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Oracle Accelerated Data Science SDK

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Oracle Accelerated Data Science (ADS)

PyPI Python Code style: black

The Oracle Accelerated Data Science (ADS) SDK is maintained by the Oracle Cloud Infrastructure (OCI) Data Science service team. It speeds up common data science activities by providing tools that automate and simplify common data science tasks. Additionally, provides data scientists a friendly pythonic interface to OCI services. Some of the more notable services are OCI Data Science, Model Catalog, Model Deployment, Jobs, ML Pipelines, Data Flow, Object Storage, Vault, Big Data Service, Data Catalog, and the Autonomous 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 Oracle Object Storage, Oracle RDBMS (ATP/ADW/On-prem), AWS S3 and other sources into Pandas dataframes.
  • Tune models using hyperparameter optimization with the ADSTuner tool.
  • Generate detailed evaluation reports of your model candidates with the ADSEvaluator module.
  • Save machine learning models to the OCI Data Science Model Catalog.
  • Deploy models as HTTP endpoints with Model Deployment.
  • Launch distributed ETL, data processing, and model training jobs in Spark with OCI Data Flow.
  • Train machine learning models in OCI Data Science Jobs.
  • Define and run an end-to-end machine learning orchestration covering all the steps of machine learning lifecycle in a repeatable, continuous ML Pipelines.
  • Manage the life cycle of conda environments through the ads conda command line interface (CLI).


You have various options when installing ADS.

Installing the oracle-ads base package

  python3 -m pip install oracle-ads

Installing OCI AI Operators

To use the AI Forecast Operator, install the "forecast" dependencies using the following command:

  python3 -m pip install 'oracle_ads[forecast]>=2.9.0'

Installing extras libraries

To work with gradient boosting models, install the boosted module. This module includes XGBoost and LightGBM model classes.

  python3 -m pip install 'oracle-ads[boosted]'

For big data use cases using Oracle Big Data Service (BDS), install the bds module. It includes the following libraries, ibis-framework[impala], hdfs[kerberos] and sqlalchemy.

  python3 -m pip install 'oracle-ads[bds]'

To work with a broad set of data formats (for example, Excel, Avro, etc.) install the data module. It includes the fastavro, openpyxl, pandavro, asteval, datefinder, htmllistparse, and sqlalchemy libraries.

  python3 -m pip install 'oracle-ads[data]'

To work with geospatial data install the geo module. It includes the geopandas and libraries from the viz module.

  python3 -m pip install 'oracle-ads[geo]'

Install the notebook module to use ADS within a OCI Data Science service notebook session. This module installs ipywidgets and ipython libraries.

  python3 -m pip install 'oracle-ads[notebook]'

To work with ONNX-compatible run times and libraries designed to maximize performance and model portability, install the onnx module. It includes the following libraries, onnx, onnxruntime, onnxmltools, skl2onnx, xgboost, lightgbm and libraries from the viz module.

  python3 -m pip install 'oracle-ads[onnx]'

For infrastructure tasks, install the opctl module. It includes the following libraries, oci-cli, docker, conda-pack, nbconvert, nbformat, and inflection.

  python3 -m pip install 'oracle-ads[opctl]'

For hyperparameter optimization tasks install the optuna module. It includes the optuna and libraries from the viz module.

  python3 -m pip install 'oracle-ads[optuna]'

Install the tensorflow module to include tensorflow and libraries from the viz module.

  python3 -m pip install 'oracle-ads[tensorflow]'

For text related tasks, install the text module. This will include the wordcloud, spacy libraries.

  python3 -m pip install 'oracle-ads[text]'

Install the torch module to include pytorch and libraries from the viz module.

  python3 -m pip install 'oracle-ads[torch]'

Install the viz module to include libraries for visualization tasks. Some of the key packages are bokeh, folium, seaborn and related packages.

  python3 -m pip install 'oracle-ads[viz]'

See pyproject.toml file [project.optional-dependencies] section for full list of modules and its list of extra libraries.


Multiple extra dependencies can be installed together. For example:

  python3 -m pip install  'oracle-ads[notebook,viz,text]'



Load data from Object Storage

  import ads
  from ads.common.auth import default_signer
  import oci
  import pandas as pd

  ads.set_auth(auth="api_key", oci_config_location=oci.config.DEFAULT_LOCATION, profile="DEFAULT")
  bucket_name = <bucket_name>
  key = <key>
  namespace = <namespace>
  df = pd.read_csv(f"oci://{bucket_name}@{namespace}/{key}", storage_options=default_signer())

Load data from ADB

This example uses SQL injection safe binding variables.

  import ads
  import pandas as pd

  connection_parameters = {
      "user_name": "<user_name>",
      "password": "<password>",
      "service_name": "<tns_name>",
      "wallet_location": "<file_path>",

  df =
      SELECT *
      WHERE ROWNUM <= :max_rows
      bind_variables={ max_rows : 100 },


This project welcomes contributions from the community. Before submitting a pull request, please review our contribution guide

Find Getting Started instructions for developers in


Consult the security guide for our responsible security vulnerability disclosure process.


Copyright (c) 2020, 2024 Oracle and/or its affiliates. Licensed under the Universal Permissive License v1.0

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