diff --git a/.github/workflows/install-and-test.yml b/.github/workflows/install-and-test.yml index 8e6be806f..8dadbf9c6 100644 --- a/.github/workflows/install-and-test.yml +++ b/.github/workflows/install-and-test.yml @@ -1,23 +1,25 @@ # Configuration to test both CPU and GPU installations with both UV and pip # -# We don't use lock files in neural-lam, because that would tie us to a -# particular variant of pytorch (gpu/cpu), and want it to be possible for -# people choose which variant to install. Because we don't use lock files the -# specific package versions to install is resolved on every install. This means -# that has upstream packages evolve the specific version installed might change -# (if we don't pin it). Currently, we do not pin pytorch in pyproject.toml +# torch is a required dependency in [project.dependencies]. The CPU/GPU variant +# is selected at install time via extras defined in pyproject.toml: +# +# uv sync --extra cpu --group dev # CPU runners +# uv sync --extra gpu --group dev # GPU runners (CUDA 12.8) +# +# The extras route torch to the correct PyTorch index via [tool.uv.sources] +# and [[tool.uv.index]] in pyproject.toml. +# +# NOTE: `uv sync --torch-backend` is not yet supported (uv pip only). +# Tracking: https://github.com/astral-sh/uv/issues/12994 +# +# pip does not support extras-based index routing. The pip path uses a dry-run +# to detect the required torch version and installs from the correct index URL. # # To test both CPU and GPU installations with both UV and pip we use two sets # of hosts to run the training on: # - ubuntu-latest for CPU: these are on github # - cirun-aws-runner--${{ github.run_id }} for GPU: these are on AWS, # orchestrated by cirun.io -# -# To enable installing different pytorch variants we use an approach where we -# first install a specific torch version (and variant), and then install neural-lam. -# To ensure that the version of torch installed is compatible with neural-lam we -# first use `pip install --dry-run .` to determine the version of torch that would -# be installed. name: CPU+GPU testing on: [push, pull_request, workflow_dispatch] @@ -48,7 +50,7 @@ jobs: with: python-version: 3.13 - - name : Install uv (if applicable) + - name: Install uv (if applicable) if: matrix.package_manager == 'uv' uses: astral-sh/setup-uv@v7 @@ -66,24 +68,20 @@ jobs: echo "Torch version detected: $TORCH_VERSION" echo "TORCH_VERSION=$TORCH_VERSION" >> $GITHUB_ENV - - name: Install PyTorch - if : matrix.package_manager != 'uv' + - name: Install PyTorch (pip) + if: matrix.package_manager == 'pip' run: | python -m pip install "torch==$TORCH_VERSION" --index-url ${{ matrix.torch_index }} - - name: Install package (including dev dependencies) - if : matrix.package_manager != 'uv' + - name: Install package (pip) + if: matrix.package_manager == 'pip' run: | python -m pip install --group dev . - - name: Setup environment with uv (if applicable) + - name: Install with uv if: matrix.package_manager == 'uv' run: | - uv venv --no-project - source .venv/bin/activate - uv pip install torch --index-url ${{ matrix.torch_index }} - uv pip install --group dev -e . - uv pip list + uv sync --extra ${{ matrix.device }} --group dev --locked echo "$PWD/.venv/bin" >> $GITHUB_PATH - name: Print torch version diff --git a/CHANGELOG.md b/CHANGELOG.md index ea899ff58..dc2e1e160 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -23,6 +23,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Add comprehensive type hints to `neural_lam/create_graph.py` [\#618](https://github.com/mllam/neural-lam/pull/618) @GiGiKoneti +- Use `cpu`/`gpu` extras with `[tool.uv.sources]` and a committed `uv.lock` to simplify and pin torch installation in CI and for users [\#604](https://github.com/mllam/neural-lam/pull/604) @RajdeepKushwaha5 +- Update default CUDA version in install instructions and CI from 11.1 to 12.8. Users on older CUDA versions need to install the matching `torch` variant manually (see README) [\#604](https://github.com/mllam/neural-lam/pull/604) @RajdeepKushwaha5 + ## [v0.6.0](https://github.com/mllam/neural-lam/releases/tag/v0.6.0) This release introduces new features including GIF animation support, wandb run resumption, and improved ensemble loading, alongside a large number of bug fixes and maintenance updates. diff --git a/README.md b/README.md index 17e269841..9a35c92c3 100644 --- a/README.md +++ b/README.md @@ -70,10 +70,7 @@ keeping versions consistent (it automatically updates the `pyproject.toml` file), makes it easy to handle virtual environments and includes the development toolchain packages installation too. -**regarding `torch` installation**: because `torch` creates