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RPC tutorial audit (#157938)

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Fix [T228333894](https://www.internalfb.com/intern/tasks/?t=228333894)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157938
Approved by: https://github.com/AlannaBurke
This commit is contained in:
Howard Huang
2025-07-09 09:57:55 -07:00
committed by PyTorch MergeBot
parent 8dff457f42
commit 8532033679
1 changed files with 4 additions and 17 deletions
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21
docs/source/rpc.md
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@ -8,16 +8,14 @@ higher-level API to automatically differentiate models split across several
machines.
```{warning}
APIs in the RPC package are stable. There are multiple ongoing work items
to improve performance and error handling, which will ship in future releases.
APIs in the RPC package are stable and in maintenance mode.
```
```{warning}
CUDA support was introduced in PyTorch 1.9 and is still a **beta** feature.
CUDA support is a **beta** feature.
Not all features of the RPC package are yet compatible with CUDA support and
thus their use is discouraged. These unsupported features include: RRefs,
JIT compatibility, dist autograd and dist optimizer, and profiling. These
shortcomings will be addressed in future releases.
JIT compatibility, dist autograd and dist optimizer, and profiling.
```
```{note}
@ -102,13 +100,6 @@ device lists on source and destination workers do not match. In such cases,
applications can always explicitly move the input tensors to CPU on the caller
and move it to the desired devices on the callee if necessary.
```{warning}
TorchScript support in RPC is a prototype feature and subject to change. Since
v1.5.0, ``torch.distributed.rpc`` supports calling TorchScript functions as
RPC target functions, and this will help improve parallelism on the callee
side as executing TorchScript functions does not require GIL.
```
```{eval-rst}
.. autofunction:: rpc_sync
.. autofunction:: rpc_async
@ -159,9 +150,7 @@ multiple different transports (TCP, of course, but also shared memory, NVLink,
InfiniBand, ...) and can automatically detect their availability and negotiate
the best transport to use for each pipe.
The TensorPipe backend has been introduced in PyTorch v1.6 and is being actively
developed. At the moment, it only supports CPU tensors, with GPU support coming
soon. It comes with a TCP-based transport, just like Gloo. It is also able to
The TensorPipe backend comes with a TCP-based transport, just like Gloo. It is also able to
automatically chunk and multiplex large tensors over multiple sockets and
threads in order to achieve very high bandwidths. The agent will be able to pick
the best transport on its own, with no intervention required.
@ -301,6 +290,4 @@ to use [the profiler](https://pytorch.org/docs/stable/autograd.html#profiler) to
- [Getting started with Distributed RPC Framework](https://pytorch.org/tutorials/intermediate/rpc_tutorial.html)
- [Implementing a Parameter Server using Distributed RPC Framework](https://pytorch.org/tutorials/intermediate/rpc_param_server_tutorial.html)
- [Combining Distributed DataParallel with Distributed RPC Framework](https://pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html) (covers **RemoteModule** as well)
- [Profiling RPC-based Workloads](https://pytorch.org/tutorials/recipes/distributed_rpc_profiling.html)
- [Implementing batch RPC processing](https://pytorch.org/tutorials/intermediate/rpc_async_execution.html)
- [Distributed Pipeline Parallel](https://pytorch.org/tutorials/intermediate/dist_pipeline_parallel_tutorial.html)