MXNet Integrations

ProActive Parallel Suite, a member of the OW2 Open Source Community for acceleration and orchestration, seamlessly integrated with the management and operation of high-performance Clouds (Private, Public with bursting capabilities). ProActive Parallel Suite platforms offer high-performance workflows and application parallelization, enterprise Scheduling & Orchestration, and dynamic management of private Heterogeneous Grids & Clouds. Our users can now simultaneously manage their Enterprise Cloud and accelerate and orchestrate all of their enterprise applications with the ProActive platform.

Discover a fresh library or dataset while working in a notebook environment. Streamline your preprocessing, training, or testing processes through an automated workflow. Transform your application into a functioning product by deploying it effectively. You have the flexibility to utilize notebooks, workflows, and deployments either together or on their own. Gradient is fully compatible with all major frameworks and libraries, ensuring seamless integration. Powered by Paperspace's exceptional GPU instances, Gradient allows you to accelerate your projects significantly. Enhance your development speed with integrated source control, connecting effortlessly to GitHub to oversee all your work and computing resources. Launch a GPU-enabled Jupyter Notebook right from your browser in mere seconds, using any library or framework of your choice. It's simple to invite collaborators or share a public link for your projects. This straightforward cloud workspace operates on free GPUs, allowing you to get started almost instantly with an easy-to-navigate notebook environment that's perfect for machine learning developers. Offering a robust and hassle-free setup with numerous features, it just works. Choose from pre-existing templates or integrate your own unique configurations, and take advantage of a free GPU to kickstart your projects!

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

Traditional CPUs are struggling to meet the growing demands for enhanced computing capabilities, while GPU processors can outperform them by a factor of 100 to 200 in terms of data processing speed. We offer specialized servers tailored for machine learning and deep learning, featuring unique capabilities. Our advanced hardware incorporates the NVIDIA® GPU chipset, renowned for its exceptional operational speed. Among our offerings are the latest Tesla® V100 cards, which boast remarkable processing power. Our systems are optimized for popular deep learning frameworks such as TensorFlow™, Caffe2, Torch, Theano, CNTK, and MXNet™. We provide development tools that support programming languages including Python 2, Python 3, and C++. Additionally, we do not impose extra fees for additional services, meaning that disk space and traffic are fully integrated into the basic service package. Moreover, our servers are versatile enough to handle a range of tasks, including video processing and rendering. Customers of LeaderGPU® can easily access a graphical interface through RDP right from the start, ensuring a seamless user experience. This comprehensive approach positions us as a leading choice for those seeking powerful computational solutions.

Guild AI serves as an open-source toolkit for tracking experiments, crafted to introduce systematic oversight into machine learning processes, thereby allowing users to enhance model creation speed and quality. By automatically documenting every facet of training sessions as distinct experiments, it promotes thorough tracking and evaluation. Users can conduct comparisons and analyses of different runs, which aids in refining their understanding and progressively enhancing their models. The toolkit also streamlines hyperparameter tuning via advanced algorithms that are executed through simple commands, doing away with the necessity for intricate trial setups. Furthermore, it facilitates the automation of workflows, which not only speeds up development but also minimizes errors while yielding quantifiable outcomes. Guild AI is versatile, functioning on all major operating systems and integrating effortlessly with pre-existing software engineering tools. In addition to this, it offers support for a range of remote storage solutions, such as Amazon S3, Google Cloud Storage, Azure Blob Storage, and SSH servers, making it a highly adaptable choice for developers. This flexibility ensures that users can tailor their workflows to fit their specific needs, further enhancing the toolkit’s utility in diverse machine learning environments.

Quickly set up a virtual machine on Google Cloud for your deep learning project using the Deep Learning VM Image, which simplifies the process of launching a VM with essential AI frameworks on Google Compute Engine. This solution allows you to initiate Compute Engine instances that come equipped with popular libraries such as TensorFlow, PyTorch, and scikit-learn, eliminating concerns over software compatibility. Additionally, you have the flexibility to incorporate Cloud GPU and Cloud TPU support effortlessly. The Deep Learning VM Image is designed to support both the latest and most widely used machine learning frameworks, ensuring you have access to cutting-edge tools like TensorFlow and PyTorch. To enhance the speed of your model training and deployment, these images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers, as well as the Intel® Math Kernel Library. By using this service, you can hit the ground running with all necessary frameworks, libraries, and drivers pre-installed and validated for compatibility. Furthermore, the Deep Learning VM Image provides a smooth notebook experience through its integrated support for JupyterLab, facilitating an efficient workflow for your data science tasks. This combination of features makes it an ideal solution for both beginners and experienced practitioners in the field of machine learning.

