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Introducing Liquid Web’s GPU stack

The Liquid Web GPU Stack streamlines AI/ML with pre-configured tools, NVIDIA support, and optimization.
5 min read

The Liquid Web GPU Stack is designed to streamline the setup and management of AI/ML workloads, enabling developers, data scientists, and enterprises to focus on innovation rather than infrastructure. With a comprehensive set of tools, pre-configured frameworks, and seamless containerization support, it’s an ideal solution for those looking to deploy and scale AI initiatives efficiently.

Core Features and Integrated Technologies

NVIDIA GPU Drivers and CUDA Toolkit

The stack includes the latest NVIDIA GPU drivers, ensuring your GPUs are fully optimized for AI/ML tasks. Combined with the NVIDIA CUDA Toolkit, it provides a comprehensive environment for developing and deploying GPU-accelerated applications, allowing you to take full advantage of your hardware’s capabilities.

  • CUDA Toolkit: A parallel computing platform and programming model developed by NVIDIA. It provides developers with tools and libraries to leverage the power of GPUs for general-purpose processing. CUDA is a foundational technology for many deep learning frameworks.

NVIDIA cuDNN

NVIDIA cuDNN is integrated directly into the stack to support deep learning tasks. This GPU-accelerated library delivers highly optimized routines for neural network training and inference, significantly reducing the time required for model development and deployment.

Docker with NVIDIA Container Toolkit

Containerization is crucial for modern AI/ML workflows, and our stack includes Docker with the NVIDIA Container Toolkit. This setup allows you to deploy GPU-accelerated containers effortlessly, ensuring consistency and reliability across different environments.

Monitoring and Utilizing GPU Resources with Server Software

Modern deep learning and high-performance computing tasks often rely heavily on Graphical Processing Units (GPUs) for accelerated computation. Effectively monitoring and managing these resources is crucial for optimal performance and resource allocation. This article explores several commonly used server software tools for interacting with and monitoring GPUs, providing links to their official documentation for further exploration.

GPU Monitoring Tools

These tools provide real-time insights into GPU performance and resource utilization:

  • nvidia-smi: This command-line tool, included with the NVIDIA driver, is a cornerstone for basic GPU monitoring. It provides information on GPU utilization, memory usage, temperature, and power consumption.
  • nvtop: Inspired by the popular system monitoring tool top, nvtop offers a more visually appealing and interactive way to monitor NVIDIA GPUs. It presents real-time statistics on GPU utilization, processes, memory usage, and more.
  • htop: While not exclusively for GPU monitoring, htop is an enhanced interactive process viewer that displays CPU and memory usage, including per-core statistics. It can be helpful for identifying processes consuming significant system resources, which may indirectly impact GPU performance.
  • Glances: A cross-platform system monitoring tool that provides a comprehensive overview of system resources, including CPU, memory, network, and disk usage. Glances also offers basic GPU monitoring capabilities, displaying utilization and memory usage.
  • gpustat: A command-line utility that presents GPU status in a compact and informative table format. It retrieves information similar to nvidia-smi but displays it in a more organized and user-friendly manner.

Support for Leading AI/ML Frameworks

Our stack is pre-configured to support popular AI/ML frameworks such as TensorFlow and PyTorch. This ensures you can start working with your preferred tools immediately without the need for additional setup.

  • PyTorch: An open-source machine learning framework known for its flexibility and dynamic computation graph. It is widely used for deep learning research and production deployments.
  • TensorFlow: Another popular open-source machine learning framework developed by Google. TensorFlow is known for its scalability and production readiness, making it suitable for deploying deep learning models across various platforms.

NVIDIA NGC Integration

With NVIDIA NGC integrated, you gain direct access to a wide range of pre-trained models, SDKs, and optimized frameworks. This integration accelerates your AI/ML projects by providing you with the tools and resources needed to get started quickly.

Ollama Integration

Ollama is a platform that unifies AI/ML workflows, and it’s seamlessly integrated into our stack. With Ollama, you can streamline data preparation, model development, and deployment within a single environment. This integration simplifies the management of complex AI projects, providing a cohesive experience from development through to production.

Hardware Optimization

CPU and GPU Synergy

To maximize performance, the stack is configured to keep both the CPU and GPU in their highest performance states. This ensures that latency is minimized and your system is always ready to handle demanding computational tasks.

Optimized Memory Management

Our stack is optimized for handling large datasets, with configurations that maximize memory efficiency and reduce dependency on local storage. This ensures faster processing times and smoother performance for data-intensive tasks.

Enhanced Network Performance

For high-speed data transfer needs, the stack is optimized for 10Gbps and 25Gbps networking. By increasing socket buffer sizes and fine-tuning TCP settings, we ensure that your data moves quickly and efficiently between systems, even in the most demanding environments.

By effectively utilizing these server software tools, developers and system administrators can optimize GPU resource utilization, monitor performance, and streamline deep learning workflows. Remember to consult the official documentation for each tool to explore their full capabilities and configuration options.

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