.

Author: ahmadsharif1

README.md

@@ -93,6 +93,7 @@ ffmpeg -f lavfi -i \
 ```
 
 ## Installing TorchCodec
+### Installing CPU-only TorchCodec
 
 1. Install the latest stable version of PyTorch following the
    [official instructions](https://pytorch.org/get-started/locally/). For other
@@ -127,6 +128,46 @@ The following table indicates the compatibility between versions of
 | not yet supported  | `2.5`              | `>=3.9`, `<=3.12`   |
 | `0.0.3`            | `2.4`              | `>=3.8`, `<=3.12`   |
 
+### Installing CUDA-enabled TorchCodec
+
+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.
+
+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.
+   TorchCodec with CUDA should work with FFmpeg versions in [4, 7].
+
+   If FFmpeg is not already installed, or you need a more recent version, an
+   easy way to install it is to use `conda`:
+
+   ```bash
+   conda install ffmpeg
+   # or
+   conda install ffmpeg -c conda-forge
+   ```
+
+   If you are building FFmpeg from source you can follow Nvidia's guide to
+   configuring and installing FFmpeg with NVDEC support
+   [here](https://docs.nvidia.com/video-technologies/video-codec-sdk/12.0/ffmpeg-with-nvidia-gpu/index.html).
+
+4. 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
+   pip install torchcodec --index-url=https://download.pytorch.org/whl/nightly/cu124
+   ```
+
+   Note that without passing in the `--index-url` parameter, `pip` installs TorchCodec
+   binaries from PyPi which are CPU-only and do not have CUDA support.
+
 ## Benchmark Results
 
 The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 56-core machine.

examples/basic_cuda_example.py

@@ -43,39 +43,7 @@
 Installing TorchCodec with CUDA Enabled
 ---------------------------------------
 
-In order to use CUDA Decoding will need the following installed in your environment:
-
-#. An Nvidia GPU that supports decoding the video format you want to decode. See
-   the support matrix `here <https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new>`_
-#. `CUDA-enabled pytorch <https://pytorch.org/get-started/locally/>`_
-#. FFmpeg binaries that support
-   `NVDEC-enabled <https://docs.nvidia.com/video-technologies/video-codec-sdk/12.0/ffmpeg-with-nvidia-gpu/index.html>`_
-   codecs
-#. libnpp and nvrtc (these are usually installed when you install the full cuda-toolkit)
-#. TorchCodec built with CUDA enabled. Note that the TorchCodec binaries on PyPi do not have CUDA enabled.
-   For installing CUDA-enabled TorchCodec binaries you have two options:
-
-   #. Install TorchCodec's CUDA-enable prebuilts using one of Pytorch's package URLs. These are specified
-      to ``pip`` by passing in the ``--index-url`` argument, example:
-
-      .. code-block:: bash
-
-         # If you have CUDA toolkit version 12.4 and have the nightly pytorch version installed:
-         pip3 install --pre torchcodec --index-url https://download.pytorch.org/whl/nightly/cu124
-
-   #. Build TorchCodec from source using the environment variable ENABLE_CUDA=1
-
-
-FFmpeg versions 5, 6 and 7 from conda-forge are built with
-`NVDEC support <https://docs.nvidia.com/video-technologies/video-codec-sdk/12.0/ffmpeg-with-nvidia-gpu/index.html>`_
-and you can install them with conda. For example, to install FFmpeg version 7:
-
-
-.. code-block:: bash
-
-    conda install ffmpeg=7 -c conda-forge
-    conda install libnpp cuda-nvrtc -c nvidia
-
+Refer to the installation guide in the `README <https://github.com/pytorch/torchcodec#installing-torchcodec>`_.
 
 """