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Update examples.
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‎docs/source/user_guide/jobs/tabs/git_notebook.rst‎

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.. tabs::
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.. code-tab:: python
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:caption: Python
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from ads.jobs import Job, DataScienceJob, GitPythonRuntime
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job = (
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Job(name="My Job")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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# The following infrastructure configurations are optional
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# if you are in an OCI data science notebook session.
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# The configurations of the notebook session will be used as defaults.
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.with_compartment_id("<compartment_ocid>")
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.with_project_id("<project_ocid>")
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# For default networking, no need to specify subnet ID
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.with_subnet_id("<subnet_ocid>")
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.with_shape_name("VM.Standard.E3.Flex")
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# Shape config details are applicable only for the flexible shapes.
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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.with_block_storage_size(50)
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)
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.with_runtime(
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GitPythonRuntime()
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# Use service conda pack
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.with_service_conda("pytorch110_p38_gpu_v1")
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# Specify training source code from GitHub
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.with_source(url="https://github.com/karpathy/minGPT.git")
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# Entrypoint is a relative path from the root of the Git repository
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.with_entrypoint("demo.ipynb")
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)
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)
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.. code-tab:: yaml
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:caption: YAML
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kind: job
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spec:
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name: "My Job"
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infrastructure:
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kind: infrastructure
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type: dataScienceJob
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spec:
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blockStorageSize: 50
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compartmentId: <compartment_ocid>
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jobInfrastructureType: STANDALONE
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jobType: DEFAULT
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logGroupId: <log_group_ocid>
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logId: <log_ocid>
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projectId: <project_ocid>
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shapeConfigDetails:
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memoryInGBs: 16
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ocpus: 1
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shapeName: VM.Standard.E3.Flex
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subnetId: <subnet_ocid>
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runtime:
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kind: runtime
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type: gitPython
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spec:
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conda:
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slug: pytorch19_p37_gpu_v1
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type: service
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entrypoint: demo.ipynb
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url: https://github.com/karpathy/minGPT.git
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.. code-block:: python
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# Create the job on OCI Data Science
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job.create()
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# Start a job run
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run = job.run()
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# Stream the job run outputs
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run.watch()

‎docs/source/user_guide/jobs/tabs/git_runtime.rst‎

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@@ -6,39 +6,39 @@
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from ads.jobs import Job, DataScienceJob, GitPythonRuntime
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job = (
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Job(name="My Job")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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# The following infrastructure configurations are optional
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# if you are in an OCI data science notebook session.
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# The configurations of the notebook session will be used as defaults.
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.with_compartment_id("<compartment_ocid>")
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.with_project_id("<project_ocid>")
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# For default networking, no need to specify subnet ID
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.with_subnet_id("<subnet_ocid>")
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.with_shape_name("VM.Standard.E3.Flex")
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# Shape config details are applicable only for the flexible shapes.
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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.with_block_storage_size(50)
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)
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.with_runtime(
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GitPythonRuntime()
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.with_environment_variable(GREETINGS="Welcome to OCI Data Science")
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# Specify the service conda environment by slug name.
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.with_service_conda("pytorch19_p37_gpu_v1")
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# Specify the git repository
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# Optionally, you can specify the branch or commit
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.with_source("https://github.com/pytorch/tutorials.git")
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# Entrypoint is a relative path from the root of the git repo.
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.with_entrypoint("beginner_source/examples_nn/polynomial_nn.py")
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# Copy files in "beginner_source/examples_nn" to object storage after job finishes.
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.with_output(
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output_dir="beginner_source/examples_nn",
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output_uri="oci://bucket_name@namespace/path/to/dir"
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Job(name="My Job")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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# The following infrastructure configurations are optional
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# if you are in an OCI data science notebook session.
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# The configurations of the notebook session will be used as defaults.
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.with_compartment_id("<compartment_ocid>")
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.with_project_id("<project_ocid>")
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# For default networking, no need to specify subnet ID
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.with_subnet_id("<subnet_ocid>")
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.with_shape_name("VM.Standard.E3.Flex")
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# Shape config details are applicable only for the flexible shapes.
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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.with_block_storage_size(50)
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)
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.with_runtime(
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GitPythonRuntime()
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.with_environment_variable(GREETINGS="Welcome to OCI Data Science")
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# Specify the service conda environment by slug name.
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.with_service_conda("pytorch19_p37_gpu_v1")
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# Specify the git repository
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# Optionally, you can specify the branch or commit
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.with_source("https://github.com/pytorch/tutorials.git")
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# Entrypoint is a relative path from the root of the git repo.
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.with_entrypoint("beginner_source/examples_nn/polynomial_nn.py")
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# Copy files in "beginner_source/examples_nn" to object storage after job finishes.
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.with_output(
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output_dir="beginner_source/examples_nn",
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output_uri="oci://bucket_name@namespace/path/to/dir"
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)
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)
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)
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)
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.. code-tab:: yaml

