---
title: Run a Jupyter Notebook inside Tensorflow

---


# How to run a Jupyter Notebook inside Tensorflow Container for Accelerated Machine Learning E2E GPU Wizard?

## Launching an Instance based on Tensorflow container with Jupyter-notebook Running

### Using myaccount portal

1. First sign into the myaccount portal.
2. Navigate to GPU Wizard:
   - Under the compute menu, click on **GPU**.
   - Then click on GPU Wizard.
   - For NGC Container Tensorflow, click on Next under actions column.
   - Choose the card according to requirement. A100 is recommended.

   ![Image](images/jupytern1.png)

3. Choose the plan as per the requirement:
   - Optionally you can [`add SSH key`](/docs/tir/GettingStarted/Security/SSH_Key/#manage-ssh-keys) (recommended) or subscribe to CDP backup.
   - Click on **“Create my node”**.
   - Wait for a few minutes and confirm that node is in running state.

   ![Image](images/jupyter2.png)

4. Open terminal on your local PC and type the following command:

   ```bash
   ssh -NL localhost:1234:localhost:8888 root@<your_node_ip>

    ```

![Image](images/jupyter3.png)

- The command usually will not show any output, which indicates that the command has run without any error.
- Go to a web browser on your local PC and hit the URL: [`http://localhost:1234`](http://localhost:1234).

![Image](images/jupyter4.png)

- Congratulations! Now you can run your Python code inside this Jupyter notebook, which has TensorFlow and all the libraries frequently used in machine learning pre configured.
- To get the most out of GPU acceleration, use [`RAPIDS`](https://rapids.ai/start.html) and [`DALI`](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), which are already installed inside this container.
- RAPIDS and DALI accelerate tasks in machine learning, including data loading and preprocessing, beyond just the learning process.

## How to Run

- MNIST dataset is a kind of **“Hello world”** implementation in machine learning.
- Here we are about to use Fashion MNIST (clothing classification) as given in the NGC link [`here`](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/fashion_mnist_tf_example/files).
- Copy the `wget` command as given below.

![Image](images/jupyter5.png)

- In your Jupyter Notebook UI, create a new notebook.

![Image](images/jupyter6.png)

- Enter **‘!’** in the cell and paste the `wget` command.

![Image](images/jupyter7.png)

- Once you run the command, you can find the downloaded file on the Jupyter home page.

![Image](images/jupyter8.png)

- Now open the notebook [`FashionMNIST+Notebook.ipynb`](http://localhost:1234/notebooks/FashionMNIST%2BNotebook.ipynb).
  - You can skip up to step 4 (which was already done) and directly go to Step 5.
  - You can run cell by cell from Step 5 and observe each output, or you can go to the menu bar and select **“Cell -> Run All”** to run all of them.

![Image](images/jupyter9.png)

![Image](images/jupyter10.png)

- You can see that after 15 epochs, the accuracy will be around 92% on training data and 88% on testing data.

## How to troubleshoot if the notebook isn’t accessible

1. SSH into your node with the public IP: `ssh username@<public-ip>`.
2. Check the output of the `docker ps` command.

![Image](images/jupyter11.png)

3. If it shows a container is running using the TensorFlow image, you can use the following command in the terminal:




```bash
         cnid=$(docker ps |grep tensorflow|head -1|awk '{print $1}');/usr/bin/nohup /usr/bin/docker exec $cnid /usr/local/bin/jupyter-notebook --no-browser --port=8888 --allow-root --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.quit_button=False --NotebookApp.notebook_dir=/home/tensorflow &

```

![Image](images/jupyter3.png)

- The command usually will not show any output, which indicates that the command has run without any error.
- Go to a web browser on your local PC and hit the URL: [`http://localhost:1234`](http://localhost:1234).

![Image](images/jupyter4.png)

- Congratulations! Now you can run your Python code inside this Jupyter notebook, which has TensorFlow and all the libraries frequently used in machine learning pre configured.
- To get the most out of GPU acceleration, use [`RAPIDS`](https://rapids.ai/start.html) and [`DALI`](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), which are already installed inside this container.
- RAPIDS and DALI accelerate tasks in machine learning, including data loading and preprocessing, beyond just the learning process.

## How to Run

- MNIST dataset is a kind of **“Hello world”** implementation in machine learning.
- Here we are about to use Fashion MNIST (clothing classification) as given in the NGC link [`here`](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/fashion_mnist_tf_example/files).
- Copy the `wget` command as given below.

![Image](images/jupyter5.png)

- In your Jupyter Notebook UI, create a new notebook.

![Image](images/jupyter6.png)

- Enter **‘!’** in the cell and paste the `wget` command.

![Image](images/jupyter7.png)

- Once you run the command, you can find the downloaded file on the Jupyter home page.

![Image](images/jupyter8.png)

- Now open the notebook [`FashionMNIST+Notebook.ipynb`](http://localhost:1234/notebooks/FashionMNIST%2BNotebook.ipynb).
  - You can skip up to step 4 (which was already done) and directly go to Step 5.
  - You can run cell by cell from Step 5 and observe each output, or you can go to the menu bar and select **“Cell -> Run All”** to run all of them.

![Image](images/jupyter9.png)

![Image](images/jupyter10.png)

- You can see that after 15 epochs, the accuracy will be around 92% on training data and 88% on testing data.

## How to troubleshoot if the notebook isn’t accessible

1. SSH into your node with the public IP: `ssh username@<public-ip>`.
2. Check the output of the `docker ps` command.

![Image](images/jupyter11.png)

3. If it shows a container is running using the TensorFlow image, you can use the following command in the terminal:

```bash
         cnid=$(docker ps |grep tensorflow|head -1|awk '{print $1}');/usr/bin/nohup /usr/bin/docker exec $cnid /usr/local/bin/jupyter-notebook --no-browser --port=8888 --allow-root --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.quit_button=False --NotebookApp.notebook_dir=/home/tensorflow &

```

![Image](images/jupyter13.png)

- Now create an SSH tunnel again on your local PC with the following command:  
  **`ssh -NL localhost:1234:localhost:8888 root@<your_node_ip>`**  
  and attempt to access the Jupyter notebook.

![Image](images/jupyter3.png)

## Why doesn't my notebook have a password?

The notebook is accessed via an SSH tunnel, which creates a secure encrypted connection between the node and the PC, making it accessible only for those who have SSH access. However, if you want to set a password, you can do so by following these steps:

- Stop any existing Jupyter notebook processes inside the container.
- Edit the field **–NotebookApp.password=’Your password’** in the above commands.
- To set it on the next boot, open the file **/etc/rc.local** in your server and set the password as shown below.

![Image](images/jupyter18.png)


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