What are the different runtimes in MLRun?

I'm trying to get a feel for how MLRun executes my Python code. What different runtimes are supported and why would I use one vs the other?

MLRun has several different ways to run a piece of code. At this time, the following runtimes are supported:

• Batch runtimes
  • local - execute a Python or shell program in your local environment (i.e. Jupyter, IDE, etc.)
  • job - run the code in a Kubernetes Pod
  • dask - run the code as a Dask Distributed job (over Kubernetes)
  • mpijob - run distributed jobs and Horovod over the MPI job operator, used mainly for deep learning jobs
  • spark - run the job as a Spark job (using Spark Kubernetes Operator)
  • remote-spark - run the job on a remote Spark service/cluster (e.g. Iguazio Spark service)
• Real-time runtimes
  • nuclio - real-time serverless functions over Nuclio
  • serving - higher level real-time Graph (DAG) over one or more Nuclio functions

If you are interested in learning more about each runtime, see the documentation.