gpu

问题 I’m running a virtual vachine on GCP with a tesla GPU. And try to deploy a PyTorch -based app to accelerate it with GPU. I want to make docker use this GPU, have access to it from containers. I managed to install all drivers on host machine, and the app runs fine there, but when I try to run it in docker (based on nvidia/cuda container) pytorch fails: File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 82, in _check_driver http://www.nvidia.com/Download/index.aspx""")

问题 I’m running a virtual vachine on GCP with a tesla GPU. And try to deploy a PyTorch -based app to accelerate it with GPU. I want to make docker use this GPU, have access to it from containers. I managed to install all drivers on host machine, and the app runs fine there, but when I try to run it in docker (based on nvidia/cuda container) pytorch fails: File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 82, in _check_driver http://www.nvidia.com/Download/index.aspx""")

问题 I’m running a virtual vachine on GCP with a tesla GPU. And try to deploy a PyTorch -based app to accelerate it with GPU. I want to make docker use this GPU, have access to it from containers. I managed to install all drivers on host machine, and the app runs fine there, but when I try to run it in docker (based on nvidia/cuda container) pytorch fails: File "/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py", line 82, in _check_driver http://www.nvidia.com/Download/index.aspx""")

问题 I have several GPUs but I only want to use one GPU for my training. I am using following options: config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True) config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: Despite setting / using all these options, all of my GPUs allocate memory and #processes = #GPUs How can I prevent this from happening? Note I do not want use set the devices manually and I do not want to set CUDA_VISIBLE_DEVICES since I want

问题 I have several GPUs but I only want to use one GPU for my training. I am using following options: config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True) config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: Despite setting / using all these options, all of my GPUs allocate memory and #processes = #GPUs How can I prevent this from happening? Note I do not want use set the devices manually and I do not want to set CUDA_VISIBLE_DEVICES since I want

问题 I use this notebook from Kaggle to run LSTM neural network. I had started training of neural network and I saw that it is too slow. It is almost three times slower than CPU training. CPU perfomance: 8 min per epoch; GPU perfomance: 26 min per epoch. After this I decided to find answer in this question on Stackoverflow and I applied a CuDNNLSTM (which runs only on GPU) instead of LSTM . Hence, GPU perfomance became only 1 min per epoch and accuracy of model decreased on 3%. Questions: 1) Does

问题 I use this notebook from Kaggle to run LSTM neural network. I had started training of neural network and I saw that it is too slow. It is almost three times slower than CPU training. CPU perfomance: 8 min per epoch; GPU perfomance: 26 min per epoch. After this I decided to find answer in this question on Stackoverflow and I applied a CuDNNLSTM (which runs only on GPU) instead of LSTM . Hence, GPU perfomance became only 1 min per epoch and accuracy of model decreased on 3%. Questions: 1) Does

问题 If both use hardware acceleration (GPU) to execute code, why WebGL is so most faster than Canvas? I mean, I want to know why at low level, the chain from the code to the processor. What happens? Canvas/WebGL comunicates directly with Drivers and then with Video Card? 回答1: Canvas is slower because it's generic and therefore is hard to optimize to the same level that you can optimize WebGL. Let's take a simple example, drawing a solid circle with arc. Canvas actually runs on top of the GPU as

问题 I recently read Jeff's blog post entitled Speed Hashing, where amongst other things he mentions that you can hash things really fast by harnessing the power of your GPU. I was wondering whether or not it was possible to harness the power of the GPU to hash things in Python (md5, sha-1 etc)? I'm interested in this for trying to see how fast I can brute-force things (not real world stuff, from old leaked data dumps). At the moment, I'm doing this sort of thing (simplified example): from

问题 I recently read Jeff's blog post entitled Speed Hashing, where amongst other things he mentions that you can hash things really fast by harnessing the power of your GPU. I was wondering whether or not it was possible to harness the power of the GPU to hash things in Python (md5, sha-1 etc)? I'm interested in this for trying to see how fast I can brute-force things (not real world stuff, from old leaked data dumps). At the moment, I'm doing this sort of thing (simplified example): from