mindspore.set_offload_context

mindspore.set_offload_context(offload_config)[source]

Configure heterogeneous training detailed parameters to adjust the offload strategy. This function is deprecated and will be removed in future versions.

Note

The offload configuration is only used if the memory offload feature is enabled via mindspore.set_context(memory_offload="ON"), and the memory_optimize_level must be set to O0. On the Ascend hardware platform, the graph compilation level must be O0.

Parameters

offload_config (dict) –

A dict contains the keys and values for setting the offload context configure.It supports the following keys.

  • offload_path (str): The path of offload, relative paths are supported. Default: "./offload".

  • offload_cpu_size (str): The cpu memory size for offload. The format is "xxGB".

  • offload_disk_size (str): The disk size for offload. The format is "xxGB"

  • hbm_ratio (float): The ratio that can be used based on the maximum device memory. The range is (0,1], Default: 1.0.

  • cpu_ratio (float): The ratio that can be used based on the maximum host memory. The range is (0,1], Default: 1.0.

  • enable_pinned_mem (bool): The flag of whether enabling Pinned Memory. Default: True.

  • enable_aio (bool): The flag of whether enabling aio. Default: True.

  • aio_block_size (str): The size of aio block. The format is "xxGB".

  • aio_queue_depth (int): The depth of aio queue.

  • offload_param (str): The param for offload destination, cpu or disk, Default: "".

  • offload_checkpoint (str): The checkpoint for offload destination, only valid if recompute is turned on, cpu or disk, Default: "".

  • auto_offload (bool): The flag of whether auto offload. Default: True.

  • host_mem_block_size (str): The memory block size of host memory pool. The format is "xxGB"

Raises

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

Examples

>>> from mindspore import context
>>> context.set_offload_context(offload_config={"offload_param":"cpu"})