ads.jobs.builders.infrastructure package

Submodules

ads.jobs.builders.infrastructure.base module

class ads.jobs.builders.infrastructure.base.Infrastructure(spec: Dict | None = None, **kwargs)[source]

Bases: Builder

Base class for job infrastructure

To initialize the object, user can either pass in the specification as a dictionary or through keyword arguments.

Parameters:

  • spec (dict, optional) – Object specification, by default None

  • kwargs (dict) – Specification as keyword arguments. If spec contains the same key as the one in kwargs, the value from kwargs will be used.

create(runtime: Runtime, **kwargs)[source]

Create/deploy a job on the infrastructure.

Parameters:

  • runtime (Runtime) – a runtime object

  • kwargs (dict) – additional arguments

delete()[source]

Deletes a job from the infrastructure.

property kind: str

Kind of the object to be stored in YAML. All runtimes will have “infrastructure” as kind. Subclass will have different types.

classmethod list_jobs(**kwargs) → list[source]

List jobs from the infrastructure.

Parameters:

kwargs (keyword arguments for filtering the results)

Returns:

list of infrastructure objects, each representing a job from the infrastructure.

Return type:

list

property name: str

The name of the job from the infrastructure.

run(name: str | None = None, args: str | None = None, env_var: dict | None = None, freeform_tags: dict | None = None, defined_tags: dict | None = None, wait: bool = False)[source]

Runs a job on the infrastructure.

Parameters:

  • name (str, optional) – The name of the job run, by default None

  • args (str, optional) – Command line arguments for the job run, by default None.

  • env_var (dict, optional) – Environment variable for the job run, by default None.

  • freeform_tags (dict, optional) – Freeform tags for the job run, by default None.

  • defined_tags (dict, optional) – Defined tags for the job run, by default None.

  • wait (bool, optional) – Indicate if this method should wait for the run to finish before it returns, by default False.

update(runtime: Runtime)[source]

Updates a job.

Parameters:

runtime – a runtime object

class ads.jobs.builders.infrastructure.base.RunInstance[source]

Bases: object

cancel()[source]

Cancel a run.

create()[source]

Create a RunInstance Object.

delete()[source]

Delete a run.

Link to run details page in OCI console

Returns:

The link to the details page in OCI console.

Return type:

str

property status

Return status of the run.

watch()[source]

Show logs of a run.

ads.jobs.builders.infrastructure.dataflow module

class ads.jobs.builders.infrastructure.dataflow.DataFlow(spec: dict | None = None, **kwargs)[source]

Bases: Infrastructure

To initialize the object, user can either pass in the specification as a dictionary or through keyword arguments.

Parameters:

  • spec (dict, optional) – Object specification, by default None

  • kwargs (dict) – Specification as keyword arguments. If spec contains the same key as the one in kwargs, the value from kwargs will be used.

CONST_BUCKET_URI = 'logs_bucket_uri'
CONST_COMPARTMENT_ID = 'compartment_id'
CONST_CONFIG = 'configuration'
CONST_DEFINED_TAGS = 'defined_tags'
CONST_DRIVER_SHAPE = 'driver_shape'
CONST_DRIVER_SHAPE_CONFIG = 'driver_shape_config'
CONST_EXECUTE = 'execute'
CONST_EXECUTOR_SHAPE = 'executor_shape'
CONST_EXECUTOR_SHAPE_CONFIG = 'executor_shape_config'
CONST_FREEFORM_TAGS = 'freeform_tags'
CONST_ID = 'id'
CONST_LANGUAGE = 'language'
CONST_MEMORY_IN_GBS = 'memory_in_gbs'
CONST_METASTORE_ID = 'metastore_id'
CONST_NUM_EXECUTORS = 'num_executors'
CONST_OCPUS = 'ocpus'
CONST_POOL_ID = 'pool_id'
CONST_PRIVATE_ENDPOINT_ID = 'private_endpoint_id'
CONST_SPARK_VERSION = 'spark_version'
CONST_WAREHOUSE_BUCKET_URI = 'warehouse_bucket_uri'
attribute_map = {'compartment_id': 'compartmentId', 'configuration': 'configuration', 'defined_tags': 'definedTags', 'driver_shape': 'driverShape', 'driver_shape_config': 'driverShapeConfig', 'execute': 'execute', 'executor_shape': 'executorShape', 'executor_shape_config': 'executorShapeConfig', 'freeform_tags': 'freeformTags', 'id': 'id', 'logs_bucket_uri': 'logsBucketUri', 'memory_in_gbs': 'memoryInGBs', 'metastore_id': 'metastoreId', 'num_executors': 'numExecutors', 'ocpus': 'ocpus', 'pool_id': 'poolId', 'private_endpoint_id': 'privateEndpointId', 'spark_version': 'sparkVersion', 'warehouse_bucket_uri': 'warehouseBucketUri'}
create(runtime: DataFlowRuntime, **kwargs) → DataFlow[source]

Create a Data Flow job given a runtime.

Parameters:

  • runtime – runtime to bind to the Data Flow job

  • kwargs – additional keyword arguments

Returns:

a Data Flow job instance

Return type:

DataFlow

delete()[source]

Delete a Data Flow job and canceling associated runs.

Return type:

None

classmethod from_dict(config: dict) → DataFlow[source]

Load a Data Flow job instance from a dictionary of configurations.

Parameters:

config (dict) – dictionary of configurations

Returns:

a Data Flow job instance

Return type:

DataFlow

classmethod from_id(id: str) → DataFlow[source]

Load a Data Flow job given an id.

Parameters:

id (str) – id of the Data Flow job to load

Returns:

a Data Flow job instance

Return type:

DataFlow

init(**kwargs) → DataFlow[source]

Initializes a starter specification for the DataFlow.

Returns:

The DataFlow instance (self)

Return type:

DataFlow

property job_id: str | None

The OCID of the job

classmethod list_jobs(compartment_id: str | None = None, **kwargs) → List[DataFlow][source]

List Data Flow jobs in a given compartment.

