The context of mindspore, used to configure the current execution environment, includes the execution mode, execution backend and other feature switches.


Set context for running environment.

Context should be configured before running your program. If there is no configuration, the “Ascend” device target will be used by default. GRAPH_MODE or PYNATIVE_MODE can be set by mode attribute and both modes support all backends, default mode is PYNATIVE_MODE.

When the save_graphs attribute is set to True, attribute of save_graphs_path is used to set the intermediate compilation graph storage path. By default, the graphs are saved in the current directory. For other configurations and arguments, please refer to the corresponding module description, the configuration is optional and can be enabled when needed.


Attribute name is required for setting attributes. The mode is not recommended to be changed after net was initialized because the implementations of some operations are different in graph mode and pynative mode. Default: PYNATIVE_MODE.

Some configurations are device specific, see the below table for details:


























  • mode (int) – Running in GRAPH_MODE(0) or PYNATIVE_MODE(1). Default: PYNATIVE_MODE(1).

  • device_target (str) – The target device to run, support “Ascend”, “GPU”, and “CPU”. Default: “Ascend”.

  • device_id (int) – ID of the target device, the value must be in [0, device_num_per_host-1], while device_num_per_host should be no more than 4096. Default: 0.

  • save_graphs (bool) – Whether to save graphs. Default: False.

  • save_graphs_path (str) –

    Path to save graphs. Default: “.”.

    If the program is executed in the parallel mode, save_graphs_path should consist of the path and the current device id, to ensure that writing file conflicts won’t happen when the different processes try to create the files in the same directory. For example, the device_id can be generated by device_id = os.getenv(“DEVICE_ID”) and the save_graphs_path can be set by context.set_context(save_graphs_path=”path/to/ir/files”+device_id).

  • enable_graph_kernel (bool) – Whether to enable composition of basic primitives. These primitives would be compiled into a fused kernel automatically. Default: False.

  • reserve_class_name_in_scope (bool) – Whether to save the network class name in the scope. Default: True.

  • enable_reduce_precision (bool) – Whether to enable precision reduction. Default: True.

  • enable_dump (bool) – Whether to enable dump. Default: False.

  • save_dump_path (str) – When the program is executed on Ascend, operators can dump data in this path. The root dump path is configured in /home/HwHiAiUser/ide_daemon/ide_daemon.cfg. So the real dump path is “{configured root dump path}/{save_dump_path}”. Default: “.”.

  • variable_memory_max_size (str) – Set the maximum size of the variable memory max size. Default: “0GB”.

  • enable_profiling (bool) – Whether to open profiling. Default: False.

  • profiling_options (str) –

    Set profiling collection options, operators can profiling data here. The values of profiling collection options are as follows, supporting the collection of multiple data.

    • output: the saving the path of the profiling collection result file. The directory spectified by this parameter needs to be created in advance on the training environment (container or host side) and ensure that the running user configured during installation has read and write permissions.It supports the configuration of absolute or relative paths(relative to the current path when executing the command line). The absolute path configuration starts with ‘/’, for example:/home/data/output. The relative path configuration directly starts with the directory name,for example:output.

    • training_trace: collect iterative trajectory data, that is, the training task and software information of the AI software stack, to achieve performance analysis of the training task, focusing on data enhancement, forward and backward calculation, gradient aggregation update and other related data. The value is on/off.

    • task_trace: collect task trajectory data, that is, the hardware information of the HWTS/AICore of the Ascend 910 processor, and analyze the information of beginning and ending of the task. The value is on/off.

    • aicpu: collect profiling data enhanced by aicpu data. The value is on/off.

    • fp_point: specify the start position of the forward operator of the training network iteration trajectory, which is used to record the start timestamp of the forward calculation.The configuration value is the name of the first operator specified in the forward direction. when the value is empty,the system will automatically obtain the forward operator name.

    • bp_point: specify the end position of the iteration trajectory reversal operator of the training network, record the end timestamp of the backward calculation. The configuration value is the name of the operator after the specified reverse. when the value is empty,the system will automatically obtain the backward operator name.

    • aic_metrics: the values are as follows: ArithmeticUtilization: percentage statistics of various calculation indicators. PipeUtilization: the time-consuming ratio of calculation unit and handling unit,this item is the default value. Memory: percentage of external memory read and write instructions. MemoryL0: percentage of internal memory read and write instructions. ResourceConflictRatio: proportion of pipline queue instructions.

    The profiling_options is like ‘{“output”:’/home/data/output’,’training_trace’:’on’}’

  • check_bprop (bool) – Whether to check bprop. Default: False.

