hpc-as-api 0.4.1

Domain-agnostic HTTP gateway for any HPC function via Globus Compute + WebSocket relay

hpc-as-api

HTTP gateway for any HPC function — real-time streaming from any HPC workload.

hpc-as-api turns any Python function running on an HPC cluster into a streaming HTTP endpoint. Register your function, define its input schema with Pydantic, and get a production-ready REST API with authentication, rate limiting, and live SSE streaming — no open ports, no VPN, no firewall changes on the HPC side.

from hpc_as_api.core import HPCApp
from pydantic import BaseModel

class SimRequest(BaseModel):
    steps: int = 1000
    grid_size: int = 100

def hpc_simulation(steps, grid_size, relay_url, channel_id, relay_secret=""):
    from streamrelay import RelayProducer
    with RelayProducer(relay_url, channel_id, relay_secret=relay_secret) as relay:
        for i in range(steps):
            result = run_timestep(i, grid_size)
            relay.send_token(f"step={i} energy={result:.4f}\n")

app = HPCApp(endpoint_id="...", relay_url="wss://relay.example.com") \
    .mount("/simulate", hpc_simulation, SimRequest) \
    .create_app()

Any output produced incrementally on the HPC side arrives in real time: simulation checkpoints, solver residuals, genome alignment progress, molecular dynamics snapshots, LLM tokens — anything.

Why

HPC clusters run workloads impossible on commodity hardware — 72B+ parameter models, climate simulations, molecular dynamics at scale. But they expose no standard API. Each cluster has its own SLURM scripts, SSH tunnels, authentication systems, and job submission conventions.

hpc-as-api provides a uniform HTTP interface over any HPC function using Globus Compute for authentication and job dispatch and streamrelay for real-time output streaming. Callers send a POST request; the framework handles everything else.

Architecture

Your Application / HTTP Client
        │  POST /your-endpoint  (any input schema)
        ▼
  hpc-as-api (FastAPI)
        │  Globus Compute (AMQP — no HPC firewall holes)
        ▼
  HPC Cluster (SLURM / PBS / …)
        │  your function runs; output flows via streamrelay
        ▼
  GPU / CPU Compute Node
        │  tokens / results / checkpoints via WebSocket relay
        ▼
  hpc-as-api → SSE stream → Your Application

Key design points:

• No open ports on HPC: Globus Compute is outbound-only from the cluster
• Real-time streaming: Any incremental output arrives as SSE via streamrelay
• E2E encryption: Optional AES-256-GCM encryption — relay sees only ciphertext
• Domain-agnostic: Register any Python function; not limited to LLMs

Installation

# Base package (no Globus SDK)
pip install hpc-as-api

# With Globus Compute support
pip install "hpc-as-api[globus]"

Quickstart: Domain-agnostic gateway

Register any HPC function and stream its output:

from hpc_as_api.core import HPCApp
from pydantic import BaseModel

class RunRequest(BaseModel):
    steps: int = 1000
    param: float = 0.5

def my_hpc_function(steps, param, relay_url, channel_id, relay_secret=""):
    from streamrelay import RelayProducer
    with RelayProducer(relay_url, channel_id, relay_secret=relay_secret) as relay:
        for i in range(steps):
            relay.send_token(f"step={i} value={compute(i, param)}\n")

gateway = HPCApp(
    endpoint_id="your-globus-endpoint-uuid",
    relay_url="wss://relay.example.com",
    relay_secret="your-relay-secret",
)
gateway.mount("/run", my_hpc_function, RunRequest)
app = gateway.create_app()

Run with:

uvicorn mymodule:app --host 0.0.0.0 --port 8001

Clients stream the output in real time:

export API_KEY="sk-your-key"
curl -N -X POST http://localhost:8001/run \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"steps": 500, "param": 0.7}'

Built-in preset: OpenAI-compatible LLM gateway

For vLLM-served language models, the OpenAI preset provides a drop-in /v1/chat/completions endpoint compatible with any OpenAI client:

from hpc_as_api.presets.openai import create_openai_app

app = create_openai_app(
    endpoint_id="8d978809-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
    models={
        "gemma4-31b": {
            "hf_name": "gemma4-31b",
            "url": "http://127.0.0.1:8001",
            "context_reserve_output": 8192,
        }
    },
    relay_url="wss://relay.example.com",
    relay_secret="your-relay-secret",
)

Or run as a service from environment variables:

export GLOBUS_COMPUTE_ENDPOINT_ID="your-endpoint-uuid"
export HPC_MODELS='{"gemma4-31b": {"hf_name": "gemma4-31b", "url": "http://127.0.0.1:8001", "context_reserve_output": 8192}}'
export RELAY_URL="wss://relay.example.com"
export RELAY_SECRET="your-relay-secret"
export PROXY_API_KEY_MYSERVICE="sk-your-key"

uvicorn hpc_as_api.app:app --host 127.0.0.1 --port 8002

Any OpenAI client works without modification:

import os
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8002/v1",
    api_key=os.environ["PROXY_API_KEY_MYSERVICE"],
)
response = client.chat.completions.create(
    model="gemma4-31b",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Multiple independent gateways

create_openai_app() returns a fresh, independent instance each time — safe to run multiple gateways with different configurations in the same process:

from hpc_as_api.presets.openai import create_openai_app

llm_a = create_openai_app(endpoint_id="endpoint-a", models={...}, relay_url="wss://relay.example.com")
llm_b = create_openai_app(endpoint_id="endpoint-b", models={...}, relay_url="wss://relay.example.com")

