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The primary purpose of the TorchForge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. TorchForge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
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The primary purpose of the torchforge ecosystem is to delineate infra concerns from model concerns thereby making RL experimentation easier. torchforge delivers this by providing clear RL abstractions and one scalable implementation of these abstractions. When you need fine-grained control over placement, fault handling/redirecting training loads during a run, or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
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Key features:
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- Usability for rapid research (isolating the RL loop from infrastructure)
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- Hackability for power users (all parts of the RL loop can be easily modified without interacting with infrastructure)
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- Scalability (ability to shift between async and synchronous training and across thousands of GPUs)
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> ⚠️ **Early Development Warning**TorchForge is currently in an experimental
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> ⚠️ **Early Development Warning**torchforge is currently in an experimental
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> stage. You should expect bugs, incomplete features, and APIs that may change
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> in future versions. The project welcomes bugfixes, but to make sure things are
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> well coordinated you should discuss any significant change before starting the
@@ -31,11 +31,11 @@ You can also find our notebook tutorials (coming soon)
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### Basic
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TorchForge requires the latest PyTorch nightly with [Monarch](https://github.com/meta-pytorch/monarch), [vLLM](https://github.com/vllm-project/vllm), and [torchtitan](https://github.com/pytorch/torchtitan). For convenience,
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torchforge requires the latest PyTorch nightly with [Monarch](https://github.com/meta-pytorch/monarch), [vLLM](https://github.com/vllm-project/vllm), and [torchtitan](https://github.com/pytorch/torchtitan). For convenience,
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we have pre-packaged these dependencies as wheels in assets/wheels. (Note that the basic install script
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uses [DNF](https://docs.fedoraproject.org/en-US/quick-docs/dnf/), but could be easily extended to other Linux OS.)
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TorchForge requires the Github CLI (gh) to download a compatible vLLM package. See [here](https://github.com/cli/cli#installation) for gh install instructions before continuting. Please login to gh with your Github account before continuing with `gh auth login`. You may use either https or ssh as the protocol for authentication.
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torchforge requires the Github CLI (gh) to download a compatible vLLM package. See [here](https://github.com/cli/cli#installation) for gh install instructions before continuting. Please login to gh with your Github account before continuing with `gh auth login`. You may use either https or ssh as the protocol for authentication.
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