Skills ai-infrastructure-modal

Serverless GPU compute platform for AI model deployment — web endpoints, GPU functions, model serving, and TypeScript client patterns

install

source · Clone the upstream repo

git clone https://github.com/agents-inc/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/agents-inc/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/dist/plugins/ai-infrastructure-modal/skills/ai-infrastructure-modal" ~/.claude/skills/agents-inc-skills-ai-infrastructure-modal && rm -rf "$T"

manifest: dist/plugins/ai-infrastructure-modal/skills/ai-infrastructure-modal/SKILL.md

source content

Modal Patterns

Quick Guide: Modal is a serverless GPU compute platform where you define Python functions with decorators and Modal handles containers, scaling, and GPU provisioning. TypeScript apps interact with Modal via HTTP endpoints (calling
@modal.fastapi_endpoint
or
@modal.asgi_app
functions) or the
modal
npm SDK (calling functions directly via gRPC). Define container images, secrets, and volumes as code -- no YAML config files. Use
modal deploy
for production,
modal serve
for dev.

<critical_requirements>

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering,
import type
, named constants)

(You MUST define Modal functions in Python -- the TypeScript SDK can call functions and manage resources but cannot define them)

(You MUST use

@modal.fastapi_endpoint

(not the old
@modal.web_endpoint
) for simple web endpoints -- renamed in Modal 1.0)

(You MUST use

modal.Volume

for model weight caching --
@modal.build
is deprecated in Modal 1.0)

(You MUST never hardcode secrets in Modal code -- use

modal.Secret.from_name()

and access via
os.environ
)

(You MUST bind to

0.0.0.0

(not
127.0.0.1
) when using
@modal.web_server
)

</critical_requirements>

Auto-detection: Modal, modal, modal.App, modal.Image, modal.Volume, modal.Secret, modal.gpu, modal.fastapi_endpoint, modal.asgi_app, modal.web_server, modal.Cron, modal.Period, modal deploy, modal serve, MODAL_TOKEN_ID, MODAL_TOKEN_SECRET, ModalClient

When to use:

Deploying ML models (vLLM, Hugging Face, custom PyTorch) on serverless GPUs
Creating HTTP API endpoints backed by GPU compute for TypeScript apps to consume
Running scheduled GPU jobs (fine-tuning, batch inference, data processing)
Calling Modal functions from TypeScript using the
```
modal
```
npm SDK
Building AI inference pipelines with auto-scaling and pay-per-second billing

Key patterns covered:

Web endpoints (

@modal.fastapi_endpoint

@modal.asgi_app

@modal.web_server

) for HTTP access

TypeScript client patterns (fetch-based and
```
modal
```
npm SDK)
Container images, secrets, volumes, and GPU configuration
Model serving with vLLM and custom inference
Scheduled functions and deployment

When NOT to use:

Pure Python ML workloads with no TypeScript consumer -- this skill focuses on the TypeScript interaction surface
Simple CPU-only tasks where a regular server or cloud function suffices
When you need persistent long-running servers (Modal scales to zero by default)
You need sub-100ms cold starts (Modal cold starts are 2-4 seconds)
You need persistent WebSocket connections beyond request/response

Examples Index

Core: Web Endpoints & TypeScript Client -- Defining endpoints, calling from TypeScript, authentication, GPU functions, images, secrets, volumes
Quick API Reference -- CLI commands, decorator parameters, URL patterns, GPU types

Philosophy

Modal eliminates infrastructure management for GPU workloads. Everything is code -- container images, GPU allocation, secrets, volumes, scaling rules. There are no YAML configs, Dockerfiles, or Kubernetes manifests.

Core principles:

Infrastructure as Python code -- Container images, GPU types, secrets, and volumes are all declared as Python decorators and objects. No separate config files.
Serverless GPU scaling -- Functions scale from zero to hundreds of GPUs automatically. You pay per second of compute, not for idle capacity.
Two interaction models for TypeScript -- Call Modal via HTTP endpoints (most common) or via the
```
modal
```
npm SDK for direct function invocation without HTTP overhead.
Immutable deployments --
```
modal deploy
```
creates a named, persistent deployment with stable URLs.
```
modal serve
```
creates ephemeral dev endpoints.

</philosophy>

Core Patterns

Pattern 1: Web Endpoint (TypeScript Consumption)

The most common pattern: define a Python endpoint on Modal, call it from TypeScript via fetch.

Python Side

# inference.py
import modal

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

image = modal.Image.debian_slim().uv_pip_install(["fastapi[standard]", "transformers", "torch"])

@app.function(image=image, gpu="A10G")
@modal.fastapi_endpoint(method="POST")
def predict(payload: dict):
    # GPU-accelerated inference
    text = payload["text"]
    result = run_model(text)
    return {"prediction": result}

TypeScript Side

const response = await fetch(MODAL_ENDPOINT, {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify(input),
  signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS), // Essential for cold starts
});

Key requirements: Named constant for URL (not hardcoded at call sites),

Content-Type: application/json

header (FastAPI rejects without it),

AbortSignal.timeout()

to handle cold start delays, typed request/response interfaces.

