gpu

问题 I have set up a simple linear regression problem in Tensorflow, and have created simple conda environments using Tensorflow CPU and GPU both in 1.13.1 (using CUDA 10.0 in the backend on an NVIDIA Quadro P600). However, it looks like the GPU environment always takes longer time than the CPU environment. The code I'm running is below. import time import warnings import numpy as np import scipy import tensorflow as tf import tensorflow_probability as tfp from tensorflow_probability import

问题 I'm trying to train a neural net on a GPU using Keras and am getting a "Resource exhausted: OOM when allocating tensor" error. The specific tensor it's trying to allocate isn't very big, so I assume some previous tensor consumed almost all the VRAM. The error message comes with a hint that suggests this: Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. That sounds good, but how do I do it?

问题 Recently i looked into reinforcement learning and there was one question bugging me, that i could not find an answer for: How is training effectively done using GPUs? To my understanding constant interaction with an environment is required, which for me seems like a huge bottleneck, since this task is often non-mathematical / non-parallelizable. Yet for example Alpha Go uses multiple TPUs/GPUs. So how are they doing it? 回答1: Indeed, you will often have interactions with the environment in

问题 Recently i looked into reinforcement learning and there was one question bugging me, that i could not find an answer for: How is training effectively done using GPUs? To my understanding constant interaction with an environment is required, which for me seems like a huge bottleneck, since this task is often non-mathematical / non-parallelizable. Yet for example Alpha Go uses multiple TPUs/GPUs. So how are they doing it? 回答1: Indeed, you will often have interactions with the environment in

问题 I am new to CUDA/GPU and I am having problems copying data from my device back to the host. I am developing for Jetson TK1 with CUDA Toolkit 6.5. It builds successfully, but gives an error during runtime. My code is below: //main.cu void allocate(double* const d_inputCurrent, double* signal, double* const d_outputCurrent, const size_t size); int main () { int data_length = 1024000; const int length=512; const size_t size= length; double signalA[length], signalB[length], signalC[length]; for

问题 I'm trying to build a docker image that can run using GPUS, this my situation: I have python 3.6 and I am starting from image nvidia/cuda:10.0-cudnn7-devel. Torch does not see my GPUs. nvidia-smi is not working too, returning error: > Failed to initialize NVML: Unknown Error > The command '/bin/sh -c nvidia-smi' returned a non-zero code: 255 I installed nvidia toolkit and nvidia-smi with RUN apt install nvidia-cuda-toolkit -y RUN apt-get install nvidia-utils-410 -y 回答1: I figured out the

问题 I have a GPU, and CUDA driver installed. I am making a docker image using the official anaconda image available at docker hub. When I use the command nvidia-smi inside the docker image, it shows there is no CUDA DRIVER installed. How should I make a docker image to be able to use Anaconda with GPU? 回答1: Docker 19.03 has native support for NVIDIA GPUs. Install nvidia-container-runtime and use the docker run --gpus all flag with your Anaconda container. Remember, that you can run Windows

问题 I have written code using numpy that takes an array of size (m x n)... The rows (m) are individual observations comprised of (n) features... and creates a square distance matrix of size (m x m). This distance matrix is the distance of a given observation from all other observations. E.g. row 0 column 9 is the distance between observation 0 and observation 9. import numpy as np #import cupy as np def l1_distance(arr): return np.linalg.norm(arr, 1) X = np.random.randint(low=0, high=255, size=

问题 I am trying to run some TensorFlow (2.2) example code on databricks/GPU (p2.xlarge) with environment as: 6.6 ML, spark 2.4.5, GPU, Scala 2.11 Keras version : 2.2.5 nvidia-smi NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 I have checked https://docs.databricks.com/applications/deep-learning/single-node-training/tensorflow.html#install-tensorflow-22-on-databricks-runtime-66-ml&language-GPU But, I do not want to run the shell commands every time the databricks GPU clusters is

问题 I have a couple questions on hardware for a Deep Learning project I'm starting, I intend to use pyTorch for Neural Networks. I am thinking about going for an 8th Gen CPU on a z390 (I'll wait month to see if prices drop after 9th gen CPU's are available) so I still get a cheaper CPU that can be upgraded later. Question 1) Are CPU cores going to be beneficial would getting the latest Intel chips be worth the extra cores, and if cores on CPU will be helpful, should I just go AMD? I am also