Environment Variables
MindSpore environment variables are as follows:
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. |
|
MS_FREE_DISK_CHECK |
Whether to enable the check of disk space. |
String |
True: Enable the check of disk space. False: Disable the check of disk space. |
Default: True, When creating MindRecords on shared storage using multiple concurrent operations, it is recommended to set it to False. |
MS_INDEPENDENT_DATASET |
Whether to enable dataset independent process mode. Dataset will run in independent child processes. Only supports Linux platform. |
String |
True: Enable the dataset independent process mode. False: Disable the dataset independent process mode. |
Default: False. This feature is currently in beta testing. Does not support use with Profiling, AutoTune, Offload, Cache, DSCallback or DVPP transforms. If you encounter any problems during use, please feel free to provide feedback. |
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.
Graph Compilation and Execution
Environment Variable |
Function |
Type |
Value Range |
Description |
|---|---|---|---|---|
MS_DEV_JIT_SYNTAX_LEVEL |
Specify the syntax support level of static graph mode. |
Integer |
0: Specify the syntax support level of static graph mode as STRICT level. Only basic syntaxes is supported, and execution performance is optimal. Can be used for MindIR load and export. 2: Specify the syntax support level of static graph mode as LAX level. More complex syntaxes are supported, compatible with all Python syntax as much as possible. Cannot be used for MindIR load and export due to some syntax that may not be able to be exported. |
|
MS_JIT_MODULES |
Specify 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. See Calling the Third-party Libraries for more details. 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 |
Specify 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_DEV_FALLBACK_DUMP_NODE |
Print syntax expressions supported by Static Graph Syntax Enhancement in the code. |
Integer |
1: Enable printing. No setting or other value: Disable printing. |
|
MS_JIT |
Specify whether to use just-in-time compilation. |
Integer |
0: Do not use just-in-time compilation, and the network script is executed directly in dynamic graph (PyNative) mode. No setting or other value: Determine whether to execute static graph (Graph) mode or dynamic graph (PyNative) mode according to the network script. |
|
MS_DEV_FORCE_USE_COMPILE_CACHE |
Specify whether to use the compilation cache directly without checking whether the network script has been modified. |
Integer |
1: Do not check whether the network script has been modified, directly use the compilation cache. It is recommended to only use it during debugging. For example, the network script only adds print statements for printing and debugging. No setting or other value: Detect changes in network scripts, and only use the compilation cache when the network scripts have not been modified. |
|
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_DEV_SAVE_GRAPHS |
Specify whether to save IR files. |
Integer |
0: Disable saving IR files. 1: Some intermediate files will be generated during graph compilation. 2: Based on level1, generate more IR files related to backend process. 3: Based on level2, generate visualization computing graphs and detailed frontend IR graphs. |
|
MS_DEV_SAVE_GRAPHS_PATH |
Specify path to save IR files. |
String |
Path to save IR files. |
|
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_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_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_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 |
|
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_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_DISABLE_TRACE |
Disable trace function |
String |
'on', indicating that disable trace function |
Experimental environment variable. |
MS_ENABLE_IO_REUSE |
Turn on the graph input/output memory multiplexing flag |
Integer |
1: Enable this function. 0: not enabled. Default value: 0 |
Ascend AI processor environment and graph compilation grade O2 process use only. |
MS_DISABLE_REF_MODE |
Forcibly setting to turn off ref mode |
Integer |
0: Does not turn off ref mode. 1: Forcibly turn off ref mode. Default value: 0. |
This environment variable will be removed subsequently and is not recommended. Ascend AI processor environment and graph compilation grade O2 process use only. |
MS_ENABLE_GRACEFUL_EXIT |
Enable training process exit gracefully |
Integer |
1: Enable graceful exit. No setting or other value: Disable graceful exit. |
Rely on the callback function to enable graceful exit. Refer to the Example of Graceful Exit . |
MS_DEV_BOOST_INFER |
Compile optimization switch for graph compilation. This switch accelerates the type inference module to speed up network compilation. |
Integer |
0: Disables the optimization. No setting or other value: Enables the optimization. |
This environment variable will be removed subsequently. |
MS_DEV_RUNTIME_CONF |
Configure the runtime environment. |
String |
