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import spaces
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import gradio as gr
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import platform
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import os;
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import socket;
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import torch
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device = "cuda";
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if not torch.cuda.is_available() and device == "cuda":
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raise RuntimeError("CUDA device unavailable, please use Dockerfile.cpu instead.")
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RandomTensor = torch.randn(1, 2)
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print("Putting Tensor in device ...", device);
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RandomTensor.to(device)
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def sysinfo(newdev = device):
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currentDevice = RandomTensor.get_device();
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tensorExample = RandomTensor.to(newdev).norm(p=2, dim=1, keepdim=True).unsqueeze(-1).to(newdev)
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tocpu = tensorExample.cpu().squeeze().half().tolist()
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return f"""
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hostname: {platform.node()} {socket.gethostname()}
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dev cur: {currentDevice}
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dev ini: {device}
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dev new: {newdev}
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tensor: {tensorExample}
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toCpu: {tocpu}
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""";
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@spaces.GPU
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def gpu():
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return sysinfo();
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def nogpu():
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return sysinfo("cpu");
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with gr.Blocks() as demo:
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outgpu = gr.Textbox(lines=5);
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outnpu = gr.Textbox(lines=5);
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btngpu = gr.Button(value="gpu");
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btngpun = gr.Button(value="ngpu");
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btngpu.click(gpu, None, [outgpu]);
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btngpun.click(nogpu, None, [outnpu]);
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if __name__ == "__main__":
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demo.launch(
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share=False,
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debug=False,
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server_port=7860,
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server_name="0.0.0.0"
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)
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