GPU Memory Usage占满而GPU-Util却为0的调试

【GPU Memory Usage占满而GPU-Util却为0的调试】的更多相关文章

GPU Memory Usage占满而GPU-Util却为0的调试

最近使用github上的一个开源项目训练基于CNN的翻译模型,使用THEANO_FLAGS='floatX=float32,device=gpu2,lib.cnmem=1' python run_nnet.py -w data/exp1/,运行时报错,打印"The image and the kernel must have the same type. inputs(float64), kerns(float32)"的错误,然后使用THEANO_FLAGS='floatX=float…

Reducing and Profiling GPU Memory Usage in Keras with TensorFlow Backend

keras 自适应分配显存 & 清理不用的变量释放 GPU 显存 Intro Are you running out of GPU memory when using keras or tensorflow deep learning models, but only some of the time? Are you curious about exactly how much GPU memory your tensorflow model uses during training? Are…

Tensorflow默认占满全部GPU的全部资源

一台服务器上装了多块GPU,默认情况下启动一个深度学习训练任务时,这个任务会占满每一块GPU的几乎全部存储空间.这就导致一个服务器基本上只能执行一个任务,而实际上任务可能并不需要如此多的资源,这相当于一种资源浪费. 针对这个问题,有如下解决方案. 一.直接设置可见GPU 写一个脚本,设置环境变量 export CUDA_VISIBLE_DEVICES=0 python model.py 二.设置每个GPU的存储上限 gpu_options = tf.GPUOptions(per_process_…

Allowing GPU memory growth

By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmen…

tensorflow 运行效率 GPU memory leak 问题解决

问题描述: Tensorflow 训练时运行越来越慢,重启后又变好. 用的是Tensorflow-GPU 1.2版本,在GPU上跑,大概就是才开始训练的时候每个batch的时间很低,然后随着训练的推进,每个batch的耗时越来越长,但是当我重启后,又一切正常了? 问题查找: 一开始查到的原因是batch_size 和 batch_num的问题,通过python yield 数据生成器解决,确保内存每次处理的数据确定是batch_size大小,但是发现运行效率还是不高,所以查阅google的一些资…

重置GPU显存 Reset GPU memory after CUDA errors

Sometimes CUDA program crashed during execution, before memory was flushed. As a result, device memory remained occupied. There are some solutions: 1. Try using: nvidia-smi --gpu-reset or simply: nvidia-smi -r 2. Although it should be unecessary to d…