docs: add "With NVIDIA GPUs monitoring" to docker install (#17167)

packaging/docker/README.md

@@ -172,6 +172,43 @@ Add `- /run/dbus:/run/dbus:ro` to the netdata service `volumes`.
 </TabItem>
 </Tabs>
 
+### With NVIDIA GPUs monitoring
+
+
+Monitoring NVIDIA GPUs requires:
+
+- Using official [NVIDIA driver](https://www.nvidia.com/Download/index.aspx).
+- Installing [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+- Allowing the Netdata container to access GPU resources.
+
+
+<Tabs>
+<TabItem value="docker_run" label="docker run">
+
+<h3> Using the <code>docker run</code> command </h3>
+
+Add `--gpus 'all,capabilities=utility'` to your `docker run`.
+
+</TabItem>
+<TabItem value="docker compose" label="docker-compose">
+
+<h3> Using the <code>docker-compose</code> command</h3>
+
+Add the following to the netdata service.
+
+```yaml
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+```
+
+</TabItem>
+</Tabs>
+
 ### With host-editable configuration
 
 Use a [bind mount](https://docs.docker.com/storage/bind-mounts/) for `/etc/netdata` rather than a volume.

src/collectors/python.d.plugin/nvidia_smi/README.md

@@ -11,22 +11,18 @@ learn_rel_path: "Integrations/Monitor/Devices"
 
 Monitors performance metrics (memory usage, fan speed, pcie bandwidth utilization, temperature, etc.) using `nvidia-smi` cli tool.
 
-## Requirements and Notes
+## Requirements
 
--   You must have the `nvidia-smi` tool installed and your NVIDIA GPU(s) must support the tool. Mostly the newer high end models used for AI / ML and Crypto or Pro range, read more about [nvidia_smi](https://developer.nvidia.com/nvidia-system-management-interface).
--   You must enable this plugin, as its disabled by default due to minor performance issues:
+-   The `nvidia-smi` tool installed and your NVIDIA GPU(s) must support the tool. Mostly the newer high end models used for AI / ML and Crypto or Pro range, read more about [nvidia_smi](https://developer.nvidia.com/nvidia-system-management-interface).
+-   Enable this plugin, as it's disabled by default due to minor performance issues:
     ```bash
     cd /etc/netdata   # Replace this path with your Netdata config directory, if different
     sudo ./edit-config python.d.conf
     ```
     Remove the '#' before nvidia_smi so it reads: `nvidia_smi: yes`.
-
 -   On some systems when the GPU is idle the `nvidia-smi` tool unloads and there is added latency again when it is next queried. If you are running GPUs under constant workload this isn't likely to be an issue.
--   Currently the `nvidia-smi` tool is being queried via cli. Updating the plugin to use the nvidia c/c++ API directly should resolve this issue. See discussion here: <https://github.com/netdata/netdata/pull/4357>
--   Contributions are welcome.
--   Make sure `netdata` user can execute `/usr/bin/nvidia-smi` or wherever your binary is.
--   If `nvidia-smi` process [is not killed after netdata restart](https://github.com/netdata/netdata/issues/7143) you need to off `loop_mode`.
--   `poll_seconds` is how often in seconds the tool is polled for as an integer.
+
+If using Docker, see [Netdata Docker container with NVIDIA GPUs monitoring](https://github.com/netdata/netdata/tree/master/packaging/docker#with-nvidia-gpus-monitoring).  
 
 ## Charts
 
@@ -83,75 +79,3 @@ Now you can manually run the `nvidia_smi` module in debug mode:
 ```bash
 ./python.d.plugin nvidia_smi debug trace
 ```
-
-## Docker
-
-GPU monitoring in a docker container is possible with [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed on the host system, and `gcompat` added to the `NETDATA_EXTRA_APK_PACKAGES` environment variable.
-
-Sample `docker-compose.yml`
-```yaml
-version: '3'
-services:
-  netdata:
-    image: netdata/netdata
-    container_name: netdata
-    hostname: example.com # set to fqdn of host
-    ports:
-      - 19999:19999
-    restart: unless-stopped
-    cap_add:
-      - SYS_PTRACE
-    security_opt:
-      - apparmor:unconfined
-    environment:
-      - NETDATA_EXTRA_APK_PACKAGES=gcompat
-    volumes:
-      - netdataconfig:/etc/netdata
-      - netdatalib:/var/lib/netdata
-      - netdatacache:/var/cache/netdata
-      - /etc/passwd:/host/etc/passwd:ro
-      - /etc/group:/host/etc/group:ro
-      - /proc:/host/proc:ro
-      - /sys:/host/sys:ro
-      - /etc/os-release:/host/etc/os-release:ro
-    deploy:
-      resources:
-        reservations:
-          devices:
-          - driver: nvidia
-            count: all
-            capabilities: [gpu]
-
-volumes:
-  netdataconfig:
-  netdatalib:
-  netdatacache:
-```
-
-Sample `docker run`
-```yaml
-docker run -d --name=netdata \
-  -p 19999:19999 \
-  -e NETDATA_EXTRA_APK_PACKAGES=gcompat \
-  -v netdataconfig:/etc/netdata \
-  -v netdatalib:/var/lib/netdata \
-  -v netdatacache:/var/cache/netdata \
-  -v /etc/passwd:/host/etc/passwd:ro \
-  -v /etc/group:/host/etc/group:ro \
-  -v /proc:/host/proc:ro \
-  -v /sys:/host/sys:ro \
-  -v /etc/os-release:/host/etc/os-release:ro \
-  --restart unless-stopped \
-  --cap-add SYS_PTRACE \
-  --security-opt apparmor=unconfined \
-  --gpus all \
-  netdata/netdata
-```
-
-### Docker Troubleshooting
-To troubleshoot `nvidia-smi` in a docker container, first confirm that `nvidia-smi` is working on the host system. If that is working correctly, run `docker exec -it netdata nvidia-smi` to confirm it's working within the docker container. If `nvidia-smi` is fuctioning both inside and outside of the container, confirm that `nvidia-smi: yes` is uncommented in `python.d.conf`.
-```bash
-docker exec -it netdata bash
-cd /etc/netdata
-./edit-config python.d.conf
-```