Oracle Cloud Infrastructure Documentation

Using Notebook Sessions to Build and Train Models

Once you have a notebook session created, you can write and execute Python code using the machine learning libraries in the JupyterLab interface to build and train models.

Authenticating to the OCI APIs from a Notebook Session

When working within a notebook session, you're operating as the Linux user datascience. This user doesn't have an OCI Identity and Access Management (IAM) identity, so it has no access to the OCI API. OCI resources include Data Science projects and models and the resources of other OCI services, such as Object Storage, Functions, Vault, Data Flow, and so on. To access these resources from the notebook environment, use one of the two authentication approaches:

(Recommended) Authenticating Using a Notebook Session's Resource Principal

A resource principal is a feature of IAM that enables resources to be authorized principal actors that can perform actions on service resources. Each resource has its own identity, and it authenticates using the certificates that are added to it. These certificates are automatically created, assigned to resources, and rotated, avoiding the need for you to store credentials in a notebook session.

The Data Science service enables you to authenticate using a notebook session's resource principal to access other OCI resources. Resource principals provides a more secure way to authenticate to resources compared to the OCI configuration and API key approach

A tenancy administrator must write policies to grant permissions for a resource principal to access other OCI resources, see Configuring Your Tenancy for Data Science.

You can authenticate with resource principals in a notebook session using the following interfaces:

Oracle Accelerated Data Science SDK:

Run the following in a notebook cell:

import ads
ads.set_auth(auth='resource_principal')

For details, see the Accelerated Data Science documentation.

OCI Python SDK:

Run the following in a notebook cell.

import oci
from oci.data_science import DataScienceClient
rps = oci.auth.signers.get_resource_principals_signer()
dsc = DataScienceClient(config={}, signer=rps)
OCI CLI:

Use the `--auth=resource_principal` flag with commands.

Note

The resource principal token is cached for 15 minutes. If you change the policy or the dynamic group, you must wait for 15 minutes to see the effect of the changes.
Important

If you don't explicitly use the resource principals when invoking an SDK or CLI, then the configuration file and API key approach is used

(Default) Authenticating Using OCI Configuration File and API Keys

You can operate as your own IAM user by setting up an OCI configuration file and API keys to access OCI resources. This is the default authentication approach

To authenticate using the configuration file and API key approach, you must upload an OCI configuration file into the notebook session's /home/datascience/.oci/ directory. For the relevant profile defined in the OCI configuration file, you also need to upload or create the required .pem files.

Set up the OCI configuration file and API key using Required Keys and OCIDs.

Working with Existing Code Files

You can create new files or work with your own existing files.

Uploading Files

Files can be uploaded from your local machine by clicking Upload in the JupyterLab interface or by dragging and dropping files.

Using Additional Terminal Commands

You can execute sftp, scp, curl, wget or rsync commands to pull files into your notebook session environment under the networking limitations imposed by your VCN and subnet selection.

Using the Provided Environment Variables in Notebook Sessions

When you start up a notebook session, the service creates useful environment variables that you can use in your code:

Service Provided Environment Variables

Name

Description

TENANCY_OCID

OCID of the tenancy the notebook belongs to.

PROJECT_OCID

The OCID of the project associated with the current notebook session.

PROJECT_COMPARTMENT_OCID

OCID of the compartment of the project the notebook is associated with.

USER_OCID

User OCID.

NB_SESSION_OCID

The OCID of the current notebook session.

NB_SESSION_COMPARTMENT_OCID

The compartment OCID of the current notebook session.

OCI_RESOURCE_PRINCIPAL_RPT_PATH

Path to the OCI resource principal token.

OCI_RESOURCE_PRINCIPAL_RPT_ID

Id of the OCI resource principal token.

To access these environment variables in your notebook session, use the Python os library. For example: 

import os 
project_ocid = os.environ[‘PROJECT_OCID’]
print(project_ocid)
Note

The NB_SESSION_COMPARTMENT_OCID and PROJECT_COMPARTMENT_OCID values do not update in a running notebook session if the resources has moved compartments after the notebook session was created.

Using Custom Environment Variables

Use your own custom environment variables in notebook sessions.

After you define your custom environment variables, access these environment variables in your notebook session with the Python os library. For example, if you define a key value pair with key of MY_CUSTOM_VAR1 and value of VALUE-1, then when you run the following code, you get VALUE-1. 

import os 
my_custom_var1 = os.environ[‘MY_CUSTOM_VAR1’]
print(my_custom_var1)
Note

The system does not allow you to overwrite the system provided environment variables with your custom ones. For example, you cannot name your custom variable, USER_OCID.

Using the Oracle Accelerated Data Science SDK

Oracle Accelerated Data Science (ADS) SDK speeds up common data science activities by providing tools that automate and simplify common data science tasks. It provides data scientists a friendly Python interface to OCI services including Data Science including jobs, Big Data, Data Flow, Object Storage, Streaming, and Vault, and to Oracle Database. ADS gives you an interface to manage the life cycle of machine learning models, from data acquisition to model evaluation, interpretation, and model deployment.

With ADS you can:

  • Read datasets from Object Storage, Oracle Database (ATP, ADW, and On premises), AWS S3, and other sources into Pandas data frames.
  • Tune models using hyperparameter optimization with the ADSTuner module.
  • Generate detailed evaluation reports of model candidates with the ADSEvaluator module.
  • Save machine learning models to the Data Science model catalog.
  • Deploy models as HTTP requests with model deployment.
  • Start distributed ETL, data processing, and model training jobs in Spark using Data Flow.

  • connect to the BDS from the notebook session, the cluster created must have Kerberos enabled.

    Use Kerberos enabled clusters to connect to Big Data from a notebook session.

  • Use feature types to characterize data, create meaning summary statistics, and plot. Use the warning and validation system to test the quality of data.
  • Train machine learning models using Data Science jobs.
  • Manage the life cycle of conda environments using the ads conda CLI.