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    Deep Learning Notebook (Python 3.11, Tensorflow 2.15, Pytorch 2.2)

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    Sold by: SigmoData 
    AWS Free Tier
    This AMI provides a jupyter notebook instance for quick experimentation with the latest software and GPU support
    Listing Thumbnail

    Deep Learning Notebook (Python 3.11, Tensorflow 2.15, Pytorch 2.2)

     Info
    Sold by: SigmoData 

    Overview

    Jupyter notebook instance ready to train deep learning models

    • Start coding in minutes
    • Automatically starts a jupyter notebook server on https port 8888. The password is your instance id.
    • Runs on GPU automatically if available (choose g5 instances), otherwise runs on CPU
    • Python version 3.11
    • Tensorflow version 2.15
    • Pytorch version 2.2
    • Scikit Learn, Matplotlib, Numpy included as dependencies
    • Nvidia CUDA version 12.3 + CUDNN version 8 (only if running on GPU instance)

    Highlights

    • Automatic support of GPUs on aws instances that have Nvidia GPUs (g3/g5 instances)
    • Latest Tensorflow and PyTorch versions

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    AmazonLinux Amazon Linux 2 - January 2024

    Typical total price

    This estimate is based on use of the seller's recommended configuration (g5.xlarge) in the US East (N. Virginia) Region. View pricing details

    $1.156/hour

    Pricing

    Deep Learning Notebook (Python 3.11, Tensorflow 2.15, Pytorch 2.2)

     Info
    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (196)

     Info
    • ...
    Instance type
    Product cost/hour
    EC2 cost/hour
    Total/hour
    t2.nano
    $0.05
    $0.006
    $0.056
    t2.micro
    AWS Free Tier
    $0.00
    $0.012
    $0.012
    t2.small
    $0.10
    $0.023
    $0.123
    t2.medium
    $0.125
    $0.046
    $0.171
    t2.large
    $0.15
    $0.093
    $0.243
    t2.xlarge
    $0.175
    $0.186
    $0.361
    t2.2xlarge
    $0.20
    $0.371
    $0.571
    t3.nano
    $0.05
    $0.005
    $0.055
    t3.micro
    AWS Free Tier
    $0.075
    $0.01
    $0.085
    t3.small
    $0.10
    $0.021
    $0.121

    Additional AWS infrastructure costs

    Type
    Cost
    EBS General Purpose SSD (gp2) volumes
    $0.10/per GB/month of provisioned storage

    Vendor refund policy

    No refunds, but you may cancel at any time.

    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

     Info

    Delivery details

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes
    • Security updates for January 2024
    • Jupyter updated with JupyterLab and Notebook 7.1
    • Updated CUDA version to 12.3
    • Updated Python version to 3.11
    • Updated Tensorflow version to 2.15.0
    • Updated PyTorch version to 2.2
    • Updated Nvidia drivers version to 535.154.05
    • Note: You may see some CUDA/NUMA warnings when Tensorflow initializes the GPU. Those warnings can be ignored

    Additional details

    Usage instructions

    • Launch the product via 1-click.
    • Access the application via web browser at https://: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://:8888

    Resources

    Vendor resources

    Support

    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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