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Merge branch 'main' of github.com:pytorch/torchcodec into benchmark_test
2 parents 6cc503c + bd9d5cb commit 64c4a79

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‎.github/workflows/build_ffmpeg.yaml

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pull_request:
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paths:
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- packaging/build_ffmpeg.sh
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- .github/workflows/build_ffmpeg.yaml # self reference
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schedule:
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- cron: '0 0 * * 0' # on sunday
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‎.github/workflows/cpp_tests.yaml

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push:
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branches: [ main ]
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pull_request:
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paths:
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- src/torchcodec/*
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- test/*
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- .github/workflows/cpp_tests.yaml # self reference
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concurrency:
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group: unit-test${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_number || github.ref }}

‎.github/workflows/docs.yaml

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push:
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branches: [ main ]
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pull_request:
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paths:
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- src/torchcodec/*
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- docs/*
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- .github/workflows/docs.yaml # self reference
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permissions:
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id-token: write
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contents: write
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defaults:
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run:
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shell: bash -l -eo pipefail {0}
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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with:
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package-type: wheel
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os: linux
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test-infra-repository: pytorch/test-infra
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test-infra-ref: main
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with-cpu: disable
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with-xpu: disable
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with-rocm: disable
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with-cuda: enable
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build-python-only: "disable"
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build:
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runs-on: ubuntu-latest
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needs: generate-matrix
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strategy:
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fail-fast: false
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name: Build and Upload wheel
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uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@main
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with:
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repository: pytorch/torchcodec
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ref: ""
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test-infra-repository: pytorch/test-infra
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test-infra-ref: main
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build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
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post-script: packaging/post_build_script.sh
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smoke-test-script: packaging/fake_smoke_test.py
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package-name: torchcodec
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trigger-event: ${{ github.event_name }}
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build-platform: "python-build-package"
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build-command: "BUILD_AGAINST_ALL_FFMPEG_FROM_S3=1 ENABLE_CUDA=1 python -m build --wheel -vvv --no-isolation"
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build-docs:
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runs-on: linux.4xlarge.nvidia.gpu
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strategy:
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fail-fast: false
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matrix:
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# 3.9 corresponds to the minimum python version for which we build
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# the wheel unless the label cliflow/binaries/all is present in the
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# PR.
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python-version: ['3.9']
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cuda-version: ['12.4']
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ffmpeg-version-for-tests: ['7']
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container:
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image: "pytorch/manylinux-builder:cuda${{ matrix.cuda-version }}"
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options: "--gpus all -e NVIDIA_DRIVER_CAPABILITIES=video,compute,utility"
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needs: build
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steps:
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- name: Check out repo
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uses: actions/checkout@v3
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- name: Setup conda env
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uses: conda-incubator/setup-miniconda@v2
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- name: Setup env vars
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run: |
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cuda_version_without_periods=$(echo "${{ matrix.cuda-version }}" | sed 's/\.//g')
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echo cuda_version_without_periods=${cuda_version_without_periods} >> $GITHUB_ENV
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- uses: actions/download-artifact@v3
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with:
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auto-update-conda: true
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miniconda-version: "latest"
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activate-environment: test
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python-version: '3.12'
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name: pytorch_torchcodec__3.9_cu${{ env.cuda_version_without_periods }}_x86_64
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path: pytorch/torchcodec/dist/
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- name: Setup miniconda using test-infra
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uses: pytorch/test-infra/.github/actions/setup-miniconda@main
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with:
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python-version: ${{ matrix.python-version }}
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#
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# For some reason nvidia::libnpp=12.4 doesn't install but nvidia/label/cuda-12.4.0::libnpp does.
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# So we use the latter convention for libnpp.
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# We install conda packages at the start because otherwise conda may have conflicts with dependencies.
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default-packages: "nvidia/label/cuda-${{ matrix.cuda-version }}.0::libnpp nvidia::cuda-nvrtc=${{ matrix.cuda-version }} nvidia::cuda-toolkit=${{ matrix.cuda-version }} nvidia::cuda-cudart=${{ matrix.cuda-version }} nvidia::cuda-driver-dev=${{ matrix.cuda-version }} conda-forge::ffmpeg=${{ matrix.ffmpeg-version-for-tests }}"
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- name: Check env
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run: |
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${CONDA_RUN} env
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${CONDA_RUN} conda info
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${CONDA_RUN} nvidia-smi
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${CONDA_RUN} conda list
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- name: Assert ffmpeg exists
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run: |
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${CONDA_RUN} ffmpeg -buildconf
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- name: Update pip
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run: python -m pip install --upgrade pip
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- name: Install dependencies and FFmpeg
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run: ${CONDA_RUN} python -m pip install --upgrade pip
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- name: Install PyTorch
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run: |
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# TODO: torchvision and torchaudio shouldn't be needed. They were only added
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# to silence an error as seen in https://github.com/pytorch/torchcodec/issues/203
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python -m pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
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conda install "ffmpeg=7.0.1" pkg-config -c conda-forge
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ffmpeg -version
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- name: Build and install torchcodec
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${CONDA_RUN} python -m pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu${{ env.cuda_version_without_periods }}
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${CONDA_RUN} python -c 'import torch; print(f"{torch.__version__}"); print(f"{torch.__file__}"); print(f"{torch.cuda.is_available()=}")'
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- name: Install torchcodec from the wheel
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run: |
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python -m pip install -e ".[dev]" --no-build-isolation -vvv
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wheel_path=`find pytorch/torchcodec/dist -type f -name "*.whl"`
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echo Installing $wheel_path
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${CONDA_RUN} python -m pip install $wheel_path -vvv
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- name: Check out repo
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uses: actions/checkout@v3
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- name: Install doc dependencies
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run: |
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cd docs
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python -m pip install -r requirements.txt
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${CONDA_RUN} python -m pip install -r requirements.txt
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- name: Build docs
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run: |
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cd docs
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make html
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${CONDA_RUN} make html
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- uses: actions/upload-artifact@v3
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with:
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name: Built-Docs

