pytorch-mutex-1.0 | cpu 3 KB pytorch torchaudio-0.11.0 | py37_cpu 2.9 MB pytorch openssl-1.1.1n | h2bbff1b_0 5.8 MB typing_extensions-4.1.1 | pyh06a4308_0 29 KB torchvision-0.12.0 | py37_cpu 7.4 MB pytorch mkl-2020.2 | 256 170.7 MB mkl_fft-1.3.0 | py37h46781fe_0 149 ...
// lock around all operations mutable std::recursive_mutex mutex; // device statistics DeviceStats stats; // unallocated cached blocks larger than 1 MB BlockPool large_blocks; // unallocated cached blocks 1 MB or smaller BlockPool small_blocks; // allocated or in use by a stream. Holds al...
① 删除CPUonly conda uninstall cpuonly 1. 有博主说执行完这条代码后会自动安装GPU版本,但是我没有。我删除后再重新安装也仍是一样的问题② 删除pytorch-mutex conda uninstall pytorch-mutex 1. ③ 删除numpy conda uninstall numpy 1. 注: ②和③都有点玄学,但有人说成功了,反正我没成功 ④ 下载安装包并...
mutex2.acquire() # mutex2 上锁 print("func1 mutex2 acquire") mutex2.release() # mutex2 解锁 mutex1.release() # mutex1 解锁 def func2(self): mutex2.acquire() print("func2 mutex2 acquire") time.sleep(1) mutex1.acquire() print("func2 mutex1 acquire") mutex1.release() mutex2.re...
l.backward()print(prof.key_averages().table(sort_by="self_cpu_time_total")) 3. 图片解码 PyTorch中默认使用的是Pillow进行图像的解码,但是其效率要比Opencv差一些,如果图片全部是JPEG格式,可以考虑使用TurboJpeg库解码。具体速度对比如下图所示:
std::lock_guard<std::mutex> lock(mutex); ++item.base->outstanding_tasks; heap.push(std::move(item)); } not_empty.notify_one(); } auto ReadyQueue::pop() -> FunctionTask { std::unique_lock<std::mutex> lock(mutex); not_empty.wait(lock, [...
对于每个GraphTask,我们维护cpu_ready_queue_,这样在设备线程(即GPU)上执行时,如果是下一个NodeTask 应该在CPU上运行,我们就知道应该推送 NodeTask 到哪个就绪队列。 mutex_ :保护如下变量:not_ready_, dependencies_, captured_vars,has_error_, future_result_, cpu_ready_queue_, and leaf_streams。
在PyTorch中,通常使用transformer做图片分类任务的数据增强,而其调用的是CPU做一些Crop、Flip、Jitter等操作。 如果你通过观察发现你的CPU利用率非常高,GPU利用率比较低,那说明瓶颈在于CPU预处理,可以使用Nvidia提供的DALI库在GPU端完成这部分数据增强操作。
[conda] pytorch-mutex 1.0 cpu pytorch [conda] torchaudio 0.13.1 py38_cpu pytorch [conda] torchvision 0.14.1 py38_cpu pytorch cc@seemethere@malfet@osalpekar@atalman zou3519addedmodule: binariesAnything related to official binaries that we release to userstriagedThis issue has been looked at a...
variable.requires_grad()) return {}; at::Tensor new_grad = callHooks(variable, std::move(grads[0])); std::lock_guard<std::mutex> lock(mutex_); at::Tensor& grad = variable.mutable_grad(); // 得到变量的mutable_grad accumulateGrad( variable, grad, new_grad, 1 + !post_hooks()....