Creating a Job
Create and run a job in Data Science.
Ensure that you have created the necessary policies, authentication, and authorization for your jobs.
Before you begin:
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To store and manage job logs, learn about logging.
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To use storage mounts, you must have an Object Storage bucket or OCI File Storage Service (FSS) mount target and export path.
To use FSS, you must first create the file system and the mount point. Use the custom networking option and ensure that the mount target and the notebook are configured with the same subnet. Configure security list rules for the subnet with the specific ports and protocols.
Ensure that service limits are allocated to
file-system-count
andmount-target-count
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To use storage mounts, you must have an Object Storage bucket or OCI File Storage Service (FSS) mount point.
When creating jobs, you can use the fast launch enabled Compute shapes when they're available in the region. This launches the job in the fastest way possible.
These environment variables control the job.
Tip
You can use the
ListFastLaunchJobConfigs
API to retrieve the configurations that are fast launch capable.Use the Data Science CLI to create a job as in this example:
The ADS SDK is also a publicly available Python library that you can install with this command:
pip install oracle-ads
It provides the wrapper that makes the creation and running jobs from notebooks or on your client machine easy.
Use the ADS SDK to create and run jobs.
Tip
You can use the
ListFastLaunchJobConfigs
API to retrieve the configurations that are fast launch capable.