Oracle Machine Learning
When using Oracle Machine Learning notebooks, you can export and import these between different projects and different environments (from ADW to ATP).
But something to watch out for when you import a notebook into your ADW or ATP environment is to reset the Interpreter Bindings.
When you create a new OML Notebook and build it up, the various Interpreter Bindings are automatically set or turned on. But for Imported OML Notebooks they are not turned on.
I’m assuming this will be fixed at some future point.
If you import an OML Notebook and turn on the Interpreter Bindings you may find the code in your notebook cells running very slowly
To turn on these binding, click on the options icon as indicated by the red box in the following image.
You will get something like the following being displayed. None of the bindings are highlighted.
To enable the Interpreter Bindings just click on each of these boxes. When you do this each one will be highlighted and will turn a blue color.
All done! You can now run your OML Notebooks without any problems or delays.
Oracle Autonomous Database (ADW) has been out a while now and have had several, behind the scenes, improvements and new/additional features added.
If you have used the Oracle Machine Learning (OML) component of ADW you will have seen the various sample OML Notebooks that come pre-loaded. These are easy to open, use and to try out the various OML features.
The above image shows the top part of the login screen for OML. To see the available sample notebooks click on the Examples icon. When you do, you will get the following sample OML Notebooks.
But what if you have a notebook you have used elsewhere. These can be exported in json format and loaded as a new notebook in OML.
To load a new notebook into OML, select the icon (three horizontal line) on the top left hand corner of the screen. Then select Notebooks from the menu.
Then select the Import button located at the top of the Notebooks screen. This will open a File window, where you can select the json file from your file system.
A couple of seconds later the notebook will be available and listed along side any other notebooks you may have created.
You have now imported a new notebook into OML and can now use it to process your data and perform machine learning using the in-database features.