Intel Tiber AI Cloud Integrations

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.

BCM One provides comprehensive, managed technology solutions to help businesses streamline their network to make communications, collaboration and connectivity simple and cost-effective. Business networks are complex by nature. Add in multiple locations and vendors, different ways of working, and evolving technology needs and the infrastructure can quickly get out of control. Without proper transparency, oversight, and expertise, companies end up relying on insufficient legacy systems, building out unsustainable networks, and overspending on services they don’t need. BCM One can help you right-fit your technology to meet your current network needs and scale as you grow. Your network may be complex, but it doesn’t have to be unmanageable. Let BCM One show you how. BCM One will optimize your network to meet all of your collaboration and communications needs now and as your business grows.

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.

Arm DDT is the number one server and HPC debugger in research, industry, and academia for software engineers and scientists developing C++, C, Fortran parallel and threaded applications on CPUs, GPUs, Intel, and Arm. Arm DDT is trusted as a powerful tool for the automatic detection of memory bugs and divergent behavior to achieve lightning-fast performance at all scales. Cross-platform for multiple servers and HPC architectures. Native parallel debugging of Python applications. Has market-leading memory debugging. Outstanding C++ debugging support. Complete Fortran debugging support. Has an offline mode for debugging non-interactively. Handles and visualizes huge data sets. Arm DDT is a powerful parallel debugger, available standalone or as part of the Arm Forge debug and profile suite. Its intuitive graphical interface provides automatic detection of memory bugs and divergent behavior at all scales.

In the hospitality industry, rapid service speed is essential. Intelligent kitchen display systems can be used to automatically deliver and categorize orders, ensuring chefs can easily understand and fulfill orders. Advantech’s intelligent kitchen display systems also support remote management software that enables remote device monitoring, database backups and recovery, and data management for reduced labor costs. Intelligent kitchen display systems automatically dispatch orders to the kitchen after categorizing each order item to ensure easy identification. This improves order management by streamlining the cooking process and increasing staff efficiency. Intelligent kitchen display systems can also be configured to schedule orders for delivery at specific times to help chefs avoid mistakes and reduce food wastage. Additionally, chefs can review the order schedule to prepare for later courses and ensure food is ready at the designated serving time.

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

Regardless of your organization’s size and requirements, HP Advance has a module and architecture that grows with your enterprise needs. Regardless of your organization’s size and requirements, HP Advance has a module and architecture that grows with your enterprise needs. HP Advance software can run on several manufacturers’ MFPs providing consistency across the entire print fleet. Increase document management efficiencies and cut costs by consolidating management of multiple solutions into a single admin console. Easily enhance document output and print management. Adding new capabilities is as simple as entering a new license key. IT departments will benefit from one installer and a consistent look and feel across all modules.

Cloud Privilege Broker gives your team the tools to visualize and manage entitlements across the multi-cloud infrastructure. Cloud-agnostic, centralized dashboard with key metrics. Continuous discovery of users, roles, policies, and endpoints across all supported cloud platforms. Granular policy recommendations across IaaS and PaaS platforms from a single interface. BeyondTrust Cloud Privilege Broker (CPB) is an entitlements and permissions management solution that enables customers to visualize and manage cloud access risk in hybrid and multi-cloud environments, all from a single interface. Each cloud service provider has its own access management tools, but they only manage their own environments and do not scale to cover others. This leaves teams to swivel from console to console, attempting to manage permissions separately for each cloud provider, with different ways to apply policy from one platform to the next.

The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease.

Intel’s Gaudi software gives developers access to a comprehensive set of tools, libraries, containers, model references, and documentation that support creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. It helps streamline every stage of AI development including training, fine-tuning, debugging, profiling, and performance optimization for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, whether in data centers or cloud environments. It includes up-to-date documentation with code samples, best practices, API references, and guides for efficient use of Gaudi solutions such as Gaudi 2 and Gaudi 3, and it integrates with popular frameworks and tools to support model portability and scalability. Users can access performance data to review training and inference benchmarks, utilize community and support resources, and take advantage of containers and libraries tailored to high-performance AI workloads.