mindspore.set_offload_context
- mindspore.set_offload_context(offload_config)[source]
Configure heterogeneous training detailed parameters to adjust the offload strategy.
Note
The offload configuration is only used if the memory offload feature is enabled via mindspore.set_context(memory_offload=”ON”).
- 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"})