oevortex/vtx-coding-agent/modal

Claude Code and agent skill by oevortex/vtx-coding-agent

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Run Python code in the cloud with Modal serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, serving APIs with GPU acceleration, or scientific computing requiring distributed compute.

Install

npx vskill@latest install oevortex/vtx-coding-agent/modal

SKILL.md

Modal

Serverless platform for running Python in the cloud. Execute functions on GPUs, scale to thousands of containers, pay only for compute used. Sign up free at https://modal.com ($30/month credits).

Setup

bash
pip install modal
modal token new        # Opens browser for login, stores token in ~/.modal.toml

Core Concepts

Container Images

Define dependencies with Modal Images:

python
import modal
image = (
    modal.Image.debian_slim(python_version="3.12")
    .uv_pip_install("torch", "transformers", "numpy")
)
app = modal.App("ml-app", image=image)

Patterns:

  • Python packages: .uv_pip_install("pandas", "scikit-learn")
  • System packages: .apt_install("ffmpeg", "git")
  • Docker base: modal.Image.from_registry("nvidia/cuda:12.1.0-base")
  • Local code: .add_local_python_source("my_module")
Functions
python
@app.function()
def process_data(file_path: str):
    import pandas as pd
    return pd.read_csv(file_path).describe()
@app.local_entrypoint()
def main():
    result = process_data.remote("data.csv")

Run: modal run script.py

GPUs
python
@app.function(gpu="H100")
def train():
    import torch
    assert torch.cuda.is_available()

Types: T4, L4 (inference), A10, A100, A100-80GB, L40S (48GB, best value), H100, H200, B200

Multi-GPU: gpu="H100:8" for 8x H100

Resources
python
@app.function(cpu=8.0, memory=32768, ephemeral_disk=10240)
def heavy_task():
    pass

Defaults: 0.125 CPU, 128 MiB RAM.

Autoscaling & Parallel Execution
python
@app.function()
def analyze(sample_id: int):
    return result
@app.local_entrypoint()
def main():
    results = list(analyze.map(range(1000)))  # Parallel across containers

Config: max_containers=100, min_containers=2, buffer_containers=5

Volumes (Persistent Storage)
python
volume = modal.Volume.from_name("my-data", create_if_missing=True)
@app.function(volumes={"/data": volume})
def save(data):
    with open("/data/results.txt", "w") as f:
        f.write(data)
    volume.commit()
Secrets
bash
modal secret create my-secret KEY=value API_TOKEN=xyz
python
@app.function(secrets=[modal.Secret.from_name("huggingface")])
def use_secret():
    import os
    token = os.environ["HF_TOKEN"]
Web Endpoints
python
@app.function()
@modal.web_endpoint(method="POST")
def predict(data: dict):
    return {"prediction": model.predict(data["input"])}

Deploy: modal deploy script.py

Scheduled Jobs
python
@app.function(schedule=modal.Cron("0 2 * * *"))
def daily_backup():
    pass
@app.function(schedule=modal.Period(hours=4))
def refresh_cache():
    pass

Common Patterns

ML Model Serving
python
import modal
image = modal.Image.debian_slim().uv_pip_install("torch", "transformers")
app = modal.App("llm-inference", image=image)
@app.cls(gpu="L40S")
class Model:
    @modal.enter()
    def load(self):
        from transformers import pipeline
        self.pipe = pipeline("text-classification", device="cuda")
    @modal.method()
    def predict(self, text: str):
        return self.pipe(text)
Batch Processing
python
@app.function(cpu=2.0, memory=4096)
def process_file(path: str):
    import pandas as pd
    return pd.read_csv(path).shape[0]
@app.local_entrypoint()
def main():
    for count in process_file.map(["f1.csv", "f2.csv", ...]):
        print(f"Processed {count} rows")
GPU Training
python
@app.function(gpu="A100:2", timeout=3600)
def train(config: dict):
    import torch
    # Multi-GPU training

CLI Commands

bash
modal run script.py              # Run function
modal deploy script.py           # Deploy endpoint
modal secret create name K=V     # Create secret
modal app list                   # List deployed apps
modal app hide app-name          # Hide app
modal app destroy app-name       # Destroy app

Best Practices

  1. Pin dependencies in .uv_pip_install() for reproducible builds
  2. L40S for inference, H100/A100 for training
  3. Use Volumes for model weights and datasets
  4. Set max_containers/min_containers for autoscaling
  5. Import packages inside function body if not available locally
  6. Use .map() for parallel processing
  7. Never hardcode API keys — use Secrets
  8. Monitor costs at https://modal.com/docs

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