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Installing Tensorflow 1.15 with CUDA and cuDNN using Conda: A Guide

Tensorflow, CUDA, and cuDNN are essential tools for any data scientist working with deep learning models. This blog post will guide you through the process of installing Tensorflow 1.15 with CUDA and cuDNN using Conda, a popular package, dependency, and environment management tool.
By Saturn Cloud | | Miscellaneous

Installing Tensorflow 1.15 with CUDA and cuDNN using Conda: A Guide

Tensorflow, CUDA, and cuDNN are essential tools for any data scientist working with deep learning models. This blog post will guide you through the process of installing Tensorflow 1.15 with CUDA and cuDNN using Conda, a popular package, dependency, and environment management tool.

Why Tensorflow 1.15, CUDA, and cuDNN?

Tensorflow is one of the most widely used libraries for implementing machine learning and deep learning algorithms. Version 1.15 is particularly important as it serves as a bridge between Tensorflow 1.x and 2.x, supporting many features of both versions.

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use CUDA-enabled GPUs for general purpose processing.

cuDNN (CUDA Deep Neural Network library) is a GPU-accelerated library for deep neural networks. It provides highly optimized primitives for deep learning.

Together, Tensorflow, CUDA, and cuDNN provide a powerful framework for designing, training, and running deep learning models.

Prerequisites

Before we start, ensure that you have the following:

  • A CUDA-compatible NVIDIA GPU
  • Anaconda or Miniconda installed on your system

Step 1: Create a New Conda Environment

Creating a new environment helps avoid conflicts with other packages. Use the following command to create a new environment named tf115:

conda create -n tf115 python=3.7

Activate the environment:

conda activate tf115

Step 2: Install CUDA Toolkit

Next, we’ll install the CUDA Toolkit. For Tensorflow 1.15, we need CUDA 10.0. Use the following command:

conda install -c anaconda cudatoolkit=10.0

Step 3: Install cuDNN

After installing the CUDA Toolkit, we’ll install cuDNN. We need cuDNN 7.6.5 for compatibility with Tensorflow 1.15 and CUDA 10.0:

conda install -c anaconda cudnn=7.6.5

Step 4: Install Tensorflow 1.15

Finally, we’ll install Tensorflow 1.15:

pip install tensorflow-gpu==1.15

Step 5: Verify the Installation

To verify that Tensorflow, CUDA, and cuDNN are installed correctly, run the following Python code:

import tensorflow as tf

print("Tensorflow Version: ", tf.__version__)
print("CUDA Version: ", tf.sysconfig.get_build_info()['cuda_version'])
print("cuDNN Version: ", tf.sysconfig.get_build_info()['cudnn_version'])

If everything is installed correctly, you should see the versions of Tensorflow, CUDA, and cuDNN that you installed.

Conclusion

In this blog post, we’ve walked through the process of installing Tensorflow 1.15 with CUDA and cuDNN using Conda. This setup will allow you to leverage the power of your GPU when training deep learning models. Remember to always check the compatibility between Tensorflow, CUDA, and cuDNN versions to avoid potential issues.

Keywords

  • Tensorflow 1.15
  • CUDA
  • cuDNN
  • Conda
  • Installation
  • Deep Learning
  • GPU
  • NVIDIA
  • Anaconda
  • Miniconda
  • Python
  • Machine Learning
  • Data Science

Meta Description

Learn how to install Tensorflow 1.15 with CUDA and cuDNN using Conda. This comprehensive guide is designed for data scientists who want to leverage the power of their GPUs for deep learning.

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