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# graph

class torch.cuda.graph(cuda_graph, pool=None, stream=None, capture_error_mode='global')

  Context-manager that captures CUDA work into a `torch.cuda.CUDAGraph` object for later replay.

  See CUDA Graphs for a general introduction, detailed use, and constraints.

  Parameters:

    - cuda_graph (torch.cuda.CUDAGraph) – Graph object used for capture.

    - pool (optional) – Opaque token (returned by a call to `graph_pool_handle()` or `other_Graph_instance.pool()`) hinting this graph’s capture may share memory from the specified pool. See Graph memory management.

    - stream (torch.cuda.Stream, optional) – If supplied, will be set as the current stream in the context. If not supplied, `graph` sets its own internal side stream as the current stream in the context.

    - capture_error_mode (str, optional) – specifies the cudaStreamCaptureMode for the graph capture stream. Can be “global”, “thread_local” or “relaxed”. During cuda graph capture, some actions, such as cudaMalloc, may be unsafe. “global” will error on actions in other threads, “thread_local” will only error for actions in the current thread, and “relaxed” will not error on actions. Do NOT change this setting unless you’re familiar with cudaStreamCaptureMode

  Note

  For effective memory sharing, if you pass a `pool` used by a previous capture and the previous capture used an explicit `stream` argument, you should pass the same `stream` argument to this capture.

  Warning

  This API is in beta and may change in future releases.

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