aclmdlExecute

Applicability

Description

Executes model inference until the result is returned.

The operations of loading, executing, and unloading a model must be performed in the same context. For details about how to create a context, see or .

Prototype

aclError aclmdlExecute(uint32_t modelId, const aclmdlDataset *input, aclmdlDataset *output)

Parameters

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aclDataBuffer *dataBuffer = aclmdlGetDatasetBuffer(output, 0); // Obtain the corresponding data buffer based on the index.
void *data = aclGetDataBufferAddr(dataBuffer);  // Obtain the device pointer to data.
aclrtFree(data ); // Free the device memory.
aclUpdateDataBuffer(dataBuffer, nullptr, 0); // Reset the content in the data buffer for next inference.

Returns

0 on success; else, failure. For details, see aclError.

Restrictions

• If the same modelId is shared by multiple threads due to service requirements, locks must be added between user threads to ensure that operations of refreshing the input and output memories and executing inference are performed continuously. For example:

  // API call sequence of thread A:
  lock(handle1) -> aclrtMemcpy refreshes the input and output memory -> aclmdlExecute runs inference -> unlock(handle1)
  
  // API call sequence of thread B:
  lock(handle1) -> aclrtMemcpy refreshes the input and output memory -> aclmdlExecute runs inference -> unlock(handle1)

• The memory for storing model input and output data is the device memory. You can call APIs such as and to allocate device memory.
  • For details about the scenarios and restrictions of each memory allocation API, see the API description in Memory Management.
  • The hi_mpi_dvpp_malloc API is a dedicated memory allocation API for media data processing. To reduce copy, the output of media data processing is used as the input of model inference to implement memory reuse.
  • Hardware has memory alignment and supplement requirements. If you use one of these APIs to allocate a large memory block, and divide and manage the memory, the alignment and supplement restrictions of the corresponding API must be met. For details, see Secondary Memory Allocation.

See Also

For details about the API call sequence and sample code, see Running a Model.