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LocalExecutor

Run tasks directly on your local machine in a separate subprocess.

Prerequisites

None. LocalExecutor works out of the box with a standard NeMo-Run installation.

Executor configuration

import nemo_run as run

executor = run.LocalExecutor()

LocalExecutor has no required parameters. Optional fields mirror the base Executor:

  • env_vars — extra environment variables passed to the subprocess
  • launcher — optional launcher ("torchrun", "ft", or None)
  • ntasks_per_node — number of tasks to launch per node (default: 1)

E2E workflow

import nemo_run as run

task = run.Script("python train.py --lr=3e-4 --max-steps=500")
executor = run.LocalExecutor()

with run.Experiment("my-experiment") as exp:
    exp.add(task, executor=executor, name="training")
    exp.run(detach=False)

exp.status()
exp.logs("training")

Advanced options

Torchrun launcher

Use torchrun for multi-GPU distributed training:

executor = run.LocalExecutor(
    launcher="torchrun",
    ntasks_per_node=4,   # number of GPUs
    env_vars={"NCCL_DEBUG": "INFO"},
)

Inline script

Pass a Python snippet directly without creating a file:

task = run.Script(inline="import socket; print(socket.gethostname())")

with run.Experiment("inline-experiment") as exp:
    exp.add(task, executor=run.LocalExecutor(), name="hostname")
    exp.run(detach=False)