Parameters:

  • compartment_id (str) – id of that compartment

  • kwargs – additional keyword arguments for filtering jobs

Returns:

list of Data Flow jobs

Return type:

List[DataFlow]

property name: str

Display name of the job

run(name: str | None = None, args: List[str] | None = None, env_vars: Dict[str, str] | None = None, freeform_tags: Dict[str, str] | None = None, defined_tags: Dict[str, Dict[str, object]] | None = None, wait: bool = False, **kwargs) → DataFlowRun[source]

Run a Data Flow job.

Parameters:

  • name (str, optional) – name of the run. If a name is not provided, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • args (List[str], optional) – list of command line arguments

  • env_vars (Dict[str, str], optional) – dictionary of environment variables (not used for data flow)

  • freeform_tags (Dict[str, str], optional) – freeform tags

  • defined_tags (Dict[str, Dict[str, object]], optional) – defined tags

  • wait (bool, optional) – whether to wait for a run to terminate

  • kwargs – additional keyword arguments

Returns:

a DataFlowRun instance

Return type:

DataFlowRun

run_list(**kwargs) → List[DataFlowRun][source]

List runs associated with a Data Flow job.

Parameters:

kwargs – additional arguments for filtering runs.

Returns:

list of DataFlowRun instances

Return type:

List[DataFlowRun]

to_dict(**kwargs) → dict[source]

Serialize job to a dictionary.

Returns:

serialized job as a dictionary

Return type:

dict

to_yaml(**kwargs) → str[source]

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

with_compartment_id(id: str) → DataFlow[source]

Set compartment id for a Data Flow job.

Parameters:

id (str) – compartment id

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_configuration(configs: dict) → DataFlow[source]

Set configuration for a Data Flow job.

Parameters:

configs (dict) – dictionary of configurations

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_defined_tag(**kwargs) → DataFlow[source]

Sets defined tags

Returns:

The DataFlow instance (self)

Return type:

DataFlow

with_driver_shape(shape: str) → DataFlow[source]

Set driver shape for a Data Flow job.

Parameters:

shape (str) – driver shape

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_driver_shape_config(memory_in_gbs: float, ocpus: float, **kwargs: Dict[str, Any]) → DataFlow[source]

Sets the driver shape config details of Data Flow job infrastructure. Specify only when a flex shape is selected. For example VM.Standard.E3.Flex allows the memory_in_gbs and cpu count to be specified.

Parameters:

  • memory_in_gbs (float) – The size of the memory in GBs.

  • ocpus (float) – The OCPUs count.

  • kwargs – Additional keyword arguments.

Returns:

the Data Flow instance itself.

Return type:

DataFlow

with_execute(exec: str) → DataFlow[source]

Set command for spark-submit.

Parameters:

exec (str) – str of commands

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_executor_shape(shape: str) → DataFlow[source]

Set executor shape for a Data Flow job.

Parameters:

shape (str) – executor shape

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_executor_shape_config(memory_in_gbs: float, ocpus: float, **kwargs: Dict[str, Any]) → DataFlow[source]

Sets the executor shape config details of Data Flow job infrastructure. Specify only when a flex shape is selected. For example VM.Standard.E3.Flex allows the memory_in_gbs and cpu count to be specified.

Parameters:

  • memory_in_gbs (float) – The size of the memory in GBs.

  • ocpus (float) – The OCPUs count.

  • kwargs – Additional keyword arguments.

Returns:

the Data Flow instance itself.

Return type:

DataFlow

with_freeform_tag(**kwargs) → DataFlow[source]

Sets freeform tags

Returns:

The DataFlow instance (self)

Return type:

DataFlow

with_id(id: str) → DataFlow[source]

Set id for a Data Flow job.

Parameters:

id (str) – id of a job

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_language(lang: str) → DataFlow[source]

Set language for a Data Flow job.

Parameters:

lang (str) – language for the job

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_logs_bucket_uri(uri: str) → DataFlow[source]

Set logs bucket uri for a Data Flow job.

Parameters:

uri (str) – uri to logs bucket

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_metastore_id(id: str) → DataFlow[source]

Set Hive metastore id for a Data Flow job.

Parameters:

id (str) – metastore id

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_num_executors(n: int) → DataFlow[source]

Set number of executors for a Data Flow job.

Parameters:

n (int) – number of executors

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_pool_id(pool_id: str) → DataFlow[source]

Set the Data Flow Pool Id for a Data Flow job.

Parameters:

pool_id (str) – The OCID of a Data Flow Pool.

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_private_endpoint_id(private_endpoint_id: str) → DataFlow[source]

Set the private endpoint ID for a Data Flow job infrastructure.

Parameters:

private_endpoint_id (str) – The OCID of a private endpoint.

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_spark_version(ver: str) → DataFlow[source]

Set spark version for a Data Flow job. Currently supported versions are 2.4.4, 3.0.2 and 3.2.1 Documentation: https://docs.oracle.com/en-us/iaas/data-flow/using/dfs_getting_started.htm#before_you_begin

Parameters:

ver (str) – spark version

Returns:

the Data Flow instance itself

Return type:

DataFlow

with_warehouse_bucket_uri(uri: str) → DataFlow[source]

Set warehouse bucket uri for a Data Flow job.

Parameters:

uri (str) – uri to warehouse bucket

Returns:

the Data Flow instance itself

Return type:

DataFlow

class ads.jobs.builders.infrastructure.dataflow.DataFlowApp(config: dict | None = None, signer: Signer | None = None, client_kwargs: dict | None = None, **kwargs)[source]

Bases: OCIModelMixin, Application

Initializes a service/resource with OCI client as a property. If config or signer is specified, it will be used to initialize the OCI client. If neither of them is specified, the client will be initialized with ads.common.auth.default_signer. If both of them are specified, both of them will be passed into the OCI client,

and the authentication will be determined by OCI Python SDK.

Parameters:

  • config (dict, optional) – OCI API key config dictionary, by default None.

  • signer (oci.signer.Signer, optional) – OCI authentication signer, by default None.

  • client_kwargs (dict, optional) – Additional keyword arguments for initializing the OCI client.

property client: DataFlowClient

OCI client

create() → DataFlowApp[source]

Create a Data Flow application.

Returns:

a DataFlowApp instance

Return type:

DataFlowApp

delete() → None[source]

Delete a Data Flow application.

