***

title: Environments
description: Learn about preconfigured software environments in NVIDIA Brev GPU instances.
------------------------------------------------------------------------------------------

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](/concepts/launchables):

| Mode               | Environment                    | Preinstalled Software                                |
| ------------------ | ------------------------------ | ---------------------------------------------------- |
| **VM Mode**        | Full Ubuntu VM                 | Python 3.10+, CUDA, Docker, NVIDIA Container Toolkit |
| **Container Mode** | Single Docker container        | Depends on your base image.                          |
| **Compose Mode**   | Multi-container Docker Compose | Depends on service images.                           |

<Callout intent="info">
  Refer to [Launchables](/concepts/launchables) for details on choosing the right deployment mode.
</Callout>

### 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](https://catalog.ngc.nvidia.com/).

<CardGroup cols={2}>
  <Card title="IDE Support" icon="window">
    * VS Code Remote SSH
    * Cursor
    * Windsurf
  </Card>

  <Card title="Development Tools" icon="terminal">
    * Git version control
    * SSH access
    * Brev CLI
  </Card>
</CardGroup>

## CUDA and GPU Support

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

```bash
# 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.

<Callout intent="info">
  The default username is `ubuntu` for most providers. Your working directory path is `/home/<username>/workspace`.
</Callout>

```bash
# 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:

```bash
# 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
```

<Callout intent="info">
  System packages and Docker images persist across instance stops. Create a setup script or Dockerfile for reproducibility.
</Callout>

## What's Next

<CardGroup cols={2}>
  <Card title="CLI Getting Started" icon="play" href="/cli/getting-started">
    Install and configure the Brev CLI.
  </Card>

  <Card title="Jupyter Notebooks" icon="file" href="/guides/development-tools/jupyter-notebooks">
    Run JupyterLab with GPU acceleration.
  </Card>
</CardGroup>