Merge 48fc5eb30e into 8452a1c5b1

nvidia/nemoclaw/README.md

@@ -172,12 +172,15 @@ Verify the NVIDIA runtime works:
 docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
 ```
 
-If you get a permission denied error on `docker`, add your user to the Docker group and log out/in:
+If you get a permission denied error on `docker`, add your user to the Docker group and activate the new group in your current session:
 
 ```bash
 sudo usermod -aG docker $USER
+newgrp docker
 ```
 
+This applies the group change immediately. Alternatively, you can log out and back in instead of running `newgrp docker`.
+
 > [!NOTE]
 > DGX Spark uses cgroup v2. OpenShell's gateway embeds k3s inside Docker and needs host cgroup namespace access. Without `default-cgroupns-mode: host`, the gateway can fail with "Failed to start ContainerManager" errors.
 
@@ -322,13 +325,21 @@ http://127.0.0.1:18789/#token=<long-token-here>
 
 **If accessing the Web UI from a remote machine**, you need to set up port forwarding.
 
+First, find your Spark's IP address. On the Spark, run:
+
+```bash
+hostname -I | awk '{print $1}'
+```
+
+This prints the primary IP address (e.g. `192.168.1.42`). You can also find it in **Settings > Wi-Fi** or **Settings > Network** on the Spark's desktop, or check your router's connected-devices list.
+
 Start the port forward on the Spark host:
 
 ```bash
 openshell forward start 18789 my-assistant --background
 ```
 
-Then from your remote machine, create an SSH tunnel to the Spark:
+Then from your remote machine, create an SSH tunnel to the Spark (replace `<your-spark-ip>` with the IP address from above):
 
 ```bash
 ssh -L 18789:127.0.0.1:18789 <your-user>@<your-spark-ip>

nvidia/txt2kg/assets/deploy/compose/docker-compose.yml

@@ -27,8 +27,8 @@ services:
       # Ollama configuration
       - OLLAMA_BASE_URL=http://ollama:11434/v1
       - OLLAMA_MODEL=llama3.1:8b
-      # Disable vLLM
-      - VLLM_BASE_URL=http://localhost:8001/v1
+      # vLLM disabled in default Ollama mode
+      # - VLLM_BASE_URL=http://localhost:8001/v1
       - VLLM_MODEL=disabled
       # Vector DB configuration
       - QDRANT_URL=http://qdrant:6333

nvidia/txt2kg/assets/frontend/lib/text-processor.ts

@@ -108,7 +108,7 @@ export class TextProcessor {
     
     // Determine which LLM provider to use based on configuration
     // Priority: vLLM > NVIDIA > Ollama
-    if (process.env.VLLM_BASE_URL) {
+    if (process.env.VLLM_BASE_URL && process.env.VLLM_MODEL && process.env.VLLM_MODEL !== 'disabled') {
       this.selectedLLMProvider = 'vllm';
     } else if (process.env.NVIDIA_API_KEY) {
       this.selectedLLMProvider = 'nvidia';

nvidia/vibe-coding/README.md

@@ -171,10 +171,12 @@ Add additional model entries for any other Ollama models you wish to host remote
 
 | 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.|
+| **WiFi connection drops or becomes unreachable** (especially in headless mode) | Aggressive WiFi power-saving settings in NetworkManager | Edit `/etc/NetworkManager/conf.d/default-wifi-powersave-on.conf`, set `wifi.powersave = 2`, and run `sudo systemctl restart NetworkManager`. |
+| **Random reboots and "00" error code on the display** | Watchdog timer module (`sbsa_gwdt`) not loaded | Add `sbsa_gwdt` to `/etc/modules-load.d/watchdog.conf` and reboot to ensure the hardware watchdog is correctly managed by the kernel. |
+| 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. |
 
 > [!NOTE]
 > DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.