From 2f703e17931b726a8d7a9cff8d3e2b33c5d0a3b1 Mon Sep 17 00:00:00 2001 From: GitLab CI Date: Thu, 4 Jun 2026 14:56:19 +0000 Subject: [PATCH] chore: Regenerate all playbooks --- nvidia/station-brev/README.md | 23 +++++++------------ nvidia/station-brev/endpoint-test.yaml | 23 +++++++------------ .../station-nanochat/endpoint-production.yaml | 4 ++-- 3 files changed, 18 insertions(+), 32 deletions(-) diff --git a/nvidia/station-brev/README.md b/nvidia/station-brev/README.md index c1ea1aa..cc64bce 100644 --- a/nvidia/station-brev/README.md +++ b/nvidia/station-brev/README.md @@ -52,21 +52,18 @@ You will also need the following: ## Step 1. Log in to Brev -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. +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. Click the “Register Compute” button and follow the instructions in the pop-up window. -## Step 2. Complete Pop-up Instructions +## Step 2. Complete Popup Instructions * Install the Brev CLI * Configure your compute * Add a name for compute - * To configure SSH, ensure the “Enable SSH access” toggle is on + * To configure ssh, ensure the “Enable SSH access” toggle is on * Run the registration command -> [!IMPORTANT] -> Run the Brev CLI install command **without `sudo`**. Prefixing the installer with `sudo` writes the `brev` binary into root's home directory, which is not on your user shell's `PATH` — the next command will fail with `brev: command not found`. Copy the install command from the pop-up and run it as your normal user. - ## Step 3. Follow Registration Flow In the CLI, you’ll be walked through registration. Go through the flow until registration is complete. @@ -83,14 +80,10 @@ Your DGX Station is now integrated into Brev as a secure, remotely accessible GP Now that your hardware is connected, you can: -* **Access your machine from anywhere:** Open the [Brev UI](https://brev.nvidia.com) and launch a session from [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute). -* **Share access with others:** Invite teammates to your DGX Station from the Brev UI: - * Go to the [Brev UI](https://brev.nvidia.com) and open [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute). - * Find your DGX Station in the list and open the row's three-dot (⋯) menu. - * Select **Share Access**. - * Enter the email address of the person you want to share with. - * Choose their role / permission level. - * Confirm to send the invitation. +* **Share Access Anywhere:** Access your machine from anywhere and share access with others through the Brev UI by: + * Adding the user to your [Team](https://brev.nvidia.com/org/team) + * Navigating to your instance in the [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute) section + * In **SSH Access** section of the instance, search for the user you wish to add and click **Modify Access** to enable access ## Step 6. Cleanup @@ -105,7 +98,7 @@ brev deregister In the UI: * Go to the [Brev UI](https://brev.nvidia.com) * Navigate to the section listing “GPU Environments” and look under “Registered Compute” -* Click the “Remove” menu item on the device you wish to delete from Brev. +* Click the “Remove” menu item on the DGX Station you wish to delete from Brev. * Confirm your selection. ## Troubleshooting diff --git a/nvidia/station-brev/endpoint-test.yaml b/nvidia/station-brev/endpoint-test.yaml index d08fa9c..8944039 100644 --- a/nvidia/station-brev/endpoint-test.yaml +++ b/nvidia/station-brev/endpoint-test.yaml @@ -82,21 +82,18 @@ spec: content: | # Step 1. Log in to Brev - 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. + 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. Click the “Register Compute” button and follow the instructions in the pop-up window. - # Step 2. Complete Pop-up Instructions + # Step 2. Complete Popup Instructions * Install the Brev CLI * Configure your compute * Add a name for compute - * To configure SSH, ensure the “Enable SSH access” toggle is on + * To configure ssh, ensure the “Enable SSH access” toggle is on * Run the registration command - > [!IMPORTANT] - > Run the Brev CLI install command **without `sudo`**. Prefixing the installer with `sudo` writes the `brev` binary into root's home directory, which is not on your user shell's `PATH` — the next command will fail with `brev: command not found`. Copy the install command from the pop-up and run it as your normal user. - # Step 3. Follow Registration Flow In the CLI, you’ll be walked through registration. Go through the flow until registration is complete. @@ -113,14 +110,10 @@ spec: Now that your hardware is connected, you can: - * **Access your machine from anywhere:** Open the [Brev UI](https://brev.nvidia.com) and launch a session from [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute). - * **Share access with others:** Invite teammates to your DGX Station from the Brev UI: - * Go to the [Brev UI](https://brev.nvidia.com) and open [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute). - * Find your DGX Station in the list and open the row's three-dot (⋯) menu. - * Select **Share Access**. - * Enter the email address of the person you want to share with. - * Choose their role / permission level. - * Confirm to send the invitation. + * **Share Access Anywhere:** Access your machine from anywhere and share access with others through the Brev UI by: + * Adding the user to your [Team](https://brev.nvidia.com/org/team) + * Navigating to your instance in the [Registered Compute](https://brev.nvidia.com/org/environments?tab=registered-compute) section + * In **SSH Access** section of the instance, search for the user you wish to add and click **Modify Access** to enable access # Step 6. Cleanup @@ -135,7 +128,7 @@ spec: In the UI: * Go to the [Brev UI](https://brev.nvidia.com) * Navigate to the section listing “GPU Environments” and look under “Registered Compute” - * Click the “Remove” menu item on the device you wish to delete from Brev. + * Click the “Remove” menu item on the DGX Station you wish to delete from Brev. * Confirm your selection. diff --git a/nvidia/station-nanochat/endpoint-production.yaml b/nvidia/station-nanochat/endpoint-production.yaml index 03a1063..8c038d2 100644 --- a/nvidia/station-nanochat/endpoint-production.yaml +++ b/nvidia/station-nanochat/endpoint-production.yaml @@ -107,7 +107,7 @@ spec: # Time & risk - - **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 16+ hours on a single GB300 Ultra. + - **Estimated time:** ~30 minutes for setup. Full d24 training takes on the order of 12+ hours on a single GB300 Ultra. - **Risk level:** Medium - Large downloads (FineWeb) can be slow; ensure stable network and disk space. - API keys (W&B, HF) must be set or `launch.sh` will exit immediately. @@ -184,7 +184,7 @@ spec: 3. **SFT** — downloads synthetic identity conversations, fine-tunes for chat 4. **Report generation** — produces `report.md` with metrics and samples - Training on a single GB300 Ultra takes on the order of 16+ hours for the full d24 run. + Training on a single GB300 Ultra takes on the order of 12+ hours for the full d24 run. # Step 4. Monitor training