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Virtual Machines

by Sarthak Chandajkar • Aug 03, 2025 • 5 min read

Virtual Machines (VMs) are software-based emulations of physical computers, enabling multiple operating systems to run on a single physical host. They provide isolated environments for running applications, testing software, or deploying Machine Learning workloads, making them essential in cloud computing, development, and system administration. This note explores VM architecture, use cases, and their role in Deep Learning, with backlinks to related concepts.

Definition

A VM is a virtualized computing environment that mimics a physical computer, including its CPU, memory, storage, and network interfaces. It runs an operating system and applications in isolation from the host system, managed by a hypervisor (e.g., VMware, VirtualBox, KVM).

Key Components

  • Guest OS: The operating system running inside the VM (e.g., Ubuntu, Windows).
  • Hypervisor: Software that creates and manages VMs, allocating resources between the host and guests.
    • Type 1 (Bare-Metal): Runs directly on hardware (e.g., VMware ESXi, Hyper-V).
    • Type 2 (Hosted): Runs on top of a host OS (e.g., VirtualBox, VMware Workstation).
  • Virtual Hardware: Emulated CPU, RAM, disk, and network interfaces assigned to the VM.

Intuition

Think of a VM as a computer within a computer, like a self-contained apartment in a larger building, with its own resources but sharing the host’s infrastructure.

How VMs Work

The hypervisor abstracts physical hardware, creating virtualized resources for each VM:

  1. Resource Allocation: The hypervisor assigns CPU cores, memory, and storage to VMs.
  2. Isolation: Each VM operates independently, ensuring crashes or security issues in one don’t affect others.
  3. Execution: The guest OS runs as if on physical hardware, interacting with virtualized devices.

VMs use virtualization technologies like Intel VT-x or AMD-V for efficient CPU performance and can leverage GPU passthrough for Deep Learning tasks.

Real-World Example

In a cloud provider like AWS, an EC2 instance (a VM) runs a Neural Network training job on Ubuntu with TensorFlow. The hypervisor (AWS Nitro) isolates the instance, ensuring secure, scalable computation.

Use Cases

  1. Development and Testing:
    • Create isolated environments to test software across different OS versions.
    • Example: Testing a web app on Windows and Linux VMs without physical hardware.
  2. Cloud Computing:
    • VMs power cloud services like AWS EC2, Azure VMs, or Google Compute Engine for scalable workloads.
    • Example: Deploying a Convolutional Neural Network on a cloud VM for image recognition.
  3. Server Consolidation:
    • Run multiple VMs on a single server to optimize resource usage.
    • Example: Hosting web, database, and Machine Learning servers on one physical machine.
  4. Machine Learning Workloads:
    • VMs provide reproducible environments for training models like BERT or Faster R-CNN.
    • Example: A VM with a GPU runs PyTorch to train a model on the CIFAR-10 dataset.

Real-World Example

In a data science team, VMs are used to create identical environments for training Transformers models. Each team member’s VM runs TensorFlow with pre-installed dependencies, ensuring consistent results across experiments.

Advantages

  • Isolation: VMs prevent interference between applications or users.
  • Flexibility: Run multiple OS types (e.g., Linux, Windows) on one host.
  • Scalability: Easily replicate or scale VMs in cloud environments.
  • Portability: VMs can be exported as images and deployed elsewhere.

Challenges

  1. Resource Overhead: VMs require significant CPU, memory, and storage compared to containers (e.g., Docker).
    • Solution: Use lightweight containers for simple apps or optimize VM resource allocation.
  2. Performance: Virtualization introduces latency compared to bare-metal execution.
    • Solution: Use Type 1 hypervisors or GPU passthrough for Deep Learning tasks.
  3. Management Complexity: Managing multiple VMs requires robust tools.
    • Solution: Use orchestration platforms like Kubernetes or VMware vSphere.

Practical Tip

For Machine Learning workloads, configure VMs with GPU support and pre-install frameworks like TensorFlow or PyTorch. Use cloud VMs with auto-scaling to handle varying compute demands.

Implementation Example

Below is a Bash script to create a VM using QEMU/KVM on a Linux host for a Machine Learning task:

#!/bin/bash
# Create a VM with Ubuntu 20.04
qemu-img create -f qcow2 ubuntu-vm.qcow2 20G
qemu-system-x86_64 \
  -m 4G \
  -cpu host \
  -enable-kvm \
  -hda ubuntu-vm.qcow2 \
  -cdrom ubuntu-20.04.iso \
  -boot d \
  -net nic -net user \
  -vga virtio
 
# After setup, install TensorFlow inside the VM
# sudo apt update && sudo apt install python3-pip
# pip install tensorflow

This script creates a VM with 4GB RAM and a 20GB disk, boots from an Ubuntu ISO, and sets up a network. Post-setup, TensorFlow can be installed for training Neural Networks.

Comparison with Containers

  • VMs vs. Containers:
    • VMs emulate full systems (OS + apps), providing strong isolation but higher overhead.
    • Containers (e.g., Docker) share the host OS, offering lightweight deployment but less isolation.
    • Example: Use VMs for secure, multi-OS environments (e.g., Windows for testing, Linux for BERT training); use containers for microservices.

Applications

  • Machine Learning: VMs host training environments for Convolutional Neural Networks or Transformers with GPU support.
  • Cloud Services: Power cloud instances for scalable Deep Learning tasks.
  • Development: Create sandboxes for testing Python scripts or PyTorch models.
  • Security: Run untrusted applications in isolated VMs to prevent system compromise.

Real-World Example

In a research lab, a VM on Google Cloud with an NVIDIA GPU trains a Faster R-CNN model for object detection on medical images, using TensorFlow and an Adam Optimizer to minimize the Objective Function, ensuring reproducible results across experiments.

Graph View

Backlinks