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Drop 12.4 support #521

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Merged
NicolasHug merged 1 commit into from
Feb 26, 2025
Merged

Drop 12.4 support #521

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3 changes: 0 additions & 3 deletions .github/workflows/docs.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -75,9 +75,6 @@ jobs:
uses: pytorch/test-infra/.github/actions/setup-miniconda@main
with:
python-version: ${{ matrix.python-version }}
#
# For some reason nvidia::libnpp=12.4 doesn't install but nvidia/label/cuda-12.4.0::libnpp does.
# So we use the latter convention for libnpp.
# We install conda packages at the start because otherwise conda may have conflicts with dependencies.
default-packages: "nvidia/label/cuda-${{ 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 }}"
- name: Check env
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8 changes: 1 addition & 7 deletions .github/workflows/linux_cuda_wheel.yaml
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Expand Up @@ -66,12 +66,9 @@ jobs:
# For the actual release we should add that label and change this to
# include more python versions.
python-version: ['3.9']
cuda-version: ['11.8', '12.4', '12.6']
cuda-version: ['11.8', '12.6']
# TODO: put back ffmpeg 5 https://github.com/pytorch/torchcodec/issues/325
ffmpeg-version-for-tests: ['4.4.2', '6', '7']
exclude:
- cuda-version: '12.4' # TODO put this back it fails with infra issue.
ffmpeg-version-for-tests: '4.4.2'

container:
image: "pytorch/manylinux2_28-builder:cuda${{ matrix.cuda-version }}"
Expand All @@ -92,9 +89,6 @@ jobs:
uses: pytorch/test-infra/.github/actions/setup-miniconda@main
with:
python-version: ${{ matrix.python-version }}
#
# For some reason nvidia::libnpp=12.4 doesn't install but nvidia/label/cuda-12.4.0::libnpp does.
# So we use the latter convention for libnpp.
# We install conda packages at the start because otherwise conda may have conflicts with dependencies.
default-packages: "nvidia/label/cuda-${{ 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 }}"
- name: Check env
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18 changes: 8 additions & 10 deletions README.md
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Expand Up @@ -146,14 +146,12 @@ First, make sure you have a GPU that has NVDEC hardware that can decode the
format you want. Refer to Nvidia's GPU support matrix for more details
[here](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new).

1. Install CUDA Toolkit. Pytorch and TorchCodec supports CUDA Toolkit
versions 11.8, 12.1 or 12.4. In particular TorchCodec depends on
CUDA libraries libnpp and libnvrtc (which are part of CUDA Toolkit).
1. Install Pytorch corresponding to your CUDA Toolkit using the
[official instructions](https://pytorch.org/get-started/locally/). You'll
need the `libnpp` and `libnvrtc` CUDA libraries, which are usually part of
the CUDA Toolkit.
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@NicolasHug NicolasHug Feb 26, 2025

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I removed the previously-first entry, because I don't think we should be in the business of telling people how to install the CUDA toolkit. We were just saying "you should install it" without providing any guidance, so it wasn't that useful anyway.

And I fear it may raise the barrier. In the majority of cases, users who have a working pytorch installation with cuda won't need to do anything special, because all the necessary libraries are already installed.


2. Install Pytorch that corresponds to your CUDA Toolkit version using the
[official instructions](https://pytorch.org/get-started/locally/).

3. Install or compile FFmpeg with NVDEC support.
2. Install or compile FFmpeg with NVDEC support.
TorchCodec with CUDA should work with FFmpeg versions in [5, 7].

If FFmpeg is not already installed, or you need a more recent version, an
Expand Down Expand Up @@ -184,13 +182,13 @@ format you want. Refer to Nvidia's GPU support matrix for more details
ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i test/resources/nasa_13013.mp4 -f null -
```

4. Install TorchCodec by passing in an `--index-url` parameter that corresponds
3. Install TorchCodec by passing in an `--index-url` parameter that corresponds
to your CUDA Toolkit version, example:

```bash
# This corresponds to CUDA Toolkit version 12.4. It should be the same one
# This corresponds to CUDA Toolkit version 12.6. It should be the same one
# you used when you installed PyTorch (If you installed PyTorch with pip).
pip install torchcodec --index-url=https://download.pytorch.org/whl/cu124
pip install torchcodec --index-url=https://download.pytorch.org/whl/cu126
```

Note that without passing in the `--index-url` parameter, `pip` installs
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