ads.jobs package#

Subpackages#

Submodules#

ads.jobs.ads_job module#

class ads.jobs.ads_job.Job(name: str | None = None, infrastructure=None, runtime=None, **kwargs)[source]#

Bases: Builder

Represents a Job defined by infrastructure and runtime.

Examples

Here is an example for creating and running a job:

from ads.jobs import Job, DataScienceJob, PythonRuntime

# Define an OCI Data Science job to run a python script
job = (
    Job(name="<job_name>")
    .with_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)
    )
    .with_runtime(
        PythonRuntime()
        # Specify the service conda environment by slug name.
        .with_service_conda("pytorch110_p38_cpu_v1")
        # The job artifact can be a single Python script, a directory or a zip file.
        .with_source("local/path/to/code_dir")
        # Environment variable
        .with_environment_variable(NAME="Welcome to OCI Data Science.")
        # Command line argument, arg1 --key arg2
        .with_argument("arg1", key="arg2")
        # Set the working directory
        # When using a directory as source, the default working dir is the parent of code_dir.
        # Working dir should be a relative path beginning from the source directory (code_dir)
        .with_working_dir("code_dir")
        # The entrypoint is applicable only to directory or zip file as source
        # The entrypoint should be a path relative to the working dir.
        # Here my_script.py is a file in the code_dir/my_package directory
        .with_entrypoint("my_package/my_script.py")
        # Add an additional Python path, relative to the working dir (code_dir/other_packages).
        .with_python_path("other_packages")
        # Copy files in "code_dir/output" to object storage after job finishes.
        .with_output("output", "oci://bucket_name@namespace/path/to/dir")
        # Tags
        .with_freeform_tag(my_tag="my_value")
        .with_defined_tag(**{"Operations": {"CostCenter": "42"}})
    )
)
# Create and Run the job
run = job.create().run()
# Stream the job run outputs
run.watch()

If you are in an OCI notebook session and you would like to use the same infrastructure configurations, the infrastructure configuration can be simplified. Here is another example of creating and running a jupyter notebook as a job:

from ads.jobs import Job, DataScienceJob, NotebookRuntime

# Define an OCI Data Science job to run a jupyter Python notebook
job = (
    Job(name="<job_name>")
    .with_infrastructure(
        # The same configurations as the OCI notebook session will be used.
        DataScienceJob()
        .with_log_group_id("<log_group_ocid>")
        .with_log_id("<log_ocid>")
    )
    .with_runtime(
        NotebookRuntime()
        .with_notebook("path/to/notebook.ipynb")
        .with_service_conda(tensorflow28_p38_cpu_v1")
        # Saves the notebook with outputs to OCI object storage.
        .with_output("oci://bucket_name@namespace/path/to/dir")
    )
).create()
# Run and monitor the job
run = job.run().watch()
# Download the notebook and outputs to local directory
run.download(to_dir="path/to/local/dir/")

See also

https

//docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/user_guide/jobs/index.html

Initializes a job.

The infrastructure and runtime can be configured when initializing the job,

or by calling with_infrastructure() and with_runtime().

The infrastructure should be a subclass of ADS job Infrastructure, e.g., DataScienceJob, DataFlow. The runtime should be a subclass of ADS job Runtime, e.g., PythonRuntime, NotebookRuntime.

Parameters:
  • name (str, optional) – The name of the job, by default None. If it is None, a default name may be generated by the infrastructure, depending on the implementation of the infrastructure. For OCI data science job, the default name contains the job artifact name and a timestamp. If no artifact, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • infrastructure (Infrastructure, optional) – Job infrastructure, by default None

  • runtime (Runtime, optional) – Job runtime, by default None.

attribute_map = {}#
auth = {}#
build() Job[source]#

Load default values from the environment for the job infrastructure.

cancel(wait_for_completion: bool = True) None[source]#

Cancels the runs of the job.

Parameters:

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

create(**kwargs) Job[source]#

Creates the job on the infrastructure.

Returns:

The job instance (self)

Return type:

Job

static dataflow_job(compartment_id: str | None = None, **kwargs) List[Job][source]#

List data flow jobs under a given compartment.

Parameters:
  • compartment_id (str) – compartment id

  • kwargs – additional keyword arguments

Returns:

list of Job instances

Return type:

List[Job]

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

Lists the existing data science jobs in the compartment.

Parameters:

compartment_id (str) – The compartment ID for listing the jobs. This is optional if running in an OCI notebook session. The jobs in the same compartment of the notebook session will be returned.

Returns:

A list of Job objects.

Return type:

list

delete() None[source]#

Deletes the job from the infrastructure.

download(to_dir: str, output_uri=None, **storage_options)[source]#

Downloads files from remote output URI to local.

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

  • output_uri ((str, optional). Default is None.) – The remote URI from which the files will be downloaded. Defaults to None. If output_uri is not specified, this method will try to get the output_uri from the runtime.

  • storage_options – Extra keyword arguments for particular storage connection. This method uses fsspec to download the files from remote URI. storage_options will to be passed into fsspec.open_files().

Returns:

The job instance (self)

Return type:

Job

Raises:

AttributeError – The output_uri is not specified and the runtime is not configured with output_uri.

static from_dataflow_job(job_id: str) Job[source]#

Create a Data Flow job given a job id.

Parameters:

job_id (str) – id of the job

Returns:

a Job instance

Return type:

Job

classmethod from_datascience_job(job_id) Job[source]#

Loads a data science job from OCI.

Parameters:

job_id (str) – OCID of an existing data science job.

Returns:

A job instance.

Return type:

Job

classmethod from_dict(config: dict) Job[source]#

Initializes a job from a dictionary containing the configurations.

Parameters:

config (dict) – A dictionary containing the infrastructure and runtime specifications.

Returns:

A job instance

Return type:

Job

Raises:

NotImplementedError – If the type of the infrastructure or runtime is not supported.

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

property id: str#

The ID of the job. For jobs running on OCI, this is the OCID.

Returns:

ID of the job.

Return type:

str

property infrastructure: DataScienceJob | DataFlow#

The job infrastructure.

Returns:

Job infrastructure.

Return type:

Infrastructure

property kind: str#

The kind of the object as showing in YAML.

Returns:

“job”

Return type:

str

property name: str#

The name of the job. For jobs running on OCI, this is the display name.

Returns:

The name of the job.

Return type:

str

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

Runs the job.

Parameters:
  • name (str, optional) – Name of the job run, by default None. The infrastructure handles the naming of the job run. For data science job, if a name is not provided, a default name will be generated containing the job name and the timestamp of the run. If no artifact, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • args (str, optional) – Command line arguments for the job run, by default None. This will override the configurations on the job. If this is None, the args from the job configuration will be used.

  • env_var (dict, optional) – Additional environment variables 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 call should wait for the job run. By default False, this method returns as soon as the job run is created. If this is set to True, this method will stream the job logs and wait until it finishes, similar to job.run().watch().

  • kwargs – additional keyword arguments

Returns:

A job run instance, depending on the infrastructure.

Return type:

Job Run Instance

Examples

To run a job and override the configurations:

job_run = job.run(
    name="<my_job_run_name>",
    args="new_arg --new_key new_val",
    env_var={"new_env": "new_val"},
    freeform_tags={"new_tag": "new_tag_val"},
    defined_tags={"Operations": {"CostCenter": "42"}}
)
run_list(**kwargs) list[source]#

Gets a list of runs of the job.

Returns:

A list of job run instances, the actual object type depends on the infrastructure.

Return type:

list

property runtime: Runtime#

The job runtime.

Returns:

The job runtime

Return type:

Runtime

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

status() str[source]#

Status of the job

Returns:

Status of the job

Return type:

str

to_dict(**kwargs: Dict) Dict[source]#

Serialize the job specifications to a dictionary.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

Returns:

A dictionary containing job specifications.

Return type:

Dict

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML.

This implementation returns the class name with the first letter coverted to lower case.

with_infrastructure(infrastructure) Job[source]#

Sets the infrastructure for the job.

Parameters:

infrastructure (Infrastructure) – Job infrastructure.

Returns:

The job instance (self)

Return type:

Job

with_name(name: str) Job[source]#

Sets the job name.

Parameters:

name (str) – Job name.

Returns:

The job instance (self)

Return type:

Job

with_runtime(runtime) Job[source]#

Sets the runtime for the job.

Parameters:

runtime (Runtime) – Job runtime.

Returns:

The job instance (self)

Return type:

Job

ads.jobs.cli module#

ads.jobs.env_var_parser module#

class ads.jobs.env_var_parser.EnvVarInterpolation[source]#

Bases: ExtendedInterpolation

Modified version of ExtendedInterpolation to ignore errors

https://github.com/python/cpython/blob/main/Lib/configparser.py

before_get(parser, section, option, value, defaults)#
before_read(parser, section, option, value)#
before_set(parser, section: str, option: str, value: str) str[source]#
before_write(parser, section, option, value)#
ads.jobs.env_var_parser.escape(s: str) str[source]#
ads.jobs.env_var_parser.parse(env_var: Dict | List[dict]) dict[source]#

Parse the environment variables and perform substitutions. This will also converts kubernetes style environment variables from a list to a dictionary.

Parameters:

env_var (dict or list) –

Environment variables specified as a list or a dictionary. If evn_var is a list, it should be in the format of:

”[{“name”: “ENV_NAME_1”, “value”: “ENV_VALUE_1”}, {“name”: “ENV_NAME_2”, “value”: “ENV_VALUE_2”}]

Returns:

Environment variable as a dictionary.

Return type:

dict

ads.jobs.extension module#

ads.jobs.extension.dataflow(line, cell=None)[source]#
ads.jobs.extension.dataflow_log(options, args)[source]#
ads.jobs.extension.dataflow_run(options, args, cell)[source]#
ads.jobs.extension.load_ipython_extension(ipython)[source]#

ads.jobs.serializer module#

class ads.jobs.serializer.Serializable[source]#

Bases: ABC

Base class that represents a serializable item.

abstract classmethod from_dict(obj_dict: dict)[source]#

Initialize an instance of the class from a dictionary

Parameters:

obj_dict (dict) – Dictionary representation of the object

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self[source]#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self[source]#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self[source]#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

abstract to_dict(**kwargs: Dict) Dict[source]#

Serializes an instance of class into a dictionary.

Parameters:

**kwargs (Dict) – Additional arguments.

Returns:

The result dictionary.

Return type:

Dict

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str[source]#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None[source]#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

ads.jobs.utils module#

class ads.jobs.utils.DataFlowConfig(path: str | None = None, oci_profile: str | None = None)[source]#

Bases: Application

Create a DataFlowConfig object. If a path to config file is given it is loaded from the path.

Parameters:
  • path (str, optional) – path to configuration file, by default None

  • oci_profile (str, optional) – oci profile to use, by default None

LANGUAGE_JAVA = 'JAVA'#

A constant which can be used with the language property of a Application. This constant has a value of “JAVA”

LANGUAGE_PYTHON = 'PYTHON'#

A constant which can be used with the language property of a Application. This constant has a value of “PYTHON”

LANGUAGE_SCALA = 'SCALA'#

A constant which can be used with the language property of a Application. This constant has a value of “SCALA”

LANGUAGE_SQL = 'SQL'#

A constant which can be used with the language property of a Application. This constant has a value of “SQL”

LIFECYCLE_STATE_ACTIVE = 'ACTIVE'#

A constant which can be used with the lifecycle_state property of a Application. This constant has a value of “ACTIVE”

LIFECYCLE_STATE_DELETED = 'DELETED'#

A constant which can be used with the lifecycle_state property of a Application. This constant has a value of “DELETED”

LIFECYCLE_STATE_INACTIVE = 'INACTIVE'#

A constant which can be used with the lifecycle_state property of a Application. This constant has a value of “INACTIVE”

TYPE_BATCH = 'BATCH'#

A constant which can be used with the type property of a Application. This constant has a value of “BATCH”

TYPE_SESSION = 'SESSION'#

A constant which can be used with the type property of a Application. This constant has a value of “SESSION”

TYPE_STREAMING = 'STREAMING'#

A constant which can be used with the type property of a Application. This constant has a value of “STREAMING”

property application_log_config#

Gets the application_log_config of this Application.

