From 3c3578c62038b2eada5c4cf9be2aa7118ac7e1f4 Mon Sep 17 00:00:00 2001 From: GitLab CI Date: Tue, 21 Oct 2025 03:40:46 +0000 Subject: [PATCH] chore: Regenerate all playbooks --- nvidia/vibe-coding/README.md | 91 +++++++++++++++++------------------- 1 file changed, 42 insertions(+), 49 deletions(-) diff --git a/nvidia/vibe-coding/README.md b/nvidia/vibe-coding/README.md index f54f5e3..cb5eb5a 100644 --- a/nvidia/vibe-coding/README.md +++ b/nvidia/vibe-coding/README.md @@ -7,7 +7,7 @@ - [Overview](#overview) - [What You'll Accomplish](#what-youll-accomplish) - [Prerequisites](#prerequisites) - - [Requirements](#requirements) + - [Time & risk](#time-risk) - [Instructions](#instructions) - [Troubleshooting](#troubleshooting) @@ -15,10 +15,10 @@ ## Overview -## DGX Spark Vibe Coding +## Basic idea This playbook walks you through setting up DGX Spark as a **Vibe Coding assistant** — locally or as a remote coding companion for VSCode with Continue.dev. -This guide uses **Ollama** with **GPT-OSS 120B** to provide easy deployment of a coding assistant to VSCode. Included is advanced instructions to allow DGX Spark and Ollama to provide the coding assistant to be available over your local network. This guide is also written on a **fresh installation* of the OS. If your OS is not freshly installed and you have issues, see the troubleshooting section at the bottom of the document. +This guide uses **Ollama** with **GPT-OSS 120B** to provide easy deployment of a coding assistant to VSCode. Included is advanced instructions to allow DGX Spark and Ollama to provide the coding assistant to be available over your local network. This guide is also written on a **fresh installation** of the OS. If your OS is not freshly installed and you have issues, see the troubleshooting section at the bottom of the document. ### What You'll Accomplish @@ -30,17 +30,19 @@ You'll have a fully configured DGX Spark system capable of: ### Prerequisites - DGX Spark (128GB unified memory recommended) -- Internet access for model downloads -- Basic familiarity with the terminal -- Optional: firewall control for remote access configuration - -### Requirements - - **Ollama** and an LLM of your choice (e.g., `gpt-oss:120b`) - **VSCode** - **Continue** VSCode extension +- Internet access for model downloads - Basic familiarity with opening the Linux terminal, copying and pasting commands. - Having sudo access. +- Optional: firewall control for remote access configuration + +### Time & risk +* **Duration:** About 30 minutes +* **Risks:** + * Data download slowness or failure due to network issues +* **Rollback:** No permanent system changes made during normal usage. ## Instructions @@ -91,8 +93,8 @@ Verify that the workstation can connect to your DGX Spark's Ollama server: ```bash curl -v http://YOUR_SPARK_IP:11434/api/version ``` - Replace YOUR_SPARK_IP with your DGX Spark's IP address. - If the connection fails please see the troubleshooting section at the bottom of this document. + Replace **YOUR_SPARK_IP** with your DGX Spark's IP address. + If the connection fails please see the Troubleshooting tab. ## Step 3. Install VSCode @@ -107,15 +109,16 @@ If using a remote workstation, **install VSCode appropriate for your system arch ## Step 4. Install Continue.dev Extension -Open VSCode and install **Continue.dev** from the Marketplace. +Open VSCode and install **Continue.dev** from the Marketplace: +- Go to **Extensions view** in VSCode +- Search for **Continue** published by [Continue.dev](https://www.continue.dev/) and install the extension. After installation, click the Continue icon on the right-hand bar. - ## Step 5. Local Inference Setup -- Click Select **Or, configure your own models** -- Click **Click here to view more providers** -- Choose **Ollama** as the provider. -- For **Model**, select **Autodetect**. +- Click `Or, configure your own models` +- Click `Click here to view more providers` +- Choose `Ollama` as the Provider +- For Model, select `Autodetect` - Test inference by sending a test prompt. Your downloaded model will now be the default (e.g., `gpt-oss:120b`) for inference. @@ -123,18 +126,18 @@ Your downloaded model will now be the default (e.g., `gpt-oss:120b`) for inferen ## Step 6. Setting up a Workstation to Connect to the DGX Spark' Ollama Server To connect a workstation running VSCode to a remote DGX Spark instance the following must be completed on that workstation: - - Install Continue from the marketplace. - - Click on the Continue icon on the left pane. - - Click ***Or, configure your own models*** - - Click **Click here to view more providers. - - Select ***Ollama*** from the provider list. - - Select ***Autodetect*** as the model. + - Install Continue as instructed in Step 4 + - Click on the `Continue` icon on the left pane + - Click `Or, configure your own models` + - Click `Click here to view more providers` + - Select `Ollama` as the Provider + - Select `Autodetect` as the Model. -Continue **wil** fail to detect the model as it is attempting to connect to a locally hosted Ollama server. - - Find the **gear** icon in the upper right corner of the chat window and click on it. +Continue **will** fail to detect the model as it is attempting to connect to a locally hosted Ollama server. + - Find the `**gear**` icon in the upper right corner of the Continue window and click on it. - On the left pane, click **Models** - Next to the first dropdown menu under **Chat** click the gear icon. - - Continue's config.yaml will open. Take note of your DGX Spark's IP address. + - Continue's `**config.yaml**` will open. Take note of your DGX Spark's IP address. - Replace the configuration with the following. **YOUR_SPARK_IP** should be replaced with your DGX Spark's IP. @@ -164,27 +167,17 @@ Add additional model entries for any other Ollama models you wish to host remote ## Troubleshooting -## Common Issues +| Symptom | Cause | Fix | +|---------|-------|-----| +|Ollama not starting|GPU drivers may not be installed correctly|Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark.| +|Continue can't connect over the network|Port 11434 may not be open or accessible|Run command `ss -tuln | grep 11434`. If the output does not reflect " tcp LISTEN 0 4096 *:11434 *:* ", go back to step 2 and run the ufw command.| +|Continue can't detect a locally running Ollama model|Configuration not properly set or detected|Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf` file. If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly, add these lines to your `~/.bashrc` file.| +|High memory usage|Model size too big|Confirm no other large models or containers are running with `nvidia-smi`. Use smaller models such as `gpt-oss:20b` for lightweight usage.| -**1. Ollama not starting** -- Verify GPU drivers are installed correctly. - Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark. - If there are no updates or updates do not correct the issue, create a thread on the DGX Spark/GB10 User forum here : - https://forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10/dgx-spark-gb10/ -- Run `ollama serve` on the DGX Spark to view Ollama logs. - -**2. Continue can't connect over the network** -- Ensure port 11434 is open and accessible from your workstation. - ```bash - ss -tuln | grep 11434 - ``` - If the output does not reflect " tcp LISTEN 0 4096 *:11434 *:* " - go back to step 2 and run the ufw command. - -**3. Continue can't detect a locally running Ollama model -- Check `OLLAMA_HOST` and `OLLAMA_ORIGINS` in `/etc/systemd/system/ollama.service.d/override.conf`. -- If `OLLAMA_HOST` and `OLLAMA_ORIGINS` are set correctly you should add these lines to your .bashrc. - -**4. High memory usage** -- Use smaller models such as `gpt-oss:20b` for lightweight usage. -- Confirm no other large models or containers are running with `nvidia-smi`. +> [!NOTE] +> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. +> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within +> the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with: +```bash +sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches' +```