Environment Variables

MindSpore environment variables are as follows:

Operators Compile

Environment Variable	Function	Type	Value Range	Description
MS_BUILD_PROCESS_NUM	Specifies the number of parallel operator build processes during Ascend backend compilation.	Integer	The number of parallel operator build processes ranges from 1 to 24.
MS_COMPILER_CACHE_ENABLE	Specifies whether to save or load the cache of the graph compiled by front-end. The function is the same as the enable_compile_cache in MindSpore context. Note: This environment variable has lower precedence than the context enable_compile_cache.	Integer	0: Disable the compile cache 1: Enable the compile cache	If it is used together with MS_COMPILER_CACHE_PATH, the directory for storing the cache files is ${MS_COMPILER_CACHE_PATH} /rank_${RANK_ID} /graph_cache/. RANK_ID is the unique ID for multi-cards training, the single card scenario defaults to RANK_ID=0.
MS_COMPILER_CACHE_PATH	MindSpore compile cache directory and save the graph or operator cache files like graph_cache, kernel_meta, somas_meta.	String	File path, which can be a relative path or an absolute path.
MS_COMPILER_OP_LEVEL	Enable debug function and generate the TBE instruction mapping file during Ascend backend compilation. Note: Only Ascend backend.	Integer	The value of compiler op level should be one of [0, 1, 2, 3, 4]. 0: Turn off op debug and delete op compile cache files 1: Turn on debug, generate the *.cce and *_loc.json 2: Turn on debug, generate the *.cce and *_loc.json files and turn off the compile optimization switch (The CCEC compiler option is set to -O0-g) at the same time 3: Turn off op debug (default) 4: Turn off op debug, generate the *.cce and *_loc.json files, generate UB fusion calculation description files ({$kernel_name}_compute.json) for fusion ops	When an AICore Error occurs, if you need to save the cce file of ops, you can set the MS_COMPILER_OP_LEVEL to 1 or 2
MS_DEV_DISABLE_PREBUILD	Turn off operator prebuild processes during Ascend backend compilation. The prebuild processing may fix the attr fusion_type of the operate, and then affect the operator fusion. If the performance of fusion operator can not meet the expectations, try to turn on this environment variable to verify if there is the performance problem of fusion operator.	Boolean	true: turn off prebuild false: enable prebuild

For more information, see Incremental Operator Build and FAQ.

Parallel Training

Environment Variable	Function	Type	Value Range	Description
RANK_ID	Specifies the logical ID of the Ascend AI Processor called during deep learning.	Integer	The value ranges from 0 to 7. When multiple servers are running concurrently, DEVICE_ID`s in different servers may be the same. RANK_ID can be used to avoid this problem. `RANK_ID = SERVER_ID * DEVICE_NUM + DEVICE_ID, and DEVICE_ID indicates the sequence number of the Ascend AI processor of the current host.
RANK_SIZE	Specifies the number of Ascend AI Processors to be called during deep learning. Note: When the Ascend AI Processor is used, specified by user when a distributed case is executed.	Integer	The number of Ascend AI Processors to be called ranges from 1 to 8.	This variable is used together with RANK_TABLE_FILE
RANK_TABLE_FILE	Specifies the file to which a path points, including device_ip corresponding to multiple Ascend AI Processor device_id. Note: When the Ascend AI Processor is used, specified by user when a distributed case is executed.	String	File path, which can be a relative path or an absolute path.	This variable is used together with RANK_SIZE.
MS_COMM_COMPILER_OPT	Specifies the maximum number of communication operators that can be replaced by corresponding communication subgraph during Ascend backend compilation in graph mode. Note: When the Ascend AI Processor is used, specified by user when a distributed case is executed.	Integer	-1 or an positive integer: communication subgraph extraction and reuse is enabled. -1 means that default value will be used. A positive integer means that the user specified value will be used. if not set: communication subgraph extraction and reuse is turned off.
DEVICE_ID	The ID of the Ascend AI processor, which is the Device serial number on the AI server.	Integer	The ID of the Ascend AI processor. Value range: [0, number of actual Devices-1].

