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import torch
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA version: {torch.version.cuda}")
# GPU detection and info
gpu_count = torch.cuda.device_count()
print(f"Number of GPUs: {gpu_count}")
for i in range(gpu_count):
gpu_name = torch.cuda.get_device_name(i)
gpu_memory = torch.cuda.get_device_properties(i).total_memory / 1024**3 # Convert to GB
print(f"GPU {i}: {gpu_name}")
print(f"GPU {i} Memory: {gpu_memory:.1f} GB")
# Current GPU
current_gpu = torch.cuda.current_device()
print(f"Current GPU: {current_gpu}")
# Test GPU tensor operations
print("\nTesting GPU operations...")
try:
# Create a test tensor on GPU
test_tensor = torch.randn(1000, 1000).cuda()
print(f"✓ Successfully created tensor on GPU: {test_tensor.shape}")
print(f"✓ Tensor device: {test_tensor.device}")
# Test basic operations
result = torch.mm(test_tensor, test_tensor.T)
print(f"✓ Matrix multiplication successful: {result.shape}")
# Memory usage
allocated = torch.cuda.memory_allocated() / 1024**2 # MB
cached = torch.cuda.memory_reserved() / 1024**2 # MB
print(f"✓ GPU Memory allocated: {allocated:.1f} MB")
print(f"✓ GPU Memory cached: {cached:.1f} MB")
# Clean up
del test_tensor, result
torch.cuda.empty_cache()
print("✓ GPU memory cleaned up successfully")
except Exception as e:
print(f"✗ GPU test failed: {e}")
else:
print("CUDA not available - PyTorch will use CPU only") |