datarobot

library3.16.0pypypi✓ verified 84d ago

The DataRobot Python Client Library (v3.13.0) provides a programmatic interface for interacting with the DataRobot AI Platform, enabling users to manage projects, create models, perform predictions, and automate MLOps workflows. It follows a regular release cadence, typically monthly or bi-monthly, aligning with platform updates.

ai-ml devops llm-agents aws

pip install datarobot

package ↗homepage ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · DATAROBOT

datarobot

ai-mlpythonv3.16.0

Install

9.2s avg

Import

2770ms

Disk

182MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.16.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 2.839s · 179.7MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 9.2s · import 2.701s · 173MB

182MB installed

● package 182MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Client

✓ from datarobot import Client

✗ import datarobot as dr dr.Client()

This quickstart demonstrates how to initialize the DataRobot client, which by default reads configuration from environment variables (DATAROBOT_ENDPOINT, DATAROBOT_API_TOKEN). It then lists the first five projects accessible to the authenticated user. Ensure your environment variables are set for successful execution.

import datarobot as dr import os # Configure client using environment variables # DATAROBOT_ENDPOINT e.g., 'https://app.datarobot.com/api/v2' # DATAROBOT_API_TOKEN # DATAROBOT_USERNAME (optional, for specific auth methods) # DATAROBOT_PASSWORD (optional, for specific auth methods) # For testing, ensure these are set in your environment or provide defaults. endpoint = os.environ.get('DATAROBOT_ENDPOINT', 'YOUR_DATAROBOT_ENDPOINT') token = os.environ.get('DATAROBOT_API_TOKEN', 'YOUR_DATAROBOT_API_TOKEN') if 'YOUR_DATAROBOT_ENDPOINT' in endpoint or 'YOUR_DATAROBOT_API_TOKEN' in token: print("Please set DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN environment variables or replace placeholders.") else: try: # Client auto-discovers config from environment variables by default. # Explicit config can be passed via dr.Client(token='...', endpoint='...'). dr.Client() print("Successfully connected to DataRobot.") # List first 5 projects projects = dr.Project.list(max_projects=5) if projects: print("\nFirst 5 projects:") for p in projects: print(f" - {p.name} (ID: {p.id})") else: print("No projects found in your account.") except dr.errors.ClientInitializationError as e: print(f"Error initializing DataRobot client: {e}") print("Ensure DATAROBOT_ENDPOINT and DATAROBOT_API_TOKEN environment variables are correctly set.") except Exception as e: print(f"An unexpected error occurred: {e}")

Debug

Known issues

breakingThe `datarobot.Client` initialization changed significantly in version 3.0. Prior versions often required explicitly passing `endpoint` and `token` arguments to the `Client` constructor. Version 3.0+ defaults to auto-discovery from environment variables (DATAROBOT_ENDPOINT, DATAROBOT_API_TOKEN) or a configuration file.

fix

For versions 3.0 and newer, set `DATAROBOT_ENDPOINT` and `DATAROBOT_API_TOKEN` environment variables. If you need to explicitly pass them, use `dr.Client(endpoint='...', token='...')`.

affects: <3.0 to 3.0+

breakingMethods for interacting with the prediction API (e.g., `predict_raw`) may have changed signatures or availability across major versions, particularly regarding how data is passed and results are retrieved.

fix

Consult the DataRobot Python Client documentation for your specific version, especially for `Project.get_recommended_model().predict()` or `Deployment.predict()`, to ensure correct method signatures and return types.

affects: <3.0 to 3.0+

gotchaConnecting from corporate networks can often lead to `SSLError` due to proxy configurations or custom CA certificates. The Python `requests` library (used by DataRobot client) might not trust these certificates by default.

fix

Set the `REQUESTS_CA_BUNDLE` environment variable to the path of your corporate CA certificate bundle or `CURL_CA_BUNDLE` for similar effect. Alternatively, you might need to configure proxy settings via `HTTP_PROXY` and `HTTPS_PROXY` environment variables. As a last resort (not recommended for production), `dr.Client(verify_ssl=False)` can temporarily bypass SSL verification.

affects: All versions

gotchaWhen working with large datasets, uploading or downloading them directly through the client can be slow or hit memory limits. DataRobot provides specific methods for large data handling.

fix

For large dataset uploads, consider using `dr.Dataset.create_from_file(filepath, max_wait=3600)` and ensure your client timeout is sufficient. For downloading predictions, use methods that allow for streaming or chunking, or retrieve predictions from a prediction server/deployment directly.

affects: All versions

Upgrade

Version history

3.16.0latest on PyPI · released May 27, 2026

Audit

Dependencies

No dependency data recorded yet.

Agent activity

40 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/datarobot/ ↗

homepagehttps://datarobot.com ↗

docshttps://docs.datarobot.com/en/docs/api/reference/sdk/index.html ↗

changeloghttps://docs.datarobot.com/en/docs/api/reference/changelogs/py-changelog/index.html ↗