# tensorplay.cuda.streams API

Source: https://www.tensorplay.cn/docs/api/tensorplay.cuda.streams.html

## Classes 3

[#](#api-tensorplay.cuda.streams.Event)

### Event

class[Full reference ↗](/docs/generated/tensorplay.cuda.streams.Event.html)

```python
class tensorplay.cuda.streams.Event(device=None, *, enable_timing=False, blocking=False, interprocess=False)
```

Query and record Stream status to identify or control dependencies across Stream and measure timing.

Parameters:

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device for the Event. If not given, the current the relevant documentation type will be used.

- enable_timing ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – indicates if the event should measure time (default: False)

- blocking ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – if True, [wait()](#tensorplay.cuda.streams.Event.wait) will be blocking (default: False)

- interprocess ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – if True, the event can be shared between processes (default: False)

> **Warning**
>
> Both blocking and interprocess are not supported right now and are noops.

Returns:

An tensorplay.Event object.

Return type:

[Event](#tensorplay.cuda.streams.Event)

Example:

```
>>> event = tensorplay.Event()
>>> e_cuda = tensorplay.Event(device='cuda')
```

```python
elapsed_time(end_event) → float
```

Returns the elapsed time in milliseconds between when this event and the end_event are
each recorded via tensorplay.Stream.record_event().

Parameters:

end_event (tensorplay.Event) – The ending event has been recorded.

Returns:

Time between starting and ending event in milliseconds.

Return type:

[float](https://docs.python.org/3/builtins/functions.html#float)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> e1_cuda = s_cuda.record_event()
>>> e2_cuda = s_cuda.record_event()
>>> ms = e1_cuda.elapsed_time(e2_cuda)
```

```python
classmethod from_ipc_handle(device, handle)
```

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

```python
ipc_handle()
```

Return an IPC handle of this event.

```python
query() → bool
```

Check if the stream where this event was recorded already moved past the point where the event was recorded.
Always returns True if the Event was not recorded.

Returns:

A boolean indicating if all work currently captured by event has completed.

Return type:

[bool](https://docs.python.org/3/builtins/functions.html#bool)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s_cuda.record_event()
>>> e_cuda.query()
True
```

```python
record(stream=None) → None
```

Record the event in a given stream. The stream’s device must match the event’s device.
This function is equivalent to stream.record_event(self).

Parameters:

stream (tensorplay.Stream, optional) – A stream to be recorded.
If not given, the current stream will be used.

Example:

```
>>> e_cuda = tensorplay.Event(device='cuda')
>>> e_cuda.record()
```

```python
synchronize() → None
```

Wait for the event to complete. This prevents the CPU thread from proceeding until the event completes.

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s_cuda.record_event()
>>> e_cuda.synchronize()
```

```python
wait(stream=None) → None
```

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

Parameters:

stream (tensorplay.Stream, optional) – A stream to synchronize.
If not given, the current stream will be used.

Example:

```
>>> s1_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s1_cuda.record()
>>> e_cuda.wait(s2)
```

[#](#api-tensorplay.cuda.streams.ExternalStream)

### ExternalStream

class[Full reference ↗](/docs/generated/tensorplay.cuda.streams.ExternalStream.html)

```python
class tensorplay.cuda.streams.ExternalStream(stream_ptr, device=None, **kwargs)
```

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.

> **Note**
>
> This class doesn’t manage the stream life-cycle, it is the user responsibility to keep the referenced stream alive while this class is being used.

Parameters:

- stream_ptr ([int](https://docs.python.org/3/builtins/functions.html#int)) – Integer representation of the cudaStream_t value allocated externally.

- device ([tensorplay.Device](/docs/generated/tensorplay.Device.html#tensorplay.Device) or [int](https://docs.python.org/3/builtins/functions.html#int), optional) – the device where the stream was originally allocated. If device is specified incorrectly, subsequent launches using this stream may fail.