Dynamic Networking

Environment Variable	Function	Type	Value Range	Description
MS_ROLE	Specifies the role of this process.	String	MS_SCHED: represents the Scheduler process, a training task starts only one Scheduler, which is responsible for networking, disaster recovery, etc., and does not execute the training code. MS_WORKER: represents the Worker process, which generally sets up the distributed training process for this role. MS_PSERVER: represents the Parameter Server process, and this role is only valid in Parameter Server mode. Please refer to Parameter Server mode .	The Worker and Parameter Server processes register with the Scheduler process to complete the networking.
MS_SCHED_HOST	Specifies the IP address of the Scheduler.	String	Legal IP address.	The current version does not support IPv6 addresses.
MS_SCHED_PORT	Specifies the Scheduler binding port number.	Integer	Port number in the range of 1024 to 65535.
MS_NODE_ID	Specifies the ID of this process, unique within the cluster.	String	Represents the unique ID of this process, which is automatically generated by MindSpore by default.	MS_NODE_ID needs to be set in the following cases. Normally it does not need to be set and is automatically generated by MindSpore: Enable Disaster Recovery Scenario: Disaster recovery requires obtaining the current process ID and thus re-registering with the Scheduler. Enable GLOG log redirection scenario: In order to ensure that the logs of each training process are saved independently, it is necessary to set the process ID, which is used as the log saving path suffix. Specify process rank id scenario: users can specify the rank id of this process by setting MS_NODE_ID to some integer.
MS_WORKER_NUM	Specifies the number of processes with the role MS_WORKER.	Integer	Integers greater than 0.	The number of Worker processes started by the user should be equal to the value of this environment variable. If it is less than this value, the networking fails; if it is greater than this value, the Scheduler process will complete the networking according to the order of Worker registration, and the redundant Worker processes will fail to start.
MS_SERVER_NUM	Specifies the number of processes with the role MS_PSERVER.	Integer	Integers greater than 0.	The setting is only required in Parameter Server training mode.
MS_ENABLE_RECOVERY	Turn on disaster tolerance.	Integer	1 for on, 0 for off. The default is 0.
MS_RECOVERY_PATH	Persistent path folder.	String	Legal user directory.	The Worker and Scheduler processes perform the necessary persistence during execution, such as node information for restoring the grouping and training the intermediate state of the service, and are saved via files.
MS_HCCL_CM_INIT	Whether to use the CM method to initialize the HCCL.	Integer	1 for using the method, 0 for not using. The default is 0.	This environment variable is only recommended to be turned on for Ascend hardware platforms with a large number of communication domains. Turning on this environment variable reduces the memory footprint of the HCCL collection communication libraries, and the training tasks are executed in the same way as the rank table startup.

See Dynamic Cluster for more details.

Running Data Recorder

Environment Variable	Function	Type	Value Range	Description
MS_RDR_ENABLE	Determines whether to enable running data recorder (RDR). If a running exception occurs in MindSpore, the pre-recorded data in MindSpore is automatically exported to assist in locating the cause of the running exception.	Integer	1：enables RDR 0：disables RDR	This variable is used together with MS_RDR_MODE and MS_RDR_PATH.
MS_RDR_MODE	Determines the exporting mode of running data recorder (RDR).	Integer	1：export data when training process terminates in exceptional scenario 2：export data when training process terminates in both exceptional scenario and normal scenario. Default: 1.	This variable is used together with MS_RDR_ENABLE=1.
MS_RDR_PATH	Specifies the system path for storing the data recorded by running data recorder (RDR).	String	Directory path, which should be an absolute path.	This variable is used together with MS_RDR_ENABLE=1. The final directory for recording data is ${MS_RDR_PATH} /rank_${RANK_ID}/rdr/. RANK_ID is the unique ID for multi-cards training, the single card scenario defaults to RANK_ID=0.

For more information, see Running Data Recorder.