  • max_device_memory (str) – Sets the maximum memory available for devices. Currently, it is only supported on GPU. The format is “xxGB”. Default: “1024GB”.

  • print_file_path (str) – The path of saving print data. If this parameter is set, print data is saved to a file by default, and turns off printing to the screen. If the file already exists, add a timestamp suffix to the file. Default: ‘’.

  • enable_sparse (bool) – Whether to enable sparsity feature. Default: False.

  • max_call_depth (int) – Specify the maximum depth of function call. Default: 1000.

  • env_config_path (str) – Config path for DFX.

  • auto_tune_mode (str) – The mode of auto tune when op building, get the best tiling performance, default: NO_TUNE. The value must be in [‘RL’, ‘GA’, ‘RL,GA’]. RL: rl_tune; GA: ga_tune; RL,GA: rl_tune/ga_tune(Automatic selection). - rl_tune: Reinforecement Learning tune. - ga_tune: Genetic Algorithm tune.

  • grad_for_scalar (bool) – Whether to get gradient for scalar. Default: False.


ValueError – If input key is not an attribute in context.


>>> context.set_context(mode=context.GRAPH_MODE)
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> context.set_context(device_target="Ascend")
>>> context.set_context(device_id=0)
>>> context.set_context(save_graphs=True, save_graphs_path="./model.ms")
>>> context.set_context(enable_reduce_precision=True)
>>> context.set_context(enable_dump=True, save_dump_path=".")
>>> context.set_context(reserve_class_name_in_scope=True)
>>> context.set_context(variable_memory_max_size="6GB")
>>> context.set_context(mode=context.GRAPH_MODE,
...                     device_target="Ascend",device_id=0, save_graphs=True,
...                     save_graphs_path="/mindspore")
>>> context.set_context(enable_profiling=True,
...                     profiling_options='{"output":"/home/data/output","training_trace":"on"}')
>>> context.set_context(max_device_memory="3.5GB")
>>> context.set_context(print_file_path="print.pb")
>>> context.set_context(max_call_depth=80)
>>> context.set_context(env_config_path="./env_config.json")

Get context attribute value according to the input key.


attr_key (str) – The key of the attribute.


Object, The value of given attribute key.


ValueError – If input key is not an attribute in context.


Set auto parallel context, which is valid only for Ascend and GPU target.

Auto parallel context should be configured before the initialization of your network.


Attribute name is required for setting attributes. If a program has tasks with different parallel modes, then before setting new parallel mode for the next task, interface mindspore.context.reset_auto_parallel_context() needs to be called to reset the configuration. Setting or changing parallel modes must be called before any creating Initializer, otherwise, RuntimeError may be raised when compiling the network.

Some configurations are parallel mode specific, see the below table for details:
















  • device_num (int) – Available device number, the value must be in [1, 4096]. Default: 1.

  • global_rank (int) – Global rank id, the value must be in [0, 4095]. Default: 0.

  • gradients_mean (bool) – Whether to perform mean operator after allreduce of gradients. “stand_alone” do not support gradients_mean. Default: False.

  • gradient_fp32_sync (bool) – Run allreduce of gradients in fp32. “stand_alone”, “data_parallel” and “hybrid_parallel” do not support gradient_fp32_sync. Default: True.

  • parallel_mode (str) –

    There are five kinds of parallel modes, “stand_alone”, “data_parallel”, “hybrid_parallel”, “semi_auto_parallel” and “auto_parallel”. Default: “stand_alone”.

    • stand_alone: Only one processor is working.

    • data_parallel: Distributes the data across different processors.

    • hybrid_parallel: Achieves data parallelism and model parallelism manually.

    • semi_auto_parallel: Achieves data parallelism and model parallelism by setting parallel strategies.

    • auto_parallel: Achieving parallelism automatically.

  • auto_parallel_search_mode (str) –

    There are two kinds of shard strategy search modes, “recursive_programming” and “dynamic_programming”. Default: “dynamic_programming”.

    • recursive_programming: Recursive programming search mode.

    • dynamic_programming: Dynamic programming search mode.

  • parameter_broadcast (bool) – Whether to broadcast parameters before training. Before training, in order to have the same network initialization parameter values for all devices, broadcast the parameters on device 0 to other devices. Parameter broadcasting in different parallel modes is different, data_parallel mode, all parameters are broadcast except for the parameter whose attribute layerwise_parallel is True. Hybrid_parallel, semi_auto_parallel and auto_parallel mode, the segmented parameters do not participate in broadcasting. Default: False.