Returns:

The application_log_config of this Application.

Return type:

oci.data_flow.models.ApplicationLogConfig

property archive_bucket#

//<bucket-name>@<namespace>/<prefix>.

Returns:

archive bucket (path)

Return type:

str

Type:

Bucket to save archive zip. Also accept a prefix in the format of oci

property archive_uri#

Gets the archive_uri of this Application. A comma separated list of one or more archive files as Oracle Cloud Infrastructure URIs. For example, oci://path/to/a.zip,oci://path/to/b.zip. An Oracle Cloud Infrastructure URI of an archive.zip file containing custom dependencies that may be used to support the execution of a Python, Java, or Scala application. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The archive_uri of this Application.

Return type:

str

property arguments#

Gets the arguments of this Application. The arguments passed to the running application as command line arguments. An argument is either a plain text or a placeholder. Placeholders are replaced using values from the parameters map. Each placeholder specified must be represented in the parameters map else the request (POST or PUT) will fail with a HTTP 400 status code. Placeholders are specified as Service Api Spec, where name is the name of the parameter. Example: [ “–input”, “${input_file}”, “–name”, “John Doe” ] If “input_file” has a value of “mydata.xml”, then the value above will be translated to –input mydata.xml –name “John Doe”

Returns:

The arguments of this Application.

Return type:

list[str]

property class_name#

Gets the class_name of this Application. The class for the application.

Returns:

The class_name of this Application.

Return type:

str

property compartment_id#

[Required] Gets the compartment_id of this Application. The OCID of a compartment.

Returns:

The compartment_id of this Application.

Return type:

str

property configuration#

Gets the configuration of this Application. The Spark configuration passed to the running process. See https://spark.apache.org/docs/latest/configuration.html#available-properties. Example: { “spark.app.name” : “My App Name”, “spark.shuffle.io.maxRetries” : “4” } Note: Not all Spark properties are permitted to be set. Attempting to set a property that is not allowed to be overwritten will cause a 400 status to be returned.

Returns:

The configuration of this Application.

Return type:

dict(str, str)

property defined_tags#

Gets the defined_tags of this Application. Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {“Operations”: {“CostCenter”: “42”}}

Returns:

The defined_tags of this Application.

Return type:

dict(str, dict(str, object))

property description#

Gets the description of this Application. A user-friendly description.

Returns:

The description of this Application.

Return type:

str

property display_name#

[Required] Gets the display_name of this Application. A user-friendly name. This name is not necessarily unique.

Returns:

The display_name of this Application.

Return type:

str

property driver_shape#

[Required] Gets the driver_shape of this Application. The VM shape for the driver. Sets the driver cores and memory.

Returns:

The driver_shape of this Application.

Return type:

str

property driver_shape_config#

Gets the driver_shape_config of this Application.

Returns:

The driver_shape_config of this Application.

Return type:

oci.data_flow.models.ShapeConfig

property execute#

Gets the execute of this Application. The input used for spark-submit command. For more details see https://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit. Supported options include --class, --file, --jars, --conf, --py-files, and main application file with arguments. Example: --jars oci://path/to/a.jar,oci://path/to/b.jar --files oci://path/to/a.json,oci://path/to/b.csv --py-files oci://path/to/a.py,oci://path/to/b.py --conf spark.sql.crossJoin.enabled=true --class org.apache.spark.examples.SparkPi oci://path/to/main.jar 10 Note: If execute is specified together with applicationId, className, configuration, fileUri, language, arguments, parameters during application create/update, or run create/submit, Data Flow service will use derived information from execute input only.

Returns:

The execute of this Application.

Return type:

str

property executor_shape#

[Required] Gets the executor_shape of this Application. The VM shape for the executors. Sets the executor cores and memory.

Returns:

The executor_shape of this Application.

Return type:

str

property executor_shape_config#

Gets the executor_shape_config of this Application.

Returns:

The executor_shape_config of this Application.

Return type:

oci.data_flow.models.ShapeConfig

property file_uri#

[Required] Gets the file_uri of this Application. An Oracle Cloud Infrastructure URI of the file containing the application to execute. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The file_uri of this Application.

Return type:

str

property freeform_tags#

Gets the freeform_tags of this Application. Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {“Department”: “Finance”}

Returns:

The freeform_tags of this Application.

Return type:

dict(str, str)

property id#

[Required] Gets the id of this Application. The application ID.

Returns:

The id of this Application.

Return type:

str

property idle_timeout_in_minutes#

Gets the idle_timeout_in_minutes of this Application. The timeout value in minutes used to manage Runs. A Run would be stopped after inactivity for this amount of time period. Note: This parameter is currently only applicable for Runs of type SESSION. Default value is 2880 minutes (2 days)

Returns:

The idle_timeout_in_minutes of this Application.

Return type:

int

property language#

[Required] Gets the language of this Application. The Spark language.

Allowed values for this property are: “SCALA”, “JAVA”, “PYTHON”, “SQL”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The language of this Application.

Return type:

str

property lifecycle_state#

[Required] Gets the lifecycle_state of this Application. The current state of this application.

Allowed values for this property are: “ACTIVE”, “DELETED”, “INACTIVE”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The lifecycle_state of this Application.

Return type:

str

property logs_bucket_uri#

Gets the logs_bucket_uri of this Application. An Oracle Cloud Infrastructure URI of the bucket where the Spark job logs are to be uploaded. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The logs_bucket_uri of this Application.

Return type:

str

property max_duration_in_minutes#

Gets the max_duration_in_minutes of this Application. The maximum duration in minutes for which an Application should run. Data Flow Run would be terminated once it reaches this duration from the time it transitions to IN_PROGRESS state.

Returns:

The max_duration_in_minutes of this Application.

Return type:

int

property metastore_id#

Gets the metastore_id of this Application. The OCID of OCI Hive Metastore.

Returns:

The metastore_id of this Application.

Return type:

str

property num_executors#

[Required] Gets the num_executors of this Application. The number of executor VMs requested.

Returns:

The num_executors of this Application.

Return type:

int

property owner_principal_id#

[Required] Gets the owner_principal_id of this Application. The OCID of the user who created the resource.

Returns:

The owner_principal_id of this Application.

Return type:

str

property owner_user_name#

Gets the owner_user_name of this Application. The username of the user who created the resource. If the username of the owner does not exist, null will be returned and the caller should refer to the ownerPrincipalId value instead.

Returns:

The owner_user_name of this Application.

Return type:

str

property parameters#

Gets the parameters of this Application. An array of name/value pairs used to fill placeholders found in properties like Application.arguments. The name must be a string of one or more word characters (a-z, A-Z, 0-9, _). The value can be a string of 0 or more characters of any kind. Example: [ { name: “iterations”, value: “10”}, { name: “input_file”, value: “mydata.xml” }, { name: “variable_x”, value: “${x}”} ]

Returns:

The parameters of this Application.

Return type:

list[oci.data_flow.models.ApplicationParameter]

property pool_id#

Gets the pool_id of this Application. The OCID of a pool. Unique Id to indentify a dataflow pool resource.

Returns:

The pool_id of this Application.

Return type:

str

property private_endpoint_id#

Gets the private_endpoint_id of this Application. The OCID of a private endpoint.

Returns:

The private_endpoint_id of this Application.

Return type:

str

property script_bucket#

//<bucket-name>@<namespace>/<prefix>.

Returns:

script bucket (path)

Return type:

str

Type:

Bucket to save user script. Also accept a prefix in the format of oci

property spark_version#

[Required] Gets the spark_version of this Application. The Spark version utilized to run the application.

Returns:

The spark_version of this Application.

Return type:

str

property time_created#

[Required] Gets the time_created of this Application. The date and time the resource was created, expressed in RFC 3339 timestamp format. Example: 2018-04-03T21:10:29.600Z

Returns:

The time_created of this Application.

Return type:

datetime

property time_updated#

[Required] Gets the time_updated of this Application. The date and time the resource was updated, expressed in RFC 3339 timestamp format. Example: 2018-04-03T21:10:29.600Z

Returns:

The time_updated of this Application.

Return type:

datetime

property type#

Gets the type of this Application. The Spark application processing type.

Allowed values for this property are: “BATCH”, “STREAMING”, “SESSION”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The type of this Application.

Return type:

str

property warehouse_bucket_uri#

Gets the warehouse_bucket_uri of this Application. An Oracle Cloud Infrastructure URI of the bucket to be used as default warehouse directory for BATCH SQL runs. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The warehouse_bucket_uri of this Application.

Return type:

str

ads.jobs.utils.get_dataflow_config(path=None, oci_profile=None)[source]#

Module contents#

class ads.jobs.ContainerRuntime(spec: Dict | None = None, **kwargs)[source]#

Bases: MultiNodeRuntime

Represents a container job runtime

To define container runtime:

>>> ContainerRuntime()
>>> .with_image("iad.ocir.io/<your_tenancy>/<your_image>")
>>> .with_cmd("sleep 5 && echo Hello World")
>>> .with_entrypoint(["/bin/sh", "-c"])
>>> .with_environment_variable(MY_ENV="MY_VALUE")

Alternatively, you can define the entrypoint and cmd along with the image.

>>> ContainerRuntime()
>>> .with_image(
>>>     "iad.ocir.io/<your_tenancy>/<your_image>",
>>>     entrypoint=["/bin/sh", "-c"],
>>>     cmd="sleep 5 && echo Hello World",
>>> )
>>> .with_environment_variable(MY_ENV="MY_VALUE")

The entrypoint and cmd can be either “exec form” or “shell form” (See references). The exec form is used when a list is passed in. The shell form is used when a space separated string is passed in.

When using the ContainerRuntime with OCI Data Science Job, the exec form is recommended. For most images, when the entrypoint is set to ["/bin/sh", "-c"], cmd can be a string as if you are running shell command.

References

https://docs.docker.com/engine/reference/builder/#entrypoint https://docs.docker.com/engine/reference/builder/#cmd

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_ARGS = 'args'#
CONST_CMD = 'cmd'#
CONST_DEFINED_TAGS = 'definedTags'#
CONST_ENTRYPOINT = 'entrypoint'#
CONST_ENV_VAR = 'env'#
CONST_FREEFORM_TAGS = 'freeformTags'#
CONST_IMAGE = 'image'#
CONST_MAXIMUM_RUNTIME_IN_MINUTES = 'maximumRuntimeInMinutes'#
CONST_REPLICA = 'replicas'#
property args: list#

Command line arguments

attribute_map = {'args': 'args', 'cmd': 'cmd', 'definedTags': 'defined_tags', 'entrypoint': 'entrypoint', 'env': 'env', 'freeformTags': 'freeform_tags', 'image': 'image'}#
build() Self#

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

property cmd: str#

Command of the container job

property defined_tags: dict#

Defined tags

property entrypoint: str#

Entrypoint of the container job

property environment_variables: dict#

Environment variables

Returns:

The runtime environment variables. The returned dictionary is a copy.

Return type:

dict

property envs: dict#

Environment variables

property freeform_tags: dict#

Freeform tags

classmethod from_dict(obj_dict: dict) Self#

Initialize the object from a Python dictionary

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

property image: str#

The container image

init(**kwargs) ContainerRuntime[source]#

Initializes a starter specification for the runtime.

Returns:

The runtime instance.

Return type:

ContainerRuntime

property kind: str#

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

property maximum_runtime_in_minutes: int#

Maximum runtime in minutes

property replica: int#

The number of nodes (job runs).

run(dsc_job, **kwargs)#

Starts the job runs

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

to_dict(**kwargs) dict#

Converts the object to dictionary with kind, type and spec as keys.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML

with_argument(*args, **kwargs) Self#

Adds command line arguments to the runtime.

This method can be called (chained) multiple times to add various arguments.

Parameters:
  • args – Positional arguments. In a single method call, positional arguments are always added before keyword arguments. You can call with_argument() to add positional arguments after keyword arguments.

  • kwargs – Keyword arguments. To add a keyword argument without value, set the value to None.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Raises:

ValueError – Keyword arguments with space in a key.

