This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 2.6.5 instead.
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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