See examples/core.md for a complete TypeScript client with error handling and typed interfaces.

Pattern 2: Authenticated Endpoints

Modal supports proxy auth tokens that protect endpoints without spinning up containers for unauthorized requests.

Python Side

@app.function(image=image, gpu="A10G")
@modal.fastapi_endpoint(method="POST", requires_proxy_auth=True)
def predict_secure(payload: dict):
    return {"prediction": run_model(payload["text"])}

TypeScript Side

headers: {
  "Content-Type": "application/json",
  "Modal-Key": process.env.MODAL_PROXY_KEY,     // Proxy auth token
  "Modal-Secret": process.env.MODAL_PROXY_SECRET,
},

Why good: Auth handled at Modal's proxy layer (no container spin-up for bad requests), env vars for credentials. Add explicit 401 handling in your error logic.

See examples/core.md for a complete authenticated TypeScript client with error handling.

Pattern 3: Modal npm SDK (Direct Function Calls)

For TypeScript apps that need to call Modal functions without HTTP overhead. Requires Node 22+.

import { ModalClient } from "modal";
const modal = new ModalClient(); // Create once, reuse
const fn = await modal.functions.fromName("my-inference-api", "predict");
const result = await fn.remote([text]); // sync call
const call = await fn.spawn([text]); // async (fire-and-forget)
const later = await call.get(); // retrieve result later

Why good: No HTTP serialization overhead, typed SDK, supports async spawn for long-running jobs

See examples/core.md for complete TypeScript SDK patterns including error handling and fire-and-forget job IDs.

When to use: Backend-to-Modal calls where you control the Node.js runtime (Node 22+). Not for browser or edge runtimes.

Pattern 4: GPU Functions and Container Images

Modal functions define their compute environment inline.

import modal

app = modal.App("gpu-inference")

# Container image with ML dependencies
inference_image = (
    modal.Image.debian_slim(python_version="3.11")
    .uv_pip_install(["torch==2.5.0", "transformers==4.47.0", "accelerate"])
    .apt_install(["libgl1"])
)

MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct"

@app.function(
    image=inference_image,
    gpu="A100",                   # GPU type: "T4", "A10G", "A100", "H100", etc.
    secrets=[modal.Secret.from_name("huggingface-secret")],
    volumes={"/models": modal.Volume.from_name("model-cache", create_if_missing=True)},
    min_containers=1,             # Keep warm to avoid cold starts
    scaledown_window=300,         # Seconds before scaling to zero
)
def generate(prompt: str) -> str:
    from transformers import AutoModelForCausalLM, AutoTokenizer
    # Load from volume cache
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, cache_dir="/models")
    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, cache_dir="/models")
    # ... generate and return

Why good: Pinned dependency versions, volume-based model caching (avoids re-download),

min_containers

for warm starts, secrets for HF token

# BAD: No version pinning, no volume cache, model re-downloads every cold start
@app.function(gpu="A100")
def generate(prompt: str):
    from transformers import pipeline
    pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
    return pipe(prompt)[0]["generated_text"]

Why bad: Unpinned deps break reproducibility, no volume means multi-GB model download on every cold start (30-60s+ delay), no secret for gated models

Pattern 5: Full ASGI App (FastAPI)

For endpoints that need routing, middleware, or multiple routes.

import modal
from fastapi import FastAPI

app = modal.App("my-api")
web_app = FastAPI()

@web_app.post("/predict")
async def predict(payload: dict):
    return {"result": "prediction"}

@web_app.get("/health")
async def health():
    return {"status": "ok"}

@app.function(image=modal.Image.debian_slim().uv_pip_install(["fastapi"]))
@modal.asgi_app()
def serve():
    return web_app

Why good: Full FastAPI capabilities (routing, middleware, validation), multiple endpoints under one function

Pattern 6: Secrets and Environment Variables

# Creating secrets via CLI
# modal secret create my-api-keys API_KEY=sk-xxx DB_URL=postgres://...

@app.function(
    secrets=[
        modal.Secret.from_name("my-api-keys"),
        modal.Secret.from_name("huggingface-secret"),
    ]
)
def my_function():
    import os
    api_key = os.environ["API_KEY"]  # Injected by Modal
    hf_token = os.environ["HF_TOKEN"]

Why good: Secrets created via dashboard or CLI, referenced by name in code, accessed as standard env vars, multiple secrets composable

Pattern 7: Scheduled Functions

@app.function(
    schedule=modal.Cron("0 2 * * *"),  # 2 AM daily
    image=inference_image,
    gpu="A10G",
    volumes={"/data": modal.Volume.from_name("training-data")},
)
def nightly_batch_inference():
    # Process accumulated data
    # Write results to volume
    pass

@app.function(schedule=modal.Period(hours=6))
def periodic_health_check():
    # Check model freshness, data quality, etc.
    pass

Why good:

modal.Cron

for precise scheduling,

modal.Period

for intervals. Scheduled functions cannot accept arguments -- use volumes or secrets for input data.

