# ExternalStream

Source: https://www.tensorplay.cn/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)
```