Intel® Developer Cloud for the Edge

Access Everything You Need to Build for the Edge. Anywhere.

The Intel® Developer Cloud for the Edge is designed to help you evaluate, benchmark, and prototype AI and edge solutions on Intel® hardware for free. Developers can get started at any stage of edge development.

  • Research problems or ideas with the help of tutorials and reference implementations.
  • Optimize your deep learning model performance on various Intel hardware configurations.
  • Import or develop your edge inference applications on bare metal.
  • Import or build multicontainer solutions for your cloud-native applications.

To get started, choose the environment or solution that best meets your goals.

FAQ

Bare Metal Development

Get Started

Develop or import your computer vision or edge AI application using a JupyterLab environment on a bare metal infrastructure.

  • Preinstalled with the Intel® Distribution of OpenVINO™ toolkit
  • Rapid prototyping with code snippets
  • Reusable references
  • Python* debugger

 

View Tutorials | Browse Sample Applications | Partner Solutions

Learn More about Prototyping

 

Containerized Development

Get Started

Build your application in a container or import your existing containerized workloads to test on a wide range of Intel hardware.

  • Launch containerized workloads on Intel hardware using Kubernetes*.
  • Import from a prebuilt library, container registry, or Helm* chart.
  • Connect to cloud storage.
  • Import sources from Git and build container images.

 

Developer Guide

Learn More about Containers

 

Announcements

Deploy and evaluate your AI workloads on the newly hosted Intel® Xeon® Platinum 8480+ Processor.

Launch JupyterLab and Try It Now

Deploy and evaluate your AI workloads on the newly hosted Intel® Core™ i5-1250PE processor.

Learn to use GStreamer* pipelines for complex media analytics tasks such as detection, classification, and tracking using Intel® Deep Learning Streamer 2022.2.

View the Tutorial

Bare Metal Environment: The performance metrics dashboard adds a new metric called Power Usage (Watts)/Time GMT to hardware and application level metrics. This metric shows CPU power use while the application is running to help users further fine-tune their workload and hardware configuration.

Try It

Analyze and evaluate updated samples or tutorials with improved throughput performance.

Tutorial with Sync OpenVINO 2.0 API:

Samples and tutorials with Async OpenVINO 2.0 APIs:

Learn More about OpenVINO 2.0 APIs

Deploy and evaluate your AI workloads on the newly hosted Intel® Data Center GPU Flex Series 170. The GPU is optimal for pipelines that include more complex AI models, such as multiple object detection or multiple classification models.

Learn More about Intel® Data Center GPU Flex Series 170

Launch JupyterLab and Try It Now

Learn to deploy an industrial computer vision model to detect real-world analog pointer meters and extract corresponding digital readings using the Intel® Distribution of OpenVINO™ toolkit.

View Industrial Meter Reader

Deploy and evaluate your AI workloads on the newly hosted Intel® Data Center GPU Flex Series 140. With higher number of video encode and decode engines, this GPU can support a high number of streams for AI inferencing workloads.

Learn More

Intel® Deep Learning Streamer (Intel® DL Streamer) code snippets are small blocks of reusable code that can be inserted in a Jupyter* Notebook for detection, classification, and tracking applications on Intel® Developer Cloud for the Edge workloads using the OpenVINO™ toolkit. Use the tag ‘DL Streamer_LTS’ to filter out the Intel DL Streamer code snippets.

Learn More

Intel® Developer Cloud for the Edge now features the Intel® Distribution of OpenVINO™ toolkit version 2022.2. 

Note  Deep Learning Workbench is on version 2022.1 of the toolkit. 

View Release Notes

Analyze and evaluate newly added samples: Image Classification Using ResNet-50 and Sequence Classification Using BERT to Accelerate PyTorch* Model Inference Using OpenVINO™ Integration with ONNX* Runtime for PyTorch*. 

View Sample

Deploy and evaluate your AI workloads on the newly hosted Intel® Xeon® D-2712T and Intel Xeon D-2796NT processors.

Quickly browse, import, or export your AI models and test data from Amazon S3 buckets to optimize and evaluate inference performance on Intel® hardware within Intel® Developer Cloud for the Edge.

Learn More

The tutorial notebook has reusable code snippets to help you quickly create OpenVINO™ applications.

Try It Out

Try your own AI workloads on the newly hosted Intel Core i7 and i9 processors.

Accelerate PyTorch model inference using OpenVINO™ Integration with ONNX* Runtime for PyTorch* as an runtime provider to run on Intel® hardware.

