mindspore.dataset.config

The configuration module provides various functions to set and get the supported configuration parameters, and read a configuration file.

mindspore.dataset.config.get_monitor_sampling_interval()

Get the default interval of performance monitor sampling.

Returns: interval (in milliseconds) for performance monitor sampling.
Return type: Interval

mindspore.dataset.config.get_num_parallel_workers()

Get the default number of parallel workers.

Returns: Int, number of parallel workers to be used as a default for each operation

mindspore.dataset.config.get_prefetch_size()

Get the prefetch size in number of rows.

Returns: Size, total number of rows to be prefetched.

mindspore.dataset.config.get_seed()

Get the seed.

Returns: Int, seed.

mindspore.dataset.config.load(file)

Load configurations from a file.

Parameters: file (str) – Path of the configuration file to be loaded.
Raises: RuntimeError – If file is invalid and parsing fails.

Examples

>>> import mindspore.dataset as ds
>>>
>>> # Set new default configuration values according to values in the configuration file.
>>> ds.config.load("path/to/config/file")
>>> # example config file:
>>> # {
>>> #     "logFilePath": "/tmp",
>>> #     "numParallelWorkers": 4,
>>> #     "seed": 5489,
>>> #     "monitorSamplingInterval": 30
>>> # }

mindspore.dataset.config.set_monitor_sampling_interval(interval)

Set the default interval (in milliseconds) for monitor sampling.

Parameters: interval (int) – Interval (in milliseconds) to be used for performance monitor sampling.
Raises: ValueError – If interval is invalid (<= 0 or > MAX_INT_32).

Examples

>>> import mindspore.dataset as ds
>>>
>>> # Set a new global configuration value for the monitor sampling interval.
>>> ds.config.set_monitor_sampling_interval(100)

mindspore.dataset.config.set_num_parallel_workers(num)

Set the default number of parallel workers.

Parameters: num (int) – Number of parallel workers to be used as a default for each operation.
Raises: ValueError – If num_parallel_workers is invalid (<= 0 or > MAX_INT_32).

Examples

>>> import mindspore.dataset as ds
>>>
>>> # Set a new global configuration value for the number of parallel workers.
>>> # Now parallel dataset operators will run with 8 workers.
>>> ds.config.set_num_parallel_workers(8)

mindspore.dataset.config.set_prefetch_size(size)

Set the number of rows to be prefetched.

Parameters: size (int) – Total number of rows to be prefetched.
Raises: ValueError – If prefetch_size is invalid (<= 0 or > MAX_INT_32).

Examples

>>> import mindspore.dataset as ds
>>>
>>> # Set a new global configuration value for the prefetch size.
>>> ds.config.set_prefetch_size(1000)

mindspore.dataset.config.set_seed(seed)

Set the seed to be used in any random generator. This is used to produce deterministic results.

Note

This set_seed function sets the seed in the Python random library and numpy.random library for deterministic Python augmentations using randomness. This set_seed function should be called with every iterator created to reset the random seed. In the pipeline, this does not guarantee deterministic results with num_parallel_workers > 1.

Parameters: seed (int) – Seed to be set.
Raises: ValueError – If seed is invalid (< 0 or > MAX_UINT_32).

Examples

>>> import mindspore.dataset as ds
>>>
>>> # Set a new global configuration value for the seed value.
>>> # Operations with randomness will use the seed value to generate random values.
>>> ds.config.set_seed(1000)