tensorplay@1.0.0.dev20260909

Wrapper around a CUDA event.

CUDA events are synchronization markers that can be used to monitor the device’s progress, to accurately measure timing, and to synchronize CUDA streams.

Parameters:

• enable_timing (bool, optional) – indicates if the event should measure time (default: False)

• blocking (bool, optional) – if True, wait() will be blocking (default: False)

• interprocess (bool) – if True, the event can be shared between processes (default: False)

elapsed_time(end_event: Event)[source]

Return the time elapsed.

Time reported in milliseconds after the event was recorded and before the end_event was recorded.

Parameters:

end_event (Event) – the end event.

classmethod from_ipc_handle(device, handle)[source]

Reconstruct an event from an IPC handle on the given device.

ipc_handle()[source]

Return an IPC handle of this event.

query()[source]

Check if all work currently captured by event has completed.

record(stream: Stream | None = None)[source]

Record the event in a given stream.

Parameters:

stream (Stream, optional) – Uses tensorplay.cuda.current_stream() if no stream is specified.

synchronize() → None[source]

Wait for the event to complete.

wait(stream: Stream | None = None) → None[source]

Make all future work submitted to the given stream wait for this event.

Parameters:

stream (Stream, optional) – Uses tensorplay.cuda.current_stream() if no stream is specified.

Wrapper around an externally allocated CUDA stream.

This class is used to wrap streams allocated in other libraries in order to facilitate data exchange and multi-library interactions.

Parameters:

• stream_ptr (int) – Integer representation of the cudaStream_t value allocated externally.

• device (tensorplay.Device or int, optional) – the device where the stream was originally allocated. If device is specified incorrectly, subsequent launches using this stream may fail.

query() → bool

Check if all the work submitted has been completed.

record_event(event: Event | None = None)

Record an event.

Parameters:

event (Event, optional) – event to record. If not given, a new one will be allocated.

Returns:

Recorded event.

synchronize() → None

Wait for all the kernels in this stream to complete.

wait_event(event: Event | tensorplay.Event) → None

Make all future work submitted to the stream wait for an event.

wait_stream(stream: Stream) → None

Synchronize with another stream.

Wrapper around a CUDA stream.

A CUDA stream is a linear sequence of execution that belongs to a specific device, independent from other streams. It supports with statement as a context manager to ensure the operators within the with block are running on the corresponding stream. See the CUDA semantics documentation for details.

Parameters:

• device (tensorplay.Device or int, optional) – a device on which to allocate the stream. If device is None (default) or a negative integer, this will use the current device.

• priority (int, optional) – priority of the stream, which can be positive, 0, or negative. A lower number indicates a higher priority. By default, the priority is set to 0.

query() → bool[source]

Check if all the work submitted has been completed.

record_event(event: Event | None = None)[source]

Record an event.

Parameters:

event (Event, optional) – event to record. If not given, a new one will be allocated.

Returns:

Recorded event.

synchronize() → None[source]

Wait for all the kernels in this stream to complete.

wait_event(event: Event | tensorplay.Event) → None[source]

Make all future work submitted to the stream wait for an event.

wait_stream(stream: Stream) → None[source]

Synchronize with another stream.