Log

Environment Variable	Function	Type	Value Range	Description
GLOG_log_dir	Specifies the log level.	String	File path, which can be a relative path or an absolute path.	This variable is used together with GLOG_logtostderr If the value of GLOG_logtostderr is 0, this variable must be set If GLOG_log_dir is specified and the value of GLOG_logtostderr is 1, the logs are output to the screen and not to the file The log saving path is: specified path/rank_${rank_id}/logs/. Under non-distributed training scenario, rank_id is 0, while under distributed training scenario, rank_id is the ID of the current device in the cluster C++ and Python logs are output to different files. The C++ logs follow the GLOG log file naming rules. In this case mindspore.machine name. user name.log.log level.timestamp.Process ID, the Python log file name is mindspore.log.process ID. GLOG_log_dir can only contain upper and lower case letters, numbers, “-”, “_”, “/” characters, etc.
GLOG_log_max	Control the size of the MindSpore C++ module log file. You can change the default maximum value of the log file with this environment variable	Integer	Positive integer. Default value: 50MB	If the current written log file exceeds the maximum value, the new output log content is written to a new log file
GLOG_logtostderr	Specifies the log output mode.	Integer	1: logs are output to the screen 0: logs are output to a file Default: 1	This variable is used together with GLOG_log_dir
GLOG_stderrthreshold	The log module will print logs to the screen when these logs are output to a file. This environment variable is used to control the log level printed to the screen in this scenario.	Integer	0-DEBUG 1-INFO 2-WARNING 3-ERROR 4-CRITICAL Default: 2
GLOG_v	Specifies the log level.	Integer	0-DEBUG 1-INFO 2-WARNING 3-ERROR, indicating that the program execution error, output error log, and the program may not terminate 4-CRITICAL, indicating that the execution of the program is abnormal, and the program may not terminate Default: 2.	After a log level is specified, output log messages greater than or equal to that level
logger_backupCount	Controls the number of mindspore Python module log files.	Integer	Default: 30
logger_maxBytes	Controls the size of the mindspore Python module log file.	Integer	Default: 52428800 bytes
MS_SUBMODULE_LOG_v	Specifies log levels of C++ sub modules of MindSpore.	Dict {String:Integer…}	0-DEBUG 1-INFO 2-WARNING 3-ERROR	The assignment way is:MS_SUBMODULE_LOG_v=”{SubModule1:LogLevel1,SubModule2:LogLevel2,…}” The log level of the specified sub-module will override the setting of GLOG_v in this module, where the log level of the sub-module LogLevel has the same meaning as that of GLOG_v. For a detailed list of MindSpore sub-modules, see sub-module_names. For example, you can set the log level of PARSER and ANALYZER modules to WARNING and the log level of other modules to INFO by GLOG_v=1 MS_SUBMODULE_LOG_v=”{PARSER:2,ANALYZER:2}”.

Note: glog does not support log file wrapping. If you need to control the log file occupation of disk space, you can use the log file management tool provided by the operating system, for example: logrotate for Linux. Please set the log environment variables before import mindspore .

Feature Value Detection

Environment Variable	Function	Type	Value	Description
NPU_ASD_ENABLE	Whether to enable feature value detection function	Integer	0: Disable feature value detection function 1: Enable feature value detection function	Currently, this feature only supports Atlas A2 training series products, and only detects abnormal feature value that occur during the training of Transformer class models with bfloat16 data type
NPU_ASD_UPPER_THRESH	Controls the absolute numerical threshold for detection	String	The format is a pair of integers, where the first element controls the first-level absolute numerical threshold, and the second element controls the second-level absolute numerical threshold Decreasing the threshold can detect smaller fluctuations of abnormal data, increasing the detection rate, while increasing the threshold has the opposite effect By default, if this environment variable is not configured, NPU_ASD_UPPER_THRESH=1000000,10000
NPU_ASD_SIGMA_THRESH	Controls the relative numerical threshold for detection	String	The format is a pair of integers, where the first element controls the first-level relative numerical threshold, and the second element controls the second-level relative numerical threshold Decreasing the threshold can detect smaller fluctuations of abnormal data, increasing the detection rate, while increasing the threshold has the opposite effect By default, if this environment variable is not configured, NPU_ASD_SIGMA_THRESH=100000,5000

For more information on feature value detection, see Feature Value Detection.

Dump Function

Environment Variable	Function	Type	Value Range	Description
MINDSPORE_DUMP_CONFIG	Specify the path of the configuration file that the cloud-side Dump or the device-side Dump depends on.	String	File path, which can be a relative path or an absolute path.
MS_DIAGNOSTIC_DATA_PATH	When the cloud-side Dump is enabled, if the path field is not set or set to an empty string in the Dump configuration file, then $MS_DIAGNOSTIC_DATA_PATH /debug_dump is regarded as path. If the `path field in configuration file is not empty, it is still used as the path to save Dump data.	String	File path, only absolute path is supported.	This variable is used together with MINDSPORE_DUMP_CONFIG.
MS_DEV_DUMP_BPROP	Dump bprop ir file in current path	String	‘on’, indicating that dump bprop ir file in current path	Experimental environment variable.
MS_DEV_DUMP_PACK	Dump trace ir file in current path	String	‘on’, indicating that dump trace ir file in current path	Experimental environment variable.