MLReef allows domain specialists and data scientists to collaborate securely through a blend of coding and no-coding methods. This results in a remarkable 75% boost in productivity, as teams can distribute workloads more effectively. Consequently, organizations are able to expedite the completion of numerous machine learning projects. By facilitating collaboration on a unified platform, MLReef eliminates all unnecessary back-and-forth communication. The system operates on your premises, ensuring complete reproducibility and continuity of work, allowing for easy rebuilding whenever needed. It also integrates with established git repositories, enabling the creation of AI modules that are not only explorative but also versioned and interoperable. The AI modules developed by your team can be transformed into user-friendly drag-and-drop components that are customizable and easily managed within your organization. Moreover, handling data often necessitates specialized expertise that a single data scientist might not possess, making MLReef an invaluable asset by empowering field experts to take on data processing tasks, which simplifies complexities and enhances overall workflow efficiency. This collaborative environment ensures that all team members can contribute to the process effectively, further amplifying the benefits of shared knowledge and skill sets.

Enhance your product's capabilities with artificial intelligence. Our platform provides an extensive range of ready-to-use models along with an intuitive no-code interface for creating custom models. Effortlessly integrate AI into your applications for a distinct competitive advantage. Sentiment analysis, often referred to as opinion mining, involves the extraction of subjective insights from textual data, including customer reviews, social media interactions, and feedback, categorizing these insights as positive, negative, or neutral. The significance of this technology has surged in recent years, with a growing number of businesses leveraging it to comprehend customer sentiments and requirements, ultimately leading to data-driven decisions that can refine their offerings and marketing approaches. By employing sentiment analysis, organizations can gain valuable insights into customer feedback, enabling them to enhance their products, services, and promotional strategies effectively. This advancement not only aids in improving customer satisfaction but also fosters innovation within the company.

Originally created by Uber, Horovod aims to simplify and accelerate the process of distributed deep learning, significantly reducing model training durations from several days or weeks to mere hours or even minutes. By utilizing Horovod, users can effortlessly scale their existing training scripts to leverage the power of hundreds of GPUs with just a few lines of Python code. It offers flexibility for deployment, as it can be installed on local servers or seamlessly operated in various cloud environments such as AWS, Azure, and Databricks. In addition, Horovod is compatible with Apache Spark, allowing a cohesive integration of data processing and model training into one streamlined pipeline. Once set up, the infrastructure provided by Horovod supports model training across any framework, facilitating easy transitions between TensorFlow, PyTorch, MXNet, and potential future frameworks as the landscape of machine learning technologies continues to progress. This adaptability ensures that users can keep pace with the rapid advancements in the field without being locked into a single technology.

In the past, tasks such as deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take several days or even weeks to complete. Thanks to GPUonCLOUD’s specialized GPU servers, these processes can now be accomplished in just a few hours. You can choose from a range of pre-configured systems or ready-to-use instances equipped with GPUs that support popular deep learning frameworks like TensorFlow, PyTorch, MXNet, and TensorRT, along with libraries such as the real-time computer vision library OpenCV, all of which enhance your AI/ML model-building journey. Among the diverse selection of GPUs available, certain servers are particularly well-suited for graphics-intensive tasks and multiplayer accelerated gaming experiences. Furthermore, instant jumpstart frameworks significantly boost the speed and flexibility of the AI/ML environment while ensuring effective and efficient management of the entire lifecycle. This advancement not only streamlines workflows but also empowers users to innovate at an unprecedented pace.

Amazon EC2 Inf1 instances are specifically engineered to provide efficient and high-performance machine learning inference at a lower cost. These instances can achieve throughput levels that are 2.3 times higher and costs per inference that are 70% lower than those of other Amazon EC2 offerings. Equipped with up to 16 AWS Inferentia chips—dedicated ML inference accelerators developed by AWS—Inf1 instances also include 2nd generation Intel Xeon Scalable processors, facilitating up to 100 Gbps networking bandwidth which is essential for large-scale machine learning applications. They are particularly well-suited for a range of applications, including search engines, recommendation systems, computer vision tasks, speech recognition, natural language processing, personalization features, and fraud detection mechanisms. Additionally, developers can utilize the AWS Neuron SDK to deploy their machine learning models on Inf1 instances, which supports integration with widely-used machine learning frameworks such as TensorFlow, PyTorch, and Apache MXNet, thus enabling a smooth transition with minimal alterations to existing code. This combination of advanced hardware and software capabilities positions Inf1 instances as a powerful choice for organizations looking to optimize their machine learning workloads.

Amazon's EC2 P4d instances offer exceptional capabilities for machine learning training and high-performance computing tasks within the cloud environment. Equipped with NVIDIA A100 Tensor Core GPUs, these instances achieve remarkable throughput and feature low-latency networking, supporting an impressive 400 Gbps instance networking speed. P4d instances present a cost-effective solution, providing up to 60% savings in the training of ML models, along with an average performance increase of 2.5 times for deep learning applications when compared to earlier P3 and P3dn models. They are utilized in expansive clusters known as Amazon EC2 UltraClusters, which seamlessly integrate high-performance computing, networking, and storage. This allows users the flexibility to scale from a handful to thousands of NVIDIA A100 GPUs, depending on their specific project requirements. A wide array of professionals, including researchers, data scientists, and developers, can leverage P4d instances for various machine learning applications such as natural language processing, object detection and classification, and recommendation systems, in addition to executing high-performance computing tasks like drug discovery and other complex analyses. The combination of performance and scalability makes P4d instances a powerful choice for tackling diverse computational challenges.

