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nemo_microservices.types.customization_target#

Module Contents#

Classes#

CustomizationTarget

API#

class nemo_microservices.types.customization_target.CustomizationTarget(/, **data: typing.Any)#

Bases: nemo_microservices._models.BaseModel

base_model: Optional[str]#

None

Default to being the same as the the configuration entry name, maps to the name in NIM

created_at: Optional[datetime.datetime]#

None

Timestamp for when the entity was created.

custom_fields: Optional[Dict[str, object]]#

None

A set of custom fields that the user can define and use for various purposes.

description: Optional[str]#

None

The description of the entity.

enabled: Optional[bool]#

None

Enable the model for training jobs

hf_endpoint: Optional[str]#

None

Configure HuggingFace Hub base URL.

Defaults to NeMo Data Store. Set value as “https://huggingface.co” to download model_uri from HuggingFace.

id: Optional[str]#

None

The ID of the entity.

With the exception of namespaces, this is always a semantically-prefixed base58-encoded uuid4 [-base58(uuid4())].

model_path: str#

None

Path to the model checkpoints to use for training.

Absolute path or local path from the models cache

model_type: Optional[nemo_microservices.types.target_checkpoint_type.TargetCheckpointType]#

None

The detected checkpoint type of the uploaded target.

nemo model checkpoints have these key files: context/nemo_tokenizer/config.json, context/model.yaml, and weights/metadata.json

hf model checkpoints have these key files: config.json, tokenizer.json or tokenizer_config.json, and either model*.safetensors (preferred) or pytorch_model*.bin

hf-lora model checkpoints only contain the LoRA adapter for a HF model, they have these key files: adapter_config.json adapter_model.safetensors

model_uri: Optional[str]#

None

The URI of the model to download to the model cache at the model_path directory.

To download from NGC, specify ngc://org/optional-team/model-name:version. To download from Nemo Data Store, specify hf://namespace/model-name@checkpoint-name

name: Optional[str]#

None

The name of the entity.

Must be unique inside the namespace. If not specified, it will be the same as the automatically generated id.

namespace: Optional[str]#

None

The namespace of the entity.

This can be missing for namespace entities or in deployments that don’t use namespaces.

num_parameters: int#

None

Number of parameters used for training the model

ownership: Optional[nemo_microservices.types.shared.ownership.Ownership]#

None

Information about ownership of an entity.

If the entity is a namespace, the access_policies will typically apply to all entities inside the namespace.

precision: nemo_microservices.types.shared.model_precision.ModelPrecision#

None

Type of model precision.

Values

  • "int8" - 8-bit integer precision

  • "bf16" - Brain floating point precision

  • "fp16" - 16-bit floating point precision

  • "fp32" - 32-bit floating point precision

  • "fp8-mixed" - Mixed 8-bit floating point precision available on Hopper and later architectures.

  • "bf16-mixed" - Mixed Brain floating point precision

project: Optional[str]#

None

The URN of the project associated with this entity.

status: Optional[nemo_microservices.types.target_status.TargetStatus]#

None

Normalized statuses for targets.

  • CREATED: The target is created, but not yet scheduled.

  • PENDING: The target is waiting for resource allocation.

  • DOWNLOADING: The target is downloading.

  • FAILED: The target failed to execute and terminated.

  • READY: The target is ready to be used.

  • CANCELLED: The target download was cancelled.

  • UNKNOWN: The target status is unknown.

  • DELETED: The target is deleted.

  • DELETING: The target is currently being deleted.

  • DELETE_FAILED: Failed to delete the target.

tokenizer: Optional[Dict[str, object]]#

None

Overrides for the model tokenizer

updated_at: Optional[datetime.datetime]#

None

Timestamp for when the entity was last updated.