different package -variants for different CUDA versions and cpu-only support you will need to install -`torch` separately if you don't want the most recent GPU variant that also -expects the most recent version of CUDA on your system. +**regarding `torch` installation**: `torch` is a required dependency, but it ships separate variants for CPU-only and each CUDA version. With `uv`, the correct PyTorch index is chosen automatically via `[tool.uv.sources]`. With `pip`, you must pass `--index-url` explicitly (see instructions below). We cover all the installation options in our [github actions ci/cd setup](.github/workflows/) which you can use as a reference. @@ -90,16 +87,27 @@ python -m pip install neural_lam 1. Clone this repository and navigate to the root directory. 2. Install `uv` if you don't have it installed on your system (either with `pip install uv` or [following the install instructions](https://docs.astral.sh/uv/getting-started/installation)). -> If you are happy using the latest version of `torch` with GPU support (expecting the latest version of CUDA is installed on your system) you can skip to step 5. -3. Create a virtual environment for uv to use with `uv venv --no-project`. -4. Install a specific version of `torch` with `uv pip install torch --index-url https://download.pytorch.org/whl/cpu` for a CPU-only version or `uv pip install torch --index-url https://download.pytorch.org/whl/cu111` for CUDA 11.1 support (you can find the correct URL for the variant you want on [PyTorch webpage](https://pytorch.org/get-started/locally/)). -5. Install the dependencies with `uv pip install .`. If you will be developing `neural-lam` we recommend to install the development dependencies with `uv pip install --group dev -e .`. This installs the `neural-lam` package in editable mode, so you can make changes to the code and see the effects immediately. +3. Install with the CPU or GPU extra: + ```bash + uv sync --extra cpu --group dev # CPU-only + uv sync --extra gpu --group dev # GPU (CUDA 12.8) + ``` + This creates a virtual environment, installs `torch` from the correct + PyTorch index, and installs all other dependencies (including dev tools) + in one step. + + If you need a different CUDA version, install with the `cpu` extra first (smaller download) and then swap `torch` to your CUDA variant: + ```bash + uv sync --extra cpu --group dev # create env (small torch wheel) + uv pip install torch --torch-backend auto # replace torch with your CUDA variant + ``` + Note: this swap is venv-local and will be reverted by the next `uv sync`. #### Using `pip` 1. Clone this repository and navigate to the root directory. > If you are happy using the latest version of `torch` with GPU support (expecting the latest version of CUDA is installed on your system) you can skip to step 3. -2. Install a specific version of `torch` with `python -m pip install torch --index-url https://download.pytorch.org/whl/cpu` for a CPU-only version or `python -m pip install torch --index-url https://download.pytorch.org/whl/cu111` for CUDA 11.1 support (you can find the correct URL for the variant you want on [PyTorch webpage](https://pytorch.org/get-started/locally/)). +2. Install a specific version of `torch` with `python -m pip install torch --index-url https://download.pytorch.org/whl/cpu` for a CPU-only version or `python -m pip install torch --index-url https://download.pytorch.org/whl/cu128` for CUDA 12.8 support (you can find the correct URL for the variant you want on [PyTorch webpage](https://pytorch.org/get-started/locally/)). 3. Install the dependencies with `python -m pip install .`. If you will be developing `neural-lam` we recommend to install in editable mode and install the development dependencies with `python -m pip install --group dev -e .` so you can make changes to the code and see the effects immediately. diff --git a/pyproject.toml b/pyproject.toml index bccec0828..e8ce2b583 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,9 +41,36 @@ dependencies = [ ] requires-python = ">=3.10" +[project.optional-dependencies] +cpu = ["torch>=2.3.0"] +gpu = ["torch>=2.3.0"] + [dependency-groups] dev = ["pre-commit>=3.8.0", "pytest>=8.3.2", "pooch>=1.8.2"] +[tool.uv] +# uv-specific configuration for PyTorch CPU/GPU variant selection. +# NOTE: --torch-backend is only supported in `uv pip`, not `uv sync`. +# Tracking issue for project-interface support: https://github.com/astral-sh/uv/issues/12994 +# Until then, we use extras + explicit index routing for `uv sync`. +conflicts = [[{ extra = "cpu" }, { extra = "gpu" }]] + +[[tool.uv.index]] +name = "pytorch-cpu" +url 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