Classifying Iris

This is a companion sample project of the Iris tutorial that you can find from the main GitHub documentation site. Using the timeless Iris flower dataset, it walks you through the basics.

Enjoy!

Quick CLI references

If you want to try quickly from the command line window launched from the File menu:

Kick-off many local runs to observe the metrics emitted by each run in a graph.

# Only needed if you don't have matplotlib installed
$ pip install matplotlib

$ python run.py

Run iris_sklearn.py in local environment.

$ az ml experiment submit -c local iris_sklearn.py

Run iris_sklearn.py in a local Docker container.

$ az ml experiment submit -c docker-python iris_sklearn.py

Run iris_pyspark.py in a local Docker container.

$ az ml experiment submit -c docker-spark iris_pyspark.py

Create myvm.compute file to point to a remote VM

$ az ml computetarget attach --name <myvm> --address <ip address or FQDN> --username <username> --password <pwd>

Run iris_pyspark.py in a Docker container (with Spark) in a remote VM:

$ az ml experiment submit -c myvm iris_pyspark.py

Create myhdi.compute to point to an HDI cluster

$ az ml computetarget attach --name <myhdi> --address <ip address or FQDN of the head node> --username <username> --password <pwd> --cluster

Run it in a remote HDInsight cluster:

$ az ml experiment submit -c myhdi iris_pyspark.py