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Version: v0.1.7 - Created: 20 Apr 2023

Visit the landing page for more information including sample notebooks, video clips and more ... Teradata Join the community discussion at Community Home - Community - Ecosystem Management Forum, topic: Jupyter Note: The docker image is also available on Docker Hub https://hub.docker.com/r/teradata/ai-unlimited-jupyter   Teradata AI Unlimited Jupyter Teradata AI Unlimited Jupyter provide extensions to the JupyterLab platform to enhance the user experience connecting, orchestrating and executing SQL statements on Teradata AI Unlimited . These extensions include the Teradata SQL Kernel, Navigator and Connection Manager. In addition, in the Docker image, we have bundled the Teradata driver for Python and teradataml library as well as the Teradata driver for R and tdplyr library. The teradataml and tdplyr libraries contain the analytic functions that interact with Teradata in the Python and R languages respectively. The SQL Kernel Provides: Teradata AI Unlimited orchestration and management of the projects and engines using custom magic commands Integration with Github to support project execution Connection management to add, remove, connect, and list connections Query engine that uses embedded Teradata SQL driver SQL aware notebook with SQL content assist and syntax checking Result set renderer that displays result data in easy to read, scrollable grid Execution history that stores execution metadata to recall SQL commands at a later time Visualization using Vega library to display charts, graphs, plots, etc. Support for basic data load from a file  Preference settings allow users to modify logging options for the SQL kernel Magic commands that provide additional custom kernel options to enhance the Teradata user experience The Navigator Provides: The ability to explore the Advanced SQL Engine catalog, regardless of the language you are using in your notebook (SQL, Python, R). Hierarchical display of SQL object-relational model Column metadata showing data type and indexes Row Count, Show DDL, Refresh, Sample Data, Profiling Information, and Column Distribution menu options The Connection Manager Provides: Connection management to add, remove, edit, copy, list, and test connections User interface that is independent of the SQL notebook Connections that are shared with the Navigator and SQL notebooks

Version: 05.00.00.01 - Created: 07 Feb 2023

Bring Your Own Model Overview  The Vantage Bring Your Own Model (BYOM) package gives data scientists and analysts the ability to operationalize predictive models in Vantage. Predictive models trained in external tools with sample data can be used to score data stored in Vantage using the BYOM Predict. Create or convert your predictive model using a supported model interchange format (PMML, MOJO, ONNX, Dataiku, and DataRobot currently available), store it in a Vantage table, and use the BYOM PMMLPredict, H2OPredict, ONNXPredict, DataikuPredict, or DataRobotPredict to score your data with the model. For installation instructions please review the readme file above. H2O Installation Options The ability to score H2O MOJO OpenSource and Driverless AI (DAI) models has been added to the BYOM product via the H2OPredict function.  While there are no extra steps necessary to use the function to score H2O MOJO OpenSource models, there is a requirement to have a license key from H2O to be able to score the Driverless AI models.  This license key must be inserted into a Teradata table and used within the query to score H2O MOJO DAI models.     Encrypt_dai_license.sh provides an option of encrypting the H2O DAI license key before inserting it to the license table in the Teradata database. Simply follow the instructions given in the encrypt_readme.txt file to generate the encrypted license key string and insert that into the Teradata table. This script can only be run on a Linux or macOS machine.   Userexamples.zip contains the examples in the user guide. More information is contained in the userexamples_readme.txt. For additional information, please refer to the Teradata Vantage - Bring Your Own Model User Guide (B700-1111-051K)

Version: 1.4.0.1 - Created: 09 Nov 2022

tdapiclient - Teradata Third Party Analytics Integration Python Library The tdapiclient Python library integrates the Python libraries from AWS SageMaker, Azure ML, and Google Vertex AI with Teradata. Users can train and score their models using teradataml DataFrame. tdapiclient will transparantly convert the teradataml DataFrame to an S3 address, Azure ML Dataset or Blob, or Vertex AI dataset to be used for training. The user can then provide another teradataml DataFrame as input for inference. Users of tdapiclient can also deploy models trained in Azure ML, AWS SageMaker, or Vertex AI to a Teradata Vantage system for in-database scoring using BYOM functionality. This library also provides API_Request, a method to call API_Request UDF, which can be used for obtaining OpenAI and Azure OpenAI text embeddings from large language models. This method can also be used for scoring models hosted in AWS, Azure, or Google Cloud Platform, equivalent to predicting in UDF mode through the tdapiclient predict method. Teradata recommends downloading tdapiclient library from PyPi location : https://pypi.org/project/tdapiclient/. Download from downloads.teradata.com location if your organization does not allow you to install directly from https://pypi.org/project/tdapiclient/.

Version: Python3.7.7_sles12sp3 - Created: 28 Oct 2022

The sandbox environment is based on a SLES12 SP3 docker image that contains a Python distribution (interpreter and add-on libraries) based on the latest Teradata In-nodes Python release for SLES12-SP3: Python interpreter (Version 3.7.7 or Version 3.6.7) Add-on libraries for Python User can choose to setup the docker environment and test Python scripts by running them inside the docker container, or user can directly execute scripts on Vantage. The sandbox environment docker image  name is "stosandbox:1.0". The docker image size is about 4 GB. Due to the large size, Teradata recommends downloading it beforehand, and saving it into a local folder.

Version: 20.00.21.00 - Created: 06 Oct 2022

TTU macOS Package This Teradata Tools and Utilities (TTU) 20.00 package is the full collection of Teradata client tools for macOS. This includes load & unload utilities, open interfaces and drivers to be used to connect to your Teradata Advanced SQL (database) instance.  Installation is easy and simple.  The size of the download zip file is ~50 MB.   For feedback, discussion, and community support, please visit the Tools forum. For Vantage Desktop Editor FAQ's please visit FAQ's.