Install related dependencies
To enable the compilation, execution, and execution of complex neural network computations or models on our Virtual Machine using GPU acceleration, it is essential to establish the necessary foundational components. The initial stepS in this process involve installing TensorFlow and cunDNN, which serves as the foundational dependencies. Subsequently, our focus will shift towards the installation of supplementary libraries to augment this robust foundation.
1. TensorFlow and cuDNN:
a. TensorFlow
TensorFlow is an open-source machine learning framework developed by Google. It is designed to facilitate the creation, training, and deployment of machine learning models, particularly those based on neural networks. TensorFlow provides a comprehensive set of tools and libraries that enable developers and researchers to build and experiment with various types of machine learning models, including deep learning architectures.
b. cuDNN (CUDA Deep Neural Network):
cuDNN (CUDA Deep Neural Network) is a GPU-accelerated dependency developed by NVIDIA specifically designed to optimize deep learning computations, especially those involving convolutional neural networks (CNNs).
By using cuDNN, deep learning frameworks can achieve better performance and reduced execution times for neural network operations.
c. Code:
Having grasped the importance of TensorFlow and cuDNN, we can now proceed to explore the hands-on procedures for its installation and setup. By following these instructions, we will create an environment that maximizes the potential of GPU acceleration, offering a robust platform for tackling intricate machine learning tasks with enhanced efficiency and performance.
2. Install Supplementary Libraries:
Incorporating not only TensorFlow and cuDNN but also vital supplementary libraries, you can furnish your environment with the essential tools for seamless data manipulation and analysis. These include indispensable installations such as NumPy, Pandas, Seaborn, SciPy, Scikit-learn (Scikit), PyTorch, Gensim, NLTK, and XGBoost. Moreover, we'll deploy Jupyter Server— a web application that provides a central interface for managing Jupyter Notebooks and their kernels is ideal for centralized management and collaboration on team-based data science and machine learning large-scale projects.
By integrating these supplementary libraries, you gain access to a comprehensive toolkit designed to preprocess data, construct robust models, and conduct meticulous result analyses—all seamlessly intertwined within the TensorFlow framework. This integration of dependencies results in a well-rounded environment that empowers you to confidently and efficiently tackle intricate deep learning tasks, driving innovation and achieving remarkable results.
Lastly, we have opted to use the pip repository instead of conda for installing the related dependencies (except for Jupyter Server) for a couple of reasons. First, pip generally offers faster installation, which is important given the intensive nature of our project. Second, pip often provides more current and specialized libraries, particularly in areas like machine learning and deep learning. This choice ensures efficient installations and access to the latest tools, aligning perfectly with our project's performance and innovation goals.