在PyTorch中,我们可以使用torch.cuda.memory_allocated()和torch.cuda.max_memory_allocated()来监控当前显存占用和最大显存占用。这两个函数返回的是字节为单位的显存占用量。 importtorch# 获取当前显存占用current_memory=torch.cuda.memory_allocated()print(f"Current memory usage:{current_memory}bytes")# 获取最...
importpsutil# 定义一个函数来获取内存使用情况defget_memory_usage():memory_info=psutil.virtual_memory()returnmemory_info.used/1024**2# 返回MB# 监控内存使用print(f'初始内存占用:{get_memory_usage():.2f}MB')# 模型向前传播output=model(input_data)# 检查内存使用情况print(f'内存占用后:{get_memory...
This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. Moreover, it avoids the overhead of copying between gradients and allreduce communication buckets. When gradients are views, detach_() cannot be called on the gradients. If hitting such...
net, segments=10, input=x) def test_memory_usage(model, input_tensor): torch.cuda.empty_cache() # 清空缓存,确保内存测量准确 torch.cuda.reset_peak_memory_stats() # 重置显存统计 output = model(input_tensor) output.sum().backward() memory_used = torch.cuda.max_memory_allocated() / 1024...
To save peak memory usage, # call _rebuild_buckets before the peak memory usage increases # during forward computation. # This should be called only once during whole training period. # 在前向传播之前使用 _rebuild_buckets 来重置桶 # 在此函数内,也许在释放旧bucket之前分配新bucket。 # 如果要节...
This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. Moreover, it avoids the overhead of copying between gradients and allreduce communication buckets. When gradients are views, detach_() cannot be called on the gradients. If hitting such...
One way to track GPU usage is by monitoring memory usage in a console with the nvidia-smi command. The problem with this approach is that peakGPUusage and out-of-memory happen so fast that you can’t quite pinpoint which part of your code is causing the memory overflow. ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - [MTIA] (4/n) Implement PyTorch APIs to query/reset device peak memory usage · pytorch/pytorch@374b762
)# Calling _rebuild_buckets before forward compuation,# It may allocate new buckets before deallocating old buckets# inside _rebuild_buckets. To save peak memory usage,# call _rebuild_buckets before the peak memory usage increases# during forward computation.# This should be called only once dur...
[源码解析] PyTtorch 分布式 Autograd (6) 引擎(下) 0x00 摘要 上文我们介绍了引擎如何获得后向计算图的依赖,本文我们就接着看看引擎如何依据这些依赖进行后向传播。通过本文的学习,大家可以: 了解 RecvRpcBackward 如何给对应的下游节点发送 RPC 消息,可以