modal（Modal 云执行）

Modal

Overview

Modal is a cloud platform for running Python code serverlessly, with a focus on AI/ML workloads. Key capabilities:

• GPU compute on demand (T4, L4, A10, L40S, A100, H100, H200, B200)
• Serverless functions with autoscaling from zero to thousands of containers
• Custom container images built entirely in Python code
• Persistent storage via Volumes for model weights and datasets
• Web endpoints for serving models and APIs
• Scheduled jobs via cron or fixed intervals
• Sub-second cold starts for low-latency inference

Everything in Modal is defined as code — no YAML, no Dockerfiles required (though both are supported).

When to Use This Skill

Use this skill when:

• Deploy or serve AI/ML models in the cloud
• Run GPU-accelerated computations (training, inference, fine-tuning)
• Create serverless web APIs or endpoints
• Scale batch processing jobs in parallel
• Schedule recurring tasks (data pipelines, retraining, scraping)
• Need persistent cloud storage for model weights or datasets
• Want to run code in custom container environments
• Build job queues or async task processing systems

Installation and Authentication

Install

uv pip install modal

Authenticate

Prefer existing credentials before creating new ones:

1. Check whether MODAL_TOKEN_ID and MODAL_TOKEN_SECRET are already present in the current environment.
2. If not, check for those values in a local .env file and load them if appropriate for the workflow.
3. Only fall back to interactive modal setup or generating fresh tokens if neither source already provides credentials.

modal setup

This opens a browser for authentication. For CI/CD or headless environments, use environment variables:

export MODAL_TOKEN_ID=<your-token-id>
export MODAL_TOKEN_SECRET=<your-token-secret>

If tokens are not already available in the environment or .env, generate them at https://modal.com/settings

Modal offers a free tier with $30/month in credits.

Reference: See references/getting-started.md for detailed setup and first app walkthrough.

Core Concepts

App and Functions

A Modal App groups related functions. Functions decorated with @app.function() run remotely in the cloud:

import modal

app = modal.App("my-app")

@app.function()
def square(x):
    return x ** 2

@app.local_entrypoint()
def main():
    # .remote() runs in the cloud
    print(square.remote(42))

Run with modal run script.py. Deploy with modal deploy script.py.

Reference: See references/functions.md for lifecycle hooks, classes, .map(), .spawn(), and more.

Container Images

Modal builds container images from Python code. The recommended package installer is uv:

image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("torch==2.8.0", "transformers", "accelerate")
    .apt_install("git")
)

@app.function(image=image)
def inference(prompt):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="meta-llama/Llama-3-8B")
    return pipe(prompt)

Key image methods:

• .uv_pip_install() — Install Python packages with uv (recommended)
• .pip_install() — Install with pip (fallback)
• .apt_install() — Install system packages
• .run_commands() — Run shell commands during build
• .run_function() — Run Python during build (e.g., download model weights)
• .add_local_python_source() — Add local modules
• .env() — Set environment variables

Reference: See references/images.md for Dockerfiles, micromamba, caching, GPU build steps.

GPU Compute

Request GPUs via the gpu parameter:

@app.function(gpu="H100")
def train_model():
    import torch
    device = torch.device("cuda")
    # GPU training code here

# Multiple GPUs
@app.function(gpu="H100:4")
def distributed_training():
    ...

# GPU fallback chain
@app.function(gpu=["H100", "A100-80GB", "A100-40GB"])
def flexible_inference():
    ...

Available GPUs: T4, L4, A10, L40S, A100-40GB, A100-80GB, H100, H200, B200, B200+

• Up to 8 GPUs per container (except A10: up to 4)
• L40S is recommended for inference (cost/performance balance, 48 GB VRAM)
• H100/A100 can be auto-upgraded to H200/A100-80GB at no extra cost
• Use gpu="H100!" to prevent auto-upgrade

Reference: See references/gpu.md for GPU selection guidance and multi-GPU training.

Volumes (Persistent Storage)

Volumes provide distributed, persistent file storage:

vol = modal.Volume.from_name("model-weights", create_if_missing=True)

@app.function(volumes={"/data": vol})
def save_model():
    # Write to the mounted path
    with open("/data/model.pt", "wb") as f:
        torch.save(model.state_dict(), f)

@app.function(volumes={"/data": vol})
def load_model():
    model.load_state_dict(torch.load("/data/model.pt"))

• Optimized for write-once, read-many workloads (model weights, datasets)
• CLI access: modal volume ls, modal volume put, modal volume get
• Background auto-commits every few seconds

Reference: See references/volumes.md for v2 volumes, concurrent writes, and best practices.

Secrets

Securely pass credentials to functions:

@app.function(secrets=[modal.Secret.from_name("my-api-keys")])
def call_api():
    import os
    api_key = os.environ["API_KEY"]
    # Use the key

Create secrets via CLI: modal secret create my-api-keys API_KEY=sk-xxx

Or from a .env file: modal.Secret.from_dotenv()

Reference: See references/secrets.md for dashboard setup, multiple secrets, and templates.