Return type:

None

classmethod init_client(**kwargs) → DataFlowClient[source]

Initializes the OCI client specified in the “client” keyword argument Sub-class should override this method and call cls._init_client(client=OCI_CLIENT)

Parameters:

**kwargs – Additional keyword arguments for initializing the OCI client.

Return type:

An instance of OCI client.

to_yaml() → str[source]

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

class ads.jobs.builders.infrastructure.dataflow.DataFlowLogs(run_id)[source]

Bases: object

property application
property driver
property executor
class ads.jobs.builders.infrastructure.dataflow.DataFlowRun(config: dict | None = None, signer: Signer | None = None, client_kwargs: dict | None = None, **kwargs)[source]

Bases: OCIModelMixin, Run, RunInstance

Initializes a service/resource with OCI client as a property. If config or signer is specified, it will be used to initialize the OCI client. If neither of them is specified, the client will be initialized with ads.common.auth.default_signer. If both of them are specified, both of them will be passed into the OCI client,

and the authentication will be determined by OCI Python SDK.

Parameters:

  • config (dict, optional) – OCI API key config dictionary, by default None.

  • signer (oci.signer.Signer, optional) – OCI authentication signer, by default None.

  • client_kwargs (dict, optional) – Additional keyword arguments for initializing the OCI client.

TERMINATED_STATES = ['CANCELED', 'FAILED', 'SUCCEEDED']
cancel() → DataFlowRun[source]

Cancel a Data Flow run if it is not yet terminated. Will be executed synchronously.

Returns:

The dataflow run instance.

Return type:

self

property client: DataFlowClient

OCI client

create() → DataFlowRun[source]

Create a Data Flow run.

Returns:

a DataFlowRun instance

Return type:

DataFlowRun

delete() → DataFlowRun[source]

Cancel and delete a Data Flow run if it is not yet terminated. Will be executed asynchronously.

Returns:

The dataflow run instance.

Return type:

self

classmethod init_client(**kwargs) → DataFlowClient[source]

Initializes the OCI client specified in the “client” keyword argument Sub-class should override this method and call cls._init_client(client=OCI_CLIENT)

Parameters:

**kwargs – Additional keyword arguments for initializing the OCI client.

Return type:

An instance of OCI client.

property logs: DataFlowLogs

Show logs from a run. There are three types of logs: application log, driver log and executor log, each with stdout and stderr separately. To access each type of logs, >>> dfr.logs.application.stdout >>> dfr.logs.driver.stderr

Returns:

an instance of DataFlowLogs

Return type:

DataFlowLogs

property status: str

Show status (lifecycle state) of a run.

Returns:

status of the run

Return type:

str

to_yaml() → str[source]

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

wait(interval: int = 3) → DataFlowRun[source]

Wait for a run to terminate.

Parameters:

interval (int, optional) – interval to wait before probing again

Returns:

a DataFlowRun instance

Return type:

DataFlowRun

watch(interval: int = 3) → DataFlowRun[source]

This is an alias of wait() method. It waits for a run to terminate.

Parameters:

interval (int, optional) – interval to wait before probing again

Returns:

a DataFlowRun instance

Return type:

DataFlowRun

ads.jobs.builders.infrastructure.dataflow.conda_pack_name_to_dataflow_config(conda_uri)[source]

ads.jobs.builders.infrastructure.dsc_job module

class ads.jobs.builders.infrastructure.dsc_job.DSCJob(artifact: str | Artifact | None = None, **kwargs)[source]

Bases: OCIDataScienceMixin, Job

Represents an OCI Data Science Job This class contains all attributes of the oci.data_science.models.Job. The main purpose of this class is to link the oci.data_science.models.Job model and the related client methods. Mainly, linking the Job model (payload) to Create/Update/Get/List/Delete methods.

A DSCJob can be initialized by unpacking a the properties stored in a dictionary (payload):

job_properties = {
    "display_name": "my_job",
    "job_infrastructure_configuration_details": {"shape_name": "VM.MY_SHAPE"}
}
job = DSCJob(**job_properties)

The properties can also be OCI REST API payload, in which the keys are in camel format.

job_payload = {
    "projectId": "<project_ocid>",
    "compartmentId": "<compartment_ocid>",
    "displayName": "<job_name>",
    "jobConfigurationDetails": {
        "jobType": "DEFAULT",
        "commandLineArguments": "pos_arg1 pos_arg2 --key1 val1 --key2 val2",
        "environmentVariables": {
            "KEY1": "VALUE1",
            "KEY2": "VALUE2",
            # User specifies conda env via env var
            "CONDA_ENV_TYPE" : "service",
            "CONDA_ENV_SLUG" : "mlcpuv1"
        }
    },
    "jobInfrastructureConfigurationDetails": {
        "jobInfrastructureType": "STANDALONE",
        "shapeName": "VM.Standard.E3.Flex",
        "jobShapeConfigDetails": {
            "memoryInGBs": 16,
            "ocpus": 1
        },
        "blockStorageSizeInGBs": "100",
        "subnetId": "<subnet_ocid>"
    }
}
job = DSCJob(**job_payload)

Initialize a DSCJob object.

Parameters:

  • artifact (str or Artifact) – Job artifact, which can be a path or an Artifact object. Defaults to None.

  • kwargs – Same as kwargs in oci.data_science.models.Job. Keyword arguments are passed into OCI Job model to initialize the properties.

CONST_DEFAULT_BLOCK_STORAGE_SIZE = 50
DEFAULT_INFRA_TYPE = 'ME_STANDALONE'
property artifact: str | Artifact

Job artifact.

Returns:

When creating a job, this be a path or an Artifact object. When loading the job from OCI, this will be the filename of the job artifact.

Return type:

str or Artifact

create() → DSCJob[source]

Create the job on OCI Data Science platform

Returns:

The DSCJob instance (self), which allows chaining additional method.

Return type:

DSCJob

delete(force_delete: bool = False) → DSCJob[source]

Deletes the job and the corresponding job runs.

Parameters:

force_delete (bool, optional, defaults to False) – the deletion fails when associated job runs are in progress, but if force_delete to true, then the job run will be canceled, then it will be deleted. In this case, delete job has to wait till job has been canceled.

