--- title: Machine Learning on LE via Jupyter slug: solutions/machine-learning-on-le-via-jupyter docTags: createdAt: 2023-08-01T12:19:10.237Z --- ## Overview This solution describes how to run a machine learning application using Jupyter on Litmus Edge. After deploying the container, you will be able to log in to a Jupyter notebook environment running on your LitmusEdge device and use the bundled example notebooks for classification, anomaly detection, and prediction. ![](https://api.archbee.com/api/optimize/SSUUxKZUk9bFTEPNn_6Zo/hmUO88KAvsdUVNw5OLHBU_functiondiagramsolutions-ml-jypiterdrawio.png) ## Prerequisites - A running Litmus Edge instance with administrative access. - The Jupyter ML toolkit image (`.tar.gz`) provided by Litmus. - Network access to the Litmus Edge IP from the machine you will browse from. - SSH access to the Litmus Edge host (used once to retrieve the Jupyter login token). ## Step 1: Download and prepare the image Obtain the Jupyter ML toolkit `.tar.gz` file from the Litmus repository or release artifact provided to you. ## Step 2: Upload the image to Litmus Edge 1. Log in to your Litmus Edge instance. 2. Navigate to **Applications > Images**. 3. Upload the `.tar.gz` file downloaded in Step 1. Wait for the upload to complete and confirm that the image appears in the list of available images. ## Step 3: Configure and run the container 1. Navigate to **Applications > Containers**. 2. Create a new container using the image uploaded in Step 2. 3. Use the following `docker run` command: ```bash docker run -p 8888:8888 \ -v /var/lib/loopedge/analytics2/models/:/home/loopedge/work/notes/savedmodels \ : ``` This command: - Exposes the Jupyter application on port `8888`. - Mounts the LitmusEdge analytics `models` folder into the container at `/home/loopedge/work/notes/savedmodels`, so any models saved from a notebook are immediately available to the Analytics engine. :::BlockQuote **Tip:** If you want to skip the token lookup in Step 5, you can pre-set a token now by adding `-e JUPYTER_TOKEN=` to the command above. ::: ## Step 4: Open the Jupyter application in a browser In a new browser tab, navigate to your Litmus Edge IP address followed by port `8888`: `http://:8888` For example, if your Litmus Edge instance is at `10.17.10.27`, open: `http://10.17.10.27:8888` You will be redirected to the Jupyter login page, which prompts for a **Password or token**. ## Step 5: Log in to Jupyter ### 5.1 Retrieve the access token The Jupyter container generates a one-time token at startup. To retrieve it: 1. SSH into the Litmus Edge host. 2. Find the running Jupyter container: ```bash docker ps | grep jupyter ``` 3. Read the token from the container logs: ```bash docker logs 2>&1 | grep -oE 'token=[a-f0-9]+' | head -1 ``` You will see output similar to: ```javascript token=8968cc8cf0480ed3b2b52c25fed0eb527f7ea2fc888b4ea ``` 4. Copy the value after `token=`. :::BlockQuote If you set `JUPYTER_TOKEN` in the `docker run` command in Step 3, skip the lookup above and use the value you set. ::: ### 5.2 Log in using one of two options **Option A: Log in with the token (one-time)** Paste the token into the **Password or token** field at the top of the login page and click **Log in**. **Option B: Set a permanent password (recommended)** On the same login page, scroll down to the **Setup a Password** section: 1. Paste the token into the **Token** field. 2. Enter your chosen password in the **New Password** field. 3. Click **Log in and set new password**. You will be logged in and the password will be saved for future sessions, so you no longer need to look up the token. :::BlockQuote **Note:** The login page contains links labeled *"enable a password"* and *"the documentation on how to enable a password"*. These links point to legacy Jupyter documentation that has been removed and will return a 404. Use the **Setup a Password** form on the same page (Option B above) instead. ::: ## Step 6: Use the bundled notebooks Once logged in, you will see the Jupyter file browser. The following example notebooks are included under `/home/loopedge/work/notes/`: - `natsConnection.ipynb` - Example of connecting to the LitmusEdge NATS broker. - `influxConnection.ipynb` - Example of querying InfluxDB on LitmusEdge. - `classification.ipynb` - Example classification model. - `anomaly.ipynb` - Example anomaly detection model. - `prediction.ipynb` - Example prediction model. - `template_prediction.ipynb` - Template you can copy as a starting point for new prediction models. Models saved into `/home/loopedge/work/notes/savedmodels` from any notebook will appear automatically in the LitmusEdge analytics engine, thanks to the volume mount configured in Step 3. ## Troubleshooting **The "enable a password" or "the documentation on how to enable a password" links on the Jupyter login page return a 404.** This is a known issue with the upstream Jupyter login template, which references documentation URLs that have since moved. Use the **Setup a Password** form on the same login page (see Step 5.2, Option B) to set a password without needing the linked documentation. `docker logs`** does not show a token.** The container may have been started with `JUPYTER_TOKEN` set to an empty value, or with token authentication disabled. Restart the container without overriding `JUPYTER_TOKEN`, or set it explicitly using `-e JUPYTER_TOKEN=` in the `docker run` command from Step 3. **Browser cannot reach **`:8888`**.** Verify that port `8888` is published in the container configuration in Step 3, that the LitmusEdge firewall allows inbound traffic on `8888`, and that your browser machine has network access to the LitmusEdge IP.