From 660aabf37a8026f858e2a2ec25dee071a80cd5d3 Mon Sep 17 00:00:00 2001 From: Leif Denby Date: Mon, 12 Aug 2024 14:36:00 +0200 Subject: [PATCH] Add parallel processing with dask.distributed (#16) Add command line arguments `--dask-distributed-local-core-fraction` and `--dask-distributed-local-memory-fraction` which can be used to enable multiprocessing by setting the former to a non-zero fraction representing the fraction of CPU cores on the local machine to use for parallel processing with `dask.distrubted.LocalCluster`. The latter argument set what fraction of the total system memory is allocated to the workers. --- .github/workflows/ci-pre-commit.yml | 5 + CHANGELOG.md | 13 + README.md | 47 ++- docs/example_output.png | Bin 166964 -> 1171333 bytes docs/using_dask_distributed.png | Bin 0 -> 2029121 bytes mllam_data_prep/__main__.py | 46 ++- mllam_data_prep/create_dataset.py | 11 +- pdm.lock | 524 +++++++++++++++++++++++++--- pyproject.toml | 9 +- 9 files changed, 600 insertions(+), 55 deletions(-) create mode 100644 docs/using_dask_distributed.png diff --git a/.github/workflows/ci-pre-commit.yml b/.github/workflows/ci-pre-commit.yml index 92a3c07..542bd8b 100644 --- a/.github/workflows/ci-pre-commit.yml +++ b/.github/workflows/ci-pre-commit.yml @@ -10,7 +10,12 @@ jobs: linting: name: "pre-commit hooks" runs-on: ubuntu-latest + steps: - uses: actions/checkout@v2 - uses: actions/setup-python@v2 + with: + # don't use python3.12 because flake8 finds extra issues with that + # version + python-version: "3.11" - uses: pre-commit/action@v2.0.3 diff --git a/CHANGELOG.md b/CHANGELOG.md index 7585bf0..d8f9b6e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,19 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [unreleased](https://github.com/mllam/mllam-data-prep/) + +[All changes](https://github.com/mllam/mllam-data-prep/compare/HEAD...v0.2.0) + +### Added + +- add support for parallel processing using `dask.distributed` with command + line flags `--dask-distributed-local-core-fraction` and + `--dask-distributed-local-memory-fraction` to control the number of cores and + memory to use on the local machine. + ![\#16](https://github.com/mllam/mllam-data-prep/pull/16) + + ## [v0.2.0](https://github.com/mllam/mllam-data-prep/releases/tags/v0.2.0) [All changes](https://github.com/mllam/mllam-data-prep/compare/v0.2.0...v0.1.0) diff --git a/README.md b/README.md index de112bf..b6856a6 100644 --- a/README.md +++ b/README.md @@ -41,12 +41,16 @@ All the linting is handelled by `pre-commit` which can be setup to automatically pdm run pre-commit install ``` -The branch, commit, push and make a pull-request :) +Then branch, commit, push and make a pull-request :) ## Usage The package is designed to be used as a command-line tool. The main command is `mllam-data-prep` which takes a configuration file as input and outputs a training dataset in the form of a `.zarr` dataset named from the config file (e.g. `example.danra.yaml` produces `example.danra.zarr`). +The format for the [config is described below](#configuration-file). +The package can also be used as a python module to create datasets in a more programmatic way by calling `mllam_data_prep.create_dataset()` directly (see below). + +### Command-line usage ```bash python -m mllam_data_prep example.danra.yaml @@ -56,6 +60,47 @@ Example output: ![](docs/example_output.png) + +#### Creating large datasets (with `dask.distributed`) + +If you will be creating datasets larger than a few 100MB you may want to use +`dask.distributed.LocalCluster` to parallelise the creation of the dataset. This can be done +by setting the ` --dask-distributed-local-core-fraction` flag to a value +between `0.0` and `1.0`. This will create a local `dask.distributed` cluster with the +number of workers set to the number of cores on the machine multiplied by the +fraction given. For example, to use 50% of the cores on the machine you would +run: + +```bash +python -m mllam_data_prep example.danra.yaml --dask-distributed-local-core-fraction 0.5 +``` + +Unfortunately, the number of cores to use can only be worked out by trial and +error, but a good starting point is to use 50% of the cores on the machine and +then if you notice warnings suggesting that workers are running out of memory +you should reduce the fraction of cores used (so that each worker has more +memory available). +You can also adjust the fraction of the total system memory allocated with +`--dask-distributed-local-memory-fraction` (default is `0.9`). + +When you run the above command the console will print a URL to the dask +dashboard, which you can open in a browser to monitor the progress of the +dataset creation (and see the memory usage of the workers). + +![example of using mllam-data-prep with dask.distrubted for parallel processing](docs/using_dask_distributed.png) + +### Usage as a python module + +The package can also be used as a python module to create datasets directly, for example to create training datasets during training. The main function to use is `mllam_data_prep.create_dataset(config)` which takes a `mllam_data_prep.Config` as input and returns a `xarray.Dataset` object. For example: + +```python +import mllam_data_prep as mdp + +config_path = "example.danra.yaml" +config = mdp.Config.from_yaml_file(config_path) +ds = mdp.create_dataset(config=config) +``` + ## Configuration file A full example configuration file is given in [example.danra.yaml](example.danra.yaml), and reproduced here for completeness: diff --git a/docs/example_output.png b/docs/example_output.png index fcf6f296f986534d0579cf345f2f24afc4e19551..3abebbf8ac7e7c97a2b0ff6451dc1cdb03175fa9 100644 GIT binary patch literal 1171333 zcmeFY2SXEE*EWozVh=Wolz>P_BBFE>6{QG>6a}P2L7KD^>2*{@Isrk72oXULMUbL& zg3`MR(jiLd1OlOklzAt3&bjaReg47sJ#1q_X7=7?t#8BvgE90V>@`!Nkij?j)sQNjR)@bPA+zAY}ygeO*zbrTe&hU zp2b~s+_5YB$=I$Vk~^}We7j8Et8n?KP$CcerN&qpsd6rpD|Jr`A|=dr6p46qHI8u~ z*xy!Z_EFm1=wg4+WWY+0$21;%N$pPca9FHie>k)+Ry#4~9ebQe$Fb&J)bGvWqCGn5 zY`f22+@o;kbXc0Sjpo(R9cE95@X0=kY^Pichjc$N2SIey9xSeXH(SJGrRT}*osTxz z*nBUTp6+6QC^6ZYGaZr5bL*Ap(;b(q%_4gy%Wp>-Px@jz#AKiD#_+LSlj4t65@7q; 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