‎docs/source/user_guide/jobs/tabs/name_substitution.rst‎

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@@ -6,23 +6,23 @@
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from ads.jobs import Job, DataScienceJob, PythonRuntime
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job = (
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Job(name="Training on ${DATASET_NAME}")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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.with_compartment_id("<compartment_ocid>")
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.with_project_id("<project_ocid>")
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.with_shape_name("VM.Standard.E3.Flex")
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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)
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.with_runtime(
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PythonRuntime()
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.with_service_conda("pytorch110_p38_gpu_v1")
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.with_environment_variable(DATASET_NAME="MyData")
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.with_source("local/path/to/training_script.py")
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.with_output("output", "oci://bucket_name@namespace/prefix/${JOB_RUN_OCID}")
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)
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Job(name="Training on ${DATASET_NAME}")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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.with_compartment_id("<compartment_ocid>")
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.with_project_id("<project_ocid>")
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.with_shape_name("VM.Standard.E3.Flex")
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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)
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.with_runtime(
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PythonRuntime()
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.with_service_conda("pytorch110_p38_gpu_v1")
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.with_environment_variable(DATASET_NAME="MyData")
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.with_source("local/path/to/training_script.py")
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.with_output("output", "oci://bucket_name@namespace/prefix/${JOB_RUN_OCID}")
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)
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)
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.. code-tab:: yaml

‎docs/source/user_guide/jobs/tabs/notebook_runtime.rst‎

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Original file line numberDiff line numberDiff line change
@@ -6,34 +6,34 @@
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from ads.jobs import Job, DataScienceJob, NotebookRuntime
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job = (
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Job(name="My Job")
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.with_infrastructure(
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DataScienceJob()
12-
.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
14-
# The following infrastructure configurations are optional
15-
# if you are in an OCI data science notebook session.
16-
# The configurations of the notebook session will be used as defaults.
17-
.with_compartment_id("<compartment_ocid>")
18-
.with_project_id("<project_ocid>")
19-
# For default networking, no need to specify subnet ID
20-
.with_subnet_id("<subnet_ocid>")
21-
.with_shape_name("VM.Standard.E3.Flex")
22-
# Shape config details are applicable only for the flexible shapes.
23-
.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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.with_block_storage_size(50)
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)
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.with_runtime(
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NotebookRuntime()
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.with_notebook(
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path="https://raw.githubusercontent.com/tensorflow/docs/master/site/en/tutorials/customization/basics.ipynb",
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encoding='utf-8'
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Job(name="My Job")
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.with_infrastructure(
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DataScienceJob()
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.with_log_group_id("<log_group_ocid>")
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.with_log_id("<log_ocid>")
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# The following infrastructure configurations are optional
15+
# if you are in an OCI data science notebook session.
16+
# The configurations of the notebook session will be used as defaults.
17+
.with_compartment_id("<compartment_ocid>")
18+
.with_project_id("<project_ocid>")
19+
# For default networking, no need to specify subnet ID
20+
.with_subnet_id("<subnet_ocid>")
21+
.with_shape_name("VM.Standard.E3.Flex")
22+
# Shape config details are applicable only for the flexible shapes.
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.with_shape_config_details(memory_in_gbs=16, ocpus=1)
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.with_block_storage_size(50)
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)
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.with_runtime(
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NotebookRuntime()
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.with_notebook(
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path="https://raw.githubusercontent.com/tensorflow/docs/master/site/en/tutorials/customization/basics.ipynb",
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encoding='utf-8'
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)
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.with_service_conda("tensorflow28_p38_cpu_v1")
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.with_environment_variable(GREETINGS="Welcome to OCI Data Science")
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.with_exclude_tag(["ignore", "remove"])
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.with_output("oci://bucket_name@namespace/path/to/dir")
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)
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.with_service_conda("tensorflow28_p38_cpu_v1")
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.with_environment_variable(GREETINGS="Welcome to OCI Data Science")
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.with_exclude_tag(["ignore", "remove"])
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.with_output("oci://bucket_name@namespace/path/to/dir")
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)
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)
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.. code-tab:: yaml

