One of HPE Ezmeral ML Ops' advantages is the integration of Jupyter
notebook with the ability to submit large distributed training
workloads on the platform’s training clusters.
Data Scientists create the code in the Jupyter notebook to build a model and
train it either in the notebook or on a larger distributed training cluster
configuration on the HPE Ezmeral ML Ops platform within their own tenant namespace.
As a Developer, navigate to the training dashboard
to create a new training cluster.
Enter a name for the new Training cluster and if
needed we have the option of adding a description.
We could then select the application to install using the
'Run Time Image' pull-down menu, in this case it is already default filled in.
The 'Notebook Server' section will display a list of the virtual node role(s) that
have been defined for the selected distribution,
along with the minimum required flavor and/or node count.
These are the pre-configured flavors in HPE Ezmeral ML Ops platform,
but we can create different flavors to suit their needs, including some with GPUs.
Select the number of virtual nodes to create for each role by either
entering a number in the Node Count field or using the up/down arrows.
When you have finished entering the parameters for the new cluster,
click Submit to create the new training cluster.
We can now create a Notebook cluster.
Enter a name for the new Notebook cluster and if
needed we have the option of adding a description.
We then associate with the training cluster we just created.
Any training job runs in the training cluster by
attaching the training cluster from the notebook cell magic.
All the data processing work will be done in the context of the notebook.
HPE Ezmeral ML Ops simplifies the model development process by providing
users with their tools, packages and libraries of choice.
Building and training a model in a Jupyter notebook like we did in this demo,
reduces time for data processing, model building, training and deployment.