Learn More

This solution uses the Intel® Deep Learning Streamer and Intel® Distribution of OpenVINO™ toolkit to identify people and determine social distance in crowded public spaces. 

Learn More

Try your own AI workloads on the newly hosted Intel Core i5 and i7 processors.

Learn the latest inference API for automatic device selection and performance hints to dynamically optimize for latency or throughput.

View Sample

New notebooks demonstrate brain tumor segmentation, classification, object detection, and style transfer in French and German languages.

This incremental update includes support for text classification models, OpenVINO™ API 2.0 enabled in tools, Cityscapes Dataset, and initial support for natural language processing (NLP) models. 

Learn More 

Code snippets are small blocks of reusable code that can be inserted in a Jupyter* Notebook to aid and accelerate coding of sample applications on Intel® Developer Cloud for the Edge workloads using the OpenVINO™ toolkit.

Launch JupyterLabs and Try It Now

Now you can import and securely launch HELM charts, Docker* Compose images, and containers on Intel® hardware in the new Kubernetes* environment. Explore, edit, and test OpenVINO™ toolkit sample applications with JupyterLab, and optimize AI models with the improved Deep Learning Workbench.

Intel® Developer Cloud for the Edge has been updated with version 2022.1 of the OpenVINO™ toolkit.

Release Notes

Try your own AI workloads or Intel® Developer Cloud for the Edge sample applications on the newly hosted Pentium® processors.

This release adds support for Intel® Distribution of OpenVINO™ toolkit version 2021.4.2, and contains minor bug fixes and usability enhancements.

Get Started  

This latest version allows you to do out-of-the-box benchmarking, measure the accuracy of your model, and perform comprehensive comparison of accuracy between floating point and int8 models and more.  

Get Started

Provides an enhanced experience when working with sample, tutorial and prototype notebooks.

Intel® Developer Cloud for the Edge now features the Intel Distribution of OpenVINO toolkit version 2021.4.2.

Release Notes

This incremental update includes support for explainable AI for classification model types, visualization of inference results, streamlined INT8 calibration flow, and multiple UX improvements. 

Get Started

Seamlessly develop, build, and test cloud-native container applications on various target deployment hardware. 

Try It Now

Use OpenVINO toolkit optimizations with TensorFlow* inference applications across a wide range of compute devices.

Learn More

Intel Developer Cloud for the Edge now supports OpenVINO toolkit version 2021.4.1.

Release Notes

Try your own AI workloads or Intel Developer Cloud for the Edge sample applications on the newly hosted OnLogic* platforms.

Learn More

Create datasets and explore the performance benefits of converting models to int8 and TensorFlow. Now includes YOLO* model support.

Try It Now 

Explore constructing media analytics pipelines using Deep Learning Streamer in the Frictionless Retail Sample on Intel Developer Cloud for the Edge.

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Additional Developer Solutions

Edge for Industrial

Speed up development of industrial solutions with prevalidated software packages for machine learning, data analytics, and software-defined control at the edge.

OpenVINO™ Integration with TensorFlow*

This solution is designed for developers who want to use built-in TensorFlow* models in their OpenVINO™ toolkit inferencing applications. In most cases, OpenVINO™ integration with TensorFlow* can be accomplished with just two lines of code.

Deep Learning Workbench

The Deep Learning Workbench simplifies using the Intel Distribution of OpenVINO toolkit to tune, visualize, and compare the performance of deep learning models on Intel® architecture.

Post-Training Optimization Tool

Accelerate the inference of deep learning models without retraining. The Post-Training Optimization Tool (POT) offers a suite of tools that let you automate the model transformation process without changing model structure.

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Hardware Available For Edge Workloads

Test your workload performance with combinations of CPUs, GPUs, and accelerators in bare metal and cloud-native container architectures to identify the hardware platform that works best for your inferencing solutions.

Explore All Hardware

Move from Prototype to Production

Take your product from prototype to production with downloadable solutions from the Intel® Developer Catalog, and procure hardware from Intel's ecosystem of hardware partners.

Development Kits

Jump-start your application development with the latest foundational kits with an option to include an Intel® Vision Accelerator Design product.

Browse All Kits

Intel® Developer Catalog

This development resource offers computer vision and deep learning solutions to try in the Container Playground, which uses the Red Hat OpenShift platform, to install and deploy to the network edge.

Learn More

Ecosystem Partners

Run your workloads or run a sample from Intel on partner hardware hosted within Intel Developer Cloud for the Edge.

Visit Partner Program

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