Examples

>>> runtime = Runtime().with_argument(key1="val1", key2="val2").with_argument("pos1")
>>> print(runtime.args)
["--key1", "val1", "--key2", "val2", "pos1"]
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1="val1", key2="val2.1 val2.2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
['pos1', '--key1', 'val1', '--key2', 'val2.1 val2.2', 'pos2']
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1=None, key2="val2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
["pos1", "--key1", "--key2", "val2", "pos2"]
with_cmd(cmd: str) ContainerRuntime[source]#

Specifies the command for the container job.

Parameters:

cmd (str) – Command for the container job

Returns:

The runtime instance.

Return type:

ContainerRuntime

with_defined_tag(**kwargs) Self#

Sets defined tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_entrypoint(entrypoint: str | list) ContainerRuntime[source]#

Specifies the entrypoint for the container job.

Parameters:

entrypoint (str or list) – Entrypoint for the container job

Returns:

The runtime instance.

Return type:

ContainerRuntime

with_environment_variable(**kwargs) Self#

Sets environment variables

Environment variables enclosed by ${...} will be substituted.

  • You can use $$ to escape the substitution.

  • Undefined variable enclosed by ${} will be ignored.

  • Double dollar signs $$ will be substituted by a single one $.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Examples

>>> runtime = (
...     PythonRuntime()
...     .with_environment_variable(
...         HOST="10.0.0.1",
...         PORT="443",
...         URL="http://${HOST}:${PORT}/path/",
...         ESCAPED_URL="http://$${HOST}:$${PORT}/path/",
...         MISSING_VAR="This is ${UNDEFINED}",
...         VAR_WITH_DOLLAR="$10",
...         DOUBLE_DOLLAR="$$10"
...     )
... )
>>> for k, v in runtime.environment_variables.items():
...     print(f"{k}: {v}")
HOST: 10.0.0.1
PORT: 443
URL: http://10.0.0.1:443/path/
ESCAPED_URL: http://${HOST}:${PORT}/path/
MISSING_VAR: This is ${UNDEFINED}
VAR_WITH_DOLLAR: $10
DOUBLE_DOLLAR: $10
with_freeform_tag(**kwargs) Self#

Sets freeform tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_image(image: str, entrypoint: str | list | None = None, cmd: str | None = None) ContainerRuntime[source]#

Specify the image for the container job.

Parameters:
  • image (str) – The container image, e.g. iad.ocir.io/<your_tenancy>/<your_image>:<your_tag>

  • entrypoint (str or list, optional) – Entrypoint for the job, by default None (the entrypoint defined in the image will be used).

  • cmd (str, optional) – Command for the job, by default None.

Returns:

The runtime instance.

Return type:

ContainerRuntime

with_maximum_runtime_in_minutes(maximum_runtime_in_minutes: int) Self#

Sets maximum runtime in minutes

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_replica(count: int)#

Specifies the number of nodes (job runs) for the job.

Parameters:

count (int) – Number of nodes (job runs)

Returns:

The runtime instance.

Return type:

self

class ads.jobs.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'}#
build() Self#

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

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

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

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

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(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]

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

to_dict(**kwargs) dict[source]#

Serialize job to a dictionary.

Returns:

serialized job as a dictionary

Return type:

dict

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(**kwargs) str[source]#

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

property type: str#

The type of the object as showing in YAML.

This implementation returns the class name with the first letter coverted to lower case.

update(runtime: Runtime)#

Updates a job.

Parameters:

runtime – a runtime object

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.DataFlowNotebookRuntime(spec: Dict | None = None, **kwargs)[source]#

Bases: DataFlowRuntime, NotebookRuntime

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_ARCHIVE_BUCKET = 'archiveBucket'#
CONST_ARCHIVE_URI = 'archiveUri'#
CONST_ARGS = 'args'#
CONST_CONDA = 'conda'#
CONST_CONDA_AUTH_TYPE = 'condaAuthType'#
CONST_CONDA_REGION = 'region'#
CONST_CONDA_SLUG = 'slug'#
CONST_CONDA_TYPE = 'type'#
CONST_CONDA_TYPE_CUSTOM = 'published'#
CONST_CONDA_TYPE_SERVICE = 'service'#
CONST_CONDA_URI = 'uri'#
CONST_CONFIGURATION = 'configuration'#
CONST_DEFINED_TAGS = 'definedTags'#
CONST_ENTRYPOINT = 'entrypoint'#
CONST_ENV_VAR = 'env'#
CONST_EXCLUDE_TAG = 'excludeTags'#
CONST_FREEFORM_TAGS = 'freeformTags'#
CONST_MAXIMUM_RUNTIME_IN_MINUTES = 'maximumRuntimeInMinutes'#
CONST_NOTEBOOK_ENCODING = 'notebookEncoding'#
CONST_NOTEBOOK_PATH = 'notebookPathURI'#
CONST_OUTPUT_URI = 'outputUri'#
CONST_OUTPUT_URI_ALT = 'outputURI'#
CONST_OVERWRITE = 'overwrite'#
CONST_SCRIPT_BUCKET = 'scriptBucket'#
CONST_SCRIPT_PATH = 'scriptPathURI'#
CONST_SOURCE = 'source'#
property archive_bucket: str#

Bucket to save archive zip

property archive_uri#

The Uri of archive zip

property args: list#

Command line arguments

attribute_map = {'archiveUri': 'archive_uri', 'args': 'args', 'condaAuthType': 'conda_auth_type', 'configuration': 'configuration', 'definedTags': 'defined_tags', 'env': 'env', 'freeformTags': 'freeform_tags', 'overwrite': 'overwrite', 'scriptBucket': 'script_bucket', 'scriptPathURI': 'script_path_uri'}#
build() Self#

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

property conda: dict#

The conda environment specification.

For service conda environment, the specification contains:

  • type, the type of the conda environment. This is always service for service conda environment.

  • slug, the slug of the conda environment.

For custom conda environment, the specification contains:

  • type, the type of the conda environment. This is always published for custom conda environment.

  • uri, the uri of the conda environment, e.g. oci://bucket@namespace/prefix/to/conda

  • region, the region of the bucket in which the conda environment is stored. By default, ADS will determine the region based on the authenticated API key or resource principal. This is only needed if your conda environment is stored in a different region.

Returns:

A dictionary containing the conda environment specifications.

Return type:

dict

property configuration: dict#

Configuration for Spark

convert(overwrite=False)[source]#
property defined_tags: dict#

Defined tags

property environment_variables: dict#

Environment variables

Returns:

The runtime environment variables. The returned dictionary is a copy.

Return type:

dict

property envs: dict#

Environment variables

property exclude_tag: list#

A list of cell tags indicating cells to be excluded from the job

property freeform_tags: dict#

Freeform tags

classmethod from_dict(obj_dict: dict) Self#

Initialize the object from a Python dictionary

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

init(**kwargs) DataFlowRuntime#

Initializes a starter specification for the runtime.

Returns:

The runtime instance.

Return type:

DataFlowRuntime

property kind: str#

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

property maximum_runtime_in_minutes: int#

Maximum runtime in minutes

property notebook: str#

The path of the notebook relative to the source.

property notebook_encoding: str#

The encoding of the notebook

property notebook_uri: str#

The URI of the notebook

property output_uri: list#

URI for storing the output notebook and files

property overwrite: str#

Whether to overwrite the existing script in object storage (script bucket).

property script_bucket: str#

Bucket to save script

property script_uri: str#

The URI of the source code

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

property source: str#

The source code location.

to_dict(**kwargs) dict#

Converts the object to dictionary with kind, type and spec as keys.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML

with_archive_bucket(bucket) DataFlowRuntime#

Set object storage bucket to save the archive zip, in case archive uri given is local.

Parameters:

bucket (str) – name of the bucket

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_archive_uri(uri: str) DataFlowRuntime#

Set archive uri (which is a zip file containing dependencies).

Parameters:

uri (str) – uri to the archive zip

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_argument(*args, **kwargs) Self#

Adds command line arguments to the runtime.

This method can be called (chained) multiple times to add various arguments.

Parameters:
  • args – Positional arguments. In a single method call, positional arguments are always added before keyword arguments. You can call with_argument() to add positional arguments after keyword arguments.

  • kwargs – Keyword arguments. To add a keyword argument without value, set the value to None.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Raises:

ValueError – Keyword arguments with space in a key.

Examples

>>> runtime = Runtime().with_argument(key1="val1", key2="val2").with_argument("pos1")
>>> print(runtime.args)
["--key1", "val1", "--key2", "val2", "pos1"]
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1="val1", key2="val2.1 val2.2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
['pos1', '--key1', 'val1', '--key2', 'val2.1 val2.2', 'pos2']
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1=None, key2="val2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
["pos1", "--key1", "--key2", "val2", "pos2"]
with_conda(conda_spec: dict | None = None)#
with_configuration(config: dict) DataFlowRuntime#

Set Configuration for Spark.

Parameters:

config (dict) – dictionary of configuration details https://spark.apache.org/docs/latest/configuration.html#available-properties. Example: { “spark.app.name” : “My App Name”, “spark.shuffle.io.maxRetries” : “4” }

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_custom_conda(uri: str, region: str | None = None, auth_type: str | None = None)#

Specifies the custom conda pack for running the job

Parameters:
  • uri (str) – The OCI object storage URI for the conda pack, e.g. “oci://your_bucket@namespace/object_name.” In the Environment Explorer of an OCI notebook session, this is shown as the “source” of the conda pack.

  • region (str, optional) – The region of the bucket storing the custom conda pack, by default None. If region is not specified, ADS will use the region from your authentication credentials, * For API Key, config[“region”] is used. * For Resource Principal, signer.region is used. This is required if the conda pack is stored in a different region.

  • auth_type (str, (="resource_principal")) – One of “resource_principal”, “api_keys”, “instance_principal”, etc. Auth mechanism used to read the conda back uri provided.

Returns:

The runtime instance.

Return type:

self

See also

https

//docs.oracle.com/en-us/iaas/data-science/using/conda_publishs_object.htm

with_defined_tag(**kwargs) Self#

Sets defined tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_environment_variable(**kwargs) Self#

Sets environment variables

Environment variables enclosed by ${...} will be substituted.

  • You can use $$ to escape the substitution.

  • Undefined variable enclosed by ${} will be ignored.

  • Double dollar signs $$ will be substituted by a single one $.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Examples

>>> runtime = (
...     PythonRuntime()
...     .with_environment_variable(
...         HOST="10.0.0.1",
...         PORT="443",
...         URL="http://${HOST}:${PORT}/path/",
...         ESCAPED_URL="http://$${HOST}:$${PORT}/path/",
...         MISSING_VAR="This is ${UNDEFINED}",
...         VAR_WITH_DOLLAR="$10",
...         DOUBLE_DOLLAR="$$10"
...     )
... )
>>> for k, v in runtime.environment_variables.items():
...     print(f"{k}: {v}")
HOST: 10.0.0.1
PORT: 443
URL: http://10.0.0.1:443/path/
ESCAPED_URL: http://${HOST}:${PORT}/path/
MISSING_VAR: This is ${UNDEFINED}
VAR_WITH_DOLLAR: $10
DOUBLE_DOLLAR: $10
with_exclude_tag(*tags) NotebookRuntime#

Specifies the cell tags in the notebook to exclude cells from the job script.

Parameters:

*tags (list) – A list of tags (strings).

Returns:

The runtime instance.

Return type:

self

with_freeform_tag(**kwargs) Self#

Sets freeform tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_maximum_runtime_in_minutes(maximum_runtime_in_minutes: int) Self#

Sets maximum runtime in minutes

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_notebook(path: str, encoding='utf-8') NotebookRuntime#

Specifies the notebook to be run as a job. Use this method if you would like to run a single notebook. Use with_source() method if you would like to run a notebook with additional dependency files.

Parameters:
  • path (str) – The path of the Jupyter notebook

  • encoding (str) – The encoding for opening the notebook. Defaults to utf-8.

Returns:

The runtime instance.

Return type:

self

with_output(output_uri: str) NotebookRuntime#

Specifies the output URI for storing the output notebook and files. All files in the directory containing the notebook will be saved.

Parameters:

output_uri (str) – URI for a directory storing the output notebook and files. For example, oci://bucket@namespace/path/to/dir

Returns:

The runtime instance.