‎docs/source/user_guide/jobs/tabs/script_runtime.rst‎

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Original file line numberDiff line numberDiff line change
@@ -6,38 +6,38 @@
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from ads.jobs import Job, DataScienceJob, ScriptRuntime
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88
job = (
9-
Job(name="My Job")
10-
.with_infrastructure(
11-
DataScienceJob()
12-
.with_log_group_id("<log_group_ocid>")
13-
.with_log_id("<log_ocid>")
14-
# The following infrastructure configurations are optional
15-
# if you are in an OCI data science notebook session.
16-
# The configurations of the notebook session will be used as defaults.
17-
.with_compartment_id("<compartment_ocid>")
18-
.with_project_id("<project_ocid>")
19-
# For default networking, no need to specify subnet ID
20-
.with_subnet_id("<subnet_ocid>")
21-
.with_shape_name("VM.Standard.E3.Flex")
22-
# Shape config details are applicable only for the flexible shapes.
23-
.with_shape_config_details(memory_in_gbs=16, ocpus=1)
24-
.with_block_storage_size(50)
25-
)
26-
.with_runtime(
27-
ScriptRuntime()
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# Specify the service conda environment by slug name.
29-
.with_service_conda("pytorch110_p38_cpu_v1")
30-
# The job artifact can be a single Python script, a directory or a zip file.
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.with_source("local/path/to/code_dir")
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# Environment variable
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.with_environment_variable(NAME="Welcome to OCI Data Science.")
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# Command line argument
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.with_argument("100 linux \"hi there\"")
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# The entrypoint is applicable only to directory or zip file as source
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# The entrypoint should be a path relative to the working dir.
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# Here my_script.sh is a file in the code_dir/my_package directory
39-
.with_entrypoint("my_package/my_script.sh")
40-
)
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Job(name="My Job")
10+
.with_infrastructure(
11+
DataScienceJob()
12+
.with_log_group_id("<log_group_ocid>")
13+
.with_log_id("<log_ocid>")
14+
# The following infrastructure configurations are optional
15+
# if you are in an OCI data science notebook session.
16+
# The configurations of the notebook session will be used as defaults.
17+
.with_compartment_id("<compartment_ocid>")
18+
.with_project_id("<project_ocid>")
19+
# For default networking, no need to specify subnet ID
20+
.with_subnet_id("<subnet_ocid>")
21+
.with_shape_name("VM.Standard.E3.Flex")
22+
# Shape config details are applicable only for the flexible shapes.
23+
.with_shape_config_details(memory_in_gbs=16, ocpus=1)
24+
.with_block_storage_size(50)
25+
)
26+
.with_runtime(
27+
ScriptRuntime()
28+
# Specify the service conda environment by slug name.
29+
.with_service_conda("pytorch110_p38_cpu_v1")
30+
# The job artifact can be a single Python script, a directory or a zip file.
31+
.with_source("local/path/to/code_dir")
32+
# Environment variable
33+
.with_environment_variable(NAME="Welcome to OCI Data Science.")
34+
# Command line argument
35+
.with_argument("100 linux \"hi there\"")
36+
# The entrypoint is applicable only to directory or zip file as source
37+
# The entrypoint should be a path relative to the working dir.
38+
# Here my_script.sh is a file in the code_dir/my_package directory
39+
.with_entrypoint("my_package/my_script.sh")
40+
)
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)
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.. code-tab:: yaml

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