31 lines
1.3 KiB
Python
31 lines
1.3 KiB
Python
import socket
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try:
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import torch
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n_gpus = torch.cuda.device_count()
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print(f"checking {n_gpus} gpus.")
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for i_gpu in range(n_gpus):
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print(i_gpu)
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device = torch.device(f"cuda:{i_gpu}")
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torch.backends.cuda.matmul.allow_tf32 = False
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.allow_tf32 = True
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data = torch.randn([4, 640, 32, 32], dtype=torch.float, device=device, requires_grad=True)
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net = torch.nn.Conv2d(640, 640, kernel_size=[3, 3], padding=[1, 1], stride=[1, 1], dilation=[1, 1], groups=1)
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net = net.to(device=device).float()
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out = net(data)
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out.backward(torch.randn_like(out))
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torch.cuda.synchronize()
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except RuntimeError as err:
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import requests
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import datetime
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import os
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device = os.environ.get("CUDA_VISIBLE_DEVICES", "?")
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hostname = socket.gethostname()
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ts = datetime.datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')
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resp = requests.get('http://169.254.169.254/latest/meta-data/instance-id')
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print(f'ERROR at {ts} on {hostname}/{resp.text} (CUDA_VISIBLE_DEVICES={device}): {type(err).__name__}: {err}', flush=True)
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raise err
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else:
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print(f"checked {socket.gethostname()}")
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