Return type:

self

with_overwrite(overwrite: bool) DataFlowRuntime#

Whether to overwrite the existing script in object storage (script bucket). If the Object Storage bucket already contains a script with the same name, then it will be overwritten with the new one if the overwrite flag equal to True.

Parameters:

overwrite (bool) – Whether to overwrite the existing script in object storage (script bucket).

Returns:

The DataFlowRuntime instance (self).

Return type:

DataFlowRuntime

with_script_bucket(bucket) DataFlowRuntime#

Set object storage bucket to save the script, in case script uri given is local.

Parameters:

bucket (str) – name of the bucket

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_script_uri(path: str) DataFlowRuntime#

Set script uri.

Parameters:

path (str) – uri to the script

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_service_conda(slug: str)#

Specifies the service conda pack for running the job

Parameters:

slug (str) – The slug name of the service conda pack

Returns:

The runtime instance.

Return type:

self

with_source(uri: str, notebook: str, encoding='utf-8')#

Specify source code directory containing the notebook and dependencies for the job. Use this method if you would like to run a notebook with additional dependency files. Use the with_notebook() method if you would like to run a single notebook.

In the following example, local folder “path/to/source” contains the notebook and dependencies, The local path of the notebook is “path/to/source/relative/path/to/notebook.ipynb”:

runtime.with_source(uri="path/to/source", notebook="relative/path/to/notebook.ipynb")
Parameters:
  • uri (str) – URI of the source code directory. This can be local or on OCI object storage.

  • notebook (str) – The relative path of the notebook from the source URI.

  • encoding (str) – The encoding for opening the notebook. Defaults to utf-8.

Returns:

The runtime instance.

Return type:

Self

class ads.jobs.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.

CONS_COMPARTMENT_ID = 'compartment_id'#
LANGUAGE_JAVA = 'JAVA'#

A constant which can be used with the language property of a Run. This constant has a value of “JAVA”

LANGUAGE_PYTHON = 'PYTHON'#

A constant which can be used with the language property of a Run. This constant has a value of “PYTHON”

LANGUAGE_SCALA = 'SCALA'#

A constant which can be used with the language property of a Run. This constant has a value of “SCALA”

LANGUAGE_SQL = 'SQL'#

A constant which can be used with the language property of a Run. This constant has a value of “SQL”

LIFECYCLE_STATE_ACCEPTED = 'ACCEPTED'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “ACCEPTED”

LIFECYCLE_STATE_CANCELED = 'CANCELED'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “CANCELED”

LIFECYCLE_STATE_CANCELING = 'CANCELING'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “CANCELING”

LIFECYCLE_STATE_FAILED = 'FAILED'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “FAILED”

LIFECYCLE_STATE_IN_PROGRESS = 'IN_PROGRESS'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “IN_PROGRESS”

LIFECYCLE_STATE_STOPPED = 'STOPPED'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “STOPPED”

LIFECYCLE_STATE_STOPPING = 'STOPPING'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “STOPPING”

LIFECYCLE_STATE_SUCCEEDED = 'SUCCEEDED'#

A constant which can be used with the lifecycle_state property of a Run. This constant has a value of “SUCCEEDED”

OCI_MODEL_PATTERN = '(oci|feature_store_client).[^.]+\\.models[\\..*]?'#
TERMINATED_STATES = ['CANCELED', 'FAILED', 'SUCCEEDED']#
TYPE_BATCH = 'BATCH'#

A constant which can be used with the type property of a Run. This constant has a value of “BATCH”

TYPE_SESSION = 'SESSION'#

A constant which can be used with the type property of a Run. This constant has a value of “SESSION”

TYPE_STREAMING = 'STREAMING'#

A constant which can be used with the type property of a Run. This constant has a value of “STREAMING”

property application_id#

[Required] Gets the application_id of this Run. The application ID.

Returns:

The application_id of this Run.

Return type:

str

property application_log_config#

Gets the application_log_config of this Run.

Returns:

The application_log_config of this Run.

Return type:

oci.data_flow.models.ApplicationLogConfig

property archive_uri#

Gets the archive_uri of this Run. A comma separated list of one or more archive files as Oracle Cloud Infrastructure URIs. For example, oci://path/to/a.zip,oci://path/to/b.zip. An Oracle Cloud Infrastructure URI of an archive.zip file containing custom dependencies that may be used to support the execution of a Python, Java, or Scala application. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The archive_uri of this Run.

Return type:

str

property arguments#

Gets the arguments of this Run. The arguments passed to the running application as command line arguments. An argument is either a plain text or a placeholder. Placeholders are replaced using values from the parameters map. Each placeholder specified must be represented in the parameters map else the request (POST or PUT) will fail with a HTTP 400 status code. Placeholders are specified as Service Api Spec, where name is the name of the parameter. Example: [ “–input”, “${input_file}”, “–name”, “John Doe” ] If “input_file” has a value of “mydata.xml”, then the value above will be translated to –input mydata.xml –name “John Doe”

Returns:

The arguments of this Run.

Return type:

list[str]

property auth: dict#

The ADS authentication config used to initialize the client. This auth has the same format as those obtained by calling functions in ads.common.auth. The config is a dict containing the following key-value pairs: config: The config contains the config loaded from the configuration loaded from oci_config. signer: The signer contains the signer object created from the api keys. client_kwargs: client_kwargs contains the client_kwargs that was passed in as input parameter.

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

static check_compartment_id(compartment_id: str | None) str#
Checks if a compartment ID has value and

return the value from NB_SESSION_COMPARTMENT_OCID environment variable if it is not specified.

Parameters:

compartment_id (str) – Compartment OCID or None

Returns:

str: Compartment OCID

Return type:

type

Raises:

ValueError – compartment_id is not specified and NB_SESSION_COMPARTMENT_OCID environment variable is not set

property class_name#

Gets the class_name of this Run. The class for the application.

Returns:

The class_name of this Run.

Return type:

str

property client: DataFlowClient#

OCI client

property compartment_id#

[Required] Gets the compartment_id of this Run. The OCID of a compartment.

Returns:

The compartment_id of this Run.

Return type:

str

config = None#
property configuration#

Gets the configuration of this Run. The Spark configuration passed to the running process. See https://spark.apache.org/docs/latest/configuration.html#available-properties. Example: { “spark.app.name” : “My App Name”, “spark.shuffle.io.maxRetries” : “4” } Note: Not all Spark properties are permitted to be set. Attempting to set a property that is not allowed to be overwritten will cause a 400 status to be returned.

Returns:

The configuration of this Run.

Return type:

dict(str, str)

create() DataFlowRun[source]#

Create a Data Flow run.

Returns:

a DataFlowRun instance

Return type:

DataFlowRun

classmethod create_instance(*args, **kwargs)#

Creates an instance using the same authentication as the class or an existing instance. If this method is called by a class, the default ADS authentication method will be used. If this method is called by an instance, the authentication method set in the instance will be used.

property data_read_in_bytes#

Gets the data_read_in_bytes of this Run. The data read by the run in bytes.

Returns:

The data_read_in_bytes of this Run.

Return type:

int

property data_written_in_bytes#

Gets the data_written_in_bytes of this Run. The data written by the run in bytes.

Returns:

The data_written_in_bytes of this Run.

Return type:

int

property defined_tags#

Gets the defined_tags of this Run. Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {“Operations”: {“CostCenter”: “42”}}

Returns:

The defined_tags of this Run.

Return type:

dict(str, dict(str, object))

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 deserialize(data: dict, to_cls: str | None = None)#

Deserialize data

Parameters:
  • data (dict) – A dictionary containing the data to be deserialized.

  • to_cls (str) – The name of the OCI model class to be initialized using the data. The OCI model class must be from the same OCI service of the OCI client (self.client). Defaults to None, the parent OCI model class name will be used if current class is inherited from an OCI model. If parent OCI model class is not found or not from the same OCI service, the data will be returned as is.

property display_name#

Gets the display_name of this Run. A user-friendly name. This name is not necessarily unique.

Returns:

The display_name of this Run.

Return type:

str

property driver_shape#

[Required] Gets the driver_shape of this Run. The VM shape for the driver. Sets the driver cores and memory.

Returns:

The driver_shape of this Run.

Return type:

str

property driver_shape_config#

Gets the driver_shape_config of this Run.

Returns:

The driver_shape_config of this Run.

Return type:

oci.data_flow.models.ShapeConfig

property execute#

Gets the execute of this Run. The input used for spark-submit command. For more details see https://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit. Supported options include --class, --file, --jars, --conf, --py-files, and main application file with arguments. Example: --jars oci://path/to/a.jar,oci://path/to/b.jar --files oci://path/to/a.json,oci://path/to/b.csv --py-files oci://path/to/a.py,oci://path/to/b.py --conf spark.sql.crossJoin.enabled=true --class org.apache.spark.examples.SparkPi oci://path/to/main.jar 10 Note: If execute is specified together with applicationId, className, configuration, fileUri, language, arguments, parameters during application create/update, or run create/submit, Data Flow service will use derived information from execute input only.

Returns:

The execute of this Run.

Return type:

str

property executor_shape#

[Required] Gets the executor_shape of this Run. The VM shape for the executors. Sets the executor cores and memory.

Returns:

The executor_shape of this Run.

Return type:

str

property executor_shape_config#

Gets the executor_shape_config of this Run.

Returns:

The executor_shape_config of this Run.

Return type:

oci.data_flow.models.ShapeConfig

property file_uri#

[Required] Gets the file_uri of this Run. An Oracle Cloud Infrastructure URI of the file containing the application to execute. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The file_uri of this Run.

Return type:

str

static flatten(data: dict) dict#

Flattens a nested dictionary.

Parameters:

data (A nested dictionary) –

Returns:

The flattened dictionary.

Return type:

dict

property freeform_tags#

Gets the freeform_tags of this Run. Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {“Department”: “Finance”}

Returns:

The freeform_tags of this Run.

Return type:

dict(str, str)

classmethod from_dict(data)#

Initialize an instance from a dictionary.

Parameters:

data (dict) – A dictionary containing the properties to initialize the class.

classmethod from_oci_model(oci_instance)#

Initialize an instance from an instance of OCI model.

Parameters:

oci_instance – An instance of an OCI model.

classmethod from_ocid(ocid: str)#

Initializes an object from OCID

Parameters:

ocid (str) – The OCID of the object

property id#

[Required] Gets the id of this Run. The ID of a run.

Returns:

The id of this Run.

Return type:

str

property idle_timeout_in_minutes#

Gets the idle_timeout_in_minutes of this Run. The timeout value in minutes used to manage Runs. A Run would be stopped after inactivity for this amount of time period. Note: This parameter is currently only applicable for Runs of type SESSION. Default value is 2880 minutes (2 days)

Returns:

The idle_timeout_in_minutes of this Run.

Return type:

int

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.

kwargs = None#
property language#

[Required] Gets the language of this Run. The Spark language.

Allowed values for this property are: “SCALA”, “JAVA”, “PYTHON”, “SQL”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The language of this Run.

Return type:

str

property lifecycle_details#

Gets the lifecycle_details of this Run. The detailed messages about the lifecycle state.

Returns:

The lifecycle_details of this Run.

Return type:

str

property lifecycle_state#

[Required] Gets the lifecycle_state of this Run. The current state of this run.

Allowed values for this property are: “ACCEPTED”, “IN_PROGRESS”, “CANCELING”, “CANCELED”, “FAILED”, “SUCCEEDED”, “STOPPING”, “STOPPED”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The lifecycle_state of this Run.

Return type:

str

classmethod list_resource(compartment_id: str | None = None, limit: int = 0, **kwargs) list#

Generic method to list OCI resources

Parameters:
  • compartment_id (str) – Compartment ID of the OCI resources. Defaults to None. If compartment_id is not specified, the value of NB_SESSION_COMPARTMENT_OCID in environment variable will be used.

  • limit (int) – The maximum number of items to return. Defaults to 0, All items will be returned

  • **kwargs – Additional keyword arguments to filter the resource. The kwargs are passed into OCI API.

