NVIDIA NCCL’s official documentation Basics ¶ These two environment variables have been pre-tuned by NCCLįor some cloud providers, such as AWS or GCP.įor a full list of NCCL environment variables, please refer to NCCL_SOCKET_NTHREADS and NCCL_NSOCKS_PERTHREAD to increase socket On some socket-based systems, users may still try tuning Performance tuning - NCCL performs automatic tuning based on its topology detection to save users’ To inspect the detailed detection result and save as reference if further help In case of topologyĭetection failure, it would be helpful to set NCCL_DEBUG_SUBSYS=GRAPH For example, NCCL_DEBUG_SUBSYS=COLL would print logs ofĬollective calls, which may be helpful when debugging hangs, especially thoseĬaused by collective type or message size mismatch. You may also use NCCL_DEBUG_SUBSYS to get more details about a specificĪspect of NCCL. Warning message as well as basic NCCL initialization information. Other NCCL environment variables ¶ĭebugging - in case of NCCL failure, you can set NCCL_DEBUG=INFO to print an explicit It is imperative that all processes specify the same number of interfaces in this variable. The backend will dispatch operations in a round-robin fashion across these interfaces. Them by a comma, like this: export GLOO_SOCKET_IFNAME=eth0,eth1,eth2,eth3. If you’re using the Gloo backend, you can specify multiple interfaces by separating GLOO_SOCKET_IFNAME, for example export GLOO_SOCKET_IFNAME=eth0 NCCL_SOCKET_IFNAME, for example export NCCL_SOCKET_IFNAME=eth0 If the automatically detected interface is not correct, you can override it using the followingĮnvironment variables (applicable to the respective backend): Use Gloo, unless you have specific reasons to use MPI.Ĭommon environment variables ¶ Choosing the network interface to use ¶īy default, both the NCCL and Gloo backends will try to find the right network interface to use. We are planning on adding InfiniBand support for If your InfiniBand has enabled IP over IB, use Gloo, otherwise, Training performance, especially for multiprocess single-node or Use NCCL, since it currently provides the best distributed GPU Use NCCL, since it’s the only backend that currently supports Use the Gloo backend for distributed CPU training. Use the NCCL backend for distributed GPU training In the past, we were often asked: “which backend should I use?”. Same as on Linux platform, you can enable TcpStore by setting environment variables, Shared file system, init_method="file:///////some_file" Local file system, init_method="file:///d:/tmp/some_file" If the init_method argument of init_process_group() points to a file it must adhere building PyTorch on a host that has MPIĪs of PyTorch v1.8, Windows supports all collective communications backend but NCCL, Included if you build PyTorch from source. MPI is an optional backend that can only be PyTorch distributed package supports Linux (stable), MacOS (stable), and Windows (prototype).īy default for Linux, the Gloo and NCCL backends are built and included in PyTorchĭistributed (NCCL only when building with CUDA). MPI supports CUDA only if the implementation used to build PyTorch supports it. The table below shows which functions are available Torch.distributed supports three built-in backends, each withĭifferent capabilities. Please refer to PyTorch Distributed Overviewįor a brief introduction to all features related to distributed training. Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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