modal-projects/modal-auto-research-skills/modal-gpu-experiment

Modal Experiments

Run training jobs and experiments on Modal GPUs — from single-GPU fine-tuning to multi-node distributed training with RDMA. Write a proper Modal training app: bake your code into the image, mount volumes for data and checkpoints, add secrets, and set retries for fault tolerance.

For general Modal platform usage (app structure, function types, CLI, deployment), see the modal-basic-skills skill.

Quick Reference

Training App Pattern

A Modal training app is a single Python file:

import modal
app = modal.App("my-training")
data_volume = modal.Volume.from_name("training-data", create_if_missing=True)
ckpt_volume = modal.Volume.from_name("training-checkpoints", create_if_missing=True)
image = (
    modal.Image.from_registry("nvidia/cuda:12.6.0-devel-ubuntu22.04", add_python="3.12")
    .pip_install("torch", "transformers", "datasets", "accelerate", "wandb")
    .add_local_file("train.py", remote_path="/root/train.py", copy=True)
    .add_local_dir("src/", remote_path="/root/src", copy=True)
)
@app.function(
    image=image,
    gpu="H100",
    timeout=60 * 60 * 12,
    volumes={"/data": data_volume, "/checkpoints": ckpt_volume},
    secrets=[modal.Secret.from_name("wandb-secret")],
    retries=modal.Retries(max_retries=3),
)
def train(config: str = "configs/default.yaml"):
    import subprocess
    subprocess.run(["python", "/root/train.py", "--config", config], check=True)
@app.local_entrypoint()
def main(config: str = "configs/default.yaml"):
    train.remote(config=config)

modal run my_train.py
modal run my_train.py --config configs/large.yaml

Key Patterns

• `add_local_file` / `add_local_dir`: Bake code into the image. Use `copy=True` if you chain .run_commands() after.
• Volumes: Mount persistent storage for data and checkpoints. Data survives across runs.
• Secrets: modal.Secret.from_name(...) for W&B, HuggingFace tokens, etc.
• Retries + auto-resume: modal.Retries(max_retries=N) combined with checkpoint detection for fault-tolerant training.

Persistent Storage

# List/download/upload via CLI
modal volume ls training-data /
modal volume get training-checkpoints /my-experiment/latest.pt ./latest.pt
modal volume put training-data ./dataset.tar.gz /dataset.tar.gz

See ./references/volumes.md for data caching, checkpoint patterns, and volume.commit().

Multi-Node Training

For models that need more than 8 GPUs, use @modal.experimental.clustered with RDMA for up to 3,200 Gbps inter-node bandwidth:

@app.function(gpu=f"H100:8", experimental_options={"efa_enabled": True})
@modal.experimental.clustered(size=2, rdma=True)
def train(config: str):
    cluster_info = modal.experimental.get_cluster_info()
    # Use cluster_info.rank, cluster_info.container_ips for torchrun

See ./references/training.md for full examples including NCCL configuration and torchrun integration.

Compute Options

See ./references/compute.md for the full GPU table and selection guide. Quick guide:

• Training (small < 1B): H100
• Training (1B-7B): H100:4 or H200:2
• Training (7B+): H100:8 or multi-node

References

• ./references/training.md — Full training guide (single-node, multi-node, data loading)
• ./references/volumes.md — Persistent storage patterns
• ./references/compute.md — GPU options and selection guide

See Also

• For interactive debugging before writing a training app: use the modal-gpu-dev skill
• For running experiments in parallel across multiple agents: use the sub-agents skill