Overview
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, Generative AI, LLMs, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on the latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), large language models (LLM), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI, and deep learning inference deployed from edge to cloud.
Highlights
- Deploy High-Performance Deep Learning Inference. Optimize and deploy deep learning solutions across multiple Intel® platforms: Intel-powered CPUs, integrated GPUs, Intel discrete GPUs, Intel NPUs, and FPGAs
- Leverage pre-optimized and open-sourced pre-trained models, code samples, and demos from the Open Model Zoo. More Generative AI coverage, Large Language Model (LLM) support, and more model compression techniques.
- This AMI includes OpenVINO, and Jupyter Interface to run OpenVINO Notebooks.
Details
Typical total price
$0.714/hour
Features and programs
Financing for AWS Marketplace purchases
Pricing
- ...
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
t2.large | $0.00 | $0.093 | $0.093 |
t2.xlarge | $0.00 | $0.186 | $0.186 |
t2.2xlarge | $0.00 | $0.371 | $0.371 |
t3.large | $0.00 | $0.083 | $0.083 |
t3.xlarge | $0.00 | $0.166 | $0.166 |
t3.2xlarge | $0.00 | $0.333 | $0.333 |
m3.xlarge | $0.00 | $0.266 | $0.266 |
m3.2xlarge | $0.00 | $0.532 | $0.532 |
m4.large | $0.00 | $0.10 | $0.10 |
m4.xlarge | $0.00 | $0.20 | $0.20 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp3) volumes | $0.08/per GB/month of provisioned storage |
Vendor refund policy
It is Free !
Legal
Vendor terms and conditions
Content disclaimer
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
Updated to OpenVINO 2024.2.0. with Amazon Linux 2023 OS. Includes OpenVINO notebooks.
Additional details
Usage instructions
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Launch the AMI. Note: Make sure the public IP address is enabled and is launched in a VPC with internet access.
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Open Jupyter Notebook by navigating to port 8888, the URL is http://<ec2-instance-public-ip>:8888 Note: It might take a few minutes for the ec2 instance to boot up, so if the webpage doesn't load, please try again in a few minutes.
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The Jupyter Notebook password is <ec2-instance-id>
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To run sample notebooks, you can navigate to /notebooks. Sample URL: http://<ec2-instance-public-ip>:8888/lab/tree/notebooks
Additional Resources: -- See step-by-step detailed Instructions here: https://github.com/IntelAI/openvino-demos/blob/master/aws/ami/Getting-Started-Guide-to-Launch-EC2-with-OpenVINO.pdf
-- Instructions to connect to OpenVINO AMI with RDP/VNC session: https://www.youtube.com/watch?v=K0eJshISQv4
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.
Similar products
Customer reviews
Run DL Models faster!
The Intel distribution of OpenVINO AMI is a versatile solution for deep learning inference workloads on AWS. The OpenVINO AMI provides a pre-configured environment for OpenVINO, and preconfigured Jupyter Notebooks which makes it easy to get started and deploy models quickly
Easy to use via Jupyter Notebook
With OpenVINO preinstalled, it is easy to quickly start experimenting with OpenVINO toolkit. Access to OpenVINO jupyter Notebook directly without logging into the console is convienent
AWS OpenVino Kit Crashes
Sir,
I installed OpenVino Image from AWS Marketplace using SpotInstance
Did RDP into the instance using the commands below.
Copying a text with the with right click mouse drag closes the remote desktop.
Very nice bug sir in the image. Please fix it.
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sudo yum -y update
sudo amazon-linux-extras install mate-desktop1.x
sudo rpm -Uvh http://li.nux.ro/download/nux/dextop/el7/x86_64/nux-dextop-release-0-1.el7.nux.noarch.rpm
sudo openssl req -x509 -sha384 -newkey rsa:3072 -nodes -keyout /etc/xrdp/key.pem -out /etc/xrdp/cert.pem -days 365
sudo yum install xrdp tigervnc-server
sudo systemctl start xrdp
sudo systemctl enable xrdp
sudo passwd ec2-user
sudo OpenVinoDev1
/etc/xrdp/xrdp.ini and comment out the line containing “channel_code = 1”.
sudo sed -i 's/<USER>/ec2-user/' /etc/systemd/system/vncserver@.service
wget https://dl.google.com/linux/direct/google-chrome-stable_current_x86_64.rpm
sudo yum install ./google-chrome-stable_current_x86_64.rpm
sudo ln -s /usr/bin/google-chrome-stable /usr/bin/chromium
sudo file -s /dev/xvdf
sudo mkfs -t ext4 /dev/xvdf
sudo mkdir /Data
sudo mount /dev/xvdf /Data/
sudo cp /etc/fstab /etc/fstab.bak
vi /etc/fstab
/dev/xvdf /Data ext4 default,nofail 0 0
sudo mount -a