Sold by: SigmoDataÂ
Deployed on AWS
AWS Free Tier
This AMI provides a jupyter notebook instance for quick experimentation with the latest software and GPU support
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
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux Amazon Linux 2 - January 2024
Deployed on AWS
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Pricing
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.
Dimension | Cost/hour |
|---|---|
g5.xlarge Recommended | $0.15 |
t3.micro AWS Free Tier | $0.075 |
t2.micro AWS Free Tier | $0.00 |
m5a.12xlarge | $0.18 |
m6a.xlarge | $0.15 |
g4ad.4xlarge | $0.15 |
g3.8xlarge | $0.20 |
x1e.8xlarge | $0.20 |
c5d.2xlarge | $0.14 |
c3.4xlarge | $0.16 |
Vendor refund policy
No refunds, but you may cancel at any time.
Legal
Vendor terms and conditions
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Content disclaimer
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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://<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
Resources
Vendor resources
Support
Vendor 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.
Updated weekly
By Hanwei Software Technology
AI generated from product descriptions
Machine Learning Framework Support
Includes pre-configured Tensorflow 2.15 and PyTorch 2.2 for deep learning model development
GPU Acceleration
Automatic GPU support with Nvidia CUDA 12.3 and CUDNN 8 for accelerated computational processing
Development Environment
Jupyter notebook server pre-configured with Python 3.11 for interactive coding and experimentation
Scientific Computing Libraries
Integrated Scikit Learn, Matplotlib, and Numpy for comprehensive data science and machine learning workflows
Computational Flexibility
Supports both GPU and CPU execution environments with automatic instance type detection
Deep Learning Framework Support
Includes multiple deep learning frameworks like TensorFlow 2.11, PyTorch 1.13, and Keras 2.11
GPU Acceleration
Automatic GPU instance support with Nvidia CUDA 11.7 and CUDNN 8.7 with TensorRT 8.5
Python Development Environment
Python 3.8 with integrated Jupyter Notebook server running on web port 8888
Machine Learning Libraries
Includes scientific computing and machine learning libraries such as Scikit Learn, Matplotlib, Numpy, and Pillow
Web-Based Development Interface
Automatically starts Jupyter Notebook server with instance ID as password for secure access
GPU Acceleration
Includes NVIDIA drivers, CUDA, and cuDNN for high-performance GPU-based deep learning computations
Deep Learning Framework
Pre-installed PyTorch 2.0.1 with support for advanced machine learning model development
Operating System
Built on Amazon Linux 2, providing a lightweight and secure cloud-optimized environment
Machine Learning Libraries
Pre-configured with essential libraries including TorchVision, NumPy, and PyTorch-related packages
Cloud Instance Compatibility
Supports multiple GPU-based EC2 instances for scalable AI training and inference tasks
Standard contract
No
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