TensorFlow

Initially created by Google Brain’s Machine Intelligence team, TensorFlow was designed for machine learning and neural network research. This flexible, scalable system works on various platforms using different accelerators, such as GPUs, NPUs, and TPUs.

It provides robust support for the training and development of generative adversarial networks (GANs). These sophisticated neural network architectures consist of two competing models: a generator that creates synthetic data and a discriminator that evaluates its authenticity. Developers can efficiently implement and fine-tune GAN’s for various applications, including image generation, style transfer, and data augmentation.

Many well-known applications leverage the power of TensorFlow. This versatile tool is utilized by a diverse range of companies across various industries:

• Google Translate uses TensorFlow for its advanced language translation capabilities.
• Airbnb utilizes this technology to enhance its user experience and optimize its services.
• Dropbox incorporates TensorFlow into its infrastructure to improve file management and search capabilities.
• Uber harnesses the power of TensorFlow for route optimization and demand prediction.
• Snapchat employs this library to power its innovative augmented reality filters and features.

It’s crucial to understand that TensorFlow is a powerful developer tool, but it’s not useful on its own. Simply installing it on a server won’t help you train a neural network model or run inference. However, when you start coding and integrate the TensorFlow library into your project, you unlock a wide range of possibilities. Given the many options available, we recommend consulting the official documentation before diving into experiments.

Installation

TensorFlow can be installed in two main methods. The first method is suitable for situations where containerization isn’t needed. The second uses Docker containers, allowing you to quickly set up a convenient test platform with Jupyter Notebook.

Standalone

Before installation, it’s recommended to update the current version of the pip package manager:

pip install --upgrade pip

Now, install TensorFlow on the server:

pip install tensorflow

After completing the process, you can create your own neural networks using the power of this wonderful library.

Docker

Before starting, ensure you have installed the latest version of Docker Engine on your server. Now, download the official container image from DockerHub library:

sudo docker pull tensorflow/tensorflow:latest

Tensorflow, while a powerful machine learning library doesn’t come with a built-in web interface for user interaction. This limitation necessitates the use of third-party software solutions to effectively work with TensorFlow. One popular and widely used option is Jupyter Notebook, which provides an interactive environment for coding and visualization.

To set up our working environment, you’ll need to launch the Jupyter Notebook and configure it for remote access. Additionally, forward port 8888 to allow connections from external devices or networks:

sudo docker run -it -p 8888:8888 tensorflow/tensorflow:latest-jupyter

After launching, open your web browser and navigate to the page:

http://[LeaderGPU_server_IP]:8888

To verify the installed version of Tensorflow and its availability, enter and execute the following code in Jupyter Notebook:

import tensorflow as tf
tf.__version__