Overview

A pre-configured and fully integrated minimal runtime environment with PyTorch, an open source machine learning library for Python, Jupyter Notebook, a browser-based interactive notebook for programming, mathematics, and data science, and the Python programming language. The stack is optimized for running on CPU. The stack also includes Development preset, program development and building tools, including C compiler, make etc.

Highlights

High-performance execution environment optimized for training or inference
Carefully integrated and tested software packages with new features and bug fixes
Stable and production ready, with long-term support and updates

Details

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Jetware

Features and programs

Financing for AWS Marketplace purchases

AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.

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Pricing

PyTorch CPU MKL Notebook

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This product is free. Subscriptions have no end date and can be canceled anytime.

Vendor refund policy

This product is available free of charge.

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

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Delivery option

Delivery details

PyTorch 0.4, Python 3.6

Supported services: Learn more

Amazon ECS
Amazon EKS

Container image

Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.

Version release notes

First AWS Marketplace release

Additional details

Usage instructions

Please ensure that you have pulled the container image from the ECR using the container image details
Launch the Docker images:

docker run -p 8888:8888 <AWS MP Container image URL>

3: Access to the product via web browser using Public DNS URL at the port 8888

Resources

Vendor resources

Quickstart guide

AISE TensorFlow Lab for AWS

AISE - AI System Engineering

Support

Vendor support

We will be pleased to answer short and quick questions with the tag #jetware on Twitter. More complicated questions are welcome with the tag #jetware on Stack Overflow. Open Source Support http://jetware.io/page/services/support?us=awsmp to get web, email or phone support covering Jetware products and open source software included. Managed Services http://jetware.io/page/services/managed?us=awsmp to get 24x7 application and infrastructure management.

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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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Ratings and reviews

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1 external reviews

External reviews are sourced from G2 and are not included in the star rating for this product.

Musfira Z.

Best part about it is being open source, It can be used to design our own customized neural network

Reviewed on Dec 31, 2021

Review provided by G2

What do you like best about the product?

The tools and available libraries, fastai,TensorLy,Glow,AllenNLP. It supports wide Artificial Intelligence uses and debugging for models that are exported

What do you dislike about the product?

Pytorch packages being slow on AMD cpu compared to others.

What problems is the product solving and how is that benefiting you?

I used it to work on thesis which was stock prediction using customized neural networks and LSTM

Recommendations to others considering the product:

Should go through the tutorial first as it is helpful to standardize the design process if you want to contribute to the community