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106 lines
4.0 KiB
Markdown
106 lines
4.0 KiB
Markdown
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# Register DGX Spark to Brev
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> Link your DGX Spark to Brev for remote access and shared environments
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## Table of Contents
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- [Overview](#overview)
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- [Instructions](#instructions)
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- [Troubleshooting](#troubleshooting)
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---
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## Overview
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## Basic idea
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NVIDIA Brev is an AI development platform that makes GPU environments remotely accessible, shareable, and easy to standardize using preconfigured setups called Launchables.
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This walkthrough will help you connect your NVIDIA DGX Spark to Brev so it shows up as a managed GPU environment in Brev. After a one-time registration, your Spark becomes remotely accessible and shareable.
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## What you'll accomplish
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You’ll register your DGX Spark with Brev and it will be visible as a healthy node in the Brev web UI and CLI, ready to share access and accept workloads whenever needed.
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## What to know before starting
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While Brev automates the complex configuration, understanding a few key concepts when establishing the initial connection will be useful:
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* **Terminal Basics**:
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* Familiarity with the command line to run a few simple setup commands
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## Prerequisites
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Your DGX Spark [device is set up](https://docs.nvidia.com/dgx/dgx-spark/first-boot.html). You will also need the following:
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* **Brev Account**:
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* Have an NVIDIA Brev account. Create one [here](https://login.brev.nvidia.com/signin) if you don’t have one.
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* **Permissions**:
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* You have administrative (root or sudo) access on the DGX Spark device to run the registration command.
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## Time & risk
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* **Estimated time:** 5-10 minutes
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* **Risk level:** Low - Registration configures the Spark for secure remote access without altering your existing workloads
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* **Rollback:** The Brev configuration can be removed through the UI and CLI
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## Instructions
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## Step 1. Log in to Brev
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Go to the [Brev UI](https://brev.nvidia.com), log in, and confirm you’re in the correct org (by clicking the org button on the top right hand side of the page). Once logged in, go to the [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute) section under the "GPU" tab in the main navigation.
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Click the “Register Compute” button and follow the instructions in the pop-up window.
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## Step 2. Complete Popup Instructions
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* Install the Brev CLI
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* Configure your compute
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* Add a name for compute
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* To configure ssh, ensure the “Enable SSH access” toggle is on
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* Run the registration command
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## Step 3. Follow Registration Flow
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In the CLI, you’ll be walked through registration. Go through the flow until registration is complete.
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## Step 4. Confirm Spark in Brev UI
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* Go to the [Brev UI](https://brev.nvidia.com)
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* Navigate to the [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute)
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* Confirm that the DGX Spark appears as a registered node with an **Available** status
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## Step 5. Next Steps
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Your Spark is now integrated into Brev as a secure, remotely accessible GPU environment.
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Now that your hardware is connected, you can:
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* **Share Access Anywhere:** Access your machine from anywhere and share access with others through the Brev UI under [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute).
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## Step 6. Cleanup
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If you ever decide to unregister your Spark with Brev, you can either do so through the Brev UI or the Brev CLI.
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With the CLI simply run:
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```bash
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brev deregister
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```
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In the UI:
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* Go to the [Brev UI](https://brev.nvidia.com)
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* Navigate to the section listing “GPU Environments” and look under “Registered Compute”
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* Click the “Deregister” menu item on the Spark you wish to delete from Brev.
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* Confirm your selection.
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## Troubleshooting
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| Symptom | Cause | Fix |
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|---------|-------|-----|
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| Your DGX Spark is showing up in the wrong org | You registered your DGX Spark to the wrong org | Run `brev set <my-org>` and then redo the registration |
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| Unable to `brev shell <name>` | Need to refresh | `brev refresh` |
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For the latest known issues, please review the [DGX Spark User Guide](https://docs.nvidia.com/dgx/dgx-spark/known-issues.html).
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