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The @context decorator registers an async context manager class whose lifecycle is tied to the GPU worker. Use it to load models, initialize engines, allocate GPU memory, and set up connections once at startup rather than on every request.

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Defining a Context

A context class must implement the async context manager protocol — __aenter__ and __aexit__: 
@app.context()
class MyModel:
    async def __aenter__(self):
        import torch
        self.model = torch.load("model.pt").cuda()
        self.device = torch.device("cuda")
        return self

    async def __aexit__(self, *exc):
        del self.model  # Cleanup on shutdown
  • __aenter__ runs once when the worker starts, before it enters “ready” state. Use it to load models, allocate resources, and perform any one-time setup. It must return self (or whatever object you want get_context to return).
  • __aexit__ runs when the worker shuts down. Use it to close connections, free resources, or flush buffers.

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Passing Arguments to Context

You can pass arguments to the context class constructor via the decorator: 
@app.context("Qwen/Qwen3-0.6B", max_len=512)
class LLMEngine:
    def __init__(self, model_name, max_len=1024):
        self.model_name = model_name
        self.max_len = max_len

    async def __aenter__(self):
        from vllm import AsyncLLMEngine, AsyncEngineArgs
        args = AsyncEngineArgs(model=self.model_name, max_model_len=self.max_len)
        self.engine = AsyncLLMEngine.from_engine_args(args)
        return self

    async def __aexit__(self, *exc):
        self.engine.shutdown_background_loop()

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Accessing Context in @remote Functions

Use app.get_context(ContextClass) inside a remote function to retrieve the initialized context instance: 
@app.remote(benchmark_dataset=[{"prompt": "Hello"}])
async def generate(prompt: str, max_tokens: int = 128) -> str:
    engine = app.get_context(LLMEngine)
    # Use engine.engine to generate text...
get_context returns the object that __aenter__ returned. If the context class hasn’t been registered or hasn’t been entered yet, it raises a KeyError.

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Multiple Contexts

You can register multiple context classes. They are all entered in parallel at startup: 
@app.context()
class Tokenizer:
    async def __aenter__(self):
        from transformers import AutoTokenizer
        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
        return self
    async def __aexit__(self, *exc):
        pass

@app.context()
class Model:
    async def __aenter__(self):
        from transformers import AutoModelForCausalLM
        self.model = AutoModelForCausalLM.from_pretrained("gpt2").cuda()
        return self
    async def __aexit__(self, *exc):
        pass

@app.remote(benchmark_dataset=[{"text": "Hello"}])
async def generate(text: str) -> str:
    tok = app.get_context(Tokenizer)
    model = app.get_context(Model)
    inputs = tok.tokenizer(text, return_tensors="pt").to("cuda")
    outputs = model.model.generate(**inputs)
    return tok.tokenizer.decode(outputs[0])
Since contexts are entered in parallel via asyncio.gather(), independent resources (like a tokenizer and a model) load concurrently, reducing total startup time.

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Lifecycle

  1. Registration (deploy time): @app.context() decorators execute and register context classes
  2. Startup (serve time): All registered contexts’ __aenter__() methods are awaited in parallel
  3. Serving: Remote functions access contexts via app.get_context()
  4. Shutdown: All contexts’ __aexit__() methods are awaited in parallel