Returns:

A list of OCI resources

Return type:

list

Raises:

NotImplementedError – List method is not supported or implemented.

load_properties_from_env()#

Loads properties from the environment

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 logs_bucket_uri#

Gets the logs_bucket_uri of this Run. An Oracle Cloud Infrastructure URI of the bucket where the Spark job logs are to be uploaded. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The logs_bucket_uri of this Run.

Return type:

str

property max_duration_in_minutes#

Gets the max_duration_in_minutes of this Run. The maximum duration in minutes for which an Application should run. Data Flow Run would be terminated once it reaches this duration from the time it transitions to IN_PROGRESS state.

Returns:

The max_duration_in_minutes of this Run.

Return type:

int

property metastore_id#

Gets the metastore_id of this Run. The OCID of OCI Hive Metastore.

Returns:

The metastore_id of this Run.

Return type:

str

property name: str#

Gets the name of the object.

property num_executors#

[Required] Gets the num_executors of this Run. The number of executor VMs requested.

Returns:

The num_executors of this Run.

Return type:

int

property opc_request_id#

Gets the opc_request_id of this Run. Unique Oracle assigned identifier for the request. If you need to contact Oracle about a particular request, please provide the request ID.

Returns:

The opc_request_id of this Run.

Return type:

str

property owner_principal_id#

Gets the owner_principal_id of this Run. The OCID of the user who created the resource.

Returns:

The owner_principal_id of this Run.

Return type:

str

property owner_user_name#

Gets the owner_user_name of this Run. The username of the user who created the resource. If the username of the owner does not exist, null will be returned and the caller should refer to the ownerPrincipalId value instead.

Returns:

The owner_user_name of this Run.

Return type:

str

property parameters#

Gets the parameters of this Run. An array of name/value pairs used to fill placeholders found in properties like Application.arguments. The name must be a string of one or more word characters (a-z, A-Z, 0-9, _). The value can be a string of 0 or more characters of any kind. Example: [ { name: “iterations”, value: “10”}, { name: “input_file”, value: “mydata.xml” }, { name: “variable_x”, value: “${x}”} ]

Returns:

The parameters of this Run.

Return type:

list[oci.data_flow.models.ApplicationParameter]

property pool_id#

Gets the pool_id of this Run. The OCID of a pool. Unique Id to indentify a dataflow pool resource.

Returns:

The pool_id of this Run.

Return type:

str

property private_endpoint_dns_zones#

Gets the private_endpoint_dns_zones of this Run. An array of DNS zone names. Example: [ “app.examplecorp.com”, “app.examplecorp2.com” ]

Returns:

The private_endpoint_dns_zones of this Run.

Return type:

list[str]

property private_endpoint_id#

Gets the private_endpoint_id of this Run. The OCID of a private endpoint.

Returns:

The private_endpoint_id of this Run.

Return type:

str

property private_endpoint_max_host_count#

Gets the private_endpoint_max_host_count of this Run. The maximum number of hosts to be accessed through the private endpoint. This value is used to calculate the relevant CIDR block and should be a multiple of 256. If the value is not a multiple of 256, it is rounded up to the next multiple of 256. For example, 300 is rounded up to 512.

Returns:

The private_endpoint_max_host_count of this Run.

Return type:

int

property private_endpoint_nsg_ids#

Gets the private_endpoint_nsg_ids of this Run. An array of network security group OCIDs.

Returns:

The private_endpoint_nsg_ids of this Run.

Return type:

list[str]

property private_endpoint_subnet_id#

Gets the private_endpoint_subnet_id of this Run. The OCID of a subnet.

Returns:

The private_endpoint_subnet_id of this Run.

Return type:

str

Link to run details page in OCI console

Returns:

The link to the details page in OCI console.

Return type:

str

property run_duration_in_milliseconds#

Gets the run_duration_in_milliseconds of this Run. The duration of the run in milliseconds.

Returns:

The run_duration_in_milliseconds of this Run.

Return type:

int

serialize()#

Serialize the model to a dictionary that is ready to be send to OCI API.

Returns:

A dictionary that is ready to be send to OCI API.

Return type:

dict

signer = None#
property spark_version#

[Required] Gets the spark_version of this Run. The Spark version utilized to run the application.

Returns:

The spark_version of this Run.

Return type:

str

property status: str#

Show status (lifecycle state) of a run.

Returns:

status of the run

Return type:

str

sync(merge_strategy: MergeStrategy = MergeStrategy.OVERRIDE)#

Refreshes the properties of the object from OCI

property time_created#

[Required] Gets the time_created of this Run. The date and time the resource was created, expressed in RFC 3339 timestamp format. Example: 2018-04-03T21:10:29.600Z

Returns:

The time_created of this Run.

Return type:

datetime

property time_updated#

[Required] Gets the time_updated of this Run. The date and time the resource was updated, expressed in RFC 3339 timestamp format. Example: 2018-04-03T21:10:29.600Z

Returns:

The time_updated of this Run.

Return type:

datetime

to_dict(flatten: bool = False) dict#

Converts the properties to a dictionary

Parameters:

flatten – (Default value = False)

to_oci_model(oci_model)#

Converts the object into an instance of OCI data model.

Parameters:
  • oci_model (class or str) – The OCI model to be converted to. This can be a string of the model name.

  • type_mapping (dict) – A dictionary mapping the models. Returns: An instance of the oci_model

to_yaml() str[source]#

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

property total_o_cpu#

Gets the total_o_cpu of this Run. The total number of oCPU requested by the run.

Returns:

The total_o_cpu of this Run.

Return type:

int

property type#

Gets the type of this Run. The Spark application processing type.

Allowed values for this property are: “BATCH”, “STREAMING”, “SESSION”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The type of this Run.

Return type:

str

type_mappings = None#
update_from_oci_model(oci_model_instance, merge_strategy: MergeStrategy = MergeStrategy.OVERRIDE)#

Updates the properties from OCI model with the same properties.

Parameters:

oci_model_instance – An instance of OCI model, which should have the same properties of this class.

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

property warehouse_bucket_uri#

Gets the warehouse_bucket_uri of this Run. An Oracle Cloud Infrastructure URI of the bucket to be used as default warehouse directory for BATCH SQL runs. See https://docs.cloud.oracle.com/iaas/Content/API/SDKDocs/hdfsconnector.htm#uriformat.

Returns:

The warehouse_bucket_uri of this Run.

Return type:

str

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

class ads.jobs.DataFlowRuntime(spec: Dict | None = None, **kwargs)[source]#

Bases: CondaRuntime

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_ARCHIVE_BUCKET = 'archiveBucket'#
CONST_ARCHIVE_URI = 'archiveUri'#
CONST_ARGS = 'args'#
CONST_CONDA = 'conda'#
CONST_CONDA_AUTH_TYPE = 'condaAuthType'#
CONST_CONDA_REGION = 'region'#
CONST_CONDA_SLUG = 'slug'#
CONST_CONDA_TYPE = 'type'#
CONST_CONDA_TYPE_CUSTOM = 'published'#
CONST_CONDA_TYPE_SERVICE = 'service'#
CONST_CONDA_URI = 'uri'#
CONST_CONFIGURATION = 'configuration'#
CONST_DEFINED_TAGS = 'definedTags'#
CONST_ENV_VAR = 'env'#
CONST_FREEFORM_TAGS = 'freeformTags'#
CONST_MAXIMUM_RUNTIME_IN_MINUTES = 'maximumRuntimeInMinutes'#
CONST_OVERWRITE = 'overwrite'#
CONST_SCRIPT_BUCKET = 'scriptBucket'#
CONST_SCRIPT_PATH = 'scriptPathURI'#
property archive_bucket: str#

Bucket to save archive zip

property archive_uri#

The Uri of archive zip

property args: list#

Command line arguments

attribute_map = {'archiveUri': 'archive_uri', 'args': 'args', 'condaAuthType': 'conda_auth_type', 'configuration': 'configuration', 'definedTags': 'defined_tags', 'env': 'env', 'freeformTags': 'freeform_tags', 'overwrite': 'overwrite', 'scriptBucket': 'script_bucket', 'scriptPathURI': 'script_path_uri'}#
build() Self#

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

property conda: dict#

The conda environment specification.

For service conda environment, the specification contains:

  • type, the type of the conda environment. This is always service for service conda environment.

  • slug, the slug of the conda environment.

For custom conda environment, the specification contains:

  • type, the type of the conda environment. This is always published for custom conda environment.

  • uri, the uri of the conda environment, e.g. oci://bucket@namespace/prefix/to/conda

  • region, the region of the bucket in which the conda environment is stored. By default, ADS will determine the region based on the authenticated API key or resource principal. This is only needed if your conda environment is stored in a different region.

Returns:

A dictionary containing the conda environment specifications.

Return type:

dict

property configuration: dict#

Configuration for Spark

convert(**kwargs)[source]#
property defined_tags: dict#

Defined tags

property environment_variables: dict#

Environment variables

Returns:

The runtime environment variables. The returned dictionary is a copy.

Return type:

dict

property envs: dict#

Environment variables

property freeform_tags: dict#

Freeform tags

classmethod from_dict(obj_dict: dict) Self#

Initialize the object from a Python dictionary

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

init(**kwargs) DataFlowRuntime[source]#

Initializes a starter specification for the runtime.

Returns:

The runtime instance.

Return type:

DataFlowRuntime

property kind: str#

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

property maximum_runtime_in_minutes: int#

Maximum runtime in minutes

property overwrite: str#

Whether to overwrite the existing script in object storage (script bucket).

property script_bucket: str#

Bucket to save script

property script_uri: str#

The URI of the source code

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

to_dict(**kwargs) dict#

Converts the object to dictionary with kind, type and spec as keys.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML

with_archive_bucket(bucket) DataFlowRuntime[source]#

Set object storage bucket to save the archive zip, in case archive uri given is local.

Parameters:

bucket (str) – name of the bucket

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_archive_uri(uri: str) DataFlowRuntime[source]#

Set archive uri (which is a zip file containing dependencies).

Parameters:

uri (str) – uri to the archive zip

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_argument(*args, **kwargs) Self#

Adds command line arguments to the runtime.

This method can be called (chained) multiple times to add various arguments.

Parameters:
  • args – Positional arguments. In a single method call, positional arguments are always added before keyword arguments. You can call with_argument() to add positional arguments after keyword arguments.

  • kwargs – Keyword arguments. To add a keyword argument without value, set the value to None.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Raises:

ValueError – Keyword arguments with space in a key.

Examples

>>> runtime = Runtime().with_argument(key1="val1", key2="val2").with_argument("pos1")
>>> print(runtime.args)
["--key1", "val1", "--key2", "val2", "pos1"]
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1="val1", key2="val2.1 val2.2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
['pos1', '--key1', 'val1', '--key2', 'val2.1 val2.2', 'pos2']
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1=None, key2="val2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
["pos1", "--key1", "--key2", "val2", "pos2"]
with_conda(conda_spec: dict | None = None)[source]#
with_configuration(config: dict) DataFlowRuntime[source]#

Set Configuration for Spark.

Parameters:

config (dict) – dictionary of configuration details https://spark.apache.org/docs/latest/configuration.html#available-properties. Example: { “spark.app.name” : “My App Name”, “spark.shuffle.io.maxRetries” : “4” }

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_custom_conda(uri: str, region: str | None = None, auth_type: str | None = None)[source]#

Specifies the custom conda pack for running the job

Parameters:
  • uri (str) – The OCI object storage URI for the conda pack, e.g. “oci://your_bucket@namespace/object_name.” In the Environment Explorer of an OCI notebook session, this is shown as the “source” of the conda pack.

  • region (str, optional) – The region of the bucket storing the custom conda pack, by default None. If region is not specified, ADS will use the region from your authentication credentials, * For API Key, config[“region”] is used. * For Resource Principal, signer.region is used. This is required if the conda pack is stored in a different region.

  • auth_type (str, (="resource_principal")) – One of “resource_principal”, “api_keys”, “instance_principal”, etc. Auth mechanism used to read the conda back uri provided.

Returns:

The runtime instance.

Return type:

self

See also

https

//docs.oracle.com/en-us/iaas/data-science/using/conda_publishs_object.htm

with_defined_tag(**kwargs) Self#

Sets defined tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_environment_variable(**kwargs) Self#

Sets environment variables

Environment variables enclosed by ${...} will be substituted.

