Net() self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2)) self.loss_Function = nn.MSELoss() self.optimizer = torch.optim.Adam(self.evaluate_net.parameters(), lr=LR) self.point = 0 self.learn_step = 0 def choose_action(self, s): s = torch.unsqueeze(torch.FloatTensor...
定时清缓存 if clear_cache_num == 5000: torch.cuda.empty_cache() clear_cache_num = 0 其他 torch.cuda.memory_allocated(device=None)返回给定设备device的张量所占用的当前GPU内存 torch.cuda.max_memory_allocated(device=None)返回给定设备device的张量所占用的GPU内存的最大值(从程序运行开始) torch.cud...
it is possible to temporarily disable (expandable_segments:False) the bevhavior for allocator tensors that need to be used cross-process. * CUDA runtimeAPIs related to sharing memory across process (cudaDeviceEnablePeerAccess) do not work for ...
CUDA Out of Memory :CUDA内存不足的完美解决方法 这类问题常见于使用TensorFlow、PyTorch等深度学习框架时,由于处理大规模数据集或模型超出GPU显存导致内存溢出。...显存没有释放 深度学习框架如TensorFlow或PyTorch有时会保留显存,而不立即释放。这可能导致显存不足的问题,即便GPU内存并没有完全使用。...这种情况下,...
print(torch.cuda.memory_summary()) 1. 类图示例 接下来,我们用 Mermaid 图示化来展示我们在示例代码中使用的类及其关系: classDiagram class SimpleNN { +forward(x) } class nn.Module { +__init__() } class nn.Linear { +forward(x) }
🐛 Bug Sometimes, PyTorch does not free memory after a CUDA out of memory exception. To Reproduce Consider the following function: import torch def oom(): try: x = torch.randn(100, 10000, device=1) for i in range(100): l = torch.nn.Linear...
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 记忆回放(Replay Memory) 为了训练DQN,我们将使用经验回放池(experience replay memory)来存储智能体所观测到的环境状态转移情况,在之后的训练中我们可以充分利用这些数据。通过对经验回放池中的数据进行随机采样,组成一个批次的转移情况是互...
RuntimeError: CUDA out of memory. Tried to allocate 12.50 MiB (GPU 0; 10.92 GiB total capacity; 8.57 MiB already allocated; 9.28 GiB free; 4.68 MiB cached). According to the message, I have the required space but it does not allocate the memory. Any idea what might cause this ? For...
# each model is sooo big we can't fit bothinmemory encoder_rnn.cuda(0)decoder_rnn.cuda(1)# run input through encoder onGPU0out=encoder_rnn(x.cuda(0))# run output through decoder on the nextGPUout=decoder_rnn(x.cuda(1))# normally we want to bring all outputs back toGPU0out=out...
both in memoryencoder_rnn.cuda(0)decoder_rnn.cuda(1)# run input through encoder on GPU 0encoder_out = encoder_rnn(x.cuda(0))# run output through decoder on the next GPUout = decoder_rnn(encoder_out.cuda(1))# normally we want to bring all outputs back to GPU 0out = out.cuda(0...