Environment Variable List

This document describes the environment variables that can be used when developers build AI applications and services based on CANN.

Environment variables can be implemented using commands, APIs, and configurations, including the export command, putenv/getenv/setenv/unsetenv/clearenv functions, os.environ, and os.getenv. It is recommended that the user should set environment variables before starting application processes. Otherwise, environment variable access conflicts may occur, causing program exceptions.

For details about environment variables related to collective communication, see Reference > Environment Variable Reference in Huawei Collective Communication Library (HCCL).

For details about PyPTO environment variables, see PyPTO Operator Development.

Installation

Environment Variable	Description
ASCEND_CACHE_PATH	Storage path of shared files. Set this environment variable if you require all shared files generated during component build and runtime to be flushed to a unified directory.
ASCEND_WORK_PATH	Storage path for node-exclusive files. Set this environment variable if you require all node-exclusive files generated during component build and runtime to be flushed to a unified directory.
ASCEND_CUSTOM_OPP_PATH	Installation path of the user custom operator package. Set this environment variable if the custom operator package generated during build needs to be installed in a specified path.
CANN_KNOWLEDGE_BANK_PROCESS_NUM	Number of processes for the knowledge base management module to query the knowledge base.

Graph Build

Environment Variable	Description
DUMP_GE_GRAPH	DUMP_GE_GRAPH sets the graph dump mode.
DUMP_GRAPH_LEVEL	DUMP_GRAPH_LEVEL sets the graph to dump.
DUMP_GRAPH_FORMAT	Controls the type of the dump file to be generated.
DUMP_GRAPH_PATH	Sets the path for storing dump graph files. The path can be an absolute path or a relative path of the script execution path.
OP_NO_REUSE_MEM	By default, memory reuse is enabled during graph build on the Ascend platform. In fault locating scenarios, if developers suspect that the computation result is abnormal due to memory reuse errors, they can use this environment variable to allocate memory to an operator separately.
ASCEND_ENGINE_PATH	Set this environment variable when converting a single-operator JSON file to an offline model and you intend to exclusively adopt TBE operators during model conversion. The conversion process will not search for AI CPU operators, and an error will be thrown if the corresponding TBE operator is missing.
MAX_COMPILE_CORE_NUMBER	Specifies the number of CPU cores available for graph build.
MULTI_THREAD_COMPILE	Enables or disables the single-thread build during model conversion.
ENABLE_NETWORK_ANALYSIS_DEBUG	In the TensorFlow training scenario, if computational graph build fails, the training process is terminated by default and the remaining graphs are not delivered to the device. You can set this environment variable to make TF Adapter continuously deliver computational graphs to the device without terminating the training process when graph build fails.

Operator Build

Environment Variable	Description
TE_PARALLEL_COMPILER	Parallel build configuration. Set this environment variable to enable parallel build, which delivers superior performance in large network scenarios.
ASCEND_MAX_OP_CACHE_SIZE	Maximum disk capacity of the cache directory under each AI processor. Set this environment variable when the operator build cache function is enabled.
ASCEND_REMAIN_CACHE_SIZE_RATIO	Specifies the percentage of cache space to be reserved when the operator compilation cache function is enabled. When the occupied compilation cache space reaches ASCEND_MAX_OP_CACHE_SIZE and old kernel files need to be purged, the system will reserve a proportion of the cache space as specified by this variable. The default value is 50, measured in percent.
IGNORE_INFER_ERROR	Specifies whether to skip operator prototype deliverable verification when an operator is integrated to a graph. The deliverables include the implementation of the adaptation functions for operator integration into the graph, such as shape deduction.

Resource Configuration

Environment Variable	Description
ASCEND_DEVICE_ID	Specifies the logical ID of the AI processor used by the current process.
ASCEND_RT_VISIBLE_DEVICES	Specifies the devices that are visible to the current process. One or more device IDs can be specified at a time. By using this environment variable, you can adjust the devices without modifying the application.
AUTO_USE_UC_MEMORY	Controls whether to allow operators to transfer data without passing through the L2 cache.
RESOURCE_CONFIG_PATH	Specifies the path for storing the heterogeneous resource description file.

Operator Execution

Environment Variable	Description
ACLNN_CACHE_LIMIT	Sets the number of operator information entries cached on the host for an aclnn API. The cached operator information includes the workspace size, operator executor, and tiling details.

