mindspore

Data Presentation

Tensor

mindspore.Tensor	Tensor is a data structure that stores an n-dimensional array.
mindspore.tensor	Create a new Tensor in Cell.construct() or function decorated by @jit.
mindspore.COOTensor	A sparse representation of a set of nonzero elements from a tensor at given indices.
mindspore.CSRTensor	Constructs a sparse tensor in CSR (Compressed Sparse Row) format, with specified values indicated by values and row and column positions indicated by indptr and indices.
mindspore.RowTensor	A sparse representation of a set of tensor slices at given indices.
mindspore.SparseTensor	A sparse representation of a set of nonzero elements from a tensor at given indices.

Parameter

mindspore.Parameter	Parameter is a Tensor subclass, when they are assigned as Cell attributes they are automatically added to the list of its parameters, and will appear, e.g.
mindspore.ParameterTuple	Inherited from tuple, ParameterTuple is used to save multiple parameter.

DataType

class mindspore.dtype

Create a data type object of MindSpore.

The actual path of dtype is /mindspore/common/dtype.py. Run the following command to import the package:

from mindspore import dtype as mstype

• Numeric Type

  Currently, MindSpore supports int type, uint type, float type and complex type. The following table lists the details.

  Definition	Description
  mindspore.int8 , mindspore.byte	8-bit integer
  mindspore.int16 , mindspore.short	16-bit integer
  mindspore.int32 , mindspore.intc	32-bit integer
  mindspore.int64 , mindspore.intp	64-bit integer
  mindspore.uint8 , mindspore.ubyte	unsigned 8-bit integer
  mindspore.uint16 , mindspore.ushort	unsigned 16-bit integer
  mindspore.uint32 , mindspore.uintc	unsigned 32-bit integer
  mindspore.uint64 , mindspore.uintp	unsigned 64-bit integer
  mindspore.float16 , mindspore.half	16-bit floating-point number
  mindspore.float32 , mindspore.single	32-bit floating-point number
  mindspore.float64 , mindspore.double	64-bit floating-point number
  mindspore.bfloat16	16-bit brain-floating-point number
  mindspore.complex64	64-bit complex number
  mindspore.complex128	128-bit complex number

• Other Type

  For other defined types, see the following table.

  Type	Description
  tensor	MindSpore’s tensor type. Data format uses NCHW. For details, see tensor.
  bool_	Boolean True or False.
  int_	Integer scalar.
  uint	Unsigned integer scalar.
  float_	Floating-point scalar.
  complex	Complex scalar.
  number	Number, including int_ , uint , float_ , complex and bool_ .
  list_	List constructed by tensor , such as List[T0,T1,...,Tn] , where the element Ti can be of different types.
  tuple_	Tuple constructed by tensor , such as Tuple[T0,T1,...,Tn] , where the element Ti can be of different types.
  function	Function. Return in two ways, when function is not None, returns Func directly, the other returns Func(args: List[T0,T1,…,Tn], retval: T) when function is None.
  type_type	Type definition of type.
  type_none	No matching return type, corresponding to the type(None) in Python.
  symbolic_key	The value of a variable is used as a key of the variable in env_type .
  env_type	Used to store the gradient of the free variable of a function, where the key is the symbolic_key of the free variable’s node and the value is the gradient.

mindspore.dtype_to_nptype	Convert MindSpore dtype to numpy data type.
mindspore.dtype_to_pytype	Convert MindSpore dtype to python data type.
mindspore.pytype_to_dtype	Convert python type to MindSpore type.
mindspore.get_py_obj_dtype	Get the MindSpore data type, which corresponds to python type or variable.
mindspore.QuantDtype	An enum for quant datatype, contains INT1 ~ INT16, UINT1 ~ UINT16.

Context

mindspore.set_context	Set context for running environment.
mindspore.get_context	Get context attribute value according to the input key.
mindspore.set_auto_parallel_context	Set auto parallel context, only data parallel supported on CPU.
mindspore.get_auto_parallel_context	Get auto parallel context attribute value according to the key.
mindspore.reset_auto_parallel_context	Reset auto parallel context attributes to the default values.
mindspore.ParallelMode	Parallel mode options.
mindspore.set_ps_context	Set parameter server training mode context.
mindspore.get_ps_context	Get parameter server training mode context attribute value according to the key.
mindspore.reset_ps_context	Reset parameter server training mode context attributes to the default values.
mindspore.set_algo_parameters	Set parameters in the algorithm for parallel strategy searching.
mindspore.get_algo_parameters	Get the algorithm parameter config attributes.
mindspore.reset_algo_parameters	Reset the algorithm parameter attributes.
mindspore.set_offload_context	Configure heterogeneous training detailed parameters to adjust the offload strategy.
mindspore.get_offload_context	Gets the offload configuration parameters.

Seed

mindspore.set_seed	Set global seed.
mindspore.get_seed	Get global seed.