Programmatic auth configuration

from hpc_as_api import AuthConfig
from hpc_as_api.core import HPCApp

gateway = HPCApp(
    endpoint_id="...",
    relay_url="wss://relay.example.com",
    auth=AuthConfig(
        globus_client_id="your-client-id",
        globus_client_secret="your-client-secret",
        allowed_domains=["university.edu"],
        api_keys={"my-service": "sk-xxxx"},
        rate_limit_requests=10000,
        rate_limit_window=60,
    ),
)

Configuration reference

HPCApp / create_openai_app()

Argument	Env var fallback	Description
endpoint_id	GLOBUS_COMPUTE_ENDPOINT_ID	Globus endpoint UUID for the HPC cluster
relay_url	RELAY_URL	WebSocket relay URL for streaming
relay_secret	RELAY_SECRET	Shared secret for relay auth
relay_encryption_key	RELAY_ENCRYPTION_KEY	AES-256 hex key for E2E encryption
auth	—	AuthConfig or Authenticator instance

OpenAI preset environment variables

Variable	Default	Description
HPC_MODELS	{}	JSON dict: model alias → {"hf_name", "url", "context_reserve_output"}
PROXY_API_KEY_<NAME>	—	API key for service <NAME> — any number of keys, any suffix
PROXY_RATE_LIMIT_REQUESTS	10000	Global max requests per window (per-caller sliding window)
PROXY_RATE_LIMIT_WINDOW	60	Window size in seconds
PROXY_RATE_LIMIT_REQUESTS_<NAME>	—	Per-key override; <NAME> must match the suffix in PROXY_API_KEY_<NAME> (lowercased)
USE_GLOBUS_COMPUTE	true	false to route directly to a vLLM URL without Globus

HPC_MODELS schema

{
  "my-model-alias": {
    "hf_name": "my-model-alias",
    "url": "http://127.0.0.1:8001",
    "context_reserve_output": 8192
  }
}

hf_name must exactly match --served-model-name in the vLLM SLURM script. url is where vLLM is reachable from the Globus Compute worker (usually http://127.0.0.1:PORT when workers are co-located).

Authentication

Two auth modes coexist automatically, configured via AuthConfig or environment variables:

Mode A — Globus token (for institutional users) The caller presents a Globus access token validated via introspection. The job runs under the caller's Globus identity. Set GLOBUS_CLIENT_ID, GLOBUS_CLIENT_SECRET, and optionally PROXY_ALLOWED_DOMAINS.

Mode B — API key (for service-to-service callers) The caller presents a static key. Set one or more PROXY_API_KEY_<NAME>=<value> env vars. The <NAME> suffix (lowercased) identifies the caller in logs and rate-limit overrides.

# Example: two keys, different rate limits
PROXY_API_KEY_CLASS=sk-class-key
PROXY_API_KEY_DEMO=sk-demo-key
PROXY_RATE_LIMIT_REQUESTS=10000      # class key: 10k req/min
PROXY_RATE_LIMIT_REQUESTS_DEMO=20    # demo key: 20 req/min

Scaling to per-student keys (future work): For classroom deployments with hundreds of students, the planned approach is a PROXY_KEYS_FILE pointing at a JSON file of {"student_name": "sk-..."} pairs loaded and merged with env-var keys at startup. A bulk generation script produces all keys at once; students receive theirs via Canvas. No OAuth, no login, no extra infrastructure. Not yet implemented.

Development

git clone https://github.com/uicacer/hpc-as-api
cd hpc-as-api
uv sync --extra dev

# Install pre-commit hooks (ruff, mypy, gitleaks, hygiene checks)
pre-commit install

uv run pytest

Deployment

See docs/deployment.md for the full sysadmin guide (systemd, Caddy TLS, Globus endpoint, secrets management).

See docs/tutorial.ipynb for a zero-to-hero walkthrough from relay primitives through production deployment.

Related

• streamrelay — WebSocket relay for real-time output streaming from Globus Compute
• STREAM — Full tiered LLM routing system that uses hpc-as-api

License

Apache 2.0 — see LICENSE.

Citation

If you use hpc-as-api in research, please cite:

@software{nassar2025hpcgateway,
  author = {Nassar, Anas},
  title  = {hpc-as-api: HTTP gateway for any HPC function via Globus Compute and WebSocket relay},
  year   = {2025},
  url    = {https://github.com/uicacer/hpc-as-api}
}