Returns:

The DSCJob instance (self), which allows chaining additional method.

Return type:

DSCJob

download_artifact(artifact_path: str) → DSCJob[source]

Downloads the artifact from OCI

Parameters:

artifact_path (str) – Local path to store the job artifact.

Returns:

The DSCJob instance (self), which allows chaining additional method.

Return type:

DSCJob

load_defaults() → DSCJob[source]
load_properties_from_env() → None[source]

Loads default properties from the environment

run(**kwargs) → DataScienceJobRun[source]

Runs the job

Parameters:

  • **kwargs – Keyword arguments for initializing a Data Science Job Run. The keys can be any keys in supported by OCI JobConfigurationDetails, OcirContainerJobEnvironmentConfigurationDetails and JobRun, including: * hyperparameter_values: dict(str, str) * environment_variables: dict(str, str) * command_line_arguments: str * maximum_runtime_in_minutes: int * display_name: str * freeform_tags: dict(str, str) * defined_tags: dict(str, dict(str, object)) * image: str * cmd: list[str] * entrypoint: list[str] * image_digest: str * image_signature_id: str

  • specified (If display_name is not)

  • "<JOB_NAME>-run-<TIMESTAMP>". (it will be generated as)

Returns:

An instance of DSCJobRun, which can be used to monitor the job run.

Return type:

DSCJobRun

run_list(**kwargs) → list[DataScienceJobRun][source]

Lists the runs of this job.

Parameters:

**kwargs – Keyword arguments to te passed into the OCI list_job_runs() for filtering the job runs.

Returns:

A list of DSCJobRun objects

Return type:

list

update() → DSCJob[source]

Updates the Data Science Job.

upload_artifact(artifact_path: str | None = None) → DSCJob[source]

Uploads the job artifact to OCI

Parameters:

artifact_path (str, optional) – Local path to the job artifact file to be uploaded, by default None. If artifact_path is None, the path in self.artifact will be used.

Returns:

The DSCJob instance (self), which allows chaining additional method.

Return type:

DSCJob

ads.jobs.builders.infrastructure.dsc_job.DSCJobRun

alias of DataScienceJobRun

class ads.jobs.builders.infrastructure.dsc_job.DataScienceJob(spec: Dict | None = None, **kwargs)[source]

Bases: Infrastructure

Represents the OCI Data Science Job infrastructure.

To configure the infrastructure for a Data Science Job:

infrastructure = (
    DataScienceJob()
    # Configure logging for getting the job run outputs.
    .with_log_group_id("<log_group_ocid>")
    # Log resource will be auto-generated if log ID is not specified.
    .with_log_id("<log_ocid>")
    # If you are in an OCI data science notebook session,
    # the following configurations are not required.
    # Configurations from the notebook session will be used as defaults.
    .with_compartment_id("<compartment_ocid>")
    .with_project_id("<project_ocid>")
    .with_subnet_id("<subnet_ocid>")
    .with_shape_name("VM.Standard.E3.Flex")
    # Shape config details are applicable only for the flexible shapes.
    .with_shape_config_details(memory_in_gbs=16, ocpus=1)
    # Minimum/Default block storage size is 50 (GB).
    .with_block_storage_size(50)
    # A list of file systems to be mounted
    .with_storage_mount(
        {
            "src" : "<mount_target_ip_address>:<export_path>",
            "dest" : "<destination_directory_name>"
        }
    )
    # Tags
    .with_freeform_tag(my_tag="my_value")
    .with_defined_tag(**{"Operations": {"CostCenter": "42"}})
)

Initializes a data science job infrastructure

Parameters:

  • spec (dict, optional) – Object specification, by default None

  • kwargs (dict) – Specification as keyword arguments. If spec contains the same key as the one in kwargs, the value from kwargs will be used.

CONST_BLOCK_STORAGE = 'blockStorageSize'
CONST_COMPARTMENT_ID = 'compartmentId'
CONST_DEFINED_TAGS = 'definedTags'
CONST_DISPLAY_NAME = 'displayName'
CONST_FREEFORM_TAGS = 'freeformTags'
CONST_JOB_INFRA = 'jobInfrastructureType'
CONST_JOB_TYPE = 'jobType'
CONST_LOG_GROUP_ID = 'logGroupId'
CONST_LOG_ID = 'logId'
CONST_MEMORY_IN_GBS = 'memoryInGBs'
CONST_OCPUS = 'ocpus'
CONST_PROJECT_ID = 'projectId'
CONST_SHAPE_CONFIG_DETAILS = 'shapeConfigDetails'
CONST_SHAPE_NAME = 'shapeName'
CONST_STORAGE_MOUNT = 'storageMount'
CONST_SUBNET_ID = 'subnetId'
attribute_map = {'blockStorageSize': 'block_storage_size', 'compartmentId': 'compartment_id', 'definedTags': 'defined_tags', 'displayName': 'display_name', 'freeformTags': 'freeform_tags', 'jobInfrastructureType': 'job_infrastructure_type', 'jobType': 'job_type', 'logGroupId': 'log_group_id', 'logId': 'log_id', 'projectId': 'project_id', 'shapeConfigDetails': 'shape_config_details', 'shapeName': 'shape_name', 'storageMount': 'storage_mount', 'subnetId': 'subnet_id'}
auth = {}
property block_storage_size: int

Block storage size for the job

build() → DataScienceJob[source]

Load default values from the environment for the job infrastructure. Should be implemented on the child level.

property compartment_id: str | None

The compartment OCID

create(runtime, **kwargs) → DataScienceJob[source]

Creates a job with runtime.

Parameters:

runtime (Runtime) – An ADS job runtime.

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

property defined_tags: dict

Defined tags

delete() → None[source]

Deletes a job

classmethod fast_launch_shapes(compartment_id: str | None = None, **kwargs) → list[source]

Lists the supported fast launch shapes for running jobs in a compartment.

Parameters:

compartment_id (str, optional) – The compartment ID for running the jobs, by default None. This is optional in a OCI Data Science notebook session. If this is not specified, the compartment ID of the notebook session will be used.

Returns:

A list of oci.data_science.models.FastLaunchJobConfigSummary objects containing the information of the supported shapes.