Configuration items, with the format "key: value", multiple configuration items separated by commas, for example, "export MS_DEV_RUNTIME_CONF=inline:false,pipeline:false". inline: In the scenario of sub image cell sharing, whether to enable backend inline, only effective in O0 or O1 mode, with a default value of true. switch_inline: Whether to enable backend control flow inline, only effective in O0 or O1 mode, with a default value of true. multi_stream: The backend stream diversion method, with possible values being 1) true: One stream for communication and one for computation. 2) false: Disable multi-streaming, use a single stream for both communication and computation. 3) group (default value): Communication operators are diverted based on their communication domain. pipeline: Whether to enable runtime pipeline, only effective in O0 or O1 mode, with a default value of true. all_finite: Whether to enable Allfitine in overflow detection, only effective in O0 or O1 mode, with a default value of true. synchronize: Whether to execute synchronously, only effective in O0 or O1 mode, with a default value of true. memory_statistics: Whether to enable memory statistics, with a default value of false. compile_statistics: Whether to enable compile statistics, with a default value of false. ge_kernel: Whether to enable O2/O1/O0 runtime unification, with a default value of true. backend_compile_cache: Whether to enable backend cache in O0/O1 mode, only effective when enable complie cache(MS_COMPILER_CACHE_ENABLE), with a default value of true. view: Whether to enable view kernels, only effective in O0 or O1 mode, with a default value of true. |
|
MS_DEV_VIEW_OP |
Specify certain operators to replace by view with MS_DEV_RUNTIME_CONF enabled view |
String |
Op name, can set more than one name, split by ',' |
Experimental environment variable. |
MS_ALLOC_CONF |
Configure the memory allocation. |
String |
Configuration items, with the format "key: value", multiple configuration items separated by commas, for example, "export MS_ALLOC_CONF=enable_vmm:true,memory_tracker:true". enable_vmm: Whether to enable virtual memory, with a default value of true. vmm_align_size: Set the virtual memory alignment size in MB, with a default value of 2. memory_tracker: Whether to enable memory tracker, with a default value of false. acl_allocator: Whether to enable ACL memory allocator, with a default value of true. somas_whole_block: Whether to use the entire Somas for memory allocation, with a default value of false. |
Dump Debugging
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 $MS_DIAGNOSTIC_DATA_PATH /debug_dump is regarded as path. If the `path field in configuration file is not empty, it is still used as the path to save Dump data. |
String |
File path, only absolute path is supported. |
This variable is used together with MINDSPORE_DUMP_CONFIG. |
MS_DEV_DUMP_BPROP |
Dump bprop ir file in current path |
String |
'on', indicating that dump bprop ir file in current path |
Experimental environment variable. |
MS_DEV_DUMP_PACK |
Dump trace ir file in current path |
String |
'on', indicating that dump trace ir file in current path |
Experimental environment variable. |
ENABLE_MS_DEBUGGER |
Determines whether to enable Debugger during training. |
Boolean |
1: enables Debugger. 0: disables Debugger. |
This variable is used together with MS_DEBUGGER_HOST and MS_DEBUGGER_PORT. |
MS_DEBUGGER_HOST |
Specifies the IP of the MindSpore Insight Debugger Server. |
String |
IP address of the host where the MindSpore Insight Debugger Server is located. |
This variable is used together with ENABLE_MS_DEBUGGER=1 and MS_DEBUGGER_PORT. |
MS_DEBUGGER_PARTIAL_MEM |
Determines whether to enable partial memory overcommitment. (Memory overcommitment is disabled only for nodes selected on Debugger.) |
Boolean |
1: enables memory overcommitment for nodes selected on Debugger. 0: disables memory overcommitment for nodes selected on Debugger. |
|
MS_DEBUGGER_PORT |
Specifies the port for connecting to the MindSpore Insight Debugger Server. |
Integer |
Port number ranges from 1 to 65536. |
This variable is used together with ENABLE_MS_DEBUGGER=1 and MS_DEBUGGER_HOST. |
MS_OM_PATH |
Specifies the save path for the file analyze_fail.ir/*.npy which is dumped if task exception or a compiling graph error occurred. The file will be saved to the path of the_specified_directory /rank_${rank_id}/om/. |
String |
File path, which can be a relative path or an absolute path. |
|
MS_DUMP_SLICE_SIZE |
Specify slice size of operator Print, TensorDump, TensorSummary, ImageSummary, ScalarSummary, HistogramSummary. |
Integer |
0~2048, unit: MB, default value is 0. The value 0 means the data is not sliced. |
|
MS_DUMP_WAIT_TIME |
Specify wait time of second stage for operator Print, TensorDump, TensorSummary, ImageSummary, ScalarSummary, HistogramSummary. |
Integer |
0~600, unit: Seconds, default value is 0. The value 0 means using default wait time, i.e. the value of mindspore.get_context("op_timeout"). |
This environment variable only takes effect when value of MS_DUMP_SLICE_SIZE is greater than 0. Now the wait time can not exceed value of mindspore.get_context("op_timeout"). |
For more information, see Using Dump in the Graph Mode.
