Environment Variables

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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.

Note: Only Ascend backend.

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.

Note: Only Ascend backend.

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

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.

For more information, see Distributed Parallel Training Example.

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 mindspire Python module log files.

Integer

Default: 30

logger_maxBytes

Controls the size of the mindspire 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

SubModule: COMMON, MD, DEBUG, DEVICE, COMMON, IR…

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, and the division of MindSpore sub-module is as follows

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}”.

The MindSpore submodules are divided by directory as follows:

Source Files

Sub Module Name

mindspore/ccsrc/kernel

KERNEL

mindspore/ccsrc/plugin/device/*/kernel

KERNEL

mindspore/ccsrc/backend/common/optimizer

PRE_ACT

mindspore/ccsrc/backend/common/pass

PRE_ACT

mindspore/ccsrc/plugin/device/*/optimizer

PRE_ACT

mindspore/ccsrc/backend/common/session

SESSION

mindspore/ccsrc/common

COMMON

mindspore/ccsrc/debug

DEBUG

mindspore/ccsrc/frontend/operator

ANALYZER

mindspore/ccsrc/frontend/optimizer

OPTIMIZER

mindspore/ccsrc/frontend/parallel

PARALLEL

mindspore/ccsrc/minddata/dataset

MD

mindspore/ccsrc/minddata/mindrecord

MD

mindspore/ccsrc/pipeline/jit/*.cc

PIPELINE

mindspore/ccsrc/pipeline/jit/parse

PARSER

mindspore/ccsrc/pipeline/jit/static_analysis

ANALYZER

mindspore/ccsrc/pipeline/pynative

PYNATIVE

mindspore/ccsrc/pybind_api

COMMON

mindspore/ccsrc/runtime/device

DEVICE

mindspore/ccsrc/runtime/hardware

DEVICE

mindspore/ccsrc/runtime/collective

DEVICE

mindspore/ccsrc/runtime/pynative

DEVICE

mindspore/ccsrc/runtime/addons

RUNTIME_FRAMEWORK

mindspore/ccsrc/runtime/graph_scheduler

RUNTIME_FRAMEWORK

mindspore/ccsrc/transform/graph_ir

GE_ADPT

mindspore/ccsrc/transform/express_ir

EXPRESS

mindspore/ccsrc/utils

UTILS

mindspore/ccsrc/backend/graph_compiler

VM

mindspore/ccsrc

ME

mindspore/core

CORE

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.

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.

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 bind feature. Most of time this configuration can improve performance on distribute scenario.

String

True: Enables the numa bind feature.

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.

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.

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_ENABLE_FALLBACK

Fallback function is enabled when the environment variable is set to a value other than 0.

Integer

1: enables fallback function

0: disables fallback function

Default: 1

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_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