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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