From 3eff7461e138bffbbf3038ad68b348bcba5508a1 Mon Sep 17 00:00:00 2001 From: GitLab CI Date: Tue, 2 Jun 2026 18:47:24 +0000 Subject: [PATCH] chore: Regenerate all playbooks --- nvidia/station-nanochat/README.md | 4 ++-- nvidia/station-nanochat/assets/Dockerfile | 2 ++ nvidia/station-nanochat/endpoint-test.yaml | 4 ++-- nvidia/station-nemoclaw/endpoint-production.yaml | 14 +++++++------- 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/nvidia/station-nanochat/README.md b/nvidia/station-nanochat/README.md index 29a4fd2..a6570a7 100644 --- a/nvidia/station-nanochat/README.md +++ b/nvidia/station-nanochat/README.md @@ -77,7 +77,7 @@ All required assets are in `nvidia/station-nanochat/assets/`: ## 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. @@ -149,7 +149,7 @@ The training runs inside the `nanochat` container and executes the full pipeline 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 diff --git a/nvidia/station-nanochat/assets/Dockerfile b/nvidia/station-nanochat/assets/Dockerfile index 98396bb..ff77688 100755 --- a/nvidia/station-nanochat/assets/Dockerfile +++ b/nvidia/station-nanochat/assets/Dockerfile @@ -10,6 +10,8 @@ RUN pip install \ psutil \ files-to-prompt \ uvicorn \ + fastapi \ + regex \ rustbpe CMD ["/bin/bash"] \ No newline at end of file diff --git a/nvidia/station-nanochat/endpoint-test.yaml b/nvidia/station-nanochat/endpoint-test.yaml index 03a1063..8c038d2 100644 --- a/nvidia/station-nanochat/endpoint-test.yaml +++ b/nvidia/station-nanochat/endpoint-test.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 diff --git a/nvidia/station-nemoclaw/endpoint-production.yaml b/nvidia/station-nemoclaw/endpoint-production.yaml index 01a441c..a4c129c 100644 --- a/nvidia/station-nemoclaw/endpoint-production.yaml +++ b/nvidia/station-nemoclaw/endpoint-production.yaml @@ -130,8 +130,8 @@ spec: - **Estimated time:** About 30–60 minutes for a first full pass (install, onboard, model download depending on choice and network). Optional Brave, Telegram, and cloudflared steps add time if you do them in a second session. - **Risk level:** Medium — you are running an AI agent in a sandbox; risks are reduced by isolation but not eliminated. Use a clean environment and do not connect sensitive data or production accounts. - - **Last Updated:** 05/29/2026 - - Update to latest nemoclaw installer instructions + - **Last Updated:** 06/01/2026 + - Pin nemoclaw installer to v0.0.55, the latest stable version @@ -144,10 +144,10 @@ spec: ## Step 1. Install NemoClaw - This single command handles everything: installs Node.js (if needed), installs OpenShell, clones the pinned NemoClaw **v0.55** release (set via `NEMOCLAW_VERSION`; v0.55 is the version the NemoClaw team currently recommends as the most stable), builds the CLI, and runs the onboard wizard to create a sandbox. + This single command handles everything: installs Node.js (if needed), installs OpenShell, clones the pinned NemoClaw **v0.0.55** release (set via `NEMOCLAW_INSTALL_TAG`; v0.0.55 is the version the NemoClaw team currently recommends as the most stable), builds the CLI, and runs the onboard wizard to create a sandbox. ```bash - curl -fsSL https://www.nvidia.com/nemoclaw.sh | NEMOCLAW_VERSION=v0.55 bash + curl -fsSL https://www.nvidia.com/nemoclaw.sh | NEMOCLAW_INSTALL_TAG=v0.0.55 bash ``` The installation wizard walks you through setup: @@ -165,7 +165,7 @@ spec: During custom setup, the onboard wizard walks you through: 1. **Configuring inference** -- Choose to set up local inference on your DGX Station by selecting **`7) Local Ollama`**. - 2. **Ollama models** -- Choose desired inference model. If no model is present locally, the installer will provide options to download models to start. + 2. **Ollama models** -- Choose desired inference model. If no model is present locally, the installer will download **`qwen3.6:35b`** automatically. 3. **Sandbox name** -- Pick a name (e.g. my-assistant). Each sandbox requires a unique name. 4. **Apply this configuration** -- Enter `Y` to confirm setting up local inference. 5. **Enable Brave Web Search** -- Optional. If you enable it, paste a [Brave Search API](https://brave.com/search/api/) key when prompted. @@ -341,7 +341,7 @@ spec: The cloudflared tunnel provides a **public URL for the Web UI dashboard** — it is not related to Telegram messaging. - Install cloudflared (DGX Station is arm64): + Install cloudflared (DGX Station is aarch64): ```bash curl -L --output cloudflared.deb \ @@ -371,7 +371,7 @@ spec: Set up NemoClaw Agents in general require three steps: Configure NemoClaw security policy, Run Agent Workflow Prompt, Personalize the Workflow for your own use case. - Checkout these [Example NemoClaw Agents](https://build.nvidia.com/station/nemoclaw-applications) for reference. Consider sharing your NemoClaw agent setup with the community at [DGX Station Developer Forum](https://forums.developer.nvidia.com/c/accelerated-computing/dgx-station-gb300) + Checkout these [Example NemoClaw Agents](https://build.nvidia.com/spark/nemoclaw-applications) for reference. ---