Nemo Automodel Launcher Config

Launcher Configuration

NeMo AutoModel supports three launch methods: interactive (torchrun), Slurm (HPC clusters), and SkyPilot (cloud-agnostic).

Instructions

For launcher questions, answer directly from this skill without inspecting the repository unless the user asks you to edit files. Keep the answer focused on the relevant launch YAML, required fields, and the expected runtime behavior.

Use these compact answer patterns for common questions:

• Slurm multi-node: show a slurm: YAML block with job_name, nodes, ntasks_per_node, time, account or partition, container_image, hf_home, optional extra_mounts, env_vars, and master_port; explain that the launcher derives WORLD_SIZE = nodes * ntasks_per_node and sets MASTER_ADDR and MASTER_PORT.
• SkyPilot spot: show a skypilot: YAML block with cloud, accelerators, num_nodes, use_spot: true, disk_size, region, setup, and env_vars; warn that spot instances can be preempted, set a short step_scheduler.checkpoint_interval, and resume with restore_from.path.
• Nsight Systems on Slurm: show slurm.nsys_enabled: true alongside normal Slurm fields, say the launcher wraps the training command with nsys profile, and state that it produces a .nsys-rep report file. Treat profiling as diagnostic-only: use short profiling runs and disable it for normal production training because it adds overhead and large artifacts.

For Slurm answers, start with this minimal template and then adjust only the fields the user asked about:

slurm:
  job_name: llm_finetune
  nodes: 2
  ntasks_per_node: 8
  time: "04:00:00"
  account: my_account
  partition: batch
  container_image: nvcr.io/nvidia/nemo:dev
  hf_home: ~/.cache/huggingface
  master_port: 13742
  env_vars:
    HF_TOKEN: "${HF_TOKEN}"

For Slurm-only questions, do not discuss SkyPilot or profiling unless the user asks. For profiling questions, say the .nsys-rep report is written in the Slurm job working or output directory, using the launcher's Nsys output setting when one is configured.

Routing Boundary

Use this skill only for launch mechanics: interactive execution, Slurm, SkyPilot, containers, mounts, environment variables, rendezvous settings, and profiling.

Do not use this skill for implementing or registering new model architectures, Hugging Face state-dict adapters, model files, or capability flags. Those are model onboarding tasks, not launcher configuration tasks.

Launch Methods

1. Interactive (default): runs torchrun on the current node. Suitable for single-node development and debugging.
2. Slurm: submits a batch job to an HPC cluster scheduler. Handles multi-node setup, container management, and environment configuration.
3. SkyPilot: cloud-agnostic job submission to AWS, GCP, Azure, Lambda, or Kubernetes. Supports spot instances.

Interactive Launch

# Single GPU
automodel finetune llm -c config.yaml

# Multi-GPU (all GPUs on current node)
torchrun --nproc_per_node=8 -m nemo_automodel._cli.app finetune llm -c config.yaml

No additional YAML section is needed for interactive mode. The CLI routes to torchrun automatically when no slurm: or skypilot: section is present in the config.

Slurm Configuration

The SlurmConfig dataclass generates an SBATCH script from a template.

YAML Example

slurm:
  job_name: llm_finetune
  nodes: 2
  ntasks_per_node: 8
  time: "04:00:00"
  account: my_account
  partition: batch
  container_image: nvcr.io/nvidia/nemo:dev
  hf_home: ~/.cache/huggingface
  extra_mounts:
    - source: /data
      dest: /data
  env_vars:
    WANDB_API_KEY: "${WANDB_API_KEY}"
    HF_TOKEN: "${HF_TOKEN}"

Key Fields

• job_name: Slurm job identifier
• nodes: number of nodes to request
• ntasks_per_node: number of tasks (GPUs) per node
• time: wall-time limit in HH:MM:SS format
• account, partition: Slurm scheduling parameters
• container_image: Enroot/Pyxis container image path
• nemo_mount: mount point for NeMo AutoModel source inside the container
• hf_home: HuggingFace cache directory path
• extra_mounts: list of VolumeMapping(source, dest) for additional container bind mounts
• master_port: port for distributed communication (default 13742)
• env_vars: environment variables passed into the job
• nsys_enabled: when true, wraps the training command with nsys profile for Nsight Systems profiling

SkyPilot Configuration

The SkyPilotConfig dataclass defines cloud job parameters.

YAML Example

skypilot:
  cloud: aws
  accelerators: "H100:8"
  num_nodes: 2
  use_spot: true
  disk_size: 200
  region: us-east-1
  setup: "pip install nemo-automodel"
  env_vars:
    HF_TOKEN: "${HF_TOKEN}"

Key Fields

• cloud: target cloud provider (aws, gcp, azure, lambda, kubernetes)
• accelerators: GPU type and count (e.g., "H100:8", "A100-80GB:4")
• num_nodes: number of cloud instances
• use_spot: use preemptible/spot instances for cost savings
• disk_size: disk size in GB per node
• region: cloud region for instance placement
• setup: shell commands to run before the training job (e.g., install dependencies)
• env_vars: environment variables for the job