#Anaconda cuda toolkit how to#
understand how to be sure docker is opening a port on your host machine to connect to jupyter, and how to use docker commands to directly use bash in your docker image, among other docker commands this is probably the most difficult stepĥ.1) Also you may need to specify a GPU number as well as ipc=host to use a GPU docker run -gpus 1 -it -ipc="host" -rm someidentifier/someimagename.Setup a tensorflow-docker image (the latest docker image for TF2.2 as of this posting is still running 18.04 inside, this is fine as the tensorflow-docker image contains CUDA as well as CUDNN in a configuration that is known to work (image generated by Google) )ĭocker pull tensorflow/tensorflow:latest-gpu-jupyter Setup (this actually supports a number of linux distributions, not just Ubuntu based) if not wanting to start with javascript, next consider the following suggested procedure instead for Linux on your own hardware:īe in a situation where you are ok installing an ubuntu flavor as your main OS on your laptop, or just have two laptops, or be able to partition your laptop to run an Ubuntu flavorĭownload 20.04 LTS with the proprietary NVIDIA driver preinstalled (this is an Ubuntu flavor).There is a future of even better acceleration backends to be implemented later by Tensorflow.js. Tensorflow.js runs WebGL for GPU acceleration on Nvidia/AMD/Intel, and probably any ARM processors that allocate enough memory to the GPU (currently my raspberry PI 4 4GB is not compatible with the basic MNIST demo). Next consider that there are a lot of tensorflow.js resources for immediately getting into machine learning concepts on virtually any hardware Much more prefered than installing CUDA on Windows: for python/tensorflow and just getting started: your best option are the Ready to run: “One-click” Jupyter options, especially the free ones, provided on link below I will work on re-installing CUDA/CUDNN on my Windows laptop to see if I can get a better answer for you in 2020 for Windows (this will be provided at later a later date)
#Anaconda cuda toolkit install#
I’ve been able to install Cuda and CUDNN on Windows to use for tensorflow with Rstudio and jupyter/python for the first half of the fast AI first-course years ago (when the first classifier was still dogs vs cats and not dog breeds), but in the end it was not worth the time.
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