it's public facing API. +The public facing API of each package MUST follow the following section [API paradigms](#api-paradigms). +The `darts-pipeline` is a [`virtual` project](https://rye.astral.sh/guide/virtual/), hence it can't be installed as a package and has no public facing API. + +[TOC] + +## Package overview + +| Package Name | Type | Description | (Major) Dependencies - all need Xarray | +| ----------------------- | -------- | ------------------------------------------------------------------------------------- | -------------------------------------- | +| `darts-preprocessing` | Data | Loads data and combines the features to a Xarray Dataset | GDAL | +| `darts-superresolution` | Train | Trains a supper resolution model to scale Sentinel 2 images from 10m to 3m resolution | PyTorch | +| `darts-segmentation` | Train | Trains an segmentation model | PyTorch, segmentation_models_pytorch | +| `darts-ensemble` | Ensemble | Ensembles the different models and run the multi-stage inference pipeline. | PyTorch | +| `darts-postprocessing` | Data | Further refines the output from an ensemble or segmentaion and binarizes the probs | PyTorch | +| `darts-export` | Data | Saves the results from inference and combines the result to the final DARTS dataset | GeoPandas, Scipy, Cucim | + +The following modules are planned or potential ideas for future expansion of the project: + +| Package Name | Type | Description | (Major) Dependencies - all need Xarray | +| ------------------- | ----- | ----------------------------------------------------------------------- | -------------------------------------- | +| `darts-acquisition` | Data | Fetches data from the data sources | GEE, rasterio, ? | +| `darts-detection` | Train | Trains an object detection model | PyTorch | +| `darts-?` | Train | Trains a ? model for more complex multi-stage ensembles | ? | +| `darts-evaluation` | Test | Evaluates the end-to-end process on a test dataset and external dataset | GeoPandas | +| `darts-utils` | Data | Shared utilities for data processing | Scipy, Cucim, GeoPandas | +| `darts-train-utils` | Train | Shared utilities for training | PyTorch | + +The packages should follow this architecture: +![DARTS nextgen architecture](./docs/assets/darts_nextgen_architecture.png) + +The `darts-pipeline` utilizes [Ray](https://docs.ray.io/en/latest/index.html) to automaticly parallize the different computations. +However, each package should be designed so that one could build their own pipeline without Ray. +Hence, all Ray-related functions / transformations etc. should be defined in the `darts-pipeline` project. + +The packages can decide to wrap their public functions into a CLI with typer. + +The `Train` packages should also hold the code for training specific data preparation, model training and model evaluation. +These packages should get their data from (already processed) data from the `darts-preprocessing` package. +They should expose a statefull Model class with an `inference` function, which can be used by the `darts-ensemble` package. + +### Conceptual migration from thaw-slump-segmentation + +- The `darts-ensemble` and `darts-postprocessing` packages is the successor of the `process-02-inference` and `process-03-ensemble` scripts. +- The `darts-preprocessing` and `darts-acquisition` packages are the successors of the `setup-raw-data` script and manual work of obtaining data. +- The `darts-export` package is splitted from the `inference` script, should include the previous manual works of combining everything into the final dataset. +- The `darts-superresolution` package is the successor of the `superresolution` repository. +- The `darts-segmentation` package is the successor of the `train` and `prepare_data` script. +- The `darts-evaluation` package is the successor of the different manual evaluations. + +### Create a new package + +A new package can easily created with: + +```py +rye init darts-packagename +``` + +Rye creates a minimal project structure for us. + +The following things needs to be updates: + +1. The `pyproject.toml` file inside the new package. + + Add to the `pyproject.toml` file inside the new package is the following to enable Ruff: + + ```toml + [tool.ruff] + # Extend