Function Runtimes#

When you create an MLRun function you need to specify a runtime kind (e.g. kind='job'). Each runtime supports its own specific attributes (e.g. Jars for Spark, Triggers for Nuclio, Auto-scaling for Dask, etc.).

MLRun supports these runtimes:

Real-time runtimes:

  • nuclio - real-time serverless functions over Nuclio

  • serving - higher level real-time Graph (DAG) over one or more Nuclio functions

Batch runtimes:

  • handler - execute python handler (used automatically in notebooks or for debug)

  • local - execute a Python or shell program

  • 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)

Common attributes for Kubernetes based functions

All the Kubernetes based runtimes (Job, Dask, Spark, Nuclio, MPIJob, Serving) support a common set of spec attributes and methods for setting the PODs:

function.spec attributes (similar to k8s pod spec attributes):

  • volumes

  • volume_mounts

  • env

  • resources

  • replicas

  • image_pull_policy

  • service_account

  • image_pull_secret

common function methods:

  • set_env(name, value)

  • set_envs(env_vars)

  • gpus(gpus, gpu_type)

  • with_limits(mem, cpu, gpus, gpu_type)

  • with_requests(mem, cpu)

  • set_env_from_secret(name, secret, secret_key)