DockerExecutor

Run tasks inside a Docker container on your local machine.

Prerequisites

• Docker Engine installed and running (docker info should succeed)

• The docker Python package (installed automatically with NeMo-Run)

Executor configuration

import nemo_run as run

executor = run.DockerExecutor(
    container_image="python:3.12",   # any accessible image
    num_gpus=-1,                      # -1 = all GPUs; 0 = CPU-only
    runtime="nvidia",                 # omit for CPU-only workloads
    ipc_mode="host",
    shm_size="30g",
    volumes=["/local/path:/path/in/container"],
    env_vars={"PYTHONUNBUFFERED": "1"},
    packager=run.Packager(),          # passthrough packager
)

Key parameters:

Parameter	Description
container_image	Docker image to use (required)
num_gpus	Number of GPUs to expose; -1 = all
runtime	Container runtime ("nvidia" for GPU support)
ipc_mode	IPC namespace mode ("host" for multi-GPU NCCL)
shm_size	Shared memory size
volumes	Host–container path bindings
packager	How to sync code into the container

E2E workflow

import nemo_run as run

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

executor = run.DockerExecutor(
    container_image="python:3.12",
    packager=run.Packager(),
)

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

Package your code into the container

Use GitArchivePackager to bundle committed code from your repo:

executor = run.DockerExecutor(
    container_image="nvcr.io/nvidia/pytorch:24.05-py3",
    packager=run.GitArchivePackager(subpath="src"),
    num_gpus=-1,
    runtime="nvidia",
)

The packaged archive is mounted at the working directory inside the container.

Torchrun for multi-GPU jobs

executor = run.DockerExecutor(
    container_image="nvcr.io/nvidia/pytorch:24.05-py3",
    num_gpus=-1,
    runtime="nvidia",
    ipc_mode="host",
    shm_size="16g",
    launcher="torchrun",
    ntasks_per_node=8,
)