the `pyproject.toml` file in the parent directory... + extend = "../pyproject.toml" + ``` + + Please also provide a description and a list of authors to the file. + +2. The `.github/workflows/update_version.yml` file, to include the package in the workflow. + + Under `package` and under step `Update version in pyproject.toml`. + +3. The docs by creating a `ref/name.md` file and add them to the nav inside the `mkdocs.yml`. + + To enable code detection, also add the package directory under `plugins` in the `mkdocs.yml`. + Please also add the refs to the top-level `ref.md`. + +4. The Readme of the package + +## APIs between pipeline steps + +The following diagram visualizes the steps of the major `packages` of the pipeline: +![DARTS nextgen pipeline steps](../assets/darts_nextgen_pipeline-steps.png) + +Each Tile should be represented as a single `xr.Dataset` with each feature / band as `DataVariable`. +Each DataVariable should have their `source` documented in the `attrs`. + +### Preprocessing Output + +Coordinates: `x`, `y` and `spatial_ref` (from rioxarray) + +| DataVariable | shape | dtype | attrs | +| -------------------- | ------ | ------- | -------- | +| `blue` | (x, y) | uint16 | - source | +| `green` | (x, y) | uint16 | - source | +| `red` | (x, y) | uint16 | - source | +| `nir` | (x, y) | uint16 | - source | +| `ndvi` | (x, y) | float32 | - source | +| `relative_elevation` | (x, y) | float32 | - source | +| `slope` | (x, y) | float32 | - source | +| `tc_brightness` | (x, y) | uint8 | - source | +| `tc_greenness` | (x, y) | uint8 | - source | +| `tc_wetness` | (x, y) | uint8 | - source | +| `valid_data_mask` | (x, y) | bool | - source | +| `quality_data_mask` | (x, y) | bool | - source | + +### Segmentation / Ensemble Output + +Coordinates: `x`, `y` and `spatial_ref` (from rioxarray) + +| DataVariable | shape | dtype | attrs | +| --------------------------- | ------ | ------- | ----- | +| [Output from Preprocessing] | | | | +| `probabilities` | (x, y) | float32 | | +| `probabilities-model-X*` | (x, y) | bool | | + +\*: optional intermedia probabilities in an ensemble + +### Postprocessing Output + +Coordinates: `x`, `y` and `spatial_ref` (from rioxarray) + +| DataVariable | shape | dtype | attrs | note | +| --------------------------- | ------ | ----- | ----- | -------------------------------- | +| [Output from Preprocessing] | | | | | +| `probabilities_percent` | (x, y) | uint8 | | Values between 0-100, nodata:255 | +| `binarized_segmentation` | (x, y) | uint8 | | | + +### PyTorch Model checkpoints + +Each checkpoint is stored as a torch `.pt` tensor file. The checkpoint MUST have the following structure: + +```py +{ + "config": { + "model_framework": "smp", # Identifier which framework or model was used + "model": { ... }, # Model specific hyperparameter which are needed to create the model + "input_combination": [ ... ], # List of strings of the names with which the model was trained, order is important + "patch_size": 1024, # Patch size on which the model was trained + ... # More model-framework specific parameter, e.g. normalization method and factors + }, + "statedict": model.module.state_dict(), +} +``` + +## API paradigms + +The packages should pass the data as Xarray Datasets between each other. Datasets can hold coordinate information aswell as other metadata (like CRS) in a single self-describing object. +Since different `tiles` do not share the same coordinates or metadata, each `tile` should be represented by a single Xarray `Dataset`. + +- Each public facing API function which in some way **transforms** data should accept a Xarray Dataset as input and return an Xarray Dataset. + - Data can also be accepted as a list of Xarray Dataset as input and returned as a list of Xarray Datasets for batched processing. + In this case, concattenation should happend internally and on `numpy` or `pytorch` level, NOT on `xarray` abstraction level. + The reason behind this it that the tiles don't share their coordinates, resulting in a lot of empty spaces between the tiles and high memory usage. + The name of the function should then be `function_batched`. +- Each public facing API function which **loads** data should return a single Xarray