激活检查点 (Activation Checkpointing) 是一种用于减小内存占用的技术,代价是需要更多的计算资源。它利用一个简单的观察,即如果我们只是在需要时重新计算反向传播所需的中间张量,就可以避免保存这些中间张量。 目前在PyTorch中有两种 Activation Checkpointing 的实现,即可重新进入 (reentrant) 和不可重新进入(non-reentra...
optimizer = optim.Adam(model.parameters(), lr=0.001) print("Check Model's State Dict:") for key, value in model.state_dict().items(): print(key, "\t", value.size()) print("Check Optimizer's State Dict:") for key, value in optimizer.state_dict().items(): print(key, "\t", ...
Here are the steps to run the TensorFlow checkpointing example on FloydHub. Via FloydHub's Command Mode First time training command: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 floyd run \ --gpu \ --env tensorflow-1.3 \ --data redeipirati/datasets/mnist/1:input \ 'python tf_mnist...
Again, a checkpoint contains the information you need to save your current experiment state so that you can resume training from this point. Just like in that infernalZelda II: The Adventure of Linkgame from my childhood. Checkpoint Strategies At this point, I'll assume I've convinced you th...
Pytorch提供了大量的有用的调试工具,如autograd.profiler,autograd.grad_check和autograd.anomaly_detection。在需要的时候使用它们,在不需要它们的时候关闭它们,因为它们会减慢你的训练。 14. 使用梯度剪裁 最初是用于RNNs避免爆炸梯度,有一些经验证据和一些理论支持认为剪裁梯度(粗略地说:gradient = min(gradient, thres...
def load_ckp(checkpoint_fpath, model, optimizer):""" checkpoint_path: path to save checkpoint model: model that we want to load checkpoint parameters into optimizer: optimizer we defined in previous training """ # load check point checkpoint = torch.load(checkpoint_fpath) ...
Pytorch提供了大量的有用的调试工具,如autograd.profiler,autograd.grad_check和autograd.anomaly_detection。在需要的时候使用它们,在不需要它们的时候关闭它们,因为它们会减慢你的训练。 14. 使用梯度剪裁 最初是用于RNNs避免爆炸梯度,有一些经验证据和一些理论支持认为剪裁梯度(粗略地说:gradient = min(gradient, thres...
and can also be used with an optimized DDP which only reduces to the relevant ranks. More context on ZeRO and PyTorch can be found inthis RFCTheAPIwith respect to loading and saving the state is a known pain point and should probably be discussed an updated. Other possible follow ups incl...
for point in points: box = [int(p) for p in point] boxes_list.append(box[-4:]) boxes = torch.tensor(boxes_list, dtype=torch.float) labels = torch.ones((boxes.shape[0],), dtype=torch.long) # iscrowd = torch.zeros((num_objs,), dtype=torch.int64) ...
Check the Convert Result PaddlePaddle's Official Quick Start #!/usr/bin/env python# encoding: utf-8importnumpyasnpimportpaddle.fluid.dygraphasDfromernie.tokenizing_ernieimportErnieTokenizerfromernie.modeling_ernieimportErnieModel D.guard().__enter__()# activate paddle `dygrpah` modemodel = ErnieMo...