`rhino_health.lib.endpoints.code_object.code_object_dataclass`#

Module Contents#

Classes#

`CodeExecutionMode`	A mode the CodeObject will run in
`CodeFormat`	A format the code is stored and uploaded to the system
`RequirementMode`	The format the requirements are in
`CodeTypes`	Supported CodeObject Types
`CodeObjectBuildStatus`	The build status of the CodeObject
`CodeObjectCreateInput`	Input arguments for creating CodeObject
`CodeObject`	A CodeObject which exists on the platform
`CodeObjectRunInput`	Input parameters for running generalized code with multiple input and/or output datasets per container
`CodeObjectRunAsyncResponse`	An asynchronous code run response
`CodeObjectRunSyncResponse`	A synchronous code run response
`ModelTrainInput`	Input for training an NVFlare Model
`ModelTrainAsyncResponse`	Response of training an NVFlare Model

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeExecutionMode#

Bases: str, enum.Enum

A mode the CodeObject will run in

DEFAULT = 'default'#

AUTO_CONTAINER_NVFLARE = 'nvflare'#

AUTO_CONTAINER_SNIPPET = 'snippet'#

AUTO_CONTAINER_FILE = 'file'#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeFormat#

Bases: str, enum.Enum

A format the code is stored and uploaded to the system

DEFAULT = 'single_non_binary_file'#

S3_MULTIPART_ZIP = 's3_multipart_zip'#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.RequirementMode#

Bases: str, enum.Enum

The format the requirements are in

PYTHON_PIP = 'python_pip'#

ANACONDA_ENVIRONMENT = 'anaconda_environment'#

ANACONDA_SPECFILE = 'anaconda_specfile'#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeTypes#

Bases: str, enum.Enum

Supported CodeObject Types

GENERALIZED_COMPUTE = 'Generalized Compute'#

NVIDIA_FLARE_V2_0 = 'NVIDIA FLARE v2.0'#

NVIDIA_FLARE_V2_2 = 'NVIDIA FLARE v2.2'#

NVIDIA_FLARE_V2_3 = 'NVIDIA FLARE v2.3'#

PYTHON_CODE = 'Python Code'#

INTERACTIVE_CONTAINER = 'Interactive Container'#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObjectBuildStatus#

Bases: str, enum.Enum

The build status of the CodeObject

NOT_STARTED = 'Not Started'#

IN_PROGRESS = 'In Progress'#

COMPLETE = 'Complete'#

ERROR = 'Error'#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObjectCreateInput(**data)#

Bases: rhino_health.lib.dataclass.RhinoBaseModel

Input arguments for creating CodeObject

name: str#: The name of the CodeObject

description: str#: The description of the CodeObject

input_data_schema_uids: List[str] = []#: A list of uids of data schemas this CodeObject expects input datasets to adhere to.

output_data_schema_uids: List[str | None] = []#: A list of uids of data schemas this CodeObject expects output datasets to adhere to.

project_uid: typing_extensions.Annotated[str, Field(alias='project')]#: The CodeObject project

code_type: typing_extensions.Annotated[str, Field(alias='type')]#

The code type which corresponds to the CodeTypes enum

See also

rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeTypes

base_version_uid: str | None = ''#: The first version of the CodeObject if multiple versions exist. You can also use add_version_if_exists=True when creating the code object in create_code_object if an existing code object with the same name exists

config: dict | None#

Additional configuration of the CodeObject. The contents will differ based on the model_type and code_run_type.

See also

rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeTypes
rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeExecutionMode

Examples

CodeTypes.GENERALIZED_COMPUTE and CodeTypes.INTERACTIVE_CONTAINER
- container_image_uri: URI of the container image to use for the model
CodeTypes.NVIDIA_FLARE_V2_X (existing image)
- code_execution_mode: CodeExecutionMode
- if CodeExecutionMode.DEFAULT requires the following additional parameters
  
  container_image_uri: URI of the container image to use for the model
- if CodeExecutionMode.AUTO_CONTAINER_NVFLARE, see CodeTypes.PYTHON_CODE below
CodeTypes.PYTHON_CODE
- code_execution_mode: CodeExecutionMode = CodeExecutionMode.DEFAULT - The format the code is structured in
  