Web Endpoints

Serve models and APIs as web endpoints:

@app.function()
@modal.fastapi_endpoint()
def predict(text: str):
    return {"result": model.predict(text)}

• modal serve script.py — Development with hot reload and temporary URL
• modal deploy script.py — Production deployment with permanent URL
• Supports FastAPI, ASGI (Starlette, FastHTML), WSGI (Flask, Django), WebSockets
• Request bodies up to 4 GiB, unlimited response size

Reference: See references/web-endpoints.md for ASGI/WSGI apps, streaming, auth, and WebSockets.

Scheduled Jobs

Run functions on a schedule:

@app.function(schedule=modal.Cron("0 9 * * *"))  # Daily at 9 AM UTC
def daily_pipeline():
    # ETL, retraining, scraping, etc.
    ...

@app.function(schedule=modal.Period(hours=6))
def periodic_check():
    ...

Deploy with modal deploy script.py to activate the schedule.

• modal.Cron("...") — Standard cron syntax, stable across deploys
• modal.Period(hours=N) — Fixed interval, resets on redeploy
• Monitor runs in the Modal dashboard

Reference: See references/scheduled-jobs.md for cron syntax and management.

Scaling and Concurrency

Modal autoscales containers automatically. Configure limits:

@app.function(
    max_containers=100,    # Upper limit
    min_containers=2,      # Keep warm for low latency
    buffer_containers=5,   # Reserve capacity
    scaledown_window=300,  # Idle seconds before shutdown
)
def process(data):
    ...

Process inputs in parallel with .map():

results = list(process.map([item1, item2, item3, ...]))

Enable concurrent request handling per container:

@app.function()
@modal.concurrent(max_inputs=10)
async def handle_request(req):
    ...

Reference: See references/scaling.md for .map(), .starmap(), .spawn(), and limits.

Resource Configuration

@app.function(
    cpu=4.0,              # Physical cores (not vCPUs)
    memory=16384,         # MiB
    ephemeral_disk=51200, # MiB (up to 3 TiB)
    timeout=3600,         # Seconds
)
def heavy_computation():
    ...

Defaults: 0.125 CPU cores, 128 MiB memory. Billed on max(request, usage).

Reference: See references/resources.md for limits and billing details.

Classes with Lifecycle Hooks

For stateful workloads (e.g., loading a model once and serving many requests):

@app.cls(gpu="L40S", image=image)
class Predictor:
    @modal.enter()
    def load_model(self):
        self.model = load_heavy_model()  # Runs once on container start

    @modal.method()
    def predict(self, text: str):
        return self.model(text)

    @modal.exit()
    def cleanup(self):
        ...  # Runs on container shutdown

Call with: Predictor().predict.remote("hello")

Common Workflow Patterns

GPU Model Inference Service

import modal

app = modal.App("llm-service")

image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install("vllm")
)

@app.cls(gpu="H100", image=image, min_containers=1)
class LLMService:
    @modal.enter()
    def load(self):
        from vllm import LLM
        self.llm = LLM(model="meta-llama/Llama-3-70B")

    @modal.method()
    @modal.fastapi_endpoint(method="POST")
    def generate(self, prompt: str, max_tokens: int = 256):
        outputs = self.llm.generate([prompt], max_tokens=max_tokens)
        return {"text": outputs[0].outputs[0].text}

Batch Processing Pipeline

app = modal.App("batch-pipeline")
vol = modal.Volume.from_name("pipeline-data", create_if_missing=True)

@app.function(volumes={"/data": vol}, cpu=4.0, memory=8192)
def process_chunk(chunk_id: int):
    import pandas as pd
    df = pd.read_parquet(f"/data/input/chunk_{chunk_id}.parquet")
    result = heavy_transform(df)
    result.to_parquet(f"/data/output/chunk_{chunk_id}.parquet")
    return len(result)

@app.local_entrypoint()
def main():
    chunk_ids = list(range(100))
    results = list(process_chunk.map(chunk_ids))
    print(f"Processed {sum(results)} total rows")

Scheduled Data Pipeline

app = modal.App("etl-pipeline")

@app.function(
    schedule=modal.Cron("0 */6 * * *"),  # Every 6 hours
    secrets=[modal.Secret.from_name("db-credentials")],
)
def etl_job():
    import os
    db_url = os.environ["DATABASE_URL"]
    # Extract, transform, load
    ...

CLI Reference

Reference Files

Detailed documentation for each topic:

• references/getting-started.md — Installation, authentication, first app
• references/functions.md — Functions, classes, lifecycle hooks, remote execution
• references/images.md — Container images, package installation, caching
• references/gpu.md — GPU types, selection, multi-GPU, training
• references/volumes.md — Persistent storage, file management, v2 volumes
• references/secrets.md — Credentials, environment variables, dotenv
• references/web-endpoints.md — FastAPI, ASGI/WSGI, streaming, auth, WebSockets
• references/scheduled-jobs.md — Cron, periodic schedules, management
• references/scaling.md — Autoscaling, concurrency,.map(), limits
• references/resources.md — CPU, memory, disk, timeout configuration
• references/examples.md — Common use cases and patterns
• references/api_reference.md — Key API classes and methods

Read these files when detailed information is needed beyond this overview.