LESSON 01

Lesson 01: The Execution Environment

Preparing your system to run AVA SDK with WSL2, Docker, and NVIDIA Container Toolkit

GDC 2026 Update

At GDC 2026, Razer unveiled new <strong>agentic</strong> capabilities for Project AVA: multi-step planning, autonomous cross-application task execution, and multi-assistant coordination. All content in this lesson remains valid as a foundation, but AVA is no longer just a reactive assistant.Read more →

Introduction

Before diving into the world of local language models with AVA SDK, you need to prepare your development environment. This lesson will guide you step-by-step through setting up Windows Subsystem for Linux 2 (WSL2), Docker Engine, and NVIDIA Container Toolkit.

System Requirements

0 / 5

Windows 10 (version 2004 or later) or Windows 11

Check your version in Settings > System > About

Processor with virtualization enabled (Intel VT-x / AMD-V)

Check in BIOS/UEFI

Minimum 16GB RAM (Recommended: 32GB)

To run medium-sized language models

NVIDIA GPU with CUDA support (GTX 1060 6GB or higher)

Verify compatibility at nvidia.com

Minimum 50GB free SSD space

For models, containers, and datasets

Step 1: WSL2 Installation

WSL2 provides a real Linux kernel on Windows, essential for running Docker containers efficiently.

Pro Tip

Make sure to run PowerShell as Administrator to avoid permission errors.

PowerShell (Administrador)

1# Habilitar WSL
2wsl --install
3
4# Instalar Ubuntu 24.04 LTS
5wsl --install -d Ubuntu-24.04
6
7# Verificar instalación
8wsl --list --verbose

Nota

After installation, restart your PC. When starting WSL for the first time, you will be asked to create a UNIX user and password.

Verify WSL Version

1wsl --version
2# Debe mostrar WSL version 2.x.x

Step 2: Docker Engine Installation

Docker allows us to run AVA SDK in an isolated and reproducible environment.

Advertencia

DO NOT install Docker Desktop. We will use native Docker Engine in WSL2 for better performance and lower resource consumption.

Installation on Ubuntu (WSL2)

Terminal WSL2 (Ubuntu)

1# Actualizar repositorios
2sudo apt update && sudo apt upgrade -y
3
4# Instalar dependencias
5sudo apt install -y ca-certificates curl gnupg lsb-release
6
7# Añadir clave GPG de Docker
8sudo mkdir -p /etc/apt/keyrings
9curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
10
11# Configurar repositorio
12echo \
13  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
14  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
15
16# Instalar Docker Engine
17sudo apt update
18sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
19
20# Habilitar Docker sin sudo
21sudo usermod -aG docker $USER
22newgrp docker
23
24# Verificar instalación
25docker --version
26docker run hello-world

Consejo

If you get a permission error with "docker run", close and reopen the WSL2 terminal.

Step 3: NVIDIA Container Toolkit

This toolkit allows Docker containers to access your NVIDIA GPU for inference acceleration.

Importante

Make sure you have the latest NVIDIA drivers installed on Windows before continuing.

Terminal WSL2 (Ubuntu)

1# Configurar repositorio NVIDIA
2distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
3curl -fsSL https://nvidia.github.io/nvidia-docker/gpgkey | sudo gpg --dearmor -o /etc/apt/keyrings/nvidia-docker.gpg
4echo "deb [signed-by=/etc/apt/keyrings/nvidia-docker.gpg] https://nvidia.github.io/nvidia-docker/$distribution nvidia-docker.list" | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
5
6# Instalar NVIDIA Container Toolkit
7sudo apt update
8sudo apt install -y nvidia-container-toolkit
9
10# Configurar Docker para usar NVIDIA runtime
11sudo nvidia-ctk runtime configure --runtime=docker
12sudo systemctl restart docker
13
14# Verificar que la GPU esté disponible
15docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi

Successful Verification

If you see the output of nvidia-smi showing your GPU, congratulations! Your environment is correctly configured.

Common Troubleshooting

WSL won't start

1# Verificar si la virtualización está habilitada
2systeminfo | find "Virtualization"
3
4# Debe mostrar "Enabled" o "Habilitado"

Docker won't start

1# Verificar estado del servicio
2sudo service docker status
3
4# Iniciar manualmente
5sudo service docker start

GPU not detected

1# Verificar drivers en Windows
2nvidia-smi.exe
3
4# Debe mostrar tu GPU en la lista

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