Overview

When it comes to setting up a machine with a GPU for deep learning workloads, the first steps primarily involve installing the necessary software components and dependencies. Here's a simplified version of the process:

Setting Up a Machine with GPU

1.Install Operating System

Choose a suitable operating system. Ubuntu or other Linux distributions are preferred for deep learning due to their compatibility and control. We will focus on Ubuntu and Debian for this project.

2. Install GPU Drivers

Download and install the latest GPU drivers compatible with your GPU model. Visit the GPU manufacturer's website (e.g., NVIDIA or AMD) to get the appropriate drivers.

3. Create CONDA Environment

Utilize package managers like pip conda to create a controlled environment. This ensures a clean setup for your deep learning project. For this project we are going to use Miniconda.

4. Install CUDA Toolkit

CUDA is essential for GPU acceleration in deep learning frameworks. Install the CUDA Toolkit, encompassing libraries and tools that optimize GPU performance.

In this step, we will focus on setting up the necessary dependencies, with TensorFlow as the chosen deep learning framework for this project.

a. Install TensorFlow

Within the conda environment established earlier, install TensorFlow. This powerful deep learning framework will serve as the cornerstone of our project, enabling advanced neural network computations and model training.

b. Install Supplementary Libraries

In addition to TensorFlow, we will also ensure that your environment is equipped with essential supplementary libraries, enhancing your capability to effectively manipulate and analyze data. Install libraries such as NumPy, Pandas, SciPy, Scikit-learn (Scikit),Jupyter Server and others important like Gensim, NLTK, XGBoost, etc.

These supplementary libraries will provide you with a rich toolkit to preprocess data, build models, and analyze results effectively within the TensorFlow framework. By installing these dependencies, we ensure a well-rounded environment that empowers you to undertake complex deep learning tasks with confidence and efficiency.

6. Verify Installation

Validate our setup by running sample models that exercises our GPU and tests the functionality of the TensorFlow installation.

Now, let's delve into the specifics of each step.