snowflake.ml.model.batch_inference.ResourcesSpec¶

class snowflake.ml.model.batch_inference.ResourcesSpec(*, cpu_requests: str | None = None, memory_requests: str | None = None, gpu_requests: str | None = None)¶

Bases: BaseModel

Resources block of the batch inference job specification.

cpu_requests¶

The cpu limit for CPU based inference. Can be an integer, fractional or string values. If None, we attempt to utilize all the vCPU of the node.

Type:

Optional[str]

memory_requests¶

The memory limit for inference. Can be an integer or a fractional value, but requires a unit (GiB, MiB). If None, we attempt to utilize all the memory of the node.

Type:

Optional[str]

gpu_requests¶

The gpu limit for GPU based inference. Can be integer or string values. Use CPU if None.

Type:

Optional[str]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Attributes

model_computed_fields = {}¶

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}¶

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra¶

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'cpu_requests': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'gpu_requests': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'memory_requests': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}¶

model_fields_set¶

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

cpu_requests: str | None¶

memory_requests: str | None¶

gpu_requests: str | None¶