Return type:

list

Examples

To get a list of shape names:

shapes = DataScienceJob.fast_launch_shapes(
    compartment_id=os.environ["PROJECT_COMPARTMENT_OCID"]
)
shape_names = [shape.shape_name for shape in shapes]
property freeform_tags: dict

Freeform tags

classmethod from_dict(obj_dict: dict)[source]

Initialize the object from a Python dictionary

classmethod from_dsc_job(dsc_job: DSCJob) → DataScienceJob[source]

Initialize a DataScienceJob instance from a DSCJob

Parameters:

dsc_job (DSCJob) – An instance of DSCJob

Returns:

An instance of DataScienceJob

Return type:

DataScienceJob

classmethod from_id(job_id: str) → DataScienceJob[source]

Gets an existing job using Job OCID

Parameters:

job_id (str) – Job OCID

Returns:

An instance of DataScienceJob

Return type:

DataScienceJob

init(**kwargs) → DataScienceJob[source]

Initializes a starter specification for the DataScienceJob.

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

classmethod instance_shapes(compartment_id: str | None = None, **kwargs) → list[source]

Lists the supported shapes for running jobs in a compartment.

Parameters:

compartment_id (str, optional) – The compartment ID for running the jobs, by default None. This is optional in a OCI Data Science notebook session. If this is not specified, the compartment ID of the notebook session will be used.

Returns:

A list of oci.data_science.models.JobShapeSummary objects containing the information of the supported shapes.

Return type:

list

Examples

To get a list of shape names:

shapes = DataScienceJob.fast_launch_shapes(
    compartment_id=os.environ["PROJECT_COMPARTMENT_OCID"]
)
shape_names = [shape.name for shape in shapes]
property job_id: str | None

The OCID of the job

property job_infrastructure_type: str | None

Job infrastructure type

property job_type: str | None

Job type

classmethod list_jobs(compartment_id: str | None = None, **kwargs) → List[DataScienceJob][source]

Lists all jobs in a compartment.

Parameters:

  • compartment_id (str, optional) – The compartment ID for running the jobs, by default None. This is optional in a OCI Data Science notebook session. If this is not specified, the compartment ID of the notebook session will be used.

  • **kwargs – Keyword arguments to be passed into OCI list_jobs API for filtering the jobs.

Returns:

A list of DataScienceJob object.

Return type:

List[DataScienceJob]

property log_group_id: str

Log group OCID of the data science job

Returns:

Log group OCID

Return type:

str

property log_id: str

Log OCID for the data science job.

Returns:

Log OCID

Return type:

str

property name: str

Display name of the job

payload_attribute_map = {'blockStorageSize': 'job_infrastructure_configuration_details.block_storage_size_in_gbs', 'compartmentId': 'compartment_id', 'displayName': 'display_name', 'jobInfrastructureType': 'job_infrastructure_configuration_details.job_infrastructure_type', 'jobType': 'job_configuration_details.job_type', 'logGroupId': 'job_log_configuration_details.log_group_id', 'logId': 'job_log_configuration_details.log_id', 'projectId': 'project_id', 'shapeConfigDetails': 'job_infrastructure_configuration_details.job_shape_config_details', 'shapeName': 'job_infrastructure_configuration_details.shape_name', 'subnetId': 'job_infrastructure_configuration_details.subnet_id'}
property project_id: str | None

Project OCID

run(name=None, args=None, env_var=None, freeform_tags=None, defined_tags=None, wait=False, **kwargs) → DataScienceJobRun[source]

Runs a job on OCI Data Science job

Parameters:

  • name (str, optional) – The name of the job run, by default None.

  • args (str, optional) – Command line arguments for the job run, by default None.

  • env_var (dict, optional) – Environment variable for the job run, by default None

  • freeform_tags (dict, optional) – Freeform tags for the job run, by default None

  • defined_tags (dict, optional) – Defined tags for the job run, by default None

  • wait (bool, optional) – Indicate if this method should wait for the run to finish before it returns, by default False.

  • kwargs – additional keyword arguments

Returns:

A Data Science Job Run instance.

Return type:

DataScienceJobRun

run_list(**kwargs) → List[DataScienceJobRun][source]

Gets a list of job runs.

Parameters:

**kwargs – Keyword arguments for filtering the job runs. These arguments will be passed to OCI API.

Returns:

A list of job runs.

Return type:

List[DSCJobRun]

property shape_config_details: Dict

The details for the job run shape configuration.

shape_config_details_attribute_map = {'memoryInGBs': 'memory_in_gbs', 'ocpus': 'ocpus'}
property shape_name: str | None

Shape name

snake_to_camel_map = {'block_storage_size_in_gbs': 'blockStorageSize', 'compartment_id': 'compartmentId', 'display_name': 'displayName', 'job_infrastructure_type': 'jobInfrastructureType', 'job_shape_config_details': 'shapeConfigDetails', 'job_type': 'jobType', 'log_group_id': 'logGroupId', 'log_id': 'logId', 'project_id': 'projectId', 'shape_name': 'shapeName', 'subnet_id': 'subnetId'}
static standardize_spec(spec)[source]
property status: str | None

Status of the job.

Returns:

Status of the job.

Return type:

str

property storage_mount: List[dict]

Files systems that have been mounted for the data science job

Returns:

A list of file systems that have been mounted

Return type:

list

storage_mount_type_dict = {'FILE_STORAGE': <class 'ads.common.dsc_file_system.OCIFileStorage'>, 'OBJECT_STORAGE': <class 'ads.common.dsc_file_system.OCIObjectStorage'>}
property subnet_id: str

Subnet ID

with_block_storage_size(size_in_gb: int) → DataScienceJob[source]

Sets the block storage size in GB

Parameters:

size_in_gb (int) – Block storage size in GB

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_compartment_id(compartment_id: str) → DataScienceJob[source]

Sets the compartment OCID

Parameters:

compartment_id (str) – The compartment OCID

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_defined_tag(**kwargs) → DataScienceJob[source]

Sets defined tags

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_freeform_tag(**kwargs) → DataScienceJob[source]

Sets freeform tags

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_job_infrastructure_type(infrastructure_type: str) → DataScienceJob[source]

Sets the job infrastructure type

Parameters:

infrastructure_type (str) – Job infrastructure type as string

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_job_type(job_type: str) → DataScienceJob[source]

Sets the job type

Parameters:

job_type (str) – Job type as string

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_log_group_id(log_group_id: str) → DataScienceJob[source]

Sets the log group OCID for the data science job. If log group ID is specified but log ID is not, a new log resource will be created automatically for each job run to store the logs.

