Sometimes, you just need to go back. Whether it's for reproducing an older experiment, ensuring compatibility with legacy code, or diving into the history of a project, working with previous versions of software is a common, and often necessary, part of the development journey. For those of us who rely on PyTorch, the good news is that accessing older releases is straightforward, though it requires a bit of precision.
While the latest and greatest is usually the way to go, the PyTorch team thoughtfully provides access to older binaries and installation instructions. This is incredibly helpful when you're not starting from scratch or when a specific project demands a particular version.
Let's talk about how you'd actually do this. The core of it revolves around using pip, the Python package installer. The key is specifying the exact version you need. For instance, if you're looking for version 2.9.1, the command would look something like this:
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1
This basic command installs the core PyTorch library (torch), along with its companions for computer vision (torchvision) and audio processing (torchaudio), all pinned to that specific version. But here's where it gets a little more nuanced, especially for those working with hardware acceleration like CUDA or ROCm.
When you need to leverage GPUs, you'll often need to specify the CUDA or ROCm version that your hardware and system are set up for. For example, to install PyTorch 2.9.1 with CUDA 12.6 support on Linux or Windows, you'd add an --index-url flag:
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu126
Similarly, for ROCm on Linux, you might see:
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/rocm6.4
And if you're just sticking to the CPU, there's a specific index URL for that too:
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cpu
The reference material shows a clear pattern for other versions as well. For instance, if you needed 2.9.0, you'd swap out the version numbers accordingly, and the same logic applies to older releases like 2.8.0, 2.7.1, 2.7.0, 2.6.0, and even 2.5.1 (which also shows conda as an installation option).
It's worth noting that the specific CUDA or ROCm versions available might change with older PyTorch releases. For example, version 2.8.0 lists CUDA 12.9, while 2.7.1 mentions CUDA 11.8 and 12.6. This highlights the importance of checking the documentation for the exact version you're targeting to ensure you're selecting the correct hardware acceleration path.
So, whether you're debugging an old project, replicating a research paper from a few years back, or simply exploring the evolution of the library, having these commands at your fingertips makes navigating the PyTorch archives a smooth experience. It’s a reminder that sometimes, the past holds the keys to understanding the present and building the future.