  • You can use $$ to escape the substitution.

  • Undefined variable enclosed by ${} will be ignored.

  • Double dollar signs $$ will be substituted by a single one $.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Examples

>>> runtime = (
...     PythonRuntime()
...     .with_environment_variable(
...         HOST="10.0.0.1",
...         PORT="443",
...         URL="http://${HOST}:${PORT}/path/",
...         ESCAPED_URL="http://$${HOST}:$${PORT}/path/",
...         MISSING_VAR="This is ${UNDEFINED}",
...         VAR_WITH_DOLLAR="$10",
...         DOUBLE_DOLLAR="$$10"
...     )
... )
>>> for k, v in runtime.environment_variables.items():
...     print(f"{k}: {v}")
HOST: 10.0.0.1
PORT: 443
URL: http://10.0.0.1:443/path/
ESCAPED_URL: http://${HOST}:${PORT}/path/
MISSING_VAR: This is ${UNDEFINED}
VAR_WITH_DOLLAR: $10
DOUBLE_DOLLAR: $10
with_freeform_tag(**kwargs) Self#

Sets freeform tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_maximum_runtime_in_minutes(maximum_runtime_in_minutes: int) Self#

Sets maximum runtime in minutes

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_overwrite(overwrite: bool) DataFlowRuntime[source]#

Whether to overwrite the existing script in object storage (script bucket). If the Object Storage bucket already contains a script with the same name, then it will be overwritten with the new one if the overwrite flag equal to True.

Parameters:

overwrite (bool) – Whether to overwrite the existing script in object storage (script bucket).

Returns:

The DataFlowRuntime instance (self).

Return type:

DataFlowRuntime

with_script_bucket(bucket) DataFlowRuntime[source]#

Set object storage bucket to save the script, in case script uri given is local.

Parameters:

bucket (str) – name of the bucket

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_script_uri(path: str) DataFlowRuntime[source]#

Set script uri.

Parameters:

path (str) – uri to the script

Returns:

runtime instance itself

Return type:

DataFlowRuntime

with_service_conda(slug: str)[source]#

Specifies the service conda pack for running the job

Parameters:

slug (str) – The slug name of the service conda pack

Returns:

The runtime instance.

Return type:

self

class ads.jobs.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) Self#

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

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

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

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(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]

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

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

to_dict(**kwargs) dict#

Converts the object to dictionary with kind, type and spec as keys.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML.

This implementation returns the class name with the first letter coverted to lower case.

update(runtime: Runtime)#

Updates a job.

Parameters:

runtime – a runtime object

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.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.

CONS_COMPARTMENT_ID = 'compartment_id'#
LIFECYCLE_STATE_ACCEPTED = 'ACCEPTED'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “ACCEPTED”

LIFECYCLE_STATE_CANCELED = 'CANCELED'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “CANCELED”

LIFECYCLE_STATE_CANCELING = 'CANCELING'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “CANCELING”

LIFECYCLE_STATE_DELETED = 'DELETED'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “DELETED”

LIFECYCLE_STATE_FAILED = 'FAILED'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “FAILED”

LIFECYCLE_STATE_IN_PROGRESS = 'IN_PROGRESS'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “IN_PROGRESS”

LIFECYCLE_STATE_NEEDS_ATTENTION = 'NEEDS_ATTENTION'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “NEEDS_ATTENTION”

LIFECYCLE_STATE_SUCCEEDED = 'SUCCEEDED'#

A constant which can be used with the lifecycle_state property of a JobRun. This constant has a value of “SUCCEEDED”

OCI_MODEL_PATTERN = '(oci|feature_store_client).[^.]+\\.models[\\..*]?'#
TERMINAL_STATES = ['SUCCEEDED', 'FAILED', 'CANCELED', 'DELETED']#
property auth: dict#

The ADS authentication config used to initialize the client. This auth has the same format as those obtained by calling functions in ads.common.auth. The config is a dict containing the following key-value pairs: config: The config contains the config loaded from the configuration loaded from oci_config. signer: The signer contains the signer object created from the api keys. client_kwargs: client_kwargs contains the client_kwargs that was passed in as input parameter.

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

static check_compartment_id(compartment_id: str | None) str#
Checks if a compartment ID has value and

return the value from NB_SESSION_COMPARTMENT_OCID environment variable if it is not specified.

Parameters:

compartment_id (str) – Compartment OCID or None

Returns:

str: Compartment OCID

Return type:

type

Raises:

ValueError – compartment_id is not specified and NB_SESSION_COMPARTMENT_OCID environment variable is not set

property client: DataScienceClient#

OCI client

property client_composite: DataScienceClientCompositeOperations#
property compartment_id#

[Required] Gets the compartment_id of this JobRun. The OCID of the compartment where you want to create the job.

Returns:

The compartment_id of this JobRun.

Return type:

str

config = None#
create() DataScienceJobRun[source]#

Creates a job run

classmethod create_instance(*args, **kwargs)#

Creates an instance using the same authentication as the class or an existing instance. If this method is called by a class, the default ADS authentication method will be used. If this method is called by an instance, the authentication method set in the instance will be used.

property created_by#

[Required] Gets the created_by of this JobRun. The OCID of the user who created the job run.

Returns:

The created_by of this JobRun.

Return type:

str

property defined_tags#

Gets the defined_tags of this JobRun. Defined tags for this resource. Each key is predefined and scoped to a namespace. See Resource Tags. Example: {“Operations”: {“CostCenter”: “42”}}

Returns:

The defined_tags of this JobRun.

Return type:

dict(str, dict(str, object))

delete()#

Deletes the resource

classmethod deserialize(data: dict, to_cls: str | None = None)#

Deserialize data

Parameters:
  • data (dict) – A dictionary containing the data to be deserialized.

  • to_cls (str) – The name of the OCI model class to be initialized using the data. The OCI model class must be from the same OCI service of the OCI client (self.client). Defaults to None, the parent OCI model class name will be used if current class is inherited from an OCI model. If parent OCI model class is not found or not from the same OCI service, the data will be returned as is.

property display_name#

Gets the display_name of this JobRun. A user-friendly display name for the resource.

Returns:

The display_name of this JobRun.

Return type:

str

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

static flatten(data: dict) dict#

Flattens a nested dictionary.

Parameters:

data (A nested dictionary) –

Returns:

The flattened dictionary.

Return type:

dict

property freeform_tags#

Gets the freeform_tags of this JobRun. Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. See Resource Tags. Example: {“Department”: “Finance”}

Returns:

The freeform_tags of this JobRun.

Return type:

dict(str, str)

classmethod from_dict(data)#

Initialize an instance from a dictionary.

Parameters:

data (dict) – A dictionary containing the properties to initialize the class.

classmethod from_oci_model(oci_instance)#

Initialize an instance from an instance of OCI model.

Parameters:

oci_instance – An instance of an OCI model.

classmethod from_ocid(ocid: str)#

Initializes an object from OCID

Parameters:

ocid (str) – The OCID of the object

property id#

[Required] Gets the id of this JobRun. The OCID of the job run.

Returns:

The id of this JobRun.

Return type:

str

classmethod init_client(**kwargs) DataScienceClient#

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 job#

The job instance of this run.

Returns:

An ADS Job instance

Return type:

Job

property job_configuration_override_details#

[Required] Gets the job_configuration_override_details of this JobRun.

Returns:

The job_configuration_override_details of this JobRun.

Return type:

oci.data_science.models.JobConfigurationDetails

property job_environment_configuration_override_details#

Gets the job_environment_configuration_override_details of this JobRun.

Returns:

The job_environment_configuration_override_details of this JobRun.

Return type:

oci.data_science.models.JobEnvironmentConfigurationDetails

property job_id#

[Required] Gets the job_id of this JobRun. The OCID of the job run.

Returns:

The job_id of this JobRun.

Return type:

str

property job_infrastructure_configuration_details#

[Required] Gets the job_infrastructure_configuration_details of this JobRun.

Returns:

The job_infrastructure_configuration_details of this JobRun.

Return type:

oci.data_science.models.JobInfrastructureConfigurationDetails

property job_log_configuration_override_details#

Gets the job_log_configuration_override_details of this JobRun.

Returns:

The job_log_configuration_override_details of this JobRun.

Return type:

oci.data_science.models.JobLogConfigurationDetails

property job_storage_mount_configuration_details_list#

Gets the job_storage_mount_configuration_details_list of this JobRun. Collection of JobStorageMountConfigurationDetails.

Returns:

The job_storage_mount_configuration_details_list of this JobRun.

Return type:

list[oci.data_science.models.StorageMountConfigurationDetails]

kwargs = None#
property lifecycle_details#

Gets the lifecycle_details of this JobRun. Details of the state of the job run.

Returns:

The lifecycle_details of this JobRun.

Return type:

str

property lifecycle_state#

[Required] Gets the lifecycle_state of this JobRun. The state of the job run.

Allowed values for this property are: “ACCEPTED”, “IN_PROGRESS”, “FAILED”, “SUCCEEDED”, “CANCELING”, “CANCELED”, “DELETED”, “NEEDS_ATTENTION”, ‘UNKNOWN_ENUM_VALUE’. Any unrecognized values returned by a service will be mapped to ‘UNKNOWN_ENUM_VALUE’.

Returns:

The lifecycle_state of this JobRun.

Return type:

str

classmethod list_resource(compartment_id: str | None = None, limit: int = 0, **kwargs) list#

Generic method to list OCI resources

Parameters:
  • compartment_id (str) – Compartment ID of the OCI resources. Defaults to None. If compartment_id is not specified, the value of NB_SESSION_COMPARTMENT_OCID in environment variable will be used.

  • limit (int) – The maximum number of items to return. Defaults to 0, All items will be returned

  • **kwargs – Additional keyword arguments to filter the resource. The kwargs are passed into OCI API.

Returns:

A list of OCI resources

Return type:

list

Raises:

NotImplementedError – List method is not supported or implemented.

load_properties_from_env()#

Loads properties from the environment

property log_details#

Gets the log_details of this JobRun.

Returns:

The log_details of this JobRun.

Return type:

oci.data_science.models.JobRunLogDetails

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 name: str#

Gets the name of the object.

property project_id#

[Required] Gets the project_id of this JobRun. The OCID of the project to associate the job with.

Returns:

The project_id of this JobRun.

Return type:

str

Link to run details page in OCI console

Returns:

The link to the details page in OCI console.

Return type:

str

serialize()#

Serialize the model to a dictionary that is ready to be send to OCI API.

Returns:

A dictionary that is ready to be send to OCI API.

Return type:

dict

signer = None#
property status: str#

Lifecycle status

Returns:

Status in a string.

Return type:

str

sync(merge_strategy: MergeStrategy = MergeStrategy.OVERRIDE)#

Refreshes the properties of the object from OCI

property time_accepted#

[Required] Gets the time_accepted of this JobRun. The date and time the job run was accepted in the timestamp format defined by RFC3339.

Returns:

The time_accepted of this JobRun.

Return type:

datetime

property time_finished#

Gets the time_finished of this JobRun. The date and time the job run request was finished in the timestamp format defined by RFC3339.

Returns:

The time_finished of this JobRun.

Return type:

datetime

property time_started#

Gets the time_started of this JobRun. The date and time the job run request was started in the timestamp format defined by RFC3339.

Returns:

The time_started of this JobRun.

Return type:

datetime

to_dict(flatten: bool = False) dict#

Converts the properties to a dictionary

Parameters:

flatten – (Default value = False)

to_oci_model(oci_model)#

Converts the object into an instance of OCI data model.

Parameters:
  • oci_model (class or str) – The OCI model to be converted to. This can be a string of the model name.

  • type_mapping (dict) – A dictionary mapping the models. Returns: An instance of the oci_model

to_yaml() str[source]#

Serializes the object into YAML string.

Returns:

YAML stored in a string.

Return type:

str

type_mappings = None#
update_from_oci_model(oci_model_instance, merge_strategy: MergeStrategy = MergeStrategy.OVERRIDE)#

Updates the properties from OCI model with the same properties.