  CodeTypes.PYTHON_CODE supports CodeExecutionMode.DEFAULT, CodeExecutionMode.AUTO_CONTAINER_SNIPPET, and CodeExecutionMode.AUTO_CONTAINER_FILE
  
  CodeTypes.NVIDIA_FLARE_V2_X only supports CodeExecutionMode.AUTO_CONTAINER_NVFLARE
- if CodeExecutionMode.DEFAULT requires the following additional parameters
  
  python_code: str - the python code to run
- CodeExecutionMode.AUTO_CONTAINER_SNIPPET requires the following additional parameters
  
  code: str - the python code to run
- CodeExecutionMode.AUTO_CONTAINER_FILE or CodeExecutionMode.AUTO_CONTAINER_NVFLARE
  
  base_image: choose one of the following
  
  base_image_uri: str - the base docker image to use for the container
  
  or - python_version: str - the python version to use for the container - cuda_version: Optional[str] - the cuda version to use for the container
  
  requirements_mode: Optional[RequirementMode] = RequirementMode.PYTHON_PIP - The format the requirements are in
  
  requirements: List[str] - a list of requirements to install in the container, uses the pip/conda install format
  
  code_format: CodeFormat the format used to pass in the code
  
  CodeFormat.DEFAULT
  
  code: str - the python code to run
  
  CodeFormat.S3_MULTIPART_ZIP
  
  folder_path: str | Path - the folder path to files
  
  entry_point: str - name of the file to run first. Not used for auto container nvflare

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObject(**data)#

A CodeObject which exists on the platform

property build_logs#: Logs when building the CodeObject, if building in the cloud

property input_data_schemas: List[DataSchema]#

Return the DataSchema Dataclasses associated with input_data_schema_uids

Warning

The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.

Returns:

input_data_schemas: List[DataSchema]: Dataclasses representing the DataSchema

property output_data_schemas: List[DataSchema]#

Return the DataSchema Dataclasses associated with output_data_schema_uids

Warning

The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.

Returns:

output_data_schemas: List[DataSchema]: Dataclasses representing the DataSchema

property project: Project#

Return the Project Dataclass associated with project_uid

Warning

The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.

Returns:

project: Project: Dataclass representing the Project

property creator: User#

Return the User Dataclass associated with creator_uid

Warning

The result of this function is cached. Be careful calling this function after making changes. All dataclasses must already exist on the platform before making this call.

Returns:

creator: User: Dataclass representing the User

uid: str#: The unique ID of the CodeObject

version: int | None#: The version of the CodeObject

build_status: CodeObjectBuildStatus#: The build status of the CodeObject

input_data_schema_uids: List[str] = []#: A list of uids of data schemas this CodeObject expects input datasets to adhere to.

output_data_schema_uids: List[str] = []#: A list of uids of data schemas this CodeObject expects output datasets to adhere to.

build_errors: List[str] | None#: Errors when building the CodeObject, if building in the cloud

name: str#: The name of the CodeObject

description: str#: The description of the CodeObject

code_type: typing_extensions.Annotated[str, Field(alias='type')]#

The code type which corresponds to the CodeTypes enum

See also

rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeTypes

base_version_uid: str | None = ''#: The first version of the CodeObject if multiple versions exist. You can also use add_version_if_exists=True when creating the code object in create_code_object if an existing code object with the same name exists

config: dict | None#

Additional configuration of the CodeObject. The contents will differ based on the model_type and code_run_type.

See also

rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeTypes
rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeExecutionMode

Examples

CodeTypes.GENERALIZED_COMPUTE and CodeTypes.INTERACTIVE_CONTAINER
- container_image_uri: URI of the container image to use for the model
CodeTypes.NVIDIA_FLARE_V2_X (existing image)
- code_execution_mode: CodeExecutionMode
- if CodeExecutionMode.DEFAULT requires the following additional parameters
  
  container_image_uri: URI of the container image to use for the model
- if CodeExecutionMode.AUTO_CONTAINER_NVFLARE, see CodeTypes.PYTHON_CODE below
CodeTypes.PYTHON_CODE
- code_execution_mode: CodeExecutionMode = CodeExecutionMode.DEFAULT - The format the code is structured in
  
  CodeTypes.PYTHON_CODE supports CodeExecutionMode.DEFAULT, CodeExecutionMode.AUTO_CONTAINER_SNIPPET, and CodeExecutionMode.AUTO_CONTAINER_FILE
  