Dataset for each `tile`. +- Data should NOT be saved to file internally, with `darts-export` as the only exception. Instead, data should returned in-memory as a Xarray Dataset, so the user / pipeline can decide what to save and when. +- Function names should be verbs, e.g. `process`, `ensemble`, `do_inference`. +- If a function is stateless it should NOT be part of a class or wrapper +- If a function is stateful it should be part of a class or wrapper, this is important for Ray + +### Examples + +Here are some examples, how these API paradigms should look like. + +1. Single transformation + + ```py + import darts-package + import xarray as xr + + # User loads / creates the dataset (a single tile) by themself + ds = xr.open_dataset("...") + + # User calls the function to transform the dataset + ds = darts-package.transform(ds, **kwargs) + + # User can decide by themself what to do next, e.g. save + ds.to_netcdf("...") + ``` + +2. Batched transformation + + ```py + import darts_package + import xarray as xr + + # User loads / creates multiple datasets (hence, multiple tiles) by themself + data = [xr.open_dataset("..."), xr.open_dataset("..."), ...] + + # User calls the function to transform the dataset + data = darts_package.transform_batched(data, **kwargs) + + # User can decide by themself what to do next + data[0].whatever() + ``` + +3. Load & preprocess some data + + ```py + import darts_package + + # User calls the function to transform the dataset + ds = darts_package.load("path/to/data", **kwargs) + + # User can decide by themself what to do next + ds.whatever() + ``` + +4. Custom pipeline example + + ```py + from pathlib import Path + import darts_preprocess + import darts_inference + + DATA_DIR = Path("./data/") + MODEL_DIR = Path("./models/") + OUT_DIR = Path("./out/") + + # Inference is a stateful transformation, because it needs to load the model + # Hence, the + ensemble = darts_inference.Ensemble.load(MODEL_DIR) + + # The data directory contains subfolders which then hold the input data + for dir in DATA_DIR: + name = dir.name + + # Load the files from the processing directory + ds = darts_preprocess.load_and_preprocess(dir) + + # Do the inferencce + ds = ensemble.inference(ds) + + # Save the results + ds.to_netcdf(OUT_DIR / f"{name}-result.nc") + ``` + +5. Pipeline with Ray + + ```py + from dataclasses import dataclass + from pathlib import Path + import ray + import darts_preprocess + import darts_inference + import darts_export + + DATA_DIR = Path("./data/") + MODEL_DIR = Path("./models/") + OUT_DIR = Path("./out/") + + ray.init() + + # We need to wrap the Xarray dataset in a class, so that Ray can serialize it + @dataclass + class Tile: + ds: xr.Dataset + + # Wrapper for ray + def open_dataset_ray(row: dict[str, Any]) -> dict[str, Any]: + data = xr.open_dataset(row["path"]) + tile = Tile(data) + return { + "input": tile, + } + + # Wrapper for the preprocessing -> Stateless + def preprocess_tile_ray(row: dict[str, Tile]) -> dict[str, Tile]: + ds = darts_preprocess.preprocess(row["input"].ds) + return { + "preprocessed": Tile(ds), + "input": row["input"] + } + + # Wrapper for the inference -> Statefull + class EnsembleRay: + def __init__(self): + self.ensemble = darts_inference.Ensemble.load(MODEL_DIR) + + def __call__(self, row: dict[str, Tile]) -> dict[str, Tile]: + ds = self.ensemble.inference(row["preprocessed"].ds) + return { + "output": Tile(ds), + "preprocessed": row["preprocessed"], + "input": row["input"], + } + + # We need to add 'local:///' to tell ray that we want to use the local filesystem + files = data.glob("*.nc") + file_list = [f"local:////{file.resolve().absolute()}" for file in files] + + ds = ray.data.read_binary_files(file_list, include_paths=True) + ds = ds.map(open_dataset_ray) # Lazy open + ds = ds.map(preprocess_tile_ray) # Lazy preprocess + ds = ds.map(EnsembleRay) # Lazy inference + + # Save the results + for row in ds.iter_rows(): + darts_export.save(row["output"].ds, OUT_DIR / f"{row['input'].ds.name}-result.nc") + + ``` + +### About the Xarray overhead with Ray + +Ray expects batched data to be in either numpy or pandas format and can't work with Xarray datasets directly. +Hence, a wrapper with custom stacking