</patterns>

<decision_framework>

Decision Framework

How to Expose Modal to TypeScript

Does your TypeScript app need to call Modal?
+-- Via HTTP (most common)
|   +-- Single endpoint? -> @modal.fastapi_endpoint
|   +-- Multiple routes? -> @modal.asgi_app with FastAPI
|   +-- Non-Python server (vLLM, TGI)? -> @modal.web_server(port=8000)
|   +-- Need auth? -> Add requires_proxy_auth=True
+-- Via SDK (direct gRPC)
|   +-- Node 22+ backend? -> npm install modal, use ModalClient
|   +-- Browser/edge? -> Use HTTP endpoints instead
+-- Async job?
    +-- Fire-and-forget? -> SDK spawn() + later get()
    +-- Webhook callback? -> Modal calls your endpoint on completion

When to Use Each Endpoint Type

What are you serving?
+-- Simple function -> @modal.fastapi_endpoint (auto-wraps in FastAPI)
+-- Full web app -> @modal.asgi_app (FastAPI, Starlette, FastHTML)
+-- Legacy sync app -> @modal.wsgi_app (Flask, Django)
+-- Custom server binary -> @modal.web_server(port=8000) (vLLM, TGI, Ollama)

HTTP vs SDK

How should TypeScript call Modal?
+-- Browser/edge runtime? -> HTTP (fetch)
+-- Server-side Node 22+? -> Either works
|   +-- Need simplicity? -> HTTP
|   +-- Need speed (no serialization overhead)? -> SDK
|   +-- Need async spawn? -> SDK
+-- Multiple providers? -> HTTP (vendor-agnostic)

</decision_framework>

<red_flags>

RED FLAGS

High Priority Issues:

Using deprecated
```
@modal.web_endpoint
```
instead of
```
@modal.fastapi_endpoint
```
(renamed in Modal 1.0)
Using
```
@modal.build
```
for downloading model weights (deprecated -- use
```
modal.Volume
```
instead)
Using
```
.lookup()
```
for object references (deprecated -- use
```
.from_name()
```
)
Hardcoding secrets in Python source (use
```
modal.Secret.from_name()
```
+
```
os.environ
```
)
Binding
```
@modal.web_server
```
to
```
127.0.0.1
```
instead of
```
0.0.0.0
```
(endpoint unreachable)

Medium Priority Issues:

Not pinning dependency versions in
```
uv_pip_install()
```
(breaks reproducibility)
No
```
min_containers
```
for latency-sensitive endpoints (2-4s cold starts)
Missing
```
signal: AbortSignal.timeout()
```
on TypeScript fetch calls (hangs on cold starts)
Not using volumes for model weight caching (re-downloads multi-GB models on cold starts)
Using
```
modal.Period
```
when you need exact times (use
```
modal.Cron
```
-- Period resets on redeploy)

Common Mistakes:

Confusing
```
modal serve
```
(ephemeral dev) with
```
modal deploy
```
(persistent production)
Using
```
messages
```
parameter with
```
@modal.fastapi_endpoint
```
(it is not OpenAI -- it is a plain HTTP endpoint)
Forgetting that scheduled functions cannot accept arguments -- use volumes, secrets, or global variables for input
Not setting
```
Content-Type: application/json
```
header from TypeScript (FastAPI endpoints may reject the request)
Using the
```
modal
```
npm SDK in browser or edge runtimes (requires Node 22+, native modules)

Gotchas & Edge Cases:

Web endpoint max request timeout is 150 seconds (enforced by Modal's proxy)
Rate limit: 200 requests/second default with 5-second burst multiplier
Request bodies up to 4 GiB, response bodies unlimited
```
modal serve
```
URLs get a
```
-dev
```
suffix to avoid production conflicts

URL pattern:

https://<workspace>--<app-name>-<function-name>.modal.run

Labels exceeding 63 characters are truncated with a SHA-256 hash suffix
Volumes v1 limited to 500,000 files; v2 has no limit (use
```
version=2
```
)
```
modal.Cron
```
maintains schedule across redeploys;
```
modal.Period
```
resets
Container class parameters are limited to primitives:
```
str
```
,
```
int
```
,
```
bool
```
,
```
bytes
```
Local Python files require
```
Image.add_local_python_source()
```
(automounting removed in 1.0)

</red_flags>

<critical_reminders>

CRITICAL REMINDERS

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering,
import type
, named constants)

(You MUST define Modal functions in Python -- the TypeScript SDK can call functions and manage resources but cannot define them)

(You MUST use

@modal.fastapi_endpoint

(not the old
@modal.web_endpoint
) for simple web endpoints -- renamed in Modal 1.0)

(You MUST use

modal.Volume

for model weight caching --
@modal.build
is deprecated in Modal 1.0)

(You MUST never hardcode secrets in Modal code -- use

modal.Secret.from_name()

and access via
os.environ
)

(You MUST bind to

0.0.0.0

(not
127.0.0.1
) when using
@modal.web_server
)

Failure to follow these rules will produce broken deployments, security vulnerabilities, or unreachable endpoints.

</critical_reminders>