There are few steps that needs to be performed to use Snap ML in DSXL environment. These are outlined as follows:
- Go to the IBM DSXL web console.
- Login with your username and password.
- On the IBM DSXL homepage dashboard, click Add project.
- Once you click Add project, a new window named Create Project appears where you can create a notebook from a file or a github repository. The window looks like the one shown below.
- In this example, we will use specific github repository links in order to use the snap-ml-local environment. For the current release, there are 2 such github repository links with example notebooks. These notebooks demonstrate the advantage of using
snap-ml-localenvironment for a machine learning pipeline.
- Under New tab, type a name for the project and enter a brief description for the project. For example, create a project named snapml_dsxl as shown below.
- After the project is created, you will see a page like the one shown below.
- Under Assets, select notebooks and click Add Notebook. The Create Notebook window appears.
- If you want, you can set the number of CPU cores, the number of GPUs and memory limit depending upon the machine configuration.
- Under the project title snapml_dsxl, move to the From URL tab. Name the notebook. Add a description if you want. Add the notebook url as
https://github.com/ibmsoe/snap-ml/blob/master/notebooks/credit-default-prediction-example.ipynb. Select the environment as Jupyter with Python 3.6 and PowerAI v1.5.3 for GPU. The window appears as follows after all these entries.