Parameters:

oci_model_instance – An instance of OCI model, which should have the same properties of this class.

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).

class ads.jobs.GitPythonRuntime(spec: Dict | None = None, **kwargs)[source]#

Bases: CondaRuntime, _PythonRuntimeMixin

Represents a job runtime with source code from git repository

Example:

runtime = (
    GitPythonRuntime()
    .with_environment_variable(GREETINGS="Welcome to OCI Data Science")
    # Specify the service conda environment by slug name.
    .with_service_conda("pytorch19_p37_gpu_v1")
    # Specify the git repository
    # Optionally, you can specify the branch or commit
    .with_source("https://github.com/pytorch/tutorials.git")
    # Entrypoint is a relative path from the root of the git repo.
    .with_entrypoint("beginner_source/examples_nn/polynomial_nn.py")
    # Copy files in "beginner_source/examples_nn" to object storage after job finishes.
    .with_output(
      output_dir="beginner_source/examples_nn",
      output_uri="oci://bucket_name@namespace/path/to/dir"
    )
)

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_ARGS = 'args'#
CONST_BRANCH = 'branch'#
CONST_COMMIT = 'commit'#
CONST_CONDA = 'conda'#
CONST_CONDA_REGION = 'region'#
CONST_CONDA_SLUG = 'slug'#
CONST_CONDA_TYPE = 'type'#
CONST_CONDA_TYPE_CUSTOM = 'published'#
CONST_CONDA_TYPE_SERVICE = 'service'#
CONST_CONDA_URI = 'uri'#
CONST_DEFINED_TAGS = 'definedTags'#
CONST_ENTRYPOINT = 'entrypoint'#
CONST_ENTRY_FUNCTION = 'entryFunction'#
CONST_ENV_VAR = 'env'#
CONST_FREEFORM_TAGS = 'freeformTags'#
CONST_GIT_SSH_SECRET_ID = 'gitSecretId'#
CONST_GIT_URL = 'url'#
CONST_MAXIMUM_RUNTIME_IN_MINUTES = 'maximumRuntimeInMinutes'#
CONST_OUTPUT_DIR = 'outputDir'#
CONST_OUTPUT_URI = 'outputUri'#
CONST_PYTHON_PATH = 'pythonPath'#
CONST_SKIP_METADATA = 'skipMetadataUpdate'#
CONST_WORKING_DIR = 'workingDir'#
property args: list#

Command line arguments

attribute_map = {'args': 'args', 'branch': 'branch', 'commit': 'commit', 'conda': 'conda', 'definedTags': 'defined_tags', 'entryFunction': 'entry_function', 'entrypoint': 'entrypoint', 'env': 'env', 'freeformTags': 'freeform_tags', 'gitSecretId': 'git_secret_id', 'outputDir': 'output_dir', 'outputUri': 'output_uri', 'pythonPath': 'python_path', 'skipMetadataUpdate': 'skip_metadata_update', 'url': 'url', 'workingDir': 'working_dir'}#
property branch: str#

Git branch name.

build() Self#

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

property commit: str#

Git commit ID (SHA1 hash)

property conda: dict#

The conda environment specification.

For service conda environment, the specification contains:

  • type, the type of the conda environment. This is always service for service conda environment.

  • slug, the slug of the conda environment.

For custom conda environment, the specification contains:

  • type, the type of the conda environment. This is always published for custom conda environment.

  • uri, the uri of the conda environment, e.g. oci://bucket@namespace/prefix/to/conda

  • region, the region of the bucket in which the conda environment is stored. By default, ADS will determine the region based on the authenticated API key or resource principal. This is only needed if your conda environment is stored in a different region.

Returns:

A dictionary containing the conda environment specifications.

Return type:

dict

property defined_tags: dict#

Defined tags

property entry_function: str#

The name of the entry function in the entry script

property entry_script: str#

The path of the entry script

property environment_variables: dict#

Environment variables

Returns:

The runtime environment variables. The returned dictionary is a copy.

Return type:

dict

property envs: dict#

Environment variables

property freeform_tags: dict#

Freeform tags

classmethod from_dict(obj_dict: dict) Self#

Initialize the object from a Python dictionary

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

init(**kwargs) GitPythonRuntime[source]#

Initializes a starter specification for the runtime.

Returns:

The runtime instance.

Return type:

GitPythonRuntime

property kind: str#

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

property maximum_runtime_in_minutes: int#

Maximum runtime in minutes

property output_dir: str#

Directory in the Job run container for saving output files generated in the job

property output_uri: str#

OCI object storage URI prefix for saving output files generated in the job

property python_path#

Additional python paths for running the source code.

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

property skip_metadata_update#

Indicate if the metadata update should be skipped after the job run

By default, the job run metadata will be updated with the following freeform tags: * repo: The URL of the Git repository * commit: The Git commit ID * module: The entry script/module * method: The entry function/method * outputs. The prefix of the output files in object storage.

This update step also requires resource principals to have the permission to update the job run.

Returns:

True if the metadata update will be skipped. Otherwise False.

Return type:

bool

property ssh_secret_ocid: str#

The OCID of the OCI Vault secret storing the Git SSH key.

to_dict(**kwargs) dict#

Converts the object to dictionary with kind, type and spec as keys.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML

property url: str#

URL of the Git repository.

with_argument(*args, **kwargs) Self#

Adds command line arguments to the runtime.

This method can be called (chained) multiple times to add various arguments.

Parameters:
  • args – Positional arguments. In a single method call, positional arguments are always added before keyword arguments. You can call with_argument() to add positional arguments after keyword arguments.

  • kwargs – Keyword arguments. To add a keyword argument without value, set the value to None.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Raises:

ValueError – Keyword arguments with space in a key.

Examples

>>> runtime = Runtime().with_argument(key1="val1", key2="val2").with_argument("pos1")
>>> print(runtime.args)
["--key1", "val1", "--key2", "val2", "pos1"]
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1="val1", key2="val2.1 val2.2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
['pos1', '--key1', 'val1', '--key2', 'val2.1 val2.2', 'pos2']
>>> runtime = Runtime()
>>> runtime.with_argument("pos1")
>>> runtime.with_argument(key1=None, key2="val2")
>>> runtime.with_argument("pos2")
>>> print(runtime.args)
["pos1", "--key1", "--key2", "val2", "pos2"]
with_custom_conda(uri: str, region: str | None = None)#

Specifies the custom conda pack for running the job Make sure you have configured the IAM policy for the job run to access the conda environment.

Parameters:
  • uri (str) – The OCI object storage URI for the conda pack, e.g. “oci://your_bucket@namespace/object_name.” In the Environment Explorer of an OCI notebook session, this is shown as the “source” of the conda pack.

  • region (str, optional) –

    The region of the bucket storing the custom conda pack, by default None. If region is not specified, ADS will use the region from your authentication credentials:

    • For API Key, config[“region”] is used.

    • For Resource Principal, signer.region is used.

    This is required if the conda pack is stored in a different region.

Returns:

The runtime instance.

Return type:

self

See also

https

//docs.oracle.com/en-us/iaas/data-science/using/conda_publishs_object.htm

with_defined_tag(**kwargs) Self#

Sets defined tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_entrypoint(path: str, func: str | None = None)#

Specifies the entrypoint for the job. The entrypoint can be a script or a function in a script.

Parameters:
  • script (str) – The relative path for the script/module starting the job.

  • func (str, optional) – The function name in the script for starting the job, by default None. If this is not specified, the script will be run with python command in a subprocess.

Returns:

The runtime instance.

Return type:

self

with_environment_variable(**kwargs) Self#

Sets environment variables

Environment variables enclosed by ${...} will be substituted.

  • You can use $$ to escape the substitution.

  • Undefined variable enclosed by ${} will be ignored.

  • Double dollar signs $$ will be substituted by a single one $.

Returns:

This method returns self to support chaining methods.

Return type:

Self

Examples

>>> runtime = (
...     PythonRuntime()
...     .with_environment_variable(
...         HOST="10.0.0.1",
...         PORT="443",
...         URL="http://${HOST}:${PORT}/path/",
...         ESCAPED_URL="http://$${HOST}:$${PORT}/path/",
...         MISSING_VAR="This is ${UNDEFINED}",
...         VAR_WITH_DOLLAR="$10",
...         DOUBLE_DOLLAR="$$10"
...     )
... )
>>> for k, v in runtime.environment_variables.items():
...     print(f"{k}: {v}")
HOST: 10.0.0.1
PORT: 443
URL: http://10.0.0.1:443/path/
ESCAPED_URL: http://${HOST}:${PORT}/path/
MISSING_VAR: This is ${UNDEFINED}
VAR_WITH_DOLLAR: $10
DOUBLE_DOLLAR: $10
with_freeform_tag(**kwargs) Self#

Sets freeform tags

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_maximum_runtime_in_minutes(maximum_runtime_in_minutes: int) Self#

Sets maximum runtime in minutes

Returns:

This method returns self to support chaining methods.

Return type:

Self

with_output(output_dir: str, output_uri: str)#

Specifies the outputs of the job. The output files in output_dir will be copied to remote output_uri when the job is finished.

Parameters:
  • output_dir (str) – Path to the output directory in the job run. This path should be a relative path from the working directory. The source code should write all outputs into this directory.

  • output_uri (str) – The OCI object storage URI prefix for saving the output files. For example, oci://bucket_name@namespace/path/to/directory

Returns:

The runtime instance.

Return type:

Self

with_python_path(*python_paths)#

Specifies additional python paths for running the source code.

Parameters:

*python_paths – Additional python path(s) for running the source code. Each path should be a relative path from the working directory.

Returns:

The runtime instance.

Return type:

self

with_service_conda(slug: str)#

Specifies the service conda pack for running the job

Parameters:

slug (str) – The slug name of the service conda pack

Returns:

The runtime instance.

Return type:

self

with_source(url: str, branch: str | None = None, commit: str | None = None, secret_ocid: str | None = None)[source]#

Specifies the Git repository and branch/commit for the job source code.

Parameters:
  • url (str) – URL of the Git repository.

  • branch (str, optional) – Git branch name, by default None, the default branch will be used.

  • commit (str, optional) – Git commit ID (SHA1 hash), by default None, the most recent commit will be used.

  • secret_ocid (str) – The secret OCID storing the SSH key content for checking out the Git repository.

Returns:

The runtime instance.

Return type:

self

with_working_dir(working_dir: str)#

Specifies the working directory in the job run. By default, the working directory will the directory containing the user code (job artifact directory). This can be changed by specifying a relative path to the job artifact directory.

Parameters:

working_dir (str) – The path of the working directory. This can be a relative path from the job artifact directory.

Returns:

The runtime instance.

Return type:

self

property working_dir: str#

The working directory for the job run.

class ads.jobs.Job(name: str | None = None, infrastructure=None, runtime=None, **kwargs)[source]#

Bases: Builder

Represents a Job defined by infrastructure and runtime.

