How to Setup GPU for TensorFlow in the Cloud

This tutorila will show you how to create a google cloud enviornment to perform Tensorflow machine learning tasks with a GPU graphic processing unit. We will use Google Compute Engine in conjunction with a Tesla K80 Nvidia GPU card. We will also create a jupyter notebook to use in the browser as an example. We will also use docker as an easy way to create a tensorflow enviornment. In general, if the step of the process can be described such as “do this mathematical operation thousands of times”, then send it to the GPU. Examples include matrix multiplication and computing the inverse of a matrix. In fact, many basic matrix operations are prime candidates for GPUs. As an overly broad and simple rule, other operations should be performed on the CPU. 🧠 Need expert help fast? Book a 1:1 session and get unstuck today 👉 https://bit.ly/42I10y5 🎥 NEW: Unlock members-only videos and behind-the-scenes drops 👉 https://bit.ly/4iyBm4I 🛠️ The exact tools and gear I trust (and actually use) 👉 https://amzn.to/44fKDv4 📚 Step-by-step setup guides, templates, and insider resources 👉 https://bit.ly/4ivZDID 🛒 Grab custom gear and tools designed by me 👉 https://etsy.me/4isKwjb 📩 For sponsorships or business inquiries, reach out: macgyvertechnology@gmail.com #tensorflow #nvidia #gpu Cuda Nvidia Google Startup Scripts https://cloud.google.com/compute/docs/gpus/add-gpus#install-driver-script *We used Ubuntu 18 as our operating system #!/bin/bash echo "Checking for CUDA and installing." # Check for CUDA and try to install. if ! dpkg-query -W cuda-10-0; then curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb dpkg -i ./cuda-repo-ubuntu1804_10.0.130-1_amd64.deb apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub apt-get update apt-get install cuda-10-0 -y fi # Enable persistence mode nvidia-smi -pm 1 Install Docker # Add NVIDIA's docker repository to your system. # Install nvidia-docker2 and restart the Docker daemon. curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L "https://nvidia.github.io/nvidia-docker/$(. /etc/os-release; echo $ID$VERSION_ID)/nvidia-docker.list" \ | sudo tee /etc/apt/sources.list.d/nvidia-docker.list \ && sudo apt-get update \ && sudo apt-get install -y nvidia-docker2 \ && sudo pkill -SIGKILL dockerd # Test nvidia-smi within the Docker container. sudo docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi Docker Container Pre-Configured with TensorFlow docker run -it -d macgyvertechnology/tensorflow-gpu:basic-jupyter