Serialization

mindspore.async_ckpt_thread_status	Get the status of asynchronous save checkpoint thread.
mindspore.build_searched_strategy	Build strategy of every parameter in network.
mindspore.convert_model	Convert mindir model to other format model.
mindspore.export	Export the MindSpore network into an offline model in the specified format.
mindspore.load	Load MindIR.
mindspore.load_checkpoint	Load checkpoint info from a specified file.
mindspore.load_checkpoint_async	Load checkpoint info from a specified file asyncly.
mindspore.load_distributed_checkpoint	Load checkpoint into net for distributed predication.
mindspore.load_mindir	load protobuf file.
mindspore.load_param_into_net	Load parameters into network, return parameter list that are not loaded in the network.
mindspore.load_segmented_checkpoints	Load checkpoint info from a specified file.
mindspore.merge_pipeline_strategys	Merge parallel strategy between all pipeline stages in pipeline parallel mode.
mindspore.merge_sliced_parameter	Merge parameter slices into one parameter.
mindspore.obfuscate_model	Obfuscate a model of MindIR format.
mindspore.parse_print	Parse data file generated by mindspore.ops.Print.
mindspore.rank_list_for_transform	List of original distributed checkpoint rank index for obtaining the target checkpoint of a rank_id during the distributed checkpoint conversion.
mindspore.restore_group_info_list	Build rank list, the checkpoint of ranks in the rank list has the same contents with the local rank who saves the group_info_file_name.
mindspore.save_checkpoint	Save checkpoint to a specified file.
mindspore.save_mindir	save protobuf file.
mindspore.transform_checkpoint_by_rank	Transform distributed checkpoint from source sharding strategy to destination sharding strategy by rank for a network.
mindspore.transform_checkpoints	Transform distributed checkpoint from source sharding strategy to destination sharding strategy for a rank.

Automatic Differentiation

mindspore.grad	A wrapper function to generate the gradient function for the input function.
mindspore.value_and_grad	A wrapper function to generate the function to calculate forward output and gradient for the input function.
mindspore.get_grad	When return_ids of mindspore.grad() or mindspore.grad() is set to True , use return value of mindspore.grad, or the second return value of mindspore.grad as gradients.
mindspore.jacfwd	Compute Jacobian via forward mode, corresponding to forward-mode differentiation.
mindspore.jacrev	Compute Jacobian via reverse mode, corresponding to reverse-mode differentiation.
mindspore.jvp	Compute the jacobian-vector-product of the given network.
mindspore.vjp	Compute the vector-jacobian-product of the given network.

Parallel Optimization

Automatic Vectorization

mindspore.vmap

Vectorizing map (vmap) is a kind of higher-order function to map fn along the parameter axes.

Parallel

mindspore.Layout	Parallel layout describes the detailed sharding information.
mindspore.parameter_broadcast	Broadcast parameter to other rank in data parallel dimension.
mindspore.recompute	This function is used to reduce memory, when run block, rather than storing the intermediate activation computed in forward pass, we will recompute it in backward pass.
mindspore.shard	Defining the input and output layouts of this cell and the parallel strategies of remaining ops will be generated by sharding propagation.
mindspore.sync_pipeline_shared_parameters	synchronize pipeline parallel stage shared parameters.

JIT

mindspore.JitConfig	Jit config for compile.
mindspore.jit	Create a callable MindSpore graph from a Python function.
mindspore.jit_class	Class decorator for user-defined classes.
mindspore.ms_class	Class decorator for user-defined classes.
mindspore.ms_function	Create a callable MindSpore graph from a Python function.
mindspore.ms_memory_recycle	Recycle memory used by MindSpore.
mindspore.mutable	Make a constant value mutable.
mindspore.constexpr	Used to calculate constant in graph copmpiling process and improve compile performance in GRAPH_MODE.
mindspore.lazy_inline	Make the cell to be reusable.

Tool

Dataset Helper

mindspore.DatasetHelper	DatasetHelper is a class to process the MindData dataset and provides the information of dataset.
mindspore.Symbol	Symbol is a data structure to indicate the symbolic info of shape.
mindspore.connect_network_with_dataset	Connect the network with dataset in dataset_helper.
mindspore.data_sink	A wrapper function to generate a function for the input function.

Debugging and Tuning

mindspore.Profiler	This class to enable the profiling of MindSpore neural networks.
mindspore.SummaryCollector	SummaryCollector can help you to collect some common information, such as loss, learning late, computational graph and so on.
mindspore.SummaryLandscape	SummaryLandscape can help you to collect loss landscape information.
mindspore.SummaryRecord	SummaryRecord is used to record the summary data and lineage data.
mindspore.set_dump	Enable or disable dump for the target and its contents.

Log

mindspore.get_level	Get the logger level.
mindspore.get_log_config	Get logger configurations.

Installation Verification

mindspore.run_check

Provide a convenient API to check if the installation is successful or failed.