4 OML Notebooks

Oracle Machine Learning Notebooks is an enhanced web-based notebook platform for data analyst and data scientists. You can write code, text, create visualizations, and perform data analytics including machine learning. Notebooks work with interpreters in the back-end. In Oracle Machine Learning user interface, notebooks are available in a project, where you can create, edit, delete, copy, move, and even save notebooks as templates.

• Oracle Machine Learning Notebooks
  Oracle Machine Learning Notebooks is an enhanced web-based notebook platform for data engineers, data analyst, R and Python users, and data scientists. You can write code, text, create visualizations, and perform data analytics including machine learning. Notebooks work with interpreters in the back-end.
• GitHub Notebooks
  Oracle Machine Learning UI supports direct interaction of Oracle Machine Learning Notebooks with your external GitHub repositories. You can now directly import notebooks from your GitHub repositories.
• Enable GPU Compute Capabilities in a Notebook through the Python Interpreter
  This topic demonstrates how to enable GPU compute capabilities in a notebook through the Python interpreter. It also shows how to get information about the current GPU on which the notebook is running, and other details.
• Visualize your Data in Oracle Machine Learning Notebooks
  Oracle Machine Learning Notebooks offer rich visualization capabilities of your data. The visualizations depend on the type of your dataset.
• Export a Notebook
  You can export a Notebook in Native format (.dsnb) file, Zeppelin format ( .json ) file, in Jupyter format ( .ipynb ), and later import them in to the same or a different environment.
• Import a Notebook
  You can import notebooks into your Oracle Machine Learning UI projects. Oracle Machine Learning UI supports the import of notebooks in the native format (.dsnb), Zeppelin (.json) and Jupyter (.ipynb) notebooks. Oracle Machine Learning UI also supports importing notebooks from your GitHub repository.
• Use the SQL Interpreter in a Notebook Paragraph
  An Oracle Machine Learning notebook supports multiple languages. Each paragraph is associated with a specific interpreter. For example, to run SQL statements use the SQL interpreter. To run PL/SQL statements, use the script interpreter.
• Use the Python Interpreter in a Notebook Paragraph
  An Oracle Machine Learning notebook supports multiple languages. Each paragraph is associated with a specific interpreter. To run Python commands in a notebook, you must first connect to the Python interpreter. To use OML4Py, you must import the oml module.
• Use the R Interpreter in a Notebook Paragraph
  An Oracle Machine Learning notebook supports multiple languages. Each paragraph is associated with a specific interpreter. To run R functions in an Oracle Machine Learning notebook, you must first connect to the R interpreter.
• Use the Conda Interpreter in a Notebook Paragraph
  Oracle Machine Learning Notebooks provides a Conda interpreter to enable administrators to create conda environments with custom third-party Python and R libraries. Once created, you can download and activate Conda environments inside a notebook session also using the Conda interpreter.
• Use the Scratchpad
  The Scratchpad provides you convenient one-click access to a notebook for running SQL statements, PL/SQL, R, and Python scripts that can be renamed. The Scratchpad is available on the Oracle Machine Learning User Interface (UI) home page.
• Use the Markdown Interpreter and Generate Static html from Markdown Plain Text
  Use the Markdown interpreter and generate static html from Markdown plain text.
• About Interpreters and Notebook Service Levels
  An interpreter is a plug-in that allows you to use a specific data processing language backend.