For more information, see Using Dump in the Graph Mode.

Data Processing

Environment Variable	Function	Type	Value Range	Description
DATASET_ENABLE_NUMA	Determines whether to enable numa feature for dataset module. Most of time this configuration can improve performance on distribute scenario.	String	True: Enables the numa feature for dataset module.	This variable is used together with libnuma.so.
MS_CACHE_HOST	Specifies the IP address of the host where the cache server is located when the cache function is enabled.	String	IP address of the host where the cache server is located.	This variable is used together with MS_CACHE_PORT.
MS_CACHE_PORT	Specifies the port number of the host where the cache server is located when the cache function is enabled.	String	Port number of the host where the cache server is located.	This variable is used together with MS_CACHE_HOST.
MS_DATASET_SINK_QUEUE	Specifies the size of data queue in sink mode.	Integer	1~128: Valid range of queue size.
MS_ENABLE_NUMA	Whether to enable numa feature in global context to improve end-to-end performance.	String	True: Enables the numa feature in global context.
OPTIMIZE	Determines whether to optimize the pipeline tree for dataset during data processing. This variable can improve the data processing efficiency in the data processing operator fusion scenario.	String	true: enables pipeline tree optimization. false: disables pipeline tree optimization.

For more information, see Single-Node Data Cache and Optimizing the Data Processing.

Debugger

Environment Variable	Function	Type	Value Range	Description
ENABLE_MS_DEBUGGER	Determines whether to enable Debugger during training.	Boolean	1: enables Debugger. 0: disables Debugger.	This variable is used together with MS_DEBUGGER_HOST and MS_DEBUGGER_PORT.
MS_DEBUGGER_HOST	Specifies the IP of the MindSpore Insight Debugger Server.	String	IP address of the host where the MindSpore Insight Debugger Server is located.	This variable is used together with ENABLE_MS_DEBUGGER=1 and MS_DEBUGGER_PORT.
MS_DEBUGGER_PARTIAL_MEM	Determines whether to enable partial memory overcommitment. (Memory overcommitment is disabled only for nodes selected on Debugger.)	Boolean	1: enables memory overcommitment for nodes selected on Debugger. 0: disables memory overcommitment for nodes selected on Debugger.
MS_DEBUGGER_PORT	Specifies the port for connecting to the MindSpore Insight Debugger Server.	Integer	Port number ranges from 1 to 65536.	This variable is used together with ENABLE_MS_DEBUGGER=1 and MS_DEBUGGER_HOST.

For more information, see Debugger.

CANN

For more information about CANN’s environment variables, see Ascend community . Please set the environment variables for CANN before import mindspore .

