Intel DevCloud

Intel DevCloud is a development environment offered by Intel Corporation that aims to accelerate AI and HPC workloads, optimize for edge computing, and facilitate the creation of multiarchitecture and FPGA applications. According to the vendor, this platform is suitable for businesses of all sizes, ranging from small startups to large enterprises. Data scientists, AI researchers, software developers, engineers, HPC professionals, edge computing solution providers, and FPGA developers across various industries can utilize Intel DevCloud to learn, prototype, test, and run applications on the latest Intel hardware and software.

Key Features

Accelerate AI and HPC: According to the vendor, users can access the latest hardware and software innovations, scale and accelerate AI workloads, and prototype, test, and run AI applications on the Intel DevCloud.

Optimize for the Edge: The vendor claims that users can evaluate, benchmark, and prototype AI and edge solutions, deploy containerized workloads on Intel architecture using Kubernetes, and leverage Intel hardware for edge prototyping and preproduction deployment.

Build Multiarchitecture and FPGA Applications: Users can program oneAPI multiarchitecture applications, test workloads across Intel FPGAs, CPUs, and GPUs, develop and optimize applications for various Intel architectures, and gain hands-on experience with FPGA development using Intel DevCloud, according to the vendor.

Use the Cloud Platform that Matches Your Workload: The vendor states that users can choose from several configurations tuned to different workloads, access AI training, FPGA development, edge prototyping, and preproduction deployment environments, experiment with real-world code samples and tutorials, evaluate performance and acceleration using multiple hardware configurations, and benchmark AI workloads with always-on access to the latest AI hardware.

Available Intel Hardware: According to the vendor, users can test and evaluate applications on virtual machines, bare metal systems, edge devices, and AI training platforms, access a variety of Intel hardware for development and testing purposes, leverage the latest Intel technologies for optimizing and fine-tuning applications, and explore different hardware configurations to find the best fit for specific workloads.

Available Development & Run Environments:

• Container-Based Workloads: Users can import and run containers, HELM charts, or buildable source using a powerful Kubernetes environment, easily manage and deploy containerized workloads on Intel architecture, and leverage the flexibility and scalability of container-based development, according to the vendor.
• JupyterLabs: The vendor claims that users can create code directly within a web-based environment, explore a library of tutorials and sample applications, develop and test applications using Jupyter notebooks, and access a collaborative and interactive coding environment.
• Secure Shell (SSH) Direct Connection: Users can connect directly to a node and run workloads using a command-line interface, execute commands and manage workloads remotely, and utilize SSH for secure and efficient remote access to Intel DevCloud, according to the vendor.
• cnvrg.io Full Stack Machine Learning Operating System*: The vendor states that users can utilize cnvrg.io*, a flexible end-to-end machine learning operating system, build high-impact models faster on any AI infrastructure, empower AI developers with a comprehensive machine learning platform, and access a range of tools and features for efficient AI development.