The importance of reviewing and analyzing the key issues in building a TensorFlow environment on Linux that supports GPU with Docker for the one-person enterprise, DeepNetwork.

I am 60 years old this year… I have been working in the field of Information and Communication Technology (IT) for 30 years… For the past 10 years, I have been a self-employed individual providing development services in the firmware sector of IT… I am going to talk about life in today’s world after 30 years of social life… From now on, I have been reviewing and analyzing 2-3 papers related to Large Language Models (LLM) every day for the past 3-4 years, which is my biggest area of interest… Leaving all sorts of stories aside, I am going to talk about why I have been interested in and analyzing the construction of a GPU Cloud development environment recently… I have also been reviewing and analyzing papers related to LLM for about 3 years… Through paper review and analysis, I have a certain understanding of the design structure of LLM… I have analyzed how to implement it in TensorFlow API, i.e., Python… Although it is not perfect, it has been analyzed to some extent… As I was reviewing and analyzing, there was no place to mention in detail about the development environment for the main issues of distributed learning and parallel learning when implementing LLM… I judge that the key to building a LLM development environment in response to the issue of distributed learning of LLM is to use the concept of Docker container design issue to separate each software development environment… Containers are processes that run in an isolated environment, and each container has an independent file system, network, and execution space. This allows multiple different software development environments to be operated independently on a single server PC… I support the environment for developers to develop TensorFlow containers in an isolated environment with Docker when talking about the LLM distributed development environment… So why this is important is as follows… The reason why you only need the NVIDIA driver to use TensorFlow that supports GPU is because Docker provides an image of TensorFlow that uses GPU. This image already has a CUDA environment that matches the version of TensorFlow. In other words, TensorFlow and CUDA toolkit are installed in Docker, and if there is only NVIDIA driver in the host, you can use GPU. This way, you can avoid the hassle of installing or matching the version of the CUDA toolkit… I can also build a container in an isolated environment with Docker related to distributed learning, but everything is not ready, but I almost understand the key point…

 

To understand how TensorFlow image, NVIDIA driver, and CUDA environment work together, you need to know the role of each and how they interact.

TensorFlow Image: The TensorFlow image is provided by Docker, and the CUDA environment that matches TensorFlow is already set. This image contains all the software components and libraries needed to run TensorFlow applications. Through this, users can use TensorFlow immediately without a complicated setting process.

NVIDIA Driver: The NVIDIA driver is installed on the host system and acts as an intermediary between the GPU hardware and the operating system. The driver receives requests for GPU from the operating system or application, and converts the request into a command that the GPU can understand. Therefore, the NVIDIA driver is a key element that allows TensorFlow to use GPU.

CUDA Environment: CUDA is a parallel computing platform and API set developed by NVIDIA. CUDA enables high-performance parallel computing by utilizing the computational power of the GPU. The CUDA toolkit is included in the TensorFlow image, which supports TensorFlow operations using the GPU.

 

These three elements work together so that Docker can use TensorFlow that supports GPU on Linux. The TensorFlow image provides all the necessary software and libraries, and the NVIDIA driver allows this software to communicate with the GPU hardware. Finally, the CUDA environment accelerates TensorFlow’s operations by utilizing the parallel processing power of the GPU. The reason why all three elements must operate for Docker to use TensorFlow that supports GPU on Linux is because they interact with each other to enable TensorFlow’s GPU accelerated operation.

 

Deep Network, a one-person startup specializing in consulting for super-large language models  

E-mail : sayhi7@daum.net    

Representative of a one-person startup /  SeokWeon Jang