Other

Environment Variable	Function	Type	Value Range	Description
GROUP_INFO_FILE	Specify communication group information storage path	String	Communication group information file path, supporting relative path and absolute path.
GRAPH_OP_RUN	When running the pipeline large network model in task sinking mode in graph mode, it may not be able to start as expected due to the limitation of stream resources. This environment variable can specify the execution mode of the graph mode. Set this variable to 0, indicating that model will be executed in non-task sinking mode which is the default execution mode. Set this variable to 1, indicating a non-task sinking mode, which has no flow restrictions, but has degraded performance.	Integer	0: task sinking mode. 1: non-task sinking mode.
MS_DEV_JIT_SYNTAX_LEVEL	Fallback function is enabled when the environment variable is set to 2.	Integer	2: enables fallback function 0: disables fallback function Default: 2
MS_JIT_MODULES	Specifies which modules in static graph mode require JIT static compilation, and their functions and methods will be compiled into static calculation graphs.	String	The module name, corresponding to the name of the imported top-level module. If there are more than one, separate them with commas. For example, export MS_JIT_MODULES=mindflow,mindyolo.	By default, modules other than third-party libraries will be perform JIT static compilation, and MindSpore suites such as mindflow and mindyolo will not be treated as third-party libraries. If there is a module similar to MindSpore suites, which contains nn.Cell, @ms.jit decorated functions or functions to be compiled into static calculation graphs, you can configure the environment variable, so that the module will be perform JIT static compilation instead of being treated as third-party library.
MS_JIT_IGNORE_MODULES	Specifies which modules are treated as third-party libraries in static graph mode without JIT static compilation. Their functions and methods will be interpreted and executed.	String	The module name, corresponding to the name of the imported top-level module. If there are more than one, separate them with commas. For example, export MS_JIT_IGNORE_MODULES=numpy,scipy.	Static graph mode can automatically recognize third-party libraries, and generally there is no need to set this environment variable for recognizable third-party libraries such as NumPy and Scipy. If MS_JIT_IGNORE_MODULES and MS_JIT_MODULES specify the same module name at the same time, the former takes effect and the latter does not.
MS_JIT_DISPLAY_PROGRESS	Specify whether to print compilation progress information.	Integer	1: Print main compilation progress information. No setting or other value: Do not print compilation progress information.
MS_EXCEPTION_DISPLAY_LEVEL	Control the display level of exception information	Integer	0: display exception information related to model developers and framework developers 1: display exception information related to model developers Default: 0
MS_OM_PATH	Specifies the save path for the file analyze_fail.ir/*.npy which is dumped if task exception or a compiling graph error occurred. The file will be saved to the path of the_specified_directory /rank_${rank_id}/om/.	String	File path, which can be a relative path or an absolute path.
OPTION_PROTO_LIB_PATH	Specifies the RPOTO dependent library path.	String	File path, which can be a relative path or an absolute path.
MS_KERNEL_LAUNCH_SKIP	Specifies the kernel or subgraph to skip during execution.	String	ALL or all: skip the execution of all kernels and subgraphs kernel name (such as ReLU) : skip the execution of all ReLU kernels subgraph name (such as kernel_graph_1) : skip the execution of subgraph kernel_graph_1, used for subgraph sink mode
MS_DEV_SAVE_GRAPHS_SORT_MODE	Choose the sort mode of the graphs printed in the ir files.	Integer	0: print default ir file 1: print deep sorted ir file
MS_DEV_DUMP_IR_FORMAT	Configure what information is displayed in IR graphs.	Integer	0: Except for the return node, only the operator and inputs of the node are displayed, and the detailed information of subgraph is simplified. 1: Display all information except debug info and scope. 2 or not set: Display all information.
MS_DEV_DUMP_IR_INTERVAL	Set to save an IR file every few IR files to reduce the number of IR files.	Integer	1 or not set: Save all IR files. Other values: Save IR files at specified intervals.	When this environment variable is enabled together with MS_DEV_DUMP_IR_PASSES, the rules of MS_DEV_DUMP_IR_PASSES take priority, and this environment variable will not take effect.
MS_DEV_DUMP_IR_PASSES	Specify which IR files to save based on the file name.	String	Pass’s name of part of its name. If there are multiple, use commas to separate them. For example, export MS_DEV_DUMP_IR_PASSES=recompute,renormalize.	When setting this environment variable, regardless of the value of MS_DEV_SAVE_GRAPHS, detailed frontend IR files will be filtered and printed.
MS_DEV_SIDE_EFFECT_LOAD_ELIM	Optimize redundant memory copy operations.	Integer	0: Do not do video memory optimization, occupy the most video memory. 1: Conservatively do some memory optimization. 2: Under the premise of losing a certain amount of compilation performance, optimize the video memory as much as possible. 3: The accuracy of the network is not guaranteed, and the memory consumption is minimal. Default: 1
MS_PYNATIVE_GE	Whether GE is executed in PyNative mode.	Integer	0: GE is not executed. 1: GE is executed. Default: 0	Experimental environment variable.
GC_COLLECT_IN_CELL	Whether to perform garbage collection on unused Cell objects	Integer	1: Perform garbage collection on unused Cell objects No setting or other value: not calling the garbage collection
PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION	Choose which language to use for the Protocol Buffers back-end implementation	String	“cpp”: implementation using c++ backend “python”: implementation using python back-end No setting or other value: implementation using python backend
MS_DEV_DISABLE_BPROP_CACHE	Disable to use bprop’s graph cache	String	‘on’, indicating that disable to use bprop’s graph cache	Experimental environment variable. When set env on, it will slow down building bprop’s graph
MS_DEV_USE_PY_BPROP	The op which set by environment will use python bprop instead of cpp expander bprop	String	Op name, can set more than one name, split by ‘,’	Experimental environment variable. It will run fail when python bprop does not exist
MS_DEV_DISABLE_TRACE	Disable trace function	String	‘on’, indicating that disable trace function	Experimental environment variable.
MS_ENABLE_GE	Enabling the GE Process	Integer	0: not enable the GE process. 1: Enabling the GE Process. Default value: 0.	For use in Ascend 910 AI processor environments only. Experimental environment variables.