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Browse files- README.md +1 -5
- app.py +61 -0
- requirements.txt +3 -0
README.md
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---
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title: CortexNet Demo
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emoji: 📉
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colorFrom: red
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colorTo: indigo
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sdk: gradio
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sdk_version: 6.7.0
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app_file: app.py
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pinned: false
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---
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---
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title: CortexNet Demo
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sdk: gradio
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app_file: app.py
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pinned: false
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---
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CortexNet demo Space.
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app.py
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from __future__ import annotations
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import time
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import gradio as gr
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import torch
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from cortexnet import CortexNet, CortexNetConfig, __version__
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def run_smoke(seq_len: int, iters: int) -> str:
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cfg = CortexNetConfig(
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vocab_size=4096,
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hidden_size=128,
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num_layers=2,
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num_heads=4,
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max_seq_len=max(128, seq_len),
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lite=True,
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)
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model = CortexNet(cfg).eval()
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input_ids = torch.randint(0, cfg.vocab_size, (1, seq_len))
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with torch.no_grad():
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_ = model(input_ids)
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t0 = time.perf_counter()
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with torch.no_grad():
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for _ in range(iters):
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out = model(input_ids)
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dt = (time.perf_counter() - t0) / max(iters, 1)
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msg = (
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f"CortexNet version: {__version__}\n"
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f"Average forward latency: {dt * 1000:.4f} ms\n"
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f"Logits shape: {tuple(out['logits'].shape)}"
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)
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return msg
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DESCRIPTION = """
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# CortexNet Space Demo
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This Space runs a lightweight smoke benchmark of CortexNet to verify runtime health.
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- Source: https://github.com/chaojixiaokeai/CortexNet
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- PyPI: https://pypi.org/project/cortexnet/
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"""
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with gr.Blocks() as demo:
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gr.Markdown(DESCRIPTION)
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seq_len = gr.Slider(minimum=8, maximum=256, value=64, step=8, label="Sequence Length")
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iters = gr.Slider(minimum=1, maximum=100, value=20, step=1, label="Iterations")
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run_btn = gr.Button("Run Smoke Benchmark")
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output = gr.Textbox(label="Result", lines=8)
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run_btn.click(fn=run_smoke, inputs=[seq_len, iters], outputs=[output])
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
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cortexnet>=3.2.5
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torch>=2.0.0
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gradio>=4.0.0
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