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README.md

Oracle Cloud Infrastructure Data Science Jobs Service

This project showcases the usage of Oracle Cloud Infrastructure Data Science Jobs through practical examples.

The Oracle Cloud Data Science Service Jobs offers a fully managed infrastructure that allows you to easily create and run repeatable tasks. With its customizable and user-defined features, you can apply a wide range of use cases, including data preparation, model training, hyperparameter tuning, batch inference, web scraping, and more.

By using Jobs, you can take advantage of the following benefits:

  • Streamline your machine learning and data science workflow by executing tasks outside of JupyterLab notebooks.
  • Make your data science and machine learning processes more efficient and consistent by turning them into reusable operations
  • Automate your MLOps or CI/CD pipeline to simplify and speed up your work.
  • Effortlessly perform batch inference, mini-batch and distributed batch jobs.
  • Take advantage of distributed training capabilities with popular frameworks such as Horovod, TensorFlow, PyTorch and Dask.

Jobs not only support Python and Shell scripts, but also gives you the flexibility to run custom code in any language or environment of your choice by bringing your own container image BYOC

Introduction

This repository features a collection of folders, each dedicated to a specific programming language, showcasing how to use the OCI Jobs SDK client. for example:

  • cli, node,python,shell, ts+js the programing language client SDK samples

Within each programming language folder, there are sub-folders that contain sample Jobs for that language. For example, in the Python folder, you will find samples written in Python.

  • sdk Oracle Cloud SDK Jobs API implementation samples
  • job+samples actual code samples you could run as a Job

Getting Started

The following are the key requirements that must be fulfilled before you can use Oracle Cloud Infrastructure Data Science Jobs.

If You're Doing a Proof-of-Concept

If you're just trying out Oracle Cloud Infrastructure Data Science or doing a proof-of-concept project, you may not need more than a few administrators with full access to everything. In that case, you can simply create any new users you need and add them to the Administrators group. The users will be able to do anything with any kind of resource, and you can create all your resources directly in the tenancy (the root compartment). You don't need to create any compartments yet, or any other policies beyond the Tenant Admin Policy, which automatically comes with your tenancy and can't be changed. Additionally you have to create following resources:

  1. Create a Dynamic Group in your cloud tenancy with the following matching rules:  

    all { resource.type = 'datasciencenotebooksession' }
all { resource.type = 'datasciencejobrun' }
all { resource.type = 'datasciencemodeldeployment' }
all { resource.type = 'datasciencepipelinerun' }

  2. Create a policy in your root compartment with the following statements:  

    allow service datascience to use virtual-network-family in tenancy
allow dynamic-group <your-dynamic-group-name> to manage data-science-family in tenancy
allow dynamic-group <your-dynamic-group-name> to manage all-resources in tenancy

      Replace <your-dynamic-group-name> with the name of your dynamic group!  

  3. If you create new users add them to your Administrators Group

If You're Past the Proof-of-Concept Phase

If you're past the proof-of-concept phase and want to restrict access to your resources, first:

Local Enviroment Setup

If you anticipate to test run jobs code on your local machine, you have to install OCI SDK and configure your API Auth Token.

The OCI API Auth Token is used for the OCI CLI and Python SDK, as well as all other OCI SDK supported languages. Follow the guidance from the online documentation to configure it: https://docs.oracle.com/en-us/iaas/Content/API/Concepts/apisigningkey.htm

At the high level the instructions are:

  • (1) login into your Oracle Cloud
  • (2) select your account from the top right dropdown menu
  • (3) generate a new API Auth Key
  • (4) download the private key and store it into your $HOME/.oci folder on your local machine
  • (5) copy the suggested configuration and store it into config file under your home directy $HOME/.oci/config
  • (6) change the $HOME/.oci/config with the suggested configuration in (5) to point to your private key
  • (7) test the SDK or CLI

For more detailed explanations, follow the instructions from our public documentation.

Oracle Accelerated Data Science SDK (short ADS) provides easy to use CLI to create and run your jobs, check it out!

Your First Job

Let's create simple hello world job and run it on Oracle Cloud Infrastructure Data Science Jobs Service.

Step 1 - prepare a job sample code

Create a file called hello_world_job.py

import time
from pathlib import Path

print("Hello world!")
time.sleep(3)

# 
current_path = Path(sys.path[0])
print (f'current path: {current_path}')

print("Job Done.")

Step 2 - create a job

Login with your Oracle Cloud account then:

  • In the Oracle Cloud console, select the Data Science Service
  • Click the Create Project button and create a new project
  • Select the new project and go to Jobs under Resources
  • Click the Create job button.
  • Under Upload job artifact, click the select a file link and select the file hello_world_job.py from your computer.
  • In the Logging section, make sure Enable logging is selected and choose your Log Group from the drop-down menu. If no Log Group appears, create one.
  • Ensure Enable automatic log creation is selected and click the Select button.
  • Set the Storage to at least 50GB.
  • Keep the Default Networking configuration.
  • Click Create to create the job.

Step 3 - run the job

  • in the Oracle Cloud console under the Jobs resource select the job that you created in the previous step
  • click on the Start a job run button to start the job
  • there is no need to override the job configuration, leave everything as-is
  • click on the Start button

The job will automatically provison the specified compute shape and execute your code. You can click on the newly created Job Run to monitor the progress. Under the Logging details of the Job Run a Log will be created, you can click on the link to view the stdout output from your code in the OCI Logging Service.

What's Next?

Check our code samples and follow our tutorials.

Samples

This repository has following samples:

  • Fn - Oracle Function example for how to start Job Runs
  • byoc - Bring Your Own Container guide for Jobs and samples
  • cli - Oracle OCI CLI client samples of how to create and run jobs
  • custom_metrics - emit custom metrics from your Job Run and query metric values on your local machine
  • java - Java client code implementing utilizing the Oracle Cloud Jobs Java OCI SDK
  • job_monitoring - a simple application to monitor multiple Job Run logs on your local or compute machine
  • python - OCI SDK API sample, as well as actual Job simple samples written in Python
  • shell - shell scripts example that can be executed as a Job
  • ts+js - client OCI SDK TypeScript and JavaScript samples of how to use to create and run Job
  • zip+tar - provides ZIP or TAR packaged samples that can be run as a Job

Tutorials

💡 You can find our tutorials section under the tutorials folder.

General

Model Training