‎.github/workflows/linux_cuda_wheel.yaml

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on:
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pull_request:
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paths:
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- src/torchcodec/*
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- benchmarks/*
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- packaging/*
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- test/*
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- .github/workflows/linux_cuda_wheel.yaml # self reference
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push:
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branches:
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- nightly

‎.github/workflows/linux_wheel.yaml

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on:
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paths:
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- src/torchcodec/*
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- packaging/*
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- test/*
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- .github/workflows/linux_wheel.yaml # self reference
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push:
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- nightly

‎.github/workflows/macos_wheel.yaml

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on:
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pull_request:
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paths:
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- src/torchcodec/*
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- packaging/*
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- test/*
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- .github/workflows/macos_wheel.yaml # self reference
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push:
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branches:
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- nightly

‎.github/workflows/reference_resources.yaml

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pull_request:
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paths:
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- test/generate_reference_resources.sh
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- .github/workflows/reference_resources.yaml # self reference
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schedule:
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- cron: '0 0 * * 0' # on sunday
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‎README.md

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## Installing TorchCodec
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Note: if you're on MacOS, you'll need to [build from source](./CONTRIBUTING.md).
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The instructions below assume you're on Linux.
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1. Install the latest stable version of PyTorch following the
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[official instructions](https://pytorch.org/get-started/locally/). For other
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versions, refer to the table below for compatibility between versions of
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`torch` and `torchcodec`.
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1. Install the latest stable version of PyTorch following the
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[official instructions](https://pytorch.org/get-started/locally/). TorchCodec
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requires [PyTorch 2.4](https://pytorch.org/docs/2.4/).
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2. Install FFmpeg, if it's not already installed. Linux distributions usually
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come with FFmpeg pre-installed. TorchCodec supports all major FFmpeg versions
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in [4, 7].
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2. Install FFmpeg, if it's not already installed. Your Linux distribution probably
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comes with FFmpeg pre-installed. TorchCodec supports all major FFmpeg versions
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in [4, 7].
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If FFmpeg is not already installed, or you need a more recent version, an
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easy way to install it is to use `conda`:
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If FFmpeg is not already installed, or you need a later version, install it with:
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```bash
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conda install ffmpeg
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# or
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conda install ffmpeg -c conda-forge
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```
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```bash
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conda install ffmpeg
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# or
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conda install ffmpeg -c conda-forge
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```
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3. Install TorchCodec:
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3. Install TorchCodec:
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```bash
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pip install torchcodec
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```
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```bash
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pip install torchcodec
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```
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The following table indicates the compatibility between versions of
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`torchcodec`, `torch` and Python.
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| `torchcodec` | `torch` | Python |
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| ------------------ | ------------------ | ------------------- |
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| `main` / `nightly` | `main` / `nightly` | `>=3.9`, `<=3.12` |
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| not yet supported | `2.5` | `>=3.9`, `<=3.12` |
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| `0.0.3` | `2.4` | `>=3.8`, `<=3.12` |
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## Benchmark Results
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We are actively working on the following features:
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- [Ship wheels for MacOS](https://github.com/pytorch/torchcodec/issues/111), so
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that MacOS users can `pip install torchcodec`. For now this is only supported
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on Linux, but MacOS users can [build from source](./CONTRIBUTING.md).
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- [GPU decoding](https://github.com/pytorch/torchcodec/pull/58)
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- [Audio decoding](https://github.com/pytorch/torchcodec/issues/85)
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Let us know if you have any feature requests by [opening an

‎benchmarks/decoders/benchmark_decoders.py

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from benchmark_decoders_library import (
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AbstractDecoder,
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BatchParameters,
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DecordAccurate,
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DecordAccurateBatch,
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plot_data,
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num_sequential_frames_from_start=[1, 10, 100],
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min_runtime_seconds=args.bm_video_speed_min_run_seconds,
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benchmark_video_creation=args.bm_video_creation,
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batch_parameters=BatchParameters(num_threads=8, batch_size=40),
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)
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plot_data(df_data, args.plot_path)
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