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VoltageGPU CLI

The fastest way to deploy and manage GPU pods for AI/ML workloads

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🎯 What is VoltageGPU?

VoltageGPU is a GPU cloud platform offering affordable access to high-performance GPUs for AI/ML training, inference, and development. Deploy pods with NVIDIA RTX 4090, A100, and H100 GPUs in seconds.

Why VoltageGPU?

FeatureVoltageGPUAWSGCP
RTX 4090$0.35/hrN/AN/A
A100 80GB$1.89/hr$4.10/hr$3.67/hr
H100$2.85/hr$5.12/hr$4.50/hr
Setup Time< 60 seconds5-10 minutes5-10 minutes
No Commitment✅ Pay-as-you-go❌ Reserved❌ Reserved

🚀 Quick Start

# Install the CLI
pip install voltagegpu-cli

# Configure your API key
export VOLT_API_KEY="your_api_key_here"

# List available templates
volt templates list

# Create a GPU pod
volt pods create --template pytorch-cuda12 --name my-training-pod

# SSH into your pod
volt pods ssh <pod_id>

🔑 Get your API key at voltagegpu.com/dashboard

✨ Features

🖥️ Pod Management

  • Create, start, stop, and delete GPU pods
  • Real-time status monitoring
  • SSH access with one command

📦 Pre-built Templates

  • PyTorch, TensorFlow, JAX environments
  • vLLM for LLM inference
  • Stable Diffusion for image generation
  • Custom Docker images support

🔑 SSH Key Management

  • Add and manage SSH keys
  • Secure access to your pods

💰 Cost Tracking

  • Real-time balance monitoring
  • Per-pod cost breakdown
  • Usage history

🐍 Python SDK

  • Full programmatic access
  • Async support
  • Type hints included

📦 Installation

From PyPI (Recommended)

pip install voltagegpu-cli

From Source

git clone https://github.com/Jabsama/VOLTAGEGPU-CLI.git
cd VOLTAGEGPU-CLI
pip install -e .

Requirements

  • Python 3.8+
  • pip

⚙️ Configuration

Option 1: Environment Variable (Recommended)

export VOLT_API_KEY="your_api_key_here"

Option 2: Configuration File

Create ~/.volt/config.ini:

[api]
api_key = your_api_key_here

Option 3: Pass Directly

from volt import VoltageGPUClient

client = VoltageGPUClient(api_key="your_api_key_here")

📖 Usage

Pods

# List all your pods
volt pods list

# Get pod details
volt pods get <pod_id>

# Create a new pod
volt pods create --template <template_id> --name <name> [--gpu-count 1]

# Start a stopped pod
volt pods start <pod_id>

# Stop a running pod
volt pods stop <pod_id>

# Delete a pod
volt pods delete <pod_id> --yes

# Get SSH command for a pod
volt pods ssh <pod_id>

Templates

# List all available templates
volt templates list

# Filter by category
volt templates list --category llm

# Get template details
volt templates get <template_id>

SSH Keys

# List your SSH keys
volt ssh-keys list

# Add a new SSH key from file
volt ssh-keys add --name "my-laptop" --file ~/.ssh/id_ed25519.pub

# Add a new SSH key directly
volt ssh-keys add --name "my-key" --key "ssh-ed25519 AAAA..."

# Delete an SSH key
volt ssh-keys delete <key_id>

Machines

# List available machines
volt machines list

# Filter by GPU type
volt machines list --gpu RTX4090
volt machines list --gpu A100
volt machines list --gpu H100

Account

# Check your balance
volt account balance

# Get account information
volt account info

JSON Output

All list commands support --json for machine-readable output:

volt pods list --json | jq '.[] | select(.status == "running")'

🐍 Python SDK

Basic Usage

from volt import VoltageGPUClient

# Initialize client
client = VoltageGPUClient()

# List pods
pods = client.list_pods()
for pod in pods:
    print(f"{pod.name}: {pod.status} ({pod.gpu_type})")

# Create a pod
pod = client.create_pod(
    template_id="pytorch-cuda12",
    name="my-training-pod",
    gpu_count=2
)
print(f"Created pod: {pod.id}")

# Get SSH info
print(f"SSH: ssh -p {pod.ssh_port} root@{pod.ssh_host}")

# Stop and delete
client.stop_pod(pod.id)
client.delete_pod(pod.id)

Context Manager

from volt import VoltageGPUClient

with VoltageGPUClient() as client:
    templates = client.list_templates()
    for t in templates:
        print(f"{t.name}: ${t.hourly_price}/hr")

Available Methods

MethodDescription
list_pods()List all your pods
get_pod(pod_id)Get pod details
create_pod(template_id, name, gpu_count)Create a new pod
start_pod(pod_id)Start a stopped pod
stop_pod(pod_id)Stop a running pod
delete_pod(pod_id)Delete a pod
list_templates(category)List available templates
get_template(template_id)Get template details
list_ssh_keys()List your SSH keys
add_ssh_key(name, public_key)Add a new SSH key
delete_ssh_key(key_id)Delete an SSH key
list_machines(gpu_type)List available machines
get_balance()Get account balance
get_account_info()Get account information

🎓 Examples

Launch a Training Job

# Find a suitable template
volt templates list --category ml

# Create pod with SSH key
volt pods create \
  --template pytorch-cuda12 \
  --name training-job \
  --gpu-count 4 \
  --ssh-key my-key-id

# Get SSH command
volt pods ssh <pod_id>

Deploy vLLM for Inference

# Create vLLM pod
volt pods create \
  --template vllm-inference \
  --name llm-server \
  --gpu-count 1

# Access the API
curl http://<pod_ip>:8000/v1/models

Batch Operations

# Stop all running pods
volt pods list --json | jq -r '.[] | select(.status == "running") | .id' | xargs -I {} volt pods stop {}

# Delete all stopped pods
volt pods list --json | jq -r '.[] | select(.status == "stopped") | .id' | xargs -I {} volt pods delete {} --yes

🌐 Environment Variables

VariableDescriptionDefault
VOLT_API_KEYYour VoltageGPU API key-
VOLT_BASE_URLAPI base URLhttps://voltagegpu.com/api

📚 Documentation

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

# Clone the repository
git clone https://github.com/Jabsama/VOLTAGEGPU-CLI.git
cd VOLTAGEGPU-CLI

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

🔗 Links

🌐Websitevoltagegpu.com📦GitHubgithub.com/Jabsama/VOLTAGEGPU-CLI📦PyPIpypi.org/project/voltagegpu-cli𝕏Twitter@VoltageGPU💬Discorddiscord.gg/6HktnD2GUr

⚡ Power your AI with VoltageGPU ⚡

Made with ❤️ by the VoltageGPU Team