Parameters:

log_group_id (str) – Log Group OCID

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_log_id(log_id: str) → DataScienceJob[source]

Sets the log OCID for the data science job. If log ID is specified, setting the log group ID (with_log_group_id()) is not strictly needed. ADS will look up the log group ID automatically. However, this may require additional permission, and the look up may not be available for newly created log group. Specifying both log ID (with_log_id()) and log group ID (with_log_group_id()) can avoid such lookup and speed up the job creation.

Parameters:

log_id (str) – Log resource OCID.

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_project_id(project_id: str) → DataScienceJob[source]

Sets the project OCID

Parameters:

project_id (str) – The project OCID

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_shape_config_details(memory_in_gbs: float, ocpus: float, **kwargs: Dict[str, Any]) → DataScienceJob[source]

Sets the details for the job run shape configuration. Specify only when a flex shape is selected. For example VM.Standard.E3.Flex allows the memory_in_gbs and cpu count to be specified.

Parameters:

  • memory_in_gbs (float) – The size of the memory in GBs.

  • ocpus (float) – The OCPUs count.

  • kwargs – Additional keyword arguments.

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_shape_name(shape_name: str) → DataScienceJob[source]

Sets the shape name for running the job

Parameters:

shape_name (str) – Shape name

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_storage_mount(*storage_mount: List[dict]) → DataScienceJob[source]

Sets the file systems to be mounted for the data science job. A maximum number of 5 file systems are allowed to be mounted for a single data science job.

Parameters:

storage_mount (List[dict]) – A list of file systems to be mounted.

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

with_subnet_id(subnet_id: str) → DataScienceJob[source]

Sets the subnet ID

Parameters:

subnet_id (str) – Subnet ID

Returns:

The DataScienceJob instance (self)

Return type:

DataScienceJob

class ads.jobs.builders.infrastructure.dsc_job.DataScienceJobRun(config: dict | None = None, signer: Signer | None = None, client_kwargs: dict | None = None, **kwargs)[source]

Bases: OCIDataScienceMixin, JobRun, RunInstance

Represents a Data Science Job run

Initializes a service/resource with OCI client as a property. If config or signer is specified, it will be used to initialize the OCI client. If neither of them is specified, the client will be initialized with ads.common.auth.default_signer. If both of them are specified, both of them will be passed into the OCI client,

and the authentication will be determined by OCI Python SDK.

Parameters:

  • config (dict, optional) – OCI API key config dictionary, by default None.

  • signer (oci.signer.Signer, optional) – OCI authentication signer, by default None.

  • client_kwargs (dict, optional) – Additional keyword arguments for initializing the OCI client.

TERMINAL_STATES = ['SUCCEEDED', 'FAILED', 'CANCELED', 'DELETED']
cancel(wait_for_completion: bool = True) → DataScienceJobRun[source]

Cancels a job run

Parameters:

wait_for_completion (bool) – Whether to wait for job run to be cancelled before proceeding. Defaults to True.

Returns:

The job run instance.

Return type:

self

create() → DataScienceJobRun[source]

Creates a job run

delete(force_delete: bool = False)[source]

Deletes the resource

download(to_dir)[source]

Downloads files from job run output URI to local.

Parameters:

to_dir (str) – Local directory to which the files will be downloaded to.

Returns:

The job run instance (self)

Return type:

DataScienceJobRun

property exit_code

The exit code of the job run from the lifecycle details. Note that, None will be returned if the job run is not finished or failed without exit code. 0 will be returned if job run succeeded.

property job

The job instance of this run.

Returns:

An ADS Job instance

Return type:

Job

property log_group_id: str

The log group ID from OCI logging service containing the logs from the job run.

property log_id: str

The log ID from OCI logging service containing the logs from the job run.

property logging: OCILog

The OCILog object containing the logs from the job run

logs(limit: int | None = None) → list[source]

Gets the logs of the job run.

Parameters:

limit (int, optional) – Limit the number of logs to be returned. Defaults to None. All logs will be returned.

Returns:

A list of log records. Each log record is a dictionary with the following keys: id, time, message.

Return type:

list

property status: str

Lifecycle status

Returns:

Status in a string.

Return type:

str

to_yaml() → str[source]

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

wait(interval: float = 3)[source]

Waits for the job run until if finishes.

Parameters:

interval (float) – Time interval in seconds between each request to update the logs. Defaults to 3 (seconds).

watch(interval: float = 3, wait: float = 90) → DataScienceJobRun[source]

Watches the job run until it finishes. Before the job start running, this method will output the job run status. Once the job start running, the logs will be streamed until the job is success, failed or cancelled.

Parameters:

  • interval (float) – Time interval in seconds between each request to update the logs. Defaults to 3 (seconds).

  • wait (float) – Time in seconds to keep updating the logs after the job run finished. It may take some time for logs to appear in OCI logging service after the job run is finished. Defaults to 90 (seconds).

ads.jobs.builders.infrastructure.dsc_job_runtime module

Contains classes for conversion between ADS runtime and OCI Data Science Job implementation. This module is for ADS developers only. In this module, a payload is defined as a dictionary for initializing a DSCJob object. The DSCJob can be initialized with the same arguments for initializing oci.data_science.models.Job,

plus an “artifact” argument for job artifact.

