Model Instance Group

model_navigator.triton.InstanceGroup `dataclass`

InstanceGroup(kind=None, count=None, name=None, gpus=lambda: [](), passive=False, host_policy=None, profile=lambda: []())

Configuration for model instance group.

Read more in Triton Inference server model configuration

Parameters:

kind (Optional[DeviceKind], default: None ) –

Kind of this instance group.
count (Optional[int], default: None ) –

For a group assigned to GPU, the number of instances created for each GPU listed in 'gpus'. For a group assigned to CPU the number of instances created.
name (Optional[str], default: None ) –

Optional name of this group of instances.
gpus (List[int], default: lambda: []() ) –

GPU(s) where instances should be available.
passive (bool, default: False ) –

Whether the instances within this instance group will be accepting inference requests from the scheduler.
host_policy (Optional[str], default: None ) –

The host policy name that the instance to be associated with.
profile (List[str], default: lambda: []() ) –

For TensorRT models containing multiple optimization profile, this parameter specifies a set of optimization profiles available to this instance group.

__post_init__

__post_init__()

Validate the configuration for early error handling.

Source code in model_navigator/triton/specialized_configs/common.py

def __post_init__(self) -> None:
    """Validate the configuration for early error handling."""
    if self.count is not None and self.count < 1:
        raise ModelNavigatorWrongParameterError("The `count` must be greater or equal 1.")

    if self.kind not in [None, DeviceKind.KIND_GPU, DeviceKind.KIND_AUTO] and len(self.gpus) > 0:
        raise ModelNavigatorWrongParameterError(
            f"`gpus` cannot be set when device is not {DeviceKind.KIND_GPU} or {DeviceKind.KIND_AUTO}"
        )

model_navigator.triton.DeviceKind

Bases: Enum

Device kind for model deployment.