oracle/accelerated-data-science

oracle 프로필

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수집된 skills: 8

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README 원본 파일: README.md

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# Oracle Accelerated Data Science (ADS) [![PyPI](https://img.shields.io/pypi/v/oracle-ads.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/oracle-ads/) [![Python](https://img.shields.io/pypi/pyversions/oracle-ads.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/oracle-ads/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://github.com/ambv/black) The [Oracle Accelerated Data Science (ADS) SDK](https://accelerated-data-science.readthedocs.io/en/latest/index.html) is maintained by the Oracle Cloud Infrastructure (OCI) [Data Science service](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm) 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](https://docs.oracle.com/en-us/iaas/data-science/using/models-about.htm). - Deploy models as HTTP endpoints with [Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). - Launch distributed ETL, data processing, and model training jobs in Spark with [OCI Data Flow](https://docs.oracle.com/en-us/iaas/data-flow/using/home.htm). - Train machine learning models in OCI Data Science [Jobs](https://docs.oracle.com/en-us/iaas/data-science/using/jobs-about.htm). - Define and run an end-to-end machine learning orchestration covering all the steps of machine learning lifecycle in a repeatable, continuous [ML Pipelines](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/pipeline/overview.html#). - Manage the life cycle of conda environments through the `ads conda` command line interface (CLI). ## Installation You have various options when installing ADS. ### Installing the oracle-ads base package ```bash python3 -m pip install oracle-ads ``` ### Installing OCI AI Operators To use the AI Forecast Operator, install the "forecast" dependencies using the following command: ```bash 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. ```bash 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`. ```bash 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. ```bash 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. ```bash python3 -m pip install 'oracle-ads[geo]' ``` Install the `notebook` module to use ADS within a OCI Data Science service [notebook session](https://docs.oracle.com/en-us/iaas/data-science/using/manage-notebook-sessions.htm). This module installs `ipywidgets` and `ipython` libraries. ```bash 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. ```bash 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`. ```bash 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. ```bash python3 -m pip install 'oracle-ads[optuna]' ``` Install the `tensorflow` module to include `tensorflow` and libraries from the `viz` module. ```bash python3 -m pip install 'oracle-ads[tensorflow]' ``` For text related tasks, install the `text` module. This will include the `wordcloud`, `spacy` libraries. ```bash python3 -m pip install 'oracle-ads[text]' ``` Install the `torch` module to include `pytorch` and libraries from the `viz` module. ```bash 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. ```bash 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. **Note** Multiple extra dependencies can be installed together. For example: ```bash python3 -m pip install 'oracle-ads[notebook,viz,text]' ``` ## Documentation - [Oracle Accelerated Data Science SDK (ADS) Documentation](https://accelerated-data-science.readthedocs.io/en/latest/index.html) - [OCI Data Science and AI services Examples](https://github.com/oracle/oci-data-science-ai-samples) - [Oracle AI & Data Science Blog](https://blogs.oracle.com/ai-and-datascience/) - [OCI Documentation](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm) ## Examples ### Load data from Object Storage ```python 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. ```python import ads import pandas as pd connection_parameters = { "user_name": "<user_name>", "password": "<password>", "service_name": "<tns_name>", "wallet_location": "<file_path>", } df = pd.DataFrame.ads.read_sql( """ SELECT * FROM SH.SALES WHERE ROWNUM <= :max_rows """, bind_variables={ max_rows : 100 }, connection_parameters=connection_parameters, ) ``` ## Contributing This project welcomes contributions from the community. Before submitting a pull request, please [review our contribution guide](./CONTRIBUTING.md) Find Getting Started instructions for developers in [README-development.md](https://github.com/oracle/accelerated-data-science/blob/main/README-development.md) ## Security Consult the security guide [SECURITY.md](https://github.com/oracle/accelerated-data-science/blob/main/SECURITY.md) for our responsible security vulnerability disclosure process. ## License Copyright (c) 2020, 2024 Oracle and/or its affiliates. Licensed under the [Universal Permissive License v1.0](https://oss.oracle.com/licenses/upl/)

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aqua-cli

소프트웨어 개발자

Complete CLI reference for the ADS AQUA command-line interface (ads aqua). Covers all model, deployment, evaluation, and fine-tuning commands with full parameter documentation. Triggered when user asks about CLI commands, wants to run AQUA operations from…

원문 언어: 영어

2026-02-28

aqua-deployment

소프트웨어 개발자

Deploy LLM models on OCI using AI Quick Actions (AQUA) - single model, multi-model, stacked (LoRA), with GPU shape selection, vLLM configuration, streaming, and tool calling. Triggered when user wants to deploy, update, or manage model deployments.

원문 언어: 영어

2026-02-28

aqua-evaluation

데이터 과학자

Evaluate LLM model quality using BERTScore, ROUGE, Perplexity, and Text Readability metrics on OCI AI Quick Actions (AQUA). Covers dataset preparation, evaluation job creation, and report interpretation. Triggered when user wants to evaluate or benchmark a…

원문 언어: 영어

2026-02-28

aqua-finetuning

데이터 과학자

Fine-tune LLM models using LoRA on OCI AI Quick Actions (AQUA). Covers dataset preparation (instruction, conversational, multimodal, tokenized formats), hyperparameter tuning, distributed training, and training metrics. Triggered when user wants to fine-tune…

원문 언어: 영어

2026-02-28

aqua-metrics

네트워크·컴퓨터 시스템 관리자

Set up Prometheus and Grafana monitoring for AQUA vLLM model deployments on OCI. Covers the signing proxy, container registry setup, OCI Container Instance deployment, and PromQL dashboards. Triggered when user wants to monitor LLM deployments, view…

원문 언어: 영어

2026-02-28

aqua-model-lifecycle

소프트웨어 개발자

Register, list, get, and manage LLM models in OCI AI Quick Actions (AQUA) using the ADS SDK. Triggered when user wants to import models from HuggingFace or Object Storage, browse available models, or manage model catalog entries.

원문 언어: 영어

2026-02-28

aqua-troubleshooting

소프트웨어 개발자

Diagnose and fix OCI AI Quick Actions (AQUA) issues including deployment failures, OOM errors, authorization problems, capacity issues, container errors, and policy misconfigurations. Triggered when user encounters errors or needs help debugging AQUA…

원문 언어: 영어

2026-02-28

oci-data-science

소프트웨어 개발자

OCI Data Science service patterns including Jobs, Pipelines, Model Catalog, authentication, and the ADS SDK beyond AQUA. Triggered when user asks about OCI Data Science Jobs, ML Pipelines, model artifacts, conda environments, or general ADS SDK usage.

원문 언어: 영어

2026-02-28

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