The AWS Marketplace serves as a carefully curated online platform that allows users to explore, acquire, implement, and oversee third-party software, data products, and services seamlessly within the AWS environment. It features a vast array of listings spanning various categories, including security, machine learning, enterprise applications, and DevOps tools. By offering diverse pricing options like pay-as-you-go, yearly subscriptions, and free trial periods, AWS Marketplace enhances the purchasing and billing process by consolidating expenses into a unified AWS invoice. Furthermore, it facilitates swift deployment through pre-configured software that can be readily activated on AWS infrastructure. This efficient method not only helps organizations to speed up innovation and minimize time-to-market but also empowers them to exercise greater oversight over software utilization and associated costs. As a result, businesses can focus more on strategic initiatives rather than operational hurdles.

Enhance machine learning models by capturing training metrics in real-time and generating alerts for any anomalies that arise. To minimize both time and costs associated with training, the process can be halted automatically once the target accuracy is reached. Furthermore, it is essential to continuously profile and monitor system resource usage, issuing alerts when any resource constraints are recognized to optimize resource efficiency. With Amazon SageMaker Debugger, troubleshooting during the training phase can be significantly expedited, transforming a process that typically takes days into one that lasts mere minutes by automatically identifying and notifying users about common training issues such as extreme gradient values. Alerts generated can be accessed via Amazon SageMaker Studio or set up through Amazon CloudWatch. Moreover, the SageMaker Debugger SDK is designed to autonomously identify novel categories of model-specific errors, including issues related to data sampling, hyperparameter settings, and values that exceed acceptable limits, which further enhances the robustness of your ML models. This proactive approach not only saves time but also ensures that the models are consistently performing at their best.

Amazon SageMaker equips users with all necessary tools and libraries to create machine learning models, allowing for an iterative approach in testing various algorithms and assessing their effectiveness to determine the optimal fit for specific applications. Within Amazon SageMaker, users can select from more than 15 built-in algorithms that are optimized for the platform, in addition to accessing over 150 pre-trained models from well-known model repositories with just a few clicks. The platform also includes a range of model-development resources such as Amazon SageMaker Studio Notebooks and RStudio, which facilitate small-scale experimentation to evaluate results and analyze performance data, ultimately leading to the creation of robust prototypes. By utilizing Amazon SageMaker Studio Notebooks, teams can accelerate the model-building process and enhance collaboration among members. These notebooks feature one-click access to Jupyter notebooks, allowing users to begin their work almost instantly. Furthermore, Amazon SageMaker simplifies the sharing of notebooks with just one click, promoting seamless collaboration and knowledge exchange among users. Overall, these features make Amazon SageMaker a powerful tool for anyone looking to develop effective machine learning solutions.

Amazon Elastic Inference offers a cost-effective way to enhance Amazon EC2 and SageMaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference expenses by as much as 75%. It seamlessly supports models built with TensorFlow, Apache MXNet, PyTorch, and ONNX. Inference involves predicting outcomes based on a model that has already been trained. Notably, in the realm of deep learning, inference can account for up to 90% of total operational costs due to two main factors. The first factor is that dedicated GPU instances are primarily optimized for training rather than inference; training typically involves processing numerous data samples concurrently, whereas inference often handles one input at a time in real time, leading to minimal GPU resource usage. Consequently, this results in an inefficient cost structure for standalone GPU inference. Conversely, standalone CPU instances lack the necessary specialization for matrix operations and therefore tend to be inadequate for the speed requirements of deep learning inference. By integrating Elastic Inference, users can strike a balance between performance and cost, ensuring that their inference workloads are handled more efficiently.

The Elastic Fabric Adapter (EFA) serves as a specialized network interface for Amazon EC2 instances, designed to support applications that necessitate significant inter-node communication when deployed at scale on AWS. Its unique operating system (OS) effectively circumvents traditional hardware interfaces, significantly improving the efficiency of communications between instances, which is essential for the scalability of these applications. EFA allows High-Performance Computing (HPC) applications utilizing the Message Passing Interface (MPI) and Machine Learning (ML) applications leveraging the NVIDIA Collective Communications Library (NCCL) to seamlessly expand to thousands of CPUs or GPUs. Consequently, users can experience the performance levels of traditional on-premises HPC clusters while benefiting from the flexible and on-demand nature of the AWS cloud environment. This feature is available as an optional enhancement for EC2 networking, and can be activated on any compatible EC2 instance without incurring extra charges. Additionally, EFA integrates effortlessly with most widely-used interfaces, APIs, and libraries for facilitating inter-node communications, making it a versatile choice for developers. The ability to scale applications while maintaining high performance is crucial in today’s data-driven landscape.