The payload also contain infrastructure information. The conversion from a runtime to a payload is called translate in this module. The conversion from a DSCJob to a runtime is called extract in this module.

class ads.jobs.builders.infrastructure.dsc_job_runtime.CondaRuntimeHandler(data_science_job)[source]

Bases: RuntimeHandler

Runtime Handler for CondaRuntime

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_CONDA_BUCKET = 'CONDA_ENV_BUCKET'
CONST_CONDA_NAMESPACE = 'CONDA_ENV_NAMESPACE'
CONST_CONDA_OBJ_NAME = 'CONDA_ENV_OBJECT_NAME'
CONST_CONDA_REGION = 'CONDA_ENV_REGION'
CONST_CONDA_SLUG = 'CONDA_ENV_SLUG'
CONST_CONDA_TYPE = 'CONDA_ENV_TYPE'
RUNTIME_CLASS

alias of CondaRuntime

class ads.jobs.builders.infrastructure.dsc_job_runtime.ContainerRuntimeHandler(data_science_job)[source]

Bases: RuntimeHandler

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CMD_DELIMITER = ','
RUNTIME_CLASS

alias of ContainerRuntime

static split_args(args: str) → list[source]

Splits the cmd or entrypoint arguments for BYOC job into a list. BYOC jobs uses environment variables to store the values of cmd and entrypoint. In the values, comma(,) is used to separate cmd or entrypoint arguments. In YAML, the arguments are formatted into a list (Exec form).

>>> ContainerRuntimeHandler.split_args("/bin/bash")
["/bin/bash"]
>>> ContainerRuntimeHandler.split_args("-c,echo Hello World")
['-c', 'echo Hello World']
Parameters:

args (str) – Arguments in a comma separated string.

Returns:

Arguments in a list

Return type:

list

translate(runtime: Runtime) → dict[source]

Translates the runtime into a JSON payload for OCI API. This method calls the following sub-methods: * _translate_artifact() * _translate_config()

  • _translate_env()

A sub-class can modify one of more of these methods.

Parameters:

runtime (Runtime) – An instance of the runtime to be converted to a JSON payload.

Returns:

JSON payload for defining a Data Science Job with OCI API

Return type:

dict

class ads.jobs.builders.infrastructure.dsc_job_runtime.DataScienceJobRuntimeManager(data_science_job)[source]

Bases: RuntimeHandler

This class is used by the DataScienceJob infrastructure to handle the runtime conversion. The translate() method determines the actual runtime handler by matching the RUNTIME_CLASS. The extract() method determines the actual runtime handler by checking if the runtime can be extracted. The order in runtime_handlers is used for extraction until a runtime is extracted. RuntimeHandler on the top of the list will have higher priority. If a runtime is a specify case of another runtime, the handler should be placed with higher priority.

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

extract(dsc_job)[source]

Extract the runtime from an OCI data science job object.

This method determines the actual runtime handler by checking if the runtime can be extracted.

Parameters:

dsc_job (DSCJob or oci.datascience.models.Job) – The data science job containing runtime information.

Returns:

The runtime extracted from the data science job.

Return type:

Runtime

runtime_handlers = [<class 'ads.jobs.builders.infrastructure.dsc_job_runtime.ContainerRuntimeHandler'>, <class 'ads.jobs.builders.infrastructure.dsc_job_runtime.PyTorchDistributedRuntimeHandler'>, <class 'ads.jobs.builders.infrastructure.dsc_job_runtime.GitPythonRuntimeHandler'>, <class 'ads.jobs.builders.infrastructure.dsc_job_runtime.NotebookRuntimeHandler'>, <class 'ads.jobs.builders.infrastructure.dsc_job_runtime.PythonRuntimeHandler'>, <class 'ads.jobs.builders.infrastructure.dsc_job_runtime.ScriptRuntimeHandler'>]
translate(runtime) → dict[source]

Translates the runtime into a JSON payload for OCI API. This method determines the actual runtime handler by matching the RUNTIME_CLASS.

Parameters:

runtime (Runtime) – An instance of the runtime to be converted to a JSON payload.

Returns:

JSON payload for defining a Data Science Job with OCI API

Return type:

dict

class ads.jobs.builders.infrastructure.dsc_job_runtime.GitPythonRuntimeHandler(data_science_job)[source]

Bases: CondaRuntimeHandler

Runtime Handler for GitPythonRuntime

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_ENTRYPOINT = 'GIT_ENTRYPOINT'
CONST_ENTRY_FUNCTION = 'ENTRY_FUNCTION'
CONST_GIT_BRANCH = 'GIT_BRANCH'
CONST_GIT_CODE_DIR = 'CODE_DIR'
CONST_GIT_COMMIT = 'GIT_COMMIT'
CONST_GIT_SSH_SECRET_ID = 'GIT_SECRET_OCID'
CONST_GIT_URL = 'GIT_URL'
CONST_JOB_ENTRYPOINT = 'JOB_RUN_ENTRYPOINT'
CONST_OUTPUT_DIR = 'OUTPUT_DIR'
CONST_OUTPUT_URI = 'OUTPUT_URI'
CONST_PYTHON_PATH = 'PYTHON_PATH'
CONST_SKIP_METADATA = 'SKIP_METADATA_UPDATE'
CONST_WORKING_DIR = 'WORKING_DIR'
PATH_DELIMITER = ':'
RUNTIME_CLASS

alias of GitPythonRuntime

SPEC_MAPPINGS = {'branch': 'GIT_BRANCH', 'commit': 'GIT_COMMIT', 'entryFunction': 'ENTRY_FUNCTION', 'entrypoint': 'GIT_ENTRYPOINT', 'gitSecretId': 'GIT_SECRET_OCID', 'outputDir': 'OUTPUT_DIR', 'outputUri': 'OUTPUT_URI', 'pythonPath': 'PYTHON_PATH', 'url': 'GIT_URL', 'workingDir': 'WORKING_DIR'}
exception ads.jobs.builders.infrastructure.dsc_job_runtime.IncompatibleRuntime[source]

Bases: Exception

Represents an exception when runtime is not compatible with the OCI data science job configuration. This exception is designed to be raised during the extraction of a runtime from OCI data science job. The data science job does not explicitly contain information of the type of the ADS runtime. Each runtime handler should determine if the configuration of the job can be converted to the runtime. This exception should be raised during the extract() call if the configuration cannot be converted. The RuntimeManager uses this exception to determine if the conversion is successful.

class ads.jobs.builders.infrastructure.dsc_job_runtime.NotebookRuntimeHandler(data_science_job)[source]