Graph Execution

Environment Variable	Description
ENABLE_DYNAMIC_SHAPE_MULTI_STREAM	During graph execution, enabling the multi-stream concurrency function can improve network performance in certain scenarios. Currently, the multi-stream concurrency function is disabled by default. If you want to enable this function in dynamic shape scenario, you can use this environment variable to enable it.
MAX_RUNTIME_CORE_NUMBER	In training and online inference scenarios, this environment variable can be used to enable multi-thread task scheduling of the graph executor (host) for the network in dynamic shape graph mode.

AOE Tuning

Environment Variable	Description
TUNE_BANK_PATH	Sets the path of the custom repository generated after Auto Tune.
REPEAT_TUNE	Initiates tuning again. This environment variable takes effect only when subgraph tuning or operator tuning is enabled.
AOE_MODE	Sets the AOE tuning mode in online inference and training scenarios.

AMCT Model Compression

Environment Variable	Description
AMCT_LOG_FILE_LEVEL	Sets the log level of framework-specific log files (amct_pytorch.log for PyTorch; amct_caffe.log for Caffe; amct_onnx.log for ONNX), as well as the log level of logs output by each quantization layer during precision simulation model generation.
AMCT_LOG_LEVEL	Sets the log level of information printed to the screen. This environment variable applies only to quantization of the PyTorch framework, Caffe framework, and ONNX network model.
AMCT_LOG_DUMP	Configures log flushing behaviors during post-training quantization. This environment variable applies only to quantization performed by calling aclgrphCalibration.

Profiling

Environment Variable	Description
PROFILING_MODE	Enables or disables the profiling function.
PROFILING_OPTIONS	Sets profiling configuration options in training or online inference scenarios.

Logging

Environment Variable	Description
ASCEND_PROCESS_LOG_PATH	Sets the log flush path.
ASCEND_SLOG_PRINT_TO_STDOUT	Enables or disables the printing of application logs on the host. After this function is enabled, logs are not saved in the log file. Instead, the generated logs are directly printed and displayed.
ASCEND_GLOBAL_LOG_LEVEL	Sets the level of application logs and module logs. Only debug logs are supported.
ASCEND_MODULE_LOG_LEVEL	Sets the level of each module of app logs. Only debug logs are supported.
ASCEND_GLOBAL_EVENT_ENABLE	Enables or disables event logging for applications.
ASCEND_LOG_DEVICE_FLUSH_TIMEOUT	Specifies the timeout for flushing app logs from the device to the host.
ASCEND_HOST_LOG_FILE_NUM	Sets the number of log files of each process stored in the application log directories (plog and device-id) in the Ascend EP scenario.
ASCEND_COREDUMP_SIGNAL	Sets the core dump semaphore for trace processing.
ASCEND_LOG_SYNC_SAVE	Specifies the processing mode for log congestion.
ASCEND_TRACE_RECORD_NUM	Sets the aging policy for trace log files. The value range is [10, 1000].

Fault Information Collection

Environment Variable	Description
NPU_COLLECT_PATH	Specifies the storage path for fault information, including dump graphs, abnormal data of AI Core operators, and operator compilation logs. The path can be an absolute path or a relative path (relative to the working directory of the running program or command), on which users must have the read, write, and execute permissions. If the target path does not exist, the system will automatically create all missing directories under the specified path.
ASCEND_DUMP_SCENE	Enables or disables abnormal operator dump when reproducing fault scenarios. When this function is enabled, input and output data, workspace details, and tiling information of faulty operators are exported.
ASCEND_DUMP_PATH	Specifies the storage directory for abnormal operator dump data. Both absolute paths and relative paths (relative to the executable program) are supported. The specified path can contain uppercase letters, lowercase letters, digits, underscores (_), hyphens (-), and periods (.). The user must have read, write, and execute permissions on the path. If the target directory does not exist, the system will automatically create all missing directories along the path. If this path is not configured, abnormal operator dump data will be saved in the current execution directory of the application by default.

Environment Variables to Be Deprecated in Later Versions

Environment Variable	Description
GE_USE_STATIC_MEMORY	Configures the memory allocation mode used during network running.
ENABLE_ACLNN	Determines whether to call the host execution function registered by the operator during graph execution to implement the host execution logic and kernel delivery.