Function: create_config

Applicability

Product	Supported (√/x)
Atlas A3 training products/Atlas A3 inference products	√
Atlas A2 training products/Atlas A2 inference products	√
Atlas training products	√
Atlas inference products	√
Atlas 200I/500 A2 inference products	√

Function Usage

Creates data of the aclprofConfig type as a Profiling configuration.

Created aclProfConfig data can be reused in multiple calls. You need to ensure the consistency and accuracy of the data.

To destroy data of the aclprofConfig type, call Function: destroy_config.

Prototype

C Prototype

aclprofConfig *aclprofCreateConfig(uint32_t *deviceIdlist, uint32_t deviceNums, aclprofAicoreMetrics aicoreMetrics, aclprofAicoreEvents *aicoreEvents, uint64_t dataTypeConfig)

Python Function

prof_config = acl.prof.create_config(device_list, aicore_metrics, aicore_events, data_type_config)

Parameter Description

Parameter	Description
device_list	List, device ID list. Set this parameter based on the actual device ID.
aicore_metrics	Int, aclprofAicoreMetrics.
aicore_events	Int, AI Core event, which is set to 0.
data_type_config	Int. Select from the following aclproftype values in logical OR format (for example, ACL_PROF_ACL_API\|ACL_PROF_AICORE_METRICS) as the parameter value of data_type_config. Each value indicates a type of profile data. ACL_PROF_ACL_API: collects profile data of APIs, including the synchronous/asynchronous memory copy latencies between the host and devices. ACL_PROF_TASK_TIME: collects operator delivery and execution duration data, as well as basic operator information, to provide more comprehensive profile data. ACL_PROF_TASK_TIME_L0: collects operator delivery and execution duration data. Compared with ACL_PROF_TASK_TIME, ACL_PROF_TASK_TIME_L0 does not collect basic operator information, so the performance overhead during collection is smaller, and this enables more accurate collection of statistics on time duration data. ACL_PROF_AICORE_METRICS: collects AI Core metrics. This macro must be included in the OR logic for aicore_metrics to take effect. ACL_PROF_TASK_MEMORY: specifies whether to collect the memory usage of CANN operators. Only data of GE operators is collected. ACL_PROF_AICPU: collects traces of AI CPU tasks, including the start and end of each task. ACL_PROF_L2CACHE: collects L2 cache data. ACL_PROF_HCCL_TRACE: collects communication data. ACL_PROF_MSPROFTX: profile data output by the user and upper-layer framework program. In the application script, you need to add one of the following APIs: For details about the mstx API (MindStudio Tools Extension API), see "mstx API Usage Examples" in the Profiling Instructions. msproftx Extension APIs ACL_PROF_TRAINING_TRACE: collects iteration traces. ACL_PROF_RUNTIME_API: collects runtime API profile data.

Return Value Description

Return Value	Description
prof_config	Int. If a non-zero value is returned, the operation is successful. The return value is the pointer address of data of the aclprofConfig type. If 0 is returned, the operation fails.

Return Value

Description

prof_config

Int.

If a non-zero value is returned, the operation is successful. The return value is the pointer address of data of the aclprofConfig type.
If 0 is returned, the operation fails.

Restrictions

Use the acl.prof.destroy_config API to destroy data of the aclprofConfig type. If data is not destroyed, the memory cannot be freed.

Use this API together with the acl.prof.destroy_config API. Call acl.prof.create_config first and then acl.prof.destroy_config.

Parent topic: Data Profiling APIs