NVIDIA Triton Inference Server Integrations

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Kubernetes (K8s), an open-source software that automates deployment, scaling and management of containerized apps, is available as an open-source project. It organizes containers that make up an app into logical units, which makes it easy to manage and discover. Kubernetes is based on 15 years of Google's experience in running production workloads. It also incorporates best-of-breed practices and ideas from the community. Kubernetes is built on the same principles that allow Google to run billions upon billions of containers per week. It can scale without increasing your operations team. Kubernetes flexibility allows you to deliver applications consistently and efficiently, no matter how complex they are, whether you're testing locally or working in a global enterprise. Kubernetes is an open-source project that allows you to use hybrid, on-premises, and public cloud infrastructures. This allows you to move workloads where they are most important.

Advanced apps can be run on a managed Kubernetes service that is secured and managed. GKE is an enterprise-grade platform that allows containerized applications to run, including stateful and non-stateful, Linux and Windows, AI and ML and complex web apps. It also supports APIs and backend services. You can leverage industry-first features such as four-way auto scaling and no stress management. Optimize GPU/TPU provisioning, make use of integrated developer tools, and get multicluster support from SREs. Single-click clusters allow you to quickly get started. You can leverage a high-availability control plan that includes multi-zonal clusters and regional clusters. Reduce operational overhead by using auto-repair, automatic-upgrade, or release channels. Secure by default, with vulnerability scanning of container images as well as data encryption. Integrated Cloud Monitoring with infrastructure, application and Kubernetes specific views. You can speed up app development without compromising security.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

Amazon Elastic Container Service (Amazon ECS), is a fully managed container orchestration and management service. ECS is used by customers such as Duolingo and Samsung, GE and Cook Pad to run their most sensitive and critical mission-critical applications. It offers security, reliability and scalability. ECS is a great way to run containers for a variety of reasons. AWS Fargate is serverless compute for containers. You can also run ECS clusters with Fargate. Fargate eliminates the need for provisioning and managing servers. It allows you to specify and pay per application for resources and improves security by application isolation by design. ECS is also used extensively in Amazon to power services like Amazon SageMaker and AWS Batch. It is also used by Amazon.com's recommendation engines. ECS is extensively tested for reliability, security, and availability.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Open-source monitoring solutions are able to power your alerting and metrics. Prometheus stores all data in time series. These are streams of timestamped value belonging to the same metric with the same labeled dimensions. Prometheus can also generate temporary derived times series as a result of queries. Prometheus offers a functional query language called PromQL, which allows the user to select and aggregate time series data real-time. The expression result can be displayed as a graph or tabular data in Prometheus’s expression browser. External systems can also consume the HTTP API. Prometheus can be configured using command-line flags or a configuration file. The command-line flags can be used to configure immutable system parameters such as storage locations and the amount of data to be kept on disk and in memory. . Download: https://sourceforge.net/projects/prometheus.mirror/

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Amazon Elastic Kubernetes Service is a fully managed Kubernetes services. EKS is trusted by customers such as Intel, Snap and Intuit. It also supports GoDaddy and Autodesk's mission-critical applications. EKS is reliable, secure, and scaleable. EKS is the best place for Kubernetes because of several reasons. AWS Fargate is serverless compute for containers that you can use to run your EKS clusters. Fargate eliminates the need for provisioning and managing servers. It allows you to specify and pay per application for resources and improves security by application isolation by design. EKS is also integrated with AWS Identity and Access Management, AWS CloudWatch, Auto Scaling Groups and AWS Identity and Access Management, IAM, and Amazon Virtual Private Cloud (VPC), allowing you to seamlessly monitor, scale, and load balance your applications.

The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions.

Azure Kubernetes Services (AKS), a fully managed service that manages containerized applications, makes it easy to deploy and manage them. It provides serverless Kubernetes and integrated continuous integration/continuous delivery (CI/CD), as well as enterprise-grade security, governance, and governance. You can quickly build, deliver, scale and scale applications using confidence by bringing together your operations and development teams. You can easily provision additional capacity by using elastic provisioning without having to manage the infrastructure. KEDA allows for event-driven autoscaling. Azure Dev Spaces allows for faster end-to-end development, including integration with Visual Studio Code Kubernetes tools and Azure DevOps. Azure Policy allows for advanced identity and access management, as well as dynamic rules enforcement across multiple clusters. More regions are available than any other cloud provider.

Manage, control, secure, and manage the apps, data, and IT that run your business from edge to cloud. HPE Ezmeral accelerates digital transformation initiatives by shifting resources and time from IT operations to innovation. Modernize your apps. Simplify your operations. You can harness data to transform insights into impact. Kubernetes can be deployed at scale in your data center or on the edge. It integrates persistent data storage to allow app modernization on baremetal or VMs. This will accelerate time-to-value. Operationalizing the entire process to build data pipelines will allow you to harness data faster and gain insights. DevOps agility is key to machine learning's lifecycle. This will enable you to deliver a unified data network. Automation and advanced artificial intelligence can increase efficiency and agility in IT Ops. Provide security and control to reduce risk and lower costs. The HPE Ezmeral Container Platform is an enterprise-grade platform that deploys Kubernetes at large scale for a wide variety of uses.

Tencent Cloud is a cloud computing service that provides high-performance, secure, reliable cloud computing services. Tencent is the largest Internet company in China and Asia. It provides services to hundreds of millions of people through its flagship products, QQ and WeChat. Cloud Virtual Machine (CVM), provides reliable and safe elastic computing services. Cloud Virtual Machine (CVM) allows you to increase or decrease computing resources in real-time, adapt to changing business requirements, and only pay for the actual resources used. CVM can reduce the cost of hardware and software procurement, and make IT easier to manage and maintain. Cloud databases offer enterprises complete relational, non-relational, analytical, and database ecological tools.

Alibaba CloudAP provides enterprise-level Wi Fi management capabilities and provides Wi-Fi or BLE network coverage for places like campuses, schools and hospitals. CloudAC allows remote control and management of CloudAP, which supports rapid deployment of the Wi Fi network and BLE network. CloudAP does not require you to deploy an AC or an authentication system for network access. These are both required for traditional Wi-Fi products. This greatly reduces costs. CloudAP can be wirelessly powered via Power Over Ethernet (PoE), which makes it easier to install onsite devices.

NVIDIA's Morpheus AI framework is GPU-accelerated and allows developers to create applications that are optimized for filtering, classifying, and processing large volumes of cybersecurity data. Morpheus uses AI to reduce time and costs associated with identifying and capturing threats and taking action. This brings a new level to security to data centers, clouds, and the edge. Morpheus extends the capabilities of human analysts with generative AI, automating real-time analyses and responses. It produces synthetic data for AI models to train that accurately identify risks and run what-if scenario. Morpheus can be downloaded as open-source software from GitHub by developers who are interested in the latest prerelease features and want to build their own. NVIDIA AI enterprise offers unlimited usage across all clouds, access NVIDIA AI experts and long-term support.