```python
query() → bool
```

Check if all the work submitted has been completed.

Returns:

A boolean indicating if all kernels in this stream are completed.

Return type:

[bool](https://docs.python.org/3/builtins/functions.html#bool)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> s_cuda.query()
True
```

```python
record_event(event) → Event
```

Record an event. En-queuing it into the Stream to allow further synchronization from the current point in the FIFO queue.

Parameters:

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

Returns:

Recorded event.

Return type:

[Event](/docs/generated/tensorplay.cuda.streams.Event.html#tensorplay.cuda.streams.Event)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s_cuda.record_event()
```

```python
synchronize() → None
```

Wait for all the kernels in this stream to complete.

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> s_cuda.synchronize()
```

```python
wait_event(event) → None
```

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

Parameters:

event (tensorplay.Event) – an event to wait for.

Example:

```
>>> s1_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s1_cuda.record_event()
>>> s2_cuda.wait_event(e_cuda)
```

```python
wait_stream(stream) → None
```

Synchronize with another stream. All future work submitted to this stream will wait until all kernels
already submitted to the given stream are completed.

Parameters:

stream (tensorplay.Stream) – a stream to synchronize.

Example:

```
>>> s1_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda.wait_stream(s1_cuda)
```

[#](#api-tensorplay.cuda.streams.Stream)

### Stream

class[Full reference ↗](/docs/generated/tensorplay.cuda.streams.Stream.html)

```python
class tensorplay.cuda.streams.Stream(device, *, priority) → Stream
```

An in-order queue of executing the respective tasks asynchronously in first in first out (FIFO) order.
It can control or synchronize the execution of other Stream or block the current host thread to ensure
the correct task sequencing. It supports with statement as a context manager to ensure the operators
within the with block are running on the corresponding stream.

See in-depth description of the CUDA behavior at [CUDA semantics](/docs/notes/cuda.html#cuda-semantics) for details
on the exact semantic that applies to all devices.

Parameters:

- device ([tensorplay.device](/docs/generated/tensorplay.Device.html#tensorplay.Device), optional) – the desired device for the Stream. If not given, the current the relevant documentation type will be used.

- priority ([int](https://docs.python.org/3/builtins/functions.html#int), optional) – priority of the stream, should be 0 or negative, where negative numbers indicate higher priority. By default, streams have priority 0.

Returns:

An tensorplay.Stream object.

Return type:

[Stream](#tensorplay.cuda.streams.Stream)

Example:

```
>>> with tensorplay.Stream(device='cuda') as s_cuda:
>>>     a = tensorplay.randn(10, 5, device='cuda')
>>>     b = tensorplay.randn(5, 10, device='cuda')
>>>     c = tensorplay.mm(a, b)
```

```python
query() → bool
```

Check if all the work submitted has been completed.

Returns:

A boolean indicating if all kernels in this stream are completed.

Return type:

[bool](https://docs.python.org/3/builtins/functions.html#bool)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> s_cuda.query()
True
```

```python
record_event(event) → Event
```

Record an event. En-queuing it into the Stream to allow further synchronization from the current point in the FIFO queue.

Parameters:

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

Returns:

Recorded event.

Return type:

[Event](/docs/generated/tensorplay.cuda.streams.Event.html#tensorplay.cuda.streams.Event)

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s_cuda.record_event()
```

```python
synchronize() → None
```

Wait for all the kernels in this stream to complete.

Example:

```
>>> s_cuda = tensorplay.Stream(device='cuda')
>>> s_cuda.synchronize()
```

```python
wait_event(event) → None
```

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

Parameters:

event (tensorplay.Event) – an event to wait for.

Example:

```
>>> s1_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda = tensorplay.Stream(device='cuda')
>>> e_cuda = s1_cuda.record_event()
>>> s2_cuda.wait_event(e_cuda)
```

```python
wait_stream(stream) → None
```

Synchronize with another stream. All future work submitted to this stream will wait until all kernels
already submitted to the given stream are completed.

Parameters:

stream (tensorplay.Stream) – a stream to synchronize.

Example:

```
>>> s1_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda = tensorplay.Stream(device='cuda')
>>> s2_cuda.wait_stream(s1_cuda)
```