  CodeTypes.NVIDIA_FLARE_V2_X only supports CodeExecutionMode.AUTO_CONTAINER_NVFLARE
- if CodeExecutionMode.DEFAULT requires the following additional parameters
  
  python_code: str - the python code to run
- CodeExecutionMode.AUTO_CONTAINER_SNIPPET requires the following additional parameters
  
  code: str - the python code to run
- CodeExecutionMode.AUTO_CONTAINER_FILE or CodeExecutionMode.AUTO_CONTAINER_NVFLARE
  
  base_image: choose one of the following
  
  base_image_uri: str - the base docker image to use for the container
  
  or - python_version: str - the python version to use for the container - cuda_version: Optional[str] - the cuda version to use for the container
  
  requirements_mode: Optional[RequirementMode] = RequirementMode.PYTHON_PIP - The format the requirements are in
  
  requirements: List[str] - a list of requirements to install in the container, uses the pip/conda install format
  
  code_format: CodeFormat the format used to pass in the code
  
  CodeFormat.DEFAULT
  
  code: str - the python code to run
  
  CodeFormat.S3_MULTIPART_ZIP
  
  folder_path: str | Path - the folder path to files
  
  entry_point: str - name of the file to run first. Not used for auto container nvflare

creator_uid: str#: The UID of the creator of this dataclass on the system

created_at: str#: When this dataclass was created on the system

input_data_schema_names: List[str]#: The input_data_schema names

output_data_schema_names: List[str]#: The output_data_schema names

project_name: str#: The project name

creator_name: str#: The creator name

wait_for_build(timeout_seconds: int = 900, poll_frequency: int = 30, print_progress: bool = True)#

Wait for the asynchronous CodeObject to finish building

Parameters:

timeout_seconds: int = 900: How many seconds to wait before timing out. Maximum of 1800.
poll_frequency: int = 30: How frequent to check the status, in seconds
print_progress: bool = True: Whether to print how long has elapsed since the start of the wait

Returns:

code_object: CodeObject: Dataclass representing the CodeObject

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObjectRunInput(*args, **kwargs)#

Bases: rhino_health.lib.dataclass.RhinoBaseModel

Input parameters for running generalized code with multiple input and/or output datasets per container

See also

rhino_health.lib.endpoints.code_object.code_object_endpoints.CodeObjectEndpoints.run_code_object: Example Usage

code_object_uid: str#: The unique ID of the CodeObject

run_params: str | None = '{}'#: The run params code you want to run on the datasets

timeout_seconds: int = 600#: The time before a timeout is declared for the run

secret_run_params: str | None#: The secrets for the CodeObject

external_storage_file_paths: List[str] | None#: The s3 bucket paths of files to be used in the run

sync: bool = False#: Highly recommended to use the default parameter

input_dataset_uids: List[List[str]]#

A list of lists of the input dataset uids.

[[first_dataset_for_first_run, second_dataset_for_first_run …], [first_dataset_for_second_run, second_dataset_for_second_run …], …] for N runs

Examples

Suppose we have the following CodeObject with 2 Input Data Schemas:

CodeObject
- DataSchema 1
- DataSchema 2

We want to run the CodeObject over two sites: Applegate and Bridgestone

The user passes in dataset UIDs for Datasets A, B, C, and D in the following order:

[[Dataset A, Dataset B], [Dataset C, Dataset D]]

The model will then be run over both sites with the following datasets passed to generalized compute:

Site A - Applegate:
- Dataset A - DataSchema 1
- Dataset B - DataSchema 2
Site B - Bridgestone:
- Dataset C - DataSchema 1
- Dataset D - DataSchema 2

output_dataset_naming_templates: List[str] | None#

A list of string naming templates used to name the output datasets at each site. You can use parameters in each template surrounded by double brackets {{parameter}} which will then be replaced by their corresponding values.