  • strategy_ckpt_load_file (str) – The path to load parallel strategy checkpoint. Default: ‘’

  • strategy_ckpt_save_file (str) – The path to save parallel strategy checkpoint. Default: ‘’

  • full_batch (bool) – If you load whole batch datasets in auto_parallel mode, this parameter should be set with True. Default: False.

  • enable_parallel_optimizer (bool) – This is a developing feature, which shards the weight update computation for data parallel training in the benefit of time and memory saving. Currently, auto and semi auto parallel mode support all optimizers in both Ascend and GPU. Data parallel mode only supports Lamb and AdamWeightDecay in Ascend . Default: False.

  • all_reduce_fusion_config (list) – Set allreduce fusion strategy by parameters indices. Only support ReduceOp.SUM and HCCL_WORLD_GROUP/NCCL_WORLD_GROUP. No Default, if it is not set, the fusion is closed.

  • pipeline_stages (int) – Set the stage information for pipeline parallel. This indicates how the devices are distributed alone the pipeline. The total devices will be divided into ‘pipeline_stags’ stages. This currently could only be used when parallel mode semi_auto_parallel is enabled. Default: 1.


ValueError – If input key is not attribute in auto parallel context.


>>> context.set_auto_parallel_context(device_num=8)
>>> context.set_auto_parallel_context(global_rank=0)
>>> context.set_auto_parallel_context(gradients_mean=True)
>>> context.set_auto_parallel_context(gradient_fp32_sync=False)
>>> context.set_auto_parallel_context(parallel_mode="auto_parallel")
>>> context.set_auto_parallel_context(auto_parallel_search_mode="dynamic_programming")
>>> context.set_auto_parallel_context(parameter_broadcast=False)
>>> context.set_auto_parallel_context(strategy_ckpt_load_file="./strategy_stage1.ckpt")
>>> context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_stage1.ckpt")
>>> context.set_auto_parallel_context(full_batch=True)
>>> context.set_auto_parallel_context(enable_parallel_optimizer=False)
>>> context.set_auto_parallel_context(all_reduce_fusion_config=[8, 160])
>>> context.set_auto_parallel_context(pipeline_stages=2)

Get auto parallel context attribute value according to the key.


attr_key (str) – The key of the attribute.


Returns attribute value according to the key.


ValueError – If input key is not attribute in auto parallel context.


Reset auto parallel context attributes to the default values:

  • device_num: 1.

  • global_rank: 0.

  • gradients_mean: False.

  • gradient_fp32_sync: True.

  • parallel_mode: ‘stand_alone’.

  • auto_parallel_search_mode: ‘dynamic_programming’.

  • parameter_broadcast: False.

  • strategy_ckpt_load_file: ‘’.

  • strategy_ckpt_save_file: ‘’.

  • full_batch: False.

  • enable_parallel_optimizer: False.

  • pipeline_stages: 1.

class mindspore.context.ParallelMode[source]

Parallel mode options.

There are five kinds of parallel modes, “STAND_ALONE”, “DATA_PARALLEL”, “HYBRID_PARALLEL”, “SEMI_AUTO_PARALLEL” and “AUTO_PARALLEL”. Default: “STAND_ALONE”.

  • STAND_ALONE: Only one processor is working.

  • DATA_PARALLEL: Distributes the data across different processors.

  • HYBRID_PARALLEL: Achieves data parallelism and model parallelism manually.

  • SEMI_AUTO_PARALLEL: Achieves data parallelism and model parallelism by setting parallel strategies.

  • AUTO_PARALLEL: Achieves parallelism automatically.

MODE_LIST: The list of all supported parallel modes.


Set parameter server training mode context.


Some other environment variables should also be set for parameter server training mode. These environment variables are listed below:

MS_SERVER_NUM # Server number MS_WORKER_NUM # Worker number MS_SCHED_HOST # Scheduler IP address MS_SCHED_PORT # Scheduler port MS_ROLE # The role of this process: MS_SCHED #represents the scheduler, MS_WORKER #represents the worker, MS_PSERVER #represents the Server


enable_ps (bool) – Whether to enable parameter server training mode. Only after enable_ps is set True, the environment variables will be effective. Default: False.


ValueError – If input key is not the attribute in parameter server training mode context.


>>> context.set_ps_context(enable_ps=True)

Get parameter server training mode context attribute value according to the key.


attr_key (str) – The key of the attribute.


Returns attribute value according to the key.


ValueError – If input key is not attribute in auto parallel context.


Reset parameter server training mode context attributes to the default values:

  • enable_ps: False.