functions is needed. +This tradeoff is not small, however, the benefits in terms of maintainability and readability are worth it. + +![Xarray overhead with Ray](./docs/assets/xarray_ray_overhead.png) diff --git a/docs/index.md b/docs/index.md index 951bbe0..d633cbd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -8,81 +8,6 @@ hide: Panarctic Database of Active Layer Detatchment Slides and Retrogressive Thaw Slumps from Deep Learning on High Resolution Satellite Imagery. This is te successor of the thaw-slump-segmentation (pipeline), with which the first version of the DARTS dataset was created. -[TOC] - -## Environment setup - -Prereq: - -- [Rye](https://rye.astral.sh/): `curl -sSf https://rye.astral.sh/get | bash` -- [GDAL](https://gdal.org/en/latest/index.html): `sudo apt update && sudo apt install libpq-dev gdal-bin libgdal-dev` or for HPC `conda install conda-forge::gdal` -- Clang: `sudo apt update && sudo apt install clang` or for HPC `conda install conda-forge::clang_linux-64` - -> If you install GDAL via apt for linux you can view the supported versions here: . For a finer controll over the versions please use conda. - -Now first check your gdal-version: - -```sh -$ gdal-config --version -3.9.2 -``` - -And your CUDA version (if you want to use CUDA): - -```sh -$ nvidia-smi -# Now look on the top right of the table -``` - -> The GDAL version is relevant, since the version of the python bindings needs to match the installed GDAL version - -Now, to sync with a specific `gdal` version, add `darts-preprocessing/gdalXX` to the `--features` flag. -To sync with a specific `cuda` version, add `darts-ensemble/cuda1X` or without cuda `darts-ensemble/cpu`. -E.g.: - -```sh -rye sync -f --features darts-preprocessing/gdal39,darts-ensemble/cuda12 # For CUDA 12 and GDAL 3.9.2 -``` - -As of right now, the supported `gdal` versions are: 3.9.2 (`gdal39`), 3.8.5 (`gdal38`), 3.8.4 (`gdal384`), 3.7.3 (`gdal37`) and 3.6.4 (`gdal36`). -If your GDAL version is not supported (yet) please sync without GDAL and then install GDAL to an new optional group. For example, if your GDAL version is 3.8.4: - -```sh -rye sync -f -rye add --optional=gdal384 "gdal==3.8.4" -``` - -> IMPORTANT! If you installed any of clang or gdal with conda, please ensure that while installing dependencies and working on the project to have the conda environment activated in which you installed clang and or gdal. - -### Troubleshoot: Rye can't find the right versions - -Because the `pyproject.toml` specifies additional sources, e.g. `https://download.pytorch.org/whl/cpu`, it can happen that the a package with an older version is found in these package-indexes. -If such a version is found, `uv` (the installer behind `Rye`) currently stops searching other sources for the right version and stops with an `Version not found` error. -This can look something like this: - -```sh -No solution found when resolving dependencies: - ╰─▶ Because only torchmetrics==1.0.3 is available and you require torchmetrics>=1.4.1, we can conclude that your requirements are unsatisfiable. -``` - -To fix this you can set an environment variable to tell `uv` to search all package-indicies: - -```sh -UV_INDEX_STRATEGY="unsafe-best-match" rye sync ... -``` - -I recommend adding the following to your `.zshrc` or `.bashrc`: - -```sh -# Change the behaviour of uv package resolution to enable additional sources without breaking existing version-requirements -export UV_INDEX_STRATEGY="unsafe-best-match" -``` - -Please see these issues: - -- [Rye: Can't specify per-dependency package index / can't specify uv behavior in config file](https://github.com/astral-sh/rye/issues/1210#issuecomment-2263761535) -- [UV: Add support for pinning a package to a specific index](https://github.com/astral-sh/uv/issues/171) - ## Contribute Before contributing please contact one of the authors and make sure to read the [Contribution Guidelines](contribute.md). diff --git a/docs/darts-acquisition/ref.md b/docs/ref/acquisition.md similarity index 100% rename from docs/darts-acquisition/ref.md rename to docs/ref/acquisition.md diff --git a/docs/darts-ensemble/ref.md b/docs/ref/ensemble.md similarity index 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