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Browse files- README.md +2 -8
- app.py +50 -0
- requirements.txt +2 -0
README.md
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---
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title:
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colorFrom: pink
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colorTo: pink
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sdk: gradio
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sdk_version: 4.19.2
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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title: skypilot-gradio
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app_file: app.py
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sdk: gradio
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sdk_version: 4.19.2
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---
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app.py
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import gradio as gr
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import sky
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def deploy_vllm_on_sky(model_path, gpu_type, cpus, memory, cloud_provider, region, disk_size, disk_type):
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task = sky.Task(
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name="vllm_serving",
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setup="pip install vllm",
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run=f"vllm serve --model_name_or_path {model_path} --port 8080",
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envs={"MODEL_PATH": model_path},
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workdir=".",
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ports=8080
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)
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task.set_resources(
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sky.Resources(
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cloud=sky.Cloud(provider=cloud_provider, region=region),
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accelerators=f"{gpu_type}:1",
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cpus=cpus,
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memory=memory,
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disk=sky.Disk(size=disk_size, type=disk_type)
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)
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)
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cluster = sky.Cluster(
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name="vllm-cluster",
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cloud=sky.Cloud(provider=cloud_provider, region=region)
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)
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sky.launch(task, cluster=cluster)
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return f"VLLM model deployed on SkyPilot with cluster name: {cluster.name}"
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sky_pilot_interface = gr.Interface(
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fn=deploy_vllm_on_sky,
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inputs=[
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gr.Textbox(label="Model Path", placeholder="EleutherAI/gpt-neo-2.7B"),
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gr.Dropdown(label="GPU Type", choices=["V100", "P100", "T4"], value="V100"),
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gr.Slider(label="CPUs", minimum=1, maximum=16, value=4),
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gr.Slider(label="Memory (GB)", minimum=4, maximum=64, value=16),
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gr.Dropdown(label="Cloud Provider", choices=["AWS", "GCP", "Azure"], value="AWS"),
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gr.Textbox(label="Region", placeholder="us-west-2"),
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gr.Slider(label="Disk Size (GB)", minimum=20, maximum=1000, value=100),
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gr.Dropdown(label="Disk Type", choices=["standard", "ssd"], value="ssd")
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],
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outputs="text",
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title="Deploy VLLM on SkyPilot",
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description="Configure and deploy a VLLM model on a SkyPilot-managed cloud instance with full parameter customization."
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)
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if __name__ == "__main__":
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sky_pilot_interface.launch()
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requirements.txt
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gradio
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sk
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