- Getting Started with Ray
- ray.data.read_sql
- Ray Dashboard
- Teradata AgentStack (See https://pypi.org/project/teradata-agentstack/)
- Go to Teradata AI Studio - Open Python Framework.
- Download () OPF-Ray_Demo_Notebooks.zip. The zip file contains:
- GettingStarted_With_Ray.ipynb
Provides a hands-on introduction to the Teradata Ray Management Service SDK. You will connect to the Ray Management Service and walk through the complete Ray cluster lifecycle: discovering available node groups, provisioning a new cluster, polling it until it is ready, inspecting its details, and finally tearing it down.
- opf_dask_on_ray.ipnyb
Demonstrates how to use the Open Python Framework to run Dask DataFrame workloads on a Ray cluster. Demonstrates ETL patterns such as derived columns, filtering, group-by aggregatios, rolling time windows, dimension joins, custom partition transforms, and final report generation.
- opf_ray_rapids.ipynb
Demonstrates how to use NVIDIA RAPIDS (cuDF, cuML) with the Ray distributed computing framework. It connects to a remote Ray cluster, verifies GPU availability, runs GPU-accelerated dataframe operations, trains and evaluates cuML models, and launches multiple GPU tasks in parallel from a notebook orchestrator.
- opf_ray_tune_model.ipynb
Demonstrates distributed hyperparameter tuning with Ray Tune over the Ray client while loading training data from a Teradata table through the teradatasql Python driver.
- opf_teradata_access_example.ipynb
Ray Batch Inference Example with Data Access from Teradata. Demonstrates an end-to-end distributed batch classification pipeline. Includes reads and writes from Teradata.
- opf_xgboost_distributed_training.ipynb
Demonstrates distributed, data-parallel model training using XGBoost on Ray Train.
- GettingStarted_With_Ray.ipynb