Deep Learning Notebook (Python 3.12)

• Launch the product via 1-click.
• Access the application via web browser at https://<instance-ip>:8888/
• Accept self-signed SSL certificate warning (a free certificate is generated by the instance unless you provide your own - see below for instructions)
• Login using the EC2 instance ID as the password (ex i-xxxxxxxxxxxxxxxxx)
• Click new > Python3 to create a new notebook. From then on you can experiment with Tensorflow, Keras and Pytorch
• When selecting an ec2 instance type, pick an instance with GPUs (ex g5.xlarge) to automatically enable faster model training in Tensorflow and Pytorch thanks to GPU acceleration

Optional settings via User Data:

• The instance can be configured to map a S3 bucket, and/or custom SSL certificates for the https connection (instead of the auto-generated ones)
• Provide the values in the User Data section of the EC2 launch screen
• S3_BUCKET set this user data if you wish to use S3 as storage for your notebooks. Add a line such as S3_BUCKET=your-s3-bucket-name and the instance will try to mount the bucket as the notebook directory (and also independently as /home/ec2-user/s3). This requires the right IAM role with S3 access to the bucket
• SSL_CERT, SSL_KEY set this user data if you wish to use your own SSL certificate. Add SSL_CERT=/home/ec2-user/s3/path-to-cert.crt and SSL_KEY=/home/ec2-user/s3/path-to-cert.key to let the instance copy the certificate and private key. This can be useful if you don't want a self-signed certificate to be generated.
• PORT set this optional value to a different port number than the default (8888). For example, to run on port 443 add a user-data line like "PORT=443"
• DISABLE_SSL (not recommended) this will disable SSL as well as traffic encryption between your browser and the server. To disable SSL, add the user-data line "DISABLE_SSL=true". You will have to set the url to http instead of https, for example http://<instance ip>:8888