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MMT SDK tutorial

MMT stands for multi-machine training. An MMT run launches the same workload across multiple machines and tracks the group as one managed resource. Use MMT when a training framework or distributed data job needs several workers instead of a single node.

An MMT run has the same runtime choices as a single-machine job:

  • A Studio environment, which reuses code and dependencies from a Studio.
  • A Docker image, which packages the runtime independently of Studios.

The number of machines must be greater than one.

Prerequisites

Install and authenticate:

$ pip install lightning-sdk -U
$ lightning login

For Studio-backed MMT runs, use an existing Studio in the target teamspace. The SDK example fetches that Studio by name with create_ok=False.

The SDK form lets Python own the launch, wait, and inspection flow:

.. literalinclude:: ../../../examples/mmts.py
   :language: python
   :start-after: # sdk-studio-mmt-start
   :end-before: # sdk-studio-mmt-end
   :dedent: 8

For a container runtime, replace studio= with image=:

.. literalinclude:: ../../../examples/mmts.py
   :language: python
   :start-after: # sdk-image-mmt-start
   :end-before: # sdk-image-mmt-end
   :dedent: 8

Run the companion script directly when you want to execute the SDK example:

$ python python/examples/mmts.py --teamspace teamspace --org owner studio --studio sdk-tutorial-studio
$ python python/examples/mmts.py --teamspace teamspace --org owner image

Operational notes

  • MMT.run creates a new multi-machine job; MMT("name", teamspace=...) fetches an existing one.
  • mmt.machines returns the per-machine job handles for detailed inspection.
  • MMT.run rejects num_machines values less than two.
  • Studio-backed MMT runs must use a Studio in the same teamspace and cloud account as the run.
  • The MMT object exposes high-level status and metadata; inspect the worker jobs when you need machine-level logs or failures.