Distributed Parallel
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 or MINDSPORE_HCCL_CONFIG_PATH |
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. Do not set or set other values:: communication subgraph extraction and reuse is turned off. |
|
DEVICE_ID |
The ID of the Ascend AI processor, which is the Device's serial number on the AI server. |
Integer |
The ID of the Rise AI processor, value range: [0, number of actual Devices-1]. |
|
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_INTERFERED_SAPP |
Turn on interfered sapp. |
Integer |
1 for on. No setting or other value: off. |
|
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. |
GROUP_INFO_FILE |
Specify communication group information storage path |
String |
Communication group information file path, supporting relative path and absolute path. |
|
DUMP_PARALLEL_INFO |
Enable dump parallel-related communication information in auto-parallel/semi-automatic parallelism mode. The dump path can be set by set_context(save_graphs_path="path/to/parallel_info_files") |
Integer |
1: Enable dump parallel information. No setting or other value: Disable printing. |
The JSON file saved by each card contains the following fields: hccl_algo: Ensemble communication algorithm. op_name: The name of the communication operator. op_type: The type of communication operator. shape: The shape information of the communication operator. data_type: The data type of the communication operator. global_rank_id: the global rank number. comm_group_name: the communication domain name of the communication operator. comm_group_rank_ids: The communication domain of the communication operator. src_rank: The rank_id of peer operator of the Receive operator. dest_rank: The rank_id of peer opposite of the Send operator. sr_tag: The identity ID of different send-receive pairs when src and dest are the same. |
MS_CUSTOM_DEPEND_CONFIG_PATH |
Insert the control edge based on the configuration file xxx.json specified by the user, and use the primitive ops.Depend in MindSpore expresses the dependency control relationship. |
String |
This environment variable is only enabled in Atlas A2 series product graph mode. |
The fields contained in the json file have the following meanings: get_full_op_name_list(bool): Whether to generate an operator name list, optional, default is false. stage_xxx(string): used in multi-card and multi-graph scenarios, that is, different cards execute different graphs (such as pipeline parallelism), where stage_xxx is just a serial number label, and the serial number value has no actual pointing meaning. graph_id (int): used to distinguish subgraph information. The graph_id number needs to be consistent with the actually executed graph_id. If it is inconsistent, the action of inserting control edges will be invalid. depend_src_list(List[string]): A list of source operator names that need to be inserted into control edges. They need to correspond one-to-one with the operators in depend_dest_list in order, otherwise the action of inserting control edges will fail. depend_dest_list(List[string]): A list of terminal operator names that need to be inserted into control edges. They need to correspond one-to-one with the operators in depend_src_list in order, otherwise the action of inserting control edges will fail. delete_depend_list(List[string]): A list of operator names that need to be deleted. If the operator name does not exist or does not match the graph_id, the action of deleting the node will be invalid. |
See Dynamic Cluster for more details about Dynamic Cluster.
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 compile cache. 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 |
|
MINDSPORE_OP_INFO_PATH |
Specify the path to the operator library load file |
string |
Absolute path of the file Default: No setting. |
Inference only |
MS_ASCEND_CHECK_OVERFLOW_MODE |
Setting the output mode of floating-point calculation results |
String |
SATURATION_MODE: Saturation mode. INFNAN_MODE: INF/NAN mode. Default value: INFNAN_MODE. |
Saturation mode: Saturates to floating-point extremes (+-MAX) when computation overflows. INF/NAN mode: Follows the IEEE 754 standard and outputs INF/NAN calculations as defined. Atlas A2 training series use only. |
MS_CUSTOM_AOT_WHITE_LIST |
Specify the valid path for custom operators to use dynamic libraries. |
String |
The path to validated dynamic libraries. The framework will validate based on the valid path specified for dynamic libraries used by custom operators. If the dynamic library used by a custom operator is not located in the specified path, the framework will report an error and refuse to use the corresponding dynamic library. When this setting is left empty, no validation will be performed on the dynamic libraries of custom operators. Default value: empty string. |
For more information, see FAQ.
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_max_log_size |
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}". |
GLOG_logfile_mode |
The GLOG environment variable used to control the permissions of the GLOG log files in MindSpore |
octal number |
Refer to the numerical representation of the Linux file permission setting, default value: 0640 (value taken) |
|
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. |
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 |
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 .
For more detailed information about RDR, refer to Running Data Recorder .
