Introduction

TensorFlow is a powerful open-source library used for numerical computation and machine learning. The increasing size and complexity of neural network models necessitate leveraging hardware such as GPUs to accelerate computation. Conda is a popular package manager that aids in setting up and managing different environments with distinct libraries and tools. This guide walks you through importing TensorFlow in a Jupyter Notebook using Conda with GPU support.

Prerequisites

Before you get started, ensure that you have:

• An NVIDIA GPU with CUDA Compute Capability 3.5 or higher.
• NVIDIA CUDA Toolkit (version compatible with TensorFlow).
• NVIDIA cuDNN library.
• Anaconda or Miniconda installed on your machine.

Step-by-Step Guide

Step 1: Install the NVIDIA CUDA Toolkit

1. Visit the CUDA Toolkit Download page: Download the installer corresponding to your operating system and architecture. You can check compatibility at the official NVIDIA CUDA Toolkit page.
2. Follow the installation instructions: Use the commands provided on the NVIDIA website after selecting the appropriate OS and installer type.

Step 2: Install cuDNN

1. Download cuDNN: Visit the NVIDIA Developer website to download the cuDNN library. Create a free account if required.
2. Unzip the downloaded file: Move the files into the CUDA installation directory to ensure the paths are correctly configured.

Step 3: Set Up a Conda Environment

1. Open Anaconda Prompt: Run as an administrator for necessary permissions.
2. Create a new Conda environment: Execute the following command to create a new environment named tf_gpu with Python 3.8.

• Check Environment Variables: Ensure that your system environment variables CUDA_HOME , LD_LIBRARY_PATH (Linux), or equivalent paths for Windows are correctly set to point to the CUDA and cuDNN installation directories.
• Update Drivers: Confirm that your NVIDIA GPU drivers are up to date. This can be managed via the NVIDIA control panel or the respective website.
• GPU Memory Management: Use TensorFlow's tf.config.experimental.set_memory_growth to manage how TensorFlow allocates GPU memory.
• Kernel Selection: In Jupyter, ensure the notebook uses the appropriate Conda Python kernel that's set up with TensorFlow-GPU.