I was previously using the AMI from the fast.ai course for my deep learning needs, but recently decided to try out the AWS Deep Learning AMI and I wanted to document a few tips for a few issues I encountered when I first set it up.


The AWS Deep Learning AMI does not come with the latest version of Keras, so you’ll need to update the keras package using:

sudo pip install keras --upgrade

Or, if you’re using Python 3, you can update it using pip3 instead:

sudo pip3 install keras --upgrade

You’ll also need to remove older Keras configurations (if any) using:

rm ~/.keras/keras.json


I ran into some issues with CUDA initially, which turned out to be a configuration problem. If you encounter the following error (or something similar):

ImportError: libcusolver.so.8.0: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

Try updating the LD_LIBRARY_PATH using the following:

sudo ldconfig /usr/local/cuda/lib64

Jupyter Notebook Configuration

Generating the default configuration for Jupyter notebook can be done using: jupyter notebook --generate-config and to avoid using the token, you may be interested in setting up a password instead using: jupyter notebook password

There were other issues I encountered with running this on EC2 which was actually getting the notebook up and running in the browser. If you had issues connecting to the notebook via the browser, you may find the following useful:

If you’ve generated a password, there should be a jupyter_notebook_config.json where you can set user-specific settings. Alternatively, you can also try modifying jupyter_notebook_config.py. Here’s an example of my JSON configuration:

  "NotebookApp": {
    "ip": "*",
    "open_browser": false,
    "password": "PASSWORD_HASH"

In order to get the notebook up and running in the browser, you will either need to adjust the security group associated with your EC2 instance or utilise SSH tunneling.

Security Group

Edit the security group for your EC2 instance and add a new rule to allow the following ports: 8888-8898 with TCP as the protocol and as the source for the rule. You should now be able to access your notebooks via http://EC2_INSTANCE_IP:8888/

SSH Tunnelling

Alternatively, you can use SSH tunnelling which will let you open the notebook via localhost by binding the port locally. This can be achieved using ssh -i ~/.ssh/<KEY_NAME.pem> [email protected]<EC2_INSTANCE_IP> -L 8888: where KEY_NAME is the name of your keypair that you downloaded from Amazon, and EC2_INSTANCE_IP is the IP address of your instance.

You should now be able to access the notebook using Please note that you’ll need to maintain the SSH connection for this to work properly.

Bonus: Autostarting Jupyter Notebook

Since I usually turn off my instance when I’m not using it, I wanted to autostart Jupyter whenever the instance was re-started to avoid SSHing everytime to start it up manually. Luckily, there’s a way to do that!

Create a new file called jupyter_start.sh in your home directory and make sure it can be executed, and add the following:

jupyter notebook --notebook-dir=/home/ubuntu/ --profile=nbserver > /tmp/ipynb.out 2>&1 &
  • For AWS Deep Learning AMI, change PATH_TO_ANACONDA to /home/ubuntu/anaconda3/bin
  • For fast.ai AMI, change PATH_TO_ANACONDA to /home/ubuntu/src/anaconda3/bin.

You can also change notebook-dir to the location of your Jupyter notebooks.

Next, edit the /etc/rc.local file and add the following line before exit 0:

su ubuntu -c 'bash /home/ubuntu/jupyter_start.sh'

Since Jupyter will now run as a background process, you may find it annoying to restart/stop the Jupyter process. I have created additional scripts that aim to address this problem.


Create jupyter_stop.sh in your home directory and add in the following pkill jupyter. Ensure the script is executable using chmod +x jupyter_stop.sh. The pkill command is a great way to kill a process without necessarily digging into the ps aux output and finding the process ID.


This script will simply call the two scripts we created earlier. Create jupyter_restart.sh in your home directory and add the following. You should also ensure that the script is executable using chmod +x.


You can now easily call jupyter_stop.sh or jupyter_restart.sh whenever you want, and jupyter_start.sh should be called automatically on reboot!

Let me know if you encounter any issues, or have any additional useful tips!