At last, copy and then paste the code in the terminal. Step 3: Create the yml file for the device: The user has to create the yml file in the new working directory, and after the process installs the complete dependencies, the user has to run TensorFlow. Step 4: Install cuDNN by extracting the contents into the toolkit path installed in step 2.

Step 3: Sign up on the NVIDIA Developer website and download cuDNN. Step 2: Download and install the CUDA toolkit. Here's a handy checklist to follow: Step 1: Begin by finding if the GPU is compatible with TensorFlow GPU. To set up the Virtual environment: After this use the command. Pip is already there in python 3.5.x onwards. There are few Pre-requisites before we install Tensorflow: Install Python. The steps can be found at TensorFlow's installation guide, but we've broken them down here to simplify the process based on your operating system. Installing the package itself is easy enough, but there are a few prerequisites to setting TensorFlow up properly. Docker pull tensorflow/tensorflow:latest # Download latest stable image docker run -it -p 8888:8888 tensorflow/tensorflow:latest-jupyter # Start Jupyter server Read the Docker install guide Google Colab: An easy way to learn and use TensorFlow Installing the TensorFlow Package.

Using pip on windows