API Call Sequence

If synchronization in the asynchronous scenario is involved, your app must contain the related code logic. For details about the API call sequence, see the following figure.

The key APIs are described as follows:

1. Create a callback function in advance to obtain and process the model inference or operator execution result.
2. Create a thread and customize a thread function in advance. Once acl.rt.process_report is called in the thread function to set the timeout interval, acl.rt.launch_callback will deliver a callback task after a specified period of time.
3. Call acl.rt.subscribe_report to specify the thread for processing the callback function in the stream. This thread must be consistent with that in 2.
4. Call acl.mdl.execute_async for asynchronous inference.

   Also call acl.rt.synchronize_stream to wait for the stream tasks to complete.

   You can obtain the asynchronous inference results of all images at once after the acl.rt.synchronize_stream call. However, if the image data volume is large, the wait time could be long. In this case, you can deliver a callback task at a specified interval to obtain the asynchronous inference result of the previous period.

5. Call acl.rt.launch_callback to deliver a callback task in the task queue of the stream. When the callback task is executed in the system, it is also executed in the thread subscribed to the stream (acl.rt.subscribe_report). The callback function must be the same as that in 1.

   Each time acl.rt.launch_callback is called, a callback task is delivered.

6. Call acl.rt.unsubscribe_report to unsubscribe from a thread. (The callback function in the stream is no longer processed by the specified thread.)