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, when error was detected, just print log, not thow exception 2: Enable feature value detection function, when error was detected, thow exception 3: Enable feature value detection function, when error was detected, thow exception, but at the same time write value detection info of each time to log file (this requires set ascend log level to info or debug) |
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.
Third-party Library
Environment Variable |
Function |
Type |
Value Range |
Description |
|---|---|---|---|---|
OPTION_PROTO_LIB_PATH |
Specifies the RPOTO dependent library path. |
String |
File path, which can be a relative path or an absolute path. |
|
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 |
|
ASCEND_OPP_PATH |
OPP package installation path |
String |
Absolute path for OPP package installation |
Required for Ascend AI processor environments only; the environment generally provided to the user is already configured and need not be concerned. |
ASCEND_AICPU_PATH |
AICPU package installation path |
String |
Absolute path of the AICPU package installation |
Required for Ascend AI processor environments only; the environment generally provided to the user is already configured and need not be concerned. |
ASCEND_CUSTOM_OPP_PATH |
the installation path of the custom operator package |
String |
the absolute path of custom operator package installation |
Required for Ascend AI processor environments only; the environment generally provided to the user is already configured and need not be concerned. |
ASCEND_TOOLKIT_PATH |
TOOLKIT package installation path |
String |
the absolute path of custom operator package installation |
Required for Ascend AI processor environments only; the environment generally provided to the user is already configured and need not be concerned. |
CUDA_HOME |
CUDA installation path |
String |
Absolute path for CUDA package installation |
Required for GPU environment only, generally no need to set. If multiple versions of CUDA are installed in the GPU environment, it is recommended to configure this environment variable in order to avoid confusion. |
MS_ENABLE_TFT |
Enable MindIO TFT feature |
String |
"{TTP:1,UCE:1}": enable MindIO TFT TTP and UCE feature, can enable only TTP or UCE separated. Default value: Empty. |
Required for Ascend graph mode only. |
AITURBO |
Optimize settings to enable accelerated usage of Huawei Cloud Storage. |
String |
"1": Optimize settings to enable accelerated usage of Huawei Cloud Storage. Other values: Disable accelerated usage of Huawei Cloud Storage. Default value: Empty. |
Limited to the Huawei Cloud environment. |
CANN
For more information about CANN's environment variables, see Ascend community . Please set the environment variables for CANN before import mindspore .
Environment Variable |
Function |
Type |
Value Range |
Description |
|---|---|---|---|---|
MS_FORMAT_MODE |
Set the default preferred format for Ascend and graph compilation grade O2 processes, with the entire network set to ND format |
Integer |
1: The operator prioritizes the ND format. 0: The operator prioritizes private formats. Default value: 1 |
This environment variable affects the choice of format for the operator, which has an impact on network execution performance and memory usage, and can be tested by setting this option to get a better choice of operator format in terms of performance and memory. Ascend AI processor environment and graph compilation grade O2 processes only. |
Profiler
Environment Variable |
Function |
Type |
Value Range |
Description |
|---|---|---|---|---|
MS_PROFILER_OPTIONS |
Set the Profiler's collection options |
String |
Configure the Profiler's collection options in the format of a JSON string. |
This environment variable enables one of two ways to enable performance data collection with the input parameter instantiation Profiler method. |
PROFILING_MODE |
Set the mode of CANN Profiling |
String |
true: Enable Profiling. false or not configured: Disable Profiling. dynamic: Dynamic collection of performance data model. |
This environment variable is enabled by CANN Profiling. Profiler reads this environment variable for checking to avoid repeatedly enabling CANN Profiling. Users don't need to set this environment variable manually. |
PROFILER_SAMPLECONFIG |
Set the CANN msprof command line collection options |
String |
CANN msprof configuration string. |
This environment variable configures the environment variable for CANN msprof, which is read by Profiler to check whether msprof is enabled or not. Users do not need to set this environment variable manually. |
MS_PROFILER_RUN_CONFIG |
Set the Profiler collection options |
String |
Configure the Profiler collection options in the format of a JSON string. |
This environment variable is usually set automatically by the program and the user does not need to set this environment variable manually. |
Dynamic Graph
Environment Variable |
Function |
Type |
Value Range |
Description |
|---|---|---|---|---|
MS_PYNATIVE_CONFIG_STATIC_SHAPE |
We use this switch to turn on graph distribution for calculating gradient in PyNative mode. |
String |
'1': Turn on graph distribution for calculating gradient. Not setting or other values: Turn off graph distribution. |
If turn on, we use graph distribution |