Bases: CondaRuntimeHandler

Runtime Handler for NotebookRuntime

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_ENTRYPOINT = 'JOB_RUN_ENTRYPOINT'
CONST_EXCLUDE_TAGS = 'NOTEBOOK_EXCLUDE_TAGS'
CONST_NOTEBOOK_ENCODING = 'NOTEBOOK_ENCODING'
CONST_NOTEBOOK_NAME = 'JOB_RUN_NOTEBOOK'
CONST_OUTPUT_URI = 'OUTPUT_URI'
RUNTIME_CLASS

alias of NotebookRuntime

SPEC_MAPPINGS = {'excludeTags': 'NOTEBOOK_EXCLUDE_TAGS', 'notebookEncoding': 'NOTEBOOK_ENCODING', 'outputUri': 'OUTPUT_URI'}
class ads.jobs.builders.infrastructure.dsc_job_runtime.PyTorchDistributedRuntimeHandler(data_science_job)[source]

Bases: PythonRuntimeHandler

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_COMMAND = 'OCI__LAUNCH_CMD'
CONST_DEEPSPEED = 'OCI__DEEPSPEED'
CONST_WORKER_COUNT = 'OCI__WORKER_COUNT'
GIT_SPEC_MAPPINGS = {'OCI__RUNTIME_GIT_BRANCH': 'branch', 'OCI__RUNTIME_GIT_COMMIT': 'commit', 'OCI__RUNTIME_GIT_SECRET_ID': 'gitSecretId', 'OCI__RUNTIME_URI': 'url'}
RUNTIME_CLASS

alias of PyTorchDistributedRuntime

SPEC_MAPPINGS = {'command': 'OCI__LAUNCH_CMD', 'entryFunction': 'ENTRY_FUNCTION', 'entrypoint': 'CODE_ENTRYPOINT', 'outputDir': 'OUTPUT_DIR', 'outputUri': 'OUTPUT_URI', 'pythonPath': 'PYTHON_PATH', 'workingDir': 'WORKING_DIR'}
class ads.jobs.builders.infrastructure.dsc_job_runtime.PythonRuntimeHandler(data_science_job)[source]

Bases: CondaRuntimeHandler

Runtime Handler for PythonRuntime

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_CODE_ENTRYPOINT = 'CODE_ENTRYPOINT'
CONST_ENTRY_FUNCTION = 'ENTRY_FUNCTION'
CONST_JOB_ENTRYPOINT = 'JOB_RUN_ENTRYPOINT'
CONST_OUTPUT_DIR = 'OUTPUT_DIR'
CONST_OUTPUT_URI = 'OUTPUT_URI'
CONST_PYTHON_PATH = 'PYTHON_PATH'
CONST_WORKING_DIR = 'WORKING_DIR'
PATH_DELIMITER = ':'
RUNTIME_CLASS

alias of PythonRuntime

SPEC_MAPPINGS = {'command': 'OCI__LAUNCH_CMD', 'entryFunction': 'ENTRY_FUNCTION', 'entrypoint': 'CODE_ENTRYPOINT', 'outputDir': 'OUTPUT_DIR', 'outputUri': 'OUTPUT_URI', 'pythonPath': 'PYTHON_PATH', 'workingDir': 'WORKING_DIR'}
class ads.jobs.builders.infrastructure.dsc_job_runtime.RuntimeHandler(data_science_job)[source]

Bases: object

Base class for Runtime Handler.

Each runtime handler should define the RUNTIME_CLASS to be the runtime it can handle.

Each runtime handler is initialized with a DataScienceJob instance. This instance is a reference and the modification will be exposed to the users.

Each runtime handler expose two methods: translate() and extract(). In this class, translate or extract signals the direction of conversion. All method starts with “translate” handles the conversion from ADS runtime to OCI API payload. All method starts with “extract” handles the conversion from OCI data science Job to ADS runtime. This base class defines the default handling for translate() and extract(). Each sub-class can override the two methods to provide additional handling. Alternatively, a sub-class can also override a sub-method, which is called by the translate() or extract() method. For example, _translate_env() handles the conversion of environment variables from ADS runtime to OCI API payload.

See the individual methods for more details.

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

RUNTIME_CLASS

alias of Runtime

extract(dsc_job)[source]

Extract the runtime from an OCI data science job object. This method calls the following sub-methods: * _extract_tags() * _extract_args() * _extract_envs() * _extract_artifact() * _extract_runtime_minutes() Each of these method returns a dict for specifying the runtime. The dictionaries are combined before initalizing the runtime. A sub-class can modify one of more of these methods.

Parameters:

dsc_job (DSCJob or oci.datascience.models.Job) – The data science job containing runtime information.

Returns:

The runtime extracted from the data science job.

Return type:

Runtime

translate(runtime: Runtime) → dict[source]

Translates the runtime into a JSON payload for OCI API. This method calls the following sub-methods: * _translate_artifact() * _translate_config()

  • _translate_env()

A sub-class can modify one of more of these methods.

Parameters:

runtime (Runtime) – An instance of the runtime to be converted to a JSON payload.

Returns:

JSON payload for defining a Data Science Job with OCI API

Return type:

dict

class ads.jobs.builders.infrastructure.dsc_job_runtime.ScriptRuntimeHandler(data_science_job)[source]

Bases: CondaRuntimeHandler

Runtime Handler for ScriptRuntime

Initialize the runtime handler.

Parameters:

data_science_job (DataScienceJob) – An instance of the DataScienceJob to be created or extracted from.

CONST_ENTRYPOINT = 'JOB_RUN_ENTRYPOINT'
RUNTIME_CLASS

alias of ScriptRuntime

ads.jobs.builders.infrastructure.utils module

ads.jobs.builders.infrastructure.utils.get_value(obj, attr, default=None)[source]

Gets a copy of the value from a nested dictionary of an object with nested attributes.

Parameters:

  • obj – An object or a dictionary

  • attr – Attributes as a string seprated by dot(.)

  • default – Default value to be returned if attribute is not found.

Returns:

A copy of the attribute value. For dict or list, a deepcopy will be returned.

Return type:

Any

ads.jobs.builders.infrastructure.utils.normalize_config(config: dict) → dict[source]

Normalize property names in a configuration so that they work directly with ADS.

Parameters:

config (dict) – input configuration, usually coming directly from an OCI response

Returns:

output configuration

Return type:

dict

Module contents