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Flyte SGLang Plugin

Serve large language models using SGLang with Flyte Apps.

This plugin provides the SGLangAppEnvironment class for deploying and serving LLMs using SGLang.

Installation

pip install --pre flyteplugins-sglang

Usage

import flyte
import flyte.app
from flyteplugins.sglang import SGLangAppEnvironment

# Define the SGLang app environment
sglang_app = SGLangAppEnvironment(
    name="my-llm-app",
    model="s3://your-bucket/models/your-model",
    model_id="your-model-id",
    resources=flyte.Resources(cpu="4", memory="16Gi", gpu="L40s:1"),
    stream_model=True,  # Stream model directly from blob store to GPU
    scaling=flyte.app.Scaling(
        replicas=(0, 1),
        scaledown_after=300,
    ),
)

if __name__ == "__main__":
    flyte.init_from_config()
    app = flyte.serve(sglang_app)
    print(f"Deployed SGLang app: {app.url}")

Features

  • Streaming Model Loading: Stream model weights directly from object storage to GPU memory, reducing startup time and disk requirements.
  • OpenAI-Compatible API: The deployed app exposes an OpenAI-compatible API for chat completions.
  • Auto-scaling: Configure scaling policies to scale up/down based on traffic.
  • Tensor Parallelism: Support for distributed inference across multiple GPUs.

Extra Arguments

You can pass additional arguments to the SGLang server using the extra_args parameter:

sglang_app = SGLangAppEnvironment(
    name="my-llm-app",
    model="s3://your-bucket/models/your-model",
    model_id="your-model-id",
    extra_args="--max-model-len 8192 --enforce-eager",
)

See the SGLang server arguments documentation for available options.

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