compute-modal

name	compute-modal
description	Modal remote GPU compute. Use for CUDA workloads, fine-tuning, large training, or anything the local machine can't fit. Requires explicit user approval per plan (cost > 0).
category	Compute
tags	["compute","modal","gpu","training","remote"]
triggers	["modal","train large model","fine-tune","gpu cloud","cuda"]
license	MIT
metadata	{"skill-author":"Research Copilot"}

Modal Compute

Modal backend (backend: 'modal', toolPrefix: 'modal') shells out to the modal CLI to run scripts on Modal's remote GPU infrastructure. Bills the user's Modal account. Every plan requires explicit user approval in the Compute tab before execution — no auto-approval.

Prerequisites

The user must have:

• pip install modal (or uv pip install modal) somewhere on PATH
• A Modal token: modal token new once, OR MODAL_TOKEN_ID + MODAL_TOKEN_SECRET in Settings → API Keys

If either is missing, list_compute_backends will show available: false with missingRequirements. Surface the message to the user — they need to install Modal CLI / configure credentials before Modal plans can execute.

Tool flow

1. Confirm availability: list_compute_backends().
2. compute_plan({ backend: 'modal', command, script_path, task_description }) — runs a planning sub-agent that reads the script and extracts the declared Modal image + GPU configuration, then estimates cost.
3. Response includes requires_approval: true and message — tell the user a Modal plan is awaiting approval in the Compute tab. Do NOT call modal_execute until they approve.
4. modal_execute({ plan_id }) — submits the run. If approval is still pending, returns waiting_for_approval: true; if user rejected, returns rejected: true with the rejection comments.
5. Monitor with modal_status / modal_wait. Stop with modal_stop.

Modal script template

import modal

image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("torch==2.8.0", "numpy", "pandas")
)
app = modal.App("research-copilot-job")

@app.function(gpu="A10G", image=image, timeout=60 * 60)
def run_job():
    import torch
    # Heavy imports + work inside the function body
    print('##PROGRESS## {"step": 1, "total": 3, "phase": "setup"}')
    # train / evaluate / process here

@app.local_entrypoint()
def main():
    run_job.remote()

GPU selection

The planner extracts the GPU declared by @app.function(gpu=…) and feeds it to the cost estimator.

GPU	Use case	~Rate ($/hr)
T4	Small inference / light eval	0.59
A10G	Default for ordinary PyTorch/TensorFlow training	1.10
A100	Larger models / memory-heavy training	3.72
A100-80GB	Larger context / longer sequences	4.28
H100	Very large or perf-critical jobs only	8.10
L4	Cheap inference, video / image workloads	0.80

When multiple GPUs are declared, the cost estimator picks the highest and adds a warning.

Cost awareness

compute_plan returns a cost_estimate with estimatedTotalUsd, hourlyRateUsd, and coverage: 'lower_bound' (only GPU time is modeled; CPU/RAM/idle-container are not). The user's configured Modal cost threshold (Settings → Compute) auto-kills runs whose elapsed cost exceeds the threshold.

Mention the estimated cost when surfacing the plan to the user so they know what they're approving.

Progress lines

Same convention as local: print ##PROGRESS## {...} on stdout; the runner extracts them via modal_status/modal_wait.

Image declaration tips

• Use uv_pip_install over pip_install when available — it's faster.
• Pin versions for reproducibility: torch==2.8.0, not just torch.
• Declare runtime GPU on @app.function(gpu=…), not just on the image build, so cost estimation finds it.
• Avoid .add_local_dir of the whole workspace — only the directories the script reads at runtime.