# Getting Started with the TensorPool CLI

A comprehensive guide to managing your GPU infrastructure from the command line with our powerful CLI tool.

[Tycho Svoboda](https://www.linkedin.com/in/tycho-svoboda/) Cofounder & CEO

September 27, 2025

5 min read

The TensorPool CLI is your command-line interface to powerful GPU infrastructure. Whether you're spinning up a single H100 for model training or orchestrating a multi-node cluster for distributed workloads, the CLI makes it fast and simple.

## Video Tutorial

Watch our comprehensive video guide to get started with the TensorPool CLI:

[TensorPool Tutorial 1: Spin Up Your First GPU](https://www.youtube.com/watch?v=4qPY2UHG71o) [TensorPool](https://www.youtube.com/channel/UCUqn_AUDCrfbLtpsh9hBLRw)

## Prerequisites

Before getting started, make sure you have the following:

- A TensorPool account (sign up at [tensorpool.dev](/content/site-root.html))
- Python 3.8 or later installed
- SSH keys generated (we'll show you how)

* * *

## Step-by-Step Guide

### Step 1: Install the TensorPool CLI

Install the CLI using pip:

```bash
pip install tensorpool
```

Verify the installation:

```bash
tp --version
```

### Step 2: Set Up Your API Key

Get your API key from the TensorPool Dashboard and set it as an environment variable:

```bash
your_api_key_here
```

**TIP:** Add this to your `~/.bashrc` or `~/.zshrc` to make it persistent.

### Step 3: Generate SSH Keys

If you don't have SSH keys yet, generate them:

```bash
ssh-keygen -t ed25519 -f ~/.ssh/id_ed25519
```

Press Enter to accept the defaults when prompted.

### Step 4: Create Your First Cluster

Let's create a single-node cluster with one H100 GPU:

```bash
tp cluster create \
  -i ~/.ssh/id_rsa.pub \
  -t 1xH100 \
  --name my-first-cluster
```

For a multi-node cluster with 8 B200s per node:

```bash
tp cluster create \
  -i ~/.ssh/id_rsa.pub \
  -t 8xB200 \
  -n 4 \
  --name distributed-training
```

Available instance types: `1xH100`, `2xH100`, `4xH100`, `8xH100`, `1xH200`, `8xB200`, and more.

### Step 5: List and Connect to Your Cluster

View all your clusters:

```bash
tp cluster list
```

This shows cluster ID, instance type, SSH username, IP addresses, ports, and hourly pricing. SSH into your cluster using the provided details:

```bash
ssh tensorpool@192.168.1.42
```

### Step 6: Create and Attach NFS Storage

Create a 500GB NFS volume for persistent storage:

```bash
tp nfs create \
  --s 500 \
  --name nfs-test
```

List all NFS volumes:

```bash
tp nfs list
```

Attach the NFS volume to your cluster:

```bash
tp nfs attach <storage_id> <cluster_ids>
```

### Step 7: Tear Down Resources

When you're done, clean up resources to stop billing:

```bash
# Detach NFS from cluster
tp nfs detach <storage_id> <cluster_ids>

# Destroy NFS volume
tp nfs destroy <storage_id>

# Destroy cluster
tp cluster destroy <cluster_id>
```

**WARNING:** Destroying a cluster or NFS volume is permanent. Make sure to back up any data before running destroy commands.

* * *

## Common Use Cases

### 💻 Single-GPU Development

Quick prototyping and model experimentation:

```bash
tp cluster create -i ~/.ssh/id_rsa.pub -t 1xH100 --name dev
```

### 🚀 Multinode Training

Large-scale model training across multiple nodes:

```bash
tp cluster create \
  -i ~/.ssh/id_rsa.pub \
  -t 8xB200 \
  -n 2 \
  --name llm-training
```

### 📊 Data Processing Pipeline

Attach shared NFS storage for data pipelines:

```bash
# Create cluster
tp cluster create -i ~/.ssh/id_rsa.pub -t 4xH100 --name pipeline

# Create and attach 1TB NFS
tp nfs create --size 1000 --name data-store
tp nfs attach --cluster-id <id> --nfs-id <nfs_id>
```

## Next Steps

- Check out the [full CLI documentation](https://github.com/tensorpool/tensorpool) on GitHub
- Join the TensorPool Slack to ask questions and share your projects
- Read our blog post on optimizing multi-node GPU clusters

* * *

Ready to get started? Sign up for TensorPool and get your first cluster running in minutes.