Examples

Here is an example for creating and running a job:

from ads.jobs import Job, DataScienceJob, PythonRuntime

# Define an OCI Data Science job to run a python script
job = (
    Job(name="<job_name>")
    .with_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)
    )
    .with_runtime(
        PythonRuntime()
        # Specify the service conda environment by slug name.
        .with_service_conda("pytorch110_p38_cpu_v1")
        # The job artifact can be a single Python script, a directory or a zip file.
        .with_source("local/path/to/code_dir")
        # Environment variable
        .with_environment_variable(NAME="Welcome to OCI Data Science.")
        # Command line argument, arg1 --key arg2
        .with_argument("arg1", key="arg2")
        # Set the working directory
        # When using a directory as source, the default working dir is the parent of code_dir.
        # Working dir should be a relative path beginning from the source directory (code_dir)
        .with_working_dir("code_dir")
        # The entrypoint is applicable only to directory or zip file as source
        # The entrypoint should be a path relative to the working dir.
        # Here my_script.py is a file in the code_dir/my_package directory
        .with_entrypoint("my_package/my_script.py")
        # Add an additional Python path, relative to the working dir (code_dir/other_packages).
        .with_python_path("other_packages")
        # Copy files in "code_dir/output" to object storage after job finishes.
        .with_output("output", "oci://bucket_name@namespace/path/to/dir")
        # Tags
        .with_freeform_tag(my_tag="my_value")
        .with_defined_tag(**{"Operations": {"CostCenter": "42"}})
    )
)
# Create and Run the job
run = job.create().run()
# Stream the job run outputs
run.watch()

If you are in an OCI notebook session and you would like to use the same infrastructure configurations, the infrastructure configuration can be simplified. Here is another example of creating and running a jupyter notebook as a job:

from ads.jobs import Job, DataScienceJob, NotebookRuntime

# Define an OCI Data Science job to run a jupyter Python notebook
job = (
    Job(name="<job_name>")
    .with_infrastructure(
        # The same configurations as the OCI notebook session will be used.
        DataScienceJob()
        .with_log_group_id("<log_group_ocid>")
        .with_log_id("<log_ocid>")
    )
    .with_runtime(
        NotebookRuntime()
        .with_notebook("path/to/notebook.ipynb")
        .with_service_conda(tensorflow28_p38_cpu_v1")
        # Saves the notebook with outputs to OCI object storage.
        .with_output("oci://bucket_name@namespace/path/to/dir")
    )
).create()
# Run and monitor the job
run = job.run().watch()
# Download the notebook and outputs to local directory
run.download(to_dir="path/to/local/dir/")

See also

https

//docs.oracle.com/en-us/iaas/tools/ads-sdk/latest/user_guide/jobs/index.html

Initializes a job.

The infrastructure and runtime can be configured when initializing the job,

or by calling with_infrastructure() and with_runtime().

The infrastructure should be a subclass of ADS job Infrastructure, e.g., DataScienceJob, DataFlow. The runtime should be a subclass of ADS job Runtime, e.g., PythonRuntime, NotebookRuntime.

Parameters:
  • name (str, optional) – The name of the job, by default None. If it is None, a default name may be generated by the infrastructure, depending on the implementation of the infrastructure. For OCI data science job, the default name contains the job artifact name and a timestamp. If no artifact, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • infrastructure (Infrastructure, optional) – Job infrastructure, by default None

  • runtime (Runtime, optional) – Job runtime, by default None.

attribute_map = {}#
auth = {}#
build() Job[source]#

Load default values from the environment for the job infrastructure.

cancel(wait_for_completion: bool = True) None[source]#

Cancels the runs of the job.

Parameters:

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

create(**kwargs) Job[source]#

Creates the job on the infrastructure.

Returns:

The job instance (self)

Return type:

Job

static dataflow_job(compartment_id: str | None = None, **kwargs) List[Job][source]#

List data flow jobs under a given compartment.

Parameters:
  • compartment_id (str) – compartment id

  • kwargs – additional keyword arguments

Returns:

list of Job instances

Return type:

List[Job]

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

Lists the existing data science jobs in the compartment.

Parameters:

compartment_id (str) – The compartment ID for listing the jobs. This is optional if running in an OCI notebook session. The jobs in the same compartment of the notebook session will be returned.

Returns:

A list of Job objects.

Return type:

list

delete() None[source]#

Deletes the job from the infrastructure.

download(to_dir: str, output_uri=None, **storage_options)[source]#

Downloads files from remote output URI to local.

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

  • output_uri ((str, optional). Default is None.) – The remote URI from which the files will be downloaded. Defaults to None. If output_uri is not specified, this method will try to get the output_uri from the runtime.

  • storage_options – Extra keyword arguments for particular storage connection. This method uses fsspec to download the files from remote URI. storage_options will to be passed into fsspec.open_files().

Returns:

The job instance (self)

Return type:

Job

Raises:

AttributeError – The output_uri is not specified and the runtime is not configured with output_uri.

static from_dataflow_job(job_id: str) Job[source]#

Create a Data Flow job given a job id.

Parameters:

job_id (str) – id of the job

Returns:

a Job instance

Return type:

Job

classmethod from_datascience_job(job_id) Job[source]#

Loads a data science job from OCI.

Parameters:

job_id (str) – OCID of an existing data science job.

Returns:

A job instance.

Return type:

Job

classmethod from_dict(config: dict) Job[source]#

Initializes a job from a dictionary containing the configurations.

Parameters:

config (dict) – A dictionary containing the infrastructure and runtime specifications.

Returns:

A job instance

Return type:

Job

Raises:

NotImplementedError – If the type of the infrastructure or runtime is not supported.

classmethod from_json(json_string: str | None = None, uri: str | None = None, decoder: callable = <class 'json.decoder.JSONDecoder'>, **kwargs) Self#

Creates an object from JSON string provided or from URI location containing JSON string

Parameters:
  • json_string (str, optional) – JSON string. Defaults to None.

  • uri (str, optional) – URI location of file containing JSON string. Defaults to None.

  • decoder (callable, optional) – Custom decoder. Defaults to simple JSON decoder.

  • kwargs (dict) – keyword arguments to be passed into fsspec.open(). For OCI object storage, this should be config=”path/to/.oci/config”. For other storage connections consider e.g. host, port, username, password, etc.

  • json_string – JSON string, by default None

  • uri – URI location of file containing JSON string, by default None

  • decoder – Decoder for custom data structures, by default json.JSONDecoder

  • kwargs – keyword arguments to be passed into fsspec.open(). For OCI object storage, this can be config=”path/to/.oci/config”.

Raises:
  • ValueError – Raised if neither string nor uri is provided

  • ValueError – Both json_string and uri are empty, or The input is not a valid JSON.

Returns:

Returns instance of the class

Return type:

cls

Returns:

Object initialized from JSON data.

Return type:

Type[Self]

classmethod from_string(obj_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML/JSON string or URI location containing the YAML/JSON

Parameters:
  • obj_string (str, optional) – YAML/JSON string, by default None

  • uri (str, optional) – URI location of file containing YAML/JSON, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

classmethod from_yaml(yaml_string: str | None = None, uri: str | None = None, loader: callable = <class 'yaml.loader.SafeLoader'>, **kwargs) Self#

Initializes an object from YAML string or URI location containing the YAML

Parameters:
  • yaml_string (str, optional) – YAML string, by default None

  • uri (str, optional) – URI location of file containing YAML, by default None

  • loader (callable, optional) – Custom YAML loader, by default yaml.SafeLoader

Returns:

Object initialized from the YAML.

Return type:

Self

Raises:

ValueError – Raised if neither string nor uri is provided

get_spec(key: str, default: Any | None = None) Any#

Gets the value of a specification property

Parameters:
  • key (str) – The name of the property.

  • default (Any, optional) – The default value to be used, if the property does not exist, by default None.

Returns:

The value of the property.

Return type:

Any

property id: str#

The ID of the job. For jobs running on OCI, this is the OCID.

Returns:

ID of the job.

Return type:

str

property infrastructure: DataScienceJob | DataFlow#

The job infrastructure.

Returns:

Job infrastructure.

Return type:

Infrastructure

property kind: str#

The kind of the object as showing in YAML.

Returns:

“job”

Return type:

str

property name: str#

The name of the job. For jobs running on OCI, this is the display name.

Returns:

The name of the job.

Return type:

str

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

Runs the job.

Parameters:
  • name (str, optional) – Name of the job run, by default None. The infrastructure handles the naming of the job run. For data science job, if a name is not provided, a default name will be generated containing the job name and the timestamp of the run. If no artifact, a randomly generated easy to remember name with timestamp will be generated, like ‘strange-spider-2022-08-17-23:55.02’.

  • args (str, optional) – Command line arguments for the job run, by default None. This will override the configurations on the job. If this is None, the args from the job configuration will be used.

  • env_var (dict, optional) – Additional environment variables 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 call should wait for the job run. By default False, this method returns as soon as the job run is created. If this is set to True, this method will stream the job logs and wait until it finishes, similar to job.run().watch().

  • kwargs – additional keyword arguments

Returns:

A job run instance, depending on the infrastructure.

Return type:

Job Run Instance

Examples

To run a job and override the configurations:

job_run = job.run(
    name="<my_job_run_name>",
    args="new_arg --new_key new_val",
    env_var={"new_env": "new_val"},
    freeform_tags={"new_tag": "new_tag_val"},
    defined_tags={"Operations": {"CostCenter": "42"}}
)
run_list(**kwargs) list[source]#

Gets a list of runs of the job.

Returns:

A list of job run instances, the actual object type depends on the infrastructure.

Return type:

list

property runtime: Runtime#

The job runtime.

Returns:

The job runtime

Return type:

Runtime

set_spec(k: str, v: Any) Self#

Sets a specification property for the object.

Parameters:
  • k (str) – key, the name of the property.

  • v (Any) – value, the value of the property.

Returns:

This method returns self to support chaining methods.

Return type:

Self

status() str[source]#

Status of the job

Returns:

Status of the job

Return type:

str

to_dict(**kwargs: Dict) Dict[source]#

Serialize the job specifications to a dictionary.

Parameters:

**kwargs (Dict) –

The additional arguments. - filter_by_attribute_map: bool

If True, then in the result will be included only the fields presented in the attribute_map.

Returns:

A dictionary containing job specifications.

Return type:

Dict

to_json(uri: str | None = None, encoder: callable = <class 'json.encoder.JSONEncoder'>, **kwargs) str#

Returns the object serialized as a JSON string

Parameters:
  • uri (str, optional) – URI location to save the JSON string, by default None

  • encoder (callable, optional) – Encoder for custom data structures, by default json.JSONEncoder

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

to_yaml(uri: str | None = None, dumper: callable = <class 'yaml.dumper.SafeDumper'>, **kwargs) str | None#

Returns object serialized as a YAML string

Parameters:
  • uri (str, optional) – URI location to save the YAML string, by default None

  • dumper (callable, optional) – Custom YAML Dumper, by default yaml.SafeDumper

  • kwargs (dict) –

    overwrite: (bool, optional). Defaults to True.

    Whether to overwrite existing file or not.

    note: (str, optional)

    The note that needs to be added in the beginning of the YAML. It will be added as is without any formatting.

    The other keyword arguments to be passed into fsspec.open(). For OCI object storage, this could be config=”path/to/.oci/config”.

Returns:

Serialized version of object. None in case when uri provided.

Return type:

Union[str, None]

property type: str#

The type of the object as showing in YAML.

This implementation returns the class name with the first letter coverted to lower case.

with_infrastructure(infrastructure) Job[source]#

Sets the infrastructure for the job.

Parameters:

infrastructure (Infrastructure) – Job infrastructure.

Returns:

The job instance (self)

Return type:

Job

with_name(name: str) Job[source]#

Sets the job name.

Parameters:

name (str) – Job name.

Returns:

The job instance (self)

Return type:

Job

with_runtime(runtime) Job[source]#

Sets the runtime for the job.

Parameters:

runtime (Runtime) – Job runtime.

Returns:

The job instance (self)

Return type:

Job

class ads.jobs.NotebookRuntime(spec: Dict | None = None, **kwargs)[source]#

Bases: CondaRuntime

Represents a job runtime with Jupyter notebook

To run a job with a single Jupyter notebook, you can define the run time as:

runtime = (
    NotebookRuntime()
    .with_notebook(
        path="https://raw.githubusercontent.com/tensorflow/docs/master/site/en/tutorials/customization/basics.ipynb",
        encoding='utf-8'
    )
    .with_service_conda("tensorflow28_p38_cpu_v1")
    .with_environment_variable(GREETINGS="Welcome to OCI Data Science")
    .with_exclude_tag(["ignore", "remove"])
    .with_output("oci://bucket_name@namespace/path/to/dir")
)

Note that the notebook path can be local or remote path supported by fsspec, including OCI object storage path like oci://bucket@namespace/path/to/notebook

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_ARGS = 'args'#
CONST_CONDA = 'conda'#
CONST_CONDA_REGION = 'region'#
CONST_CONDA_SLUG = 'slug'#
CONST_CONDA_TYPE = 'type'#
CONST_CONDA_TYPE_CUSTOM = 'published'#
CONST_CONDA_TYPE_SERVICE = 'service'#
CONST_CONDA_URI = 'uri'#
CONST_DEFINED_TAGS = 'definedTags'#
CONST_ENTRYPOINT = 'entrypoint'#
CONST_ENV_VAR = 'env'#