Parameters:

workgroup_name:: The name of the workgroup the CodeObject belongs to.
workgroup_uid:: The name of the workgroup the CodeObject belongs to.
model_name:: The CodeObject name
input_dataset_names.n:: The name of the nth input dataset, (zero indexed)

Examples

Suppose we have two input datasets, named “First Dataset” and “Second Dataset” and our CodeObject has two outputs:

output_dataset_naming_templates = [

“{{input_dataset_names.0}} - Train”,

“{{input_dataset_names.1}} - Test”

]

After running Generalized Compute, we will save the two outputs as “First Dataset - Train” and “Second Dataset - Test”

classmethod warn_on_usage_of_obsolete_arg(value)#

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObjectRunAsyncResponse(**data)#

Bases: CodeObjectRunResponse

An asynchronous code run response

property code_run#

Return the CodeRun associated with this CodeObject

Warning

The result of this function is cached. Be careful calling this function after making changes

Returns:

code_run: CodeRun: Dataclass representing the CodeRun

task_uids: List[str]#

status: str#: The status of the run

output_dataset_uids: List[str] | None = []#: A list of output dataset uids for the run

code_run_uid: str | None#: The UID of the model result

wait_for_completion(*args, **kwargs)#

Wait for the asynchronous Code Run to complete, convenience function call to the same function on the CodeRun object.

Returns:

code_run: CodeRun: Dataclass representing the CodeRun of the CodeObject

See also

rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun: Response object
rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun.wait_for_completion: Accepted parameters

class rhino_health.lib.endpoints.code_object.code_object_dataclass.CodeObjectRunSyncResponse(**data)#

Bases: CodeObjectRunResponse

A synchronous code run response

property code_run#

Return the CodeRun associated with this CodeObject

Warning

The result of this function is cached. Be careful calling this function after making changes

Returns:

code_run: CodeRun: Dataclass representing the CodeRun

errors: List[Any] | None = []#

warnings: List[Any] | None = []#

status: str#: The status of the run

output_dataset_uids: List[str] | None = []#: A list of output dataset uids for the run

code_run_uid: str | None#: The UID of the model result

wait_for_completion(*args, **kwargs)#

Wait for the asynchronous Code Run to complete, convenience function call to the same function on the CodeRun object.

Returns:

code_run: CodeRun: Dataclass representing the CodeRun of the CodeObject

See also

rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun: Response object
rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun.wait_for_completion: Accepted parameters

class rhino_health.lib.endpoints.code_object.code_object_dataclass.ModelTrainInput(**data)#

Bases: rhino_health.lib.dataclass.RhinoBaseModel

Input for training an NVFlare Model

See also

rhino_health.lib.endpoints.code_object.code_object_endpoints.CodeObjectEndpoints.train_model: Example Usage

code_object_uid: str#: The unique ID of the CodeObject

input_dataset_uids: List[str]#: A list of the input Dataset uids

validation_dataset_uids: List[str]#: A list of the Dohort uids for validation

validation_datasets_inference_suffix: str#: The suffix given to all output datasets

simulate_federated_learning: typing_extensions.Annotated[bool, Field(alias='one_fl_client_per_dataset')]#: Run simulated federated learning on the same on-prem installation by treating each dataset as a site

config_fed_server: str | None#: The config for the federated server

config_fed_client: str | None#: The config for the federated client

secrets_fed_server: str | None#: The secrets for the federated server

secrets_fed_client: str | None#: The secrets for the federated client

external_storage_file_paths: List[str] | None#: The s3 bucket paths of files to be used in the run

timeout_seconds: int#: The time before a timeout is declared for the run

class rhino_health.lib.endpoints.code_object.code_object_dataclass.ModelTrainAsyncResponse(**data)#

Bases: CodeRunWaitMixin

Response of training an NVFlare Model .. warning:: This feature is under development and the interface may change

property code_run#

Return the CodeRun associated with the NVFlare training

Warning

The result of this function is cached. Be careful calling this function after making changes

Returns:

code_run: CodeRun: Dataclass representing the CodeRun

status: str#: The status of the run

code_run_uid: str | None#: The UID of the model result

wait_for_completion(*args, **kwargs)#

Wait for the asynchronous Code Run to complete, convenience function call to the same function on the CodeRun object.

Returns:

code_run: CodeRun: Dataclass representing the CodeRun of the CodeObject

See also

rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun: Response object
rhino_health.lib.endpoints.code_run.code_run_dataclass.CodeRun.wait_for_completion: Accepted parameters

rhino_health.lib.endpoints.code_object.code_object_dataclass#

Module Contents#

Classes#

`rhino_health.lib.endpoints.code_object.code_object_dataclass`#