Quick Start#

ADS Python SDK#

The following sections provide sample code to define, create, and run a pipeline, including a visualization to track the pipeline run status.

The following example shows creating and runnning a pipeline with multiple steps. The steps step_1 and step_2 run in parallel and step_3 runs after step_1 and step_2 are complete.

../../_images/quick_start_example.png
  • YAML
  • Python
from ads.pipeline import Pipeline
import os

compartment_id = os.environ['NB_SESSION_COMPARTMENT_OCID']
project_id = os.environ["PROJECT_OCID"]

with open("script.py", "w") as f:
    f.write("print('Hello World!')")

yaml_string = """
kind: pipeline
spec:
  compartmentId: {compartment_id}
  displayName: An example pipeline
  projectId: {project_id}
  dag:
  - (step_1, step_2) >> step_3
  stepDetails:
  - kind: customScript
    spec:
      description: A step running a python script
      infrastructure:
        kind: infrastructure
        spec:
          blockStorageSize: 200
          shapeConfigDetails:
            memoryInGBs: 32
            ocpus: 4
          shapeName: VM.Standard3.Flex
      name: step_1
      runtime:
        kind: runtime
        spec:
          conda:
            slug: generalml_p37_cpu_v1
            type: service
          scriptPathURI: script.py
        type: script
  - kind: customScript
    spec:
      description: A step running a notebook
      infrastructure:
        kind: infrastructure
        spec:
          blockStorageSize: 200
          shapeConfigDetails:
            memoryInGBs: 32
            ocpus: 4
          shapeName: VM.Standard3.Flex
      name: step_2
      runtime:
        kind: runtime
        spec:
          conda:
            slug: tensorflow26_p37_cpu_v2
            type: service
          notebookEncoding: utf-8
          notebookPathURI: https://raw.githubusercontent.com/tensorflow/docs/master/site/en/tutorials/customization/basics.ipynb
        type: notebook
  - kind: customScript
    spec:
      description: A step running a python script
      infrastructure:
        kind: infrastructure
        spec:
          blockStorageSize: 200
          shapeConfigDetails:
            memoryInGBs: 32
            ocpus: 4
          shapeName: VM.Standard3.Flex
      name: step_3
      runtime:
        kind: runtime
        spec:
          conda:
            slug: generalml_p37_cpu_v1
            type: service
          scriptPathURI: script.py
        type: script
type: pipeline
""".format(compartment_id=compartment_id, project_id=project_id)

pipeline = Pipeline.from_yaml(yaml_string)

pipeline.create()      # create the pipeline
pipeline.show()       # visualize the pipeline

pipeline_run = pipeline.run()   # run the pipeline

pipeline_run.show()     # watch the pipeline run status

from ads.pipeline import Pipeline, PipelineStep, CustomScriptStep, ScriptRuntime, NotebookRuntime
import os

with open("script.py", "w") as f:
    f.write("print('Hello World!')")

infrastructure = (
    CustomScriptStep()
    .with_block_storage_size(200)
    .with_shape_name("VM.Standard3.Flex")
    .with_shape_config_details(ocpus=4, memory_in_gbs=32)
)

script_runtime = (
    ScriptRuntime()
    .with_source("script.py")
    .with_service_conda("generalml_p37_cpu_v1")
)

notebook_runtime = (
    NotebookRuntime()
    .with_notebook(
        path="https://raw.githubusercontent.com/tensorflow/docs/master/site/en/tutorials/customization/basics.ipynb",
        encoding='utf-8'
    )
    .with_service_conda("tensorflow26_p37_cpu_v2")
)

pipeline_step_1 = (
    PipelineStep("step_1")
    .with_description("A step running a python script")
    .with_infrastructure(infrastructure)
    .with_runtime(script_runtime)
)

pipeline_step_2 = (
    PipelineStep("step_2")
    .with_description("A step running a notebook")
    .with_infrastructure(infrastructure)
    .with_runtime(notebook_runtime)
)

pipeline_step_3 = (
    PipelineStep("step_3")
    .with_description("A step running a python script")
    .with_infrastructure(infrastructure)
    .with_runtime(script_runtime)
)

