Environments

Every Brev instance provides a development environment with popular AI/ML tools ready to use. There is no manual setup required.

What’s Included

What’s preinstalled depends on your deployment mode:

VM Mode Software

VM Mode instances include:

• Languages: Python 3.10+ with pip (version configurable)
• GPU Support: CUDA Toolkit, cuDNN, NVIDIA drivers
• Containers: Docker, Docker Compose, NVIDIA Container Toolkit
• Notebooks: JupyterLab (when enabled in configuration)

Container and Compose Modes

Preinstalled software depends on your base image. Common NVIDIA base images include PyTorch, TensorFlow, and TensorRT containers from NGC.

CUDA and GPU Support

Brev preconfigures and optimizes NVIDIA drivers and CUDA for your GPU type. Verify GPU access with:

$
# Quick check - GPU and CUDA availability
$
nvidia-smi
$
nvcc --version
$
$
# Framework verification (requires PyTorch installed)
$
pip install torch  # if not using a PyTorch base image
$
python3 -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
$
python3 -c "import torch; print(f'GPU: {torch.cuda.get_device_name(0)}')"

Working Directory

Your default working directory is /home/ubuntu/workspace. This is where you should store all your work, as it persists across instance stops.

$
# Navigate to workspace
$
cd /home/ubuntu/workspace
$
$
# Clone your project
$
git clone https://github.com/your/repo.git
$
$
# Create a virtual environment
$
python3 -m venv .venv
$
source .venv/bin/activate

Customizing Your Environment

You have full control to install additional packages and configure the environment:

$
# Navigate to workspace
$
cd /home/ubuntu/workspace
$
$
# Install Python packages
$
pip install transformers accelerate
$
$
# Install system packages
$
sudo apt update && sudo apt install -y htop
$
$
# Pull Docker images
$
docker pull nvcr.io/nvidia/pytorch:24.01-py3

What’s Next