compartment_id = os.environ['NB_SESSION_COMPARTMENT_OCID']
project_id = os.environ["PROJECT_OCID"]

pipeline = (
      Pipeline("An example pipeline")
      .with_compartment_id(compartment_id)
      .with_project_id(project_id)
      .with_step_details([pipeline_step_1, pipeline_step_2, pipeline_step_3])
      .with_dag(["(step_1, step_2) >> step_3"])
  )

pipeline.create()      # create the pipeline
pipeline.show()       # visualize the pipeline

pipeline_run = pipeline.run()   # run the pipeline

pipeline_run.show()     # watch the pipeline run status

ADS CLI#

Prerequisites

  1. Install ADS CLI

  2. Configure Defaults

Create / Run#

To build a brand new Data Science Pipeline and run it, provide the path to the pipeline YAML file with the --file option

ads opctl run --file <path_to_pipeline_yaml>

Alternatively, to run an existing pipeline, provide the pipeline OCID with the --ocid option

ads opctl run --ocid <pipeline_ocid>

Monitor#

To monitor a pipeline run, provide the pipeline run OCID and provide the log type with the -l option

Below is an example to stream the custom log

ads opctl watch <pipeline_run_ocid> -l custom_log

Tip :class: note

The allowed values for -l option are custom_log, service_log, or None.

Cancel#

To cancel a pipeline run, provide the pipeline run OCID

ads opctl cancel <pipeline_run_ocid>

Data Science Pipeline Runs can only be canceled when they are in the ACCEPTED or IN_PROGRESS state.

Delete#

To delete a pipeline run, provide the pipeline run OCID

ads opctl delete <pipeline_run_ocid>

Data Science Pipeline Runs can only be deleted when they are in the SUCCEEDED, FAILED, or CANCELED state.

To delete a pipeline, provide the pipeline OCID

ads opctl delete <pipeline_ocid>

Data Science Pipelines can only be deleted when their associated pipeline runs are all deleted.

ADS Magic Commands#

Tip :class: note

Get more information about the pipeline extension by running %pipeline -h

Installation#

Install the pipeline extension by running the following command

%load_ext ads.pipeline.extension

Create / Run#

To build a brand new Data Science Pipeline and run it, provide the path to the pipeline YAML file with the --file option

%pipeline run --file <path_to_pipeline_yaml>

Alternatively, to run an existing pipeline, provide the pipeline OCID with the --ocid option

%pipeline run --ocid <pipeline_ocid>

Visualize#

To visualize a pipeline in a graph, use the pipeline OCID

%pipeline show <pipeline_ocid>

Watch status#

To watch the status of pipeline run, use the pipeline run OCID

Tip :class: note

Get more information about watching pipeline status by running %pipeline status -h

Below is an example of watching the status of pipeline run in graph mode until it finishes

%pipeline status <pipeline_run_ocid> -w

Below is an example of watching the status of pipeline run in text mode

%pipeline status <pipeline_run_ocid> -x

Monitor logs#

To monitor a pipeline run, use the pipeline run OCID.

Tip :class: note

Get more information about monitoring pipeline logs by running %pipeline log -h

Below is an example of streaming the custom_log

%pipeline log <pipeline_run_ocid> -l custom_log

Below is an example of viewing the last 10 consolidated logs with tail

%pipeline log <pipeline_run_ocid> -t -n 10

Cancel#

To cancel a pipeline run, use the pipeline run OCID

%pipeline cancel <pipeline_run_ocid>

Data Science Pipeline Runs can only be canceled when they are in the ACCEPTED or IN_PROGRESS state.

Delete#

Tip :class: note

Get more information about deleting pipelines and pipeline runs by running %pipeline delete -h

To delete a pipeline run, use the pipeline run OCID

%pipeline delete <pipeline_run_ocid>

Data Science Pipeline Runs can only be deleted when they are in the SUCCEEDED, FAILED, or CANCELED state.

To delete a pipeline, use the pipeline OCID

%pipeline delete <pipeline_ocid>

Data Science Pipelines can only be deleted when their associated pipeline runs are all deleted.