Alternatives to MatConvNet

Your deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

Manage unstructured data to develop AI solutions in record time. Enterprise-grade data platform with vision AI. Dataloop offers a single-stop-shop for building and deploying powerful data pipelines for computer vision, data labeling, automation of data operations, customizing production pipelines, and weaving in the human for data validation. Our vision is to make machine-learning-based systems affordable, scalable and accessible for everyone. Explore and analyze large quantities of unstructured information from diverse sources. Use automated preprocessing to find similar data and identify the data you require. Curate, version, cleanse, and route data to where it's required to create exceptional AI apps.

The Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub.

VisionPro Deep Learning is the best deep learning-based image analysis program for factory automation. Its field-tested algorithms have been optimized for machine vision. The graphical user interface makes it easy to train neural networks without sacrificing performance. VisionPro Deep Learning solves complex problems that are too difficult for traditional machine vision. It also provides consistency and speed that can't be achieved with human inspection. Automation engineers can quickly choose the right tool for the job by combining VisionPro's rule-based visual libraries. VisionPro Deep Learning is a combination of a comprehensive machine vision tool collection with advanced deep learning tools within a common development-deployment framework. It makes it easy to develop highly variable vision applications.

Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for building better, faster and stronger AI. We help enterprises and public sector organizations transform their data into actionable insights. Our technology is used across many industries including Defense, Retail, Manufacturing, Media and Entertainment, and more. We help our customers create innovative AI solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been a market leader in computer vision AI since winning the top five places in image classification at the 2013 ImageNet Challenge. Clarifai is headquartered in Delaware

Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling.

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

SKY ENGINE AI is a simulation and deep learning platform that generates fully annotated, synthetic data and trains AI computer vision algorithms at scale. The platform is architected to procedurally generate highly balanced imagery data of photorealistic environments and objects and provides advanced domain adaptation algorithms. SKY ENGINE AI platform is a tool for developers: Data Scientists, ML/Software Engineers creating computer vision projects in any industry. SKY ENGINE AI is a Deep Learning environment for AI training in Virtual Reality with Sensors Physics Simulation & Fusion for any Computer Vision applications.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

Reliable and adaptable computer vision systems that can be used in any factory. Every step of manufacturing is integrated with AI and image capture. Overview's inspection systems use deep learning technology, which allows us find errors more consistently in a wider range of situations. Remote access and support for enhanced traceability. Our solutions provide a visual record that can be traced back to every unit. It is easy to identify the root cause for production problems or quality issues. Overview can help you eliminate waste from your manufacturing operations, whether you're digitizing your inspections or have an underperforming vision system. See how the Snap platform can improve your factory efficiency. Deep learning automated inspection solutions dramatically improve defect detection. Superior yields, improved traceability, easier setup, and outstanding support.

Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes.

DataMelt, or "DMelt", is an environment for numeric computations, data analysis, data mining and computational statistics. DataMelt allows you to plot functions and data in 2D or 3D, perform statistical testing, data mining, data analysis, numeric computations and function minimization. It also solves systems of linear and differential equations. There are also options for symbolic, non-linear, and linear regression. Java API integrates neural networks and data-manipulation techniques using various data-manipulation algorithms. Support is provided for elements of symbolic computations using Octave/Matlab programming. DataMelt provides a Java platform-based computational environment. It can be used on different operating systems and programming languages. It is not limited to one programming language, unlike other statistical programs. This software combines Java, the most widely used enterprise language in the world, with the most popular data science scripting languages, Jython (Python), Groovy and JRuby.

Neuralhub is an AI system that simplifies the creation, experimentation, and innovation of neural networks. It helps AI enthusiasts, researchers, engineers, and other AI professionals. Our mission goes beyond just providing tools. We're creating a community where people can share and collaborate. We want to simplify deep learning by bringing together all the tools, models, and research into a collaborative space. This will make AI research, development, and learning more accessible. Create a neural network by starting from scratch, or use our library to experiment and create something new. Construct your neural networks with just one click. Visualize and interact with each component of the network. Tune hyperparameters like epochs and features, labels, and more.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

We conduct cutting-edge research in artificial intelligence and implement it to give financial investors an advantage. Transforming the financial market through groundbreaking neuro-prediction. Our algorithms combine graph-based learning and deep reinforcement learning algorithms to model and predict time series. Neuri aims to generate synthetic data that mimics the global financial markets and test it with complex simulations. Quantum optimization is the future of supercomputing. Our simulations will be able to exceed the limits of classical supercomputing. Financial markets are dynamic and change over time. We develop AI algorithms that learn and adapt continuously to discover the connections between different financial assets, classes, and markets. The application of neuroscience-inspired models, quantum algorithms and machine learning to systematic trading at this point is underexplored.

ConvNetJS is a Javascript library that allows you to train deep learning models (neural network) in your browser. You can train by simply opening a tab. No software requirements, no compilers, no installations, no GPUs, no sweat. The library was originally created by @karpathy and allows you to create and solve neural networks using Javascript. The library has been greatly expanded by the community, and new contributions are welcome. If you don't want to develop, this link to convnet.min.js will allow you to download the library as a plug-and play. You can also download the latest version of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create an index.html file with no content and copy build/convnet.min.js to that folder.

DATAGYM allows data scientists and machine-learning experts to label images up 10x faster than before. AI-assisted annotators reduce manual labeling, give you more time for fine tuning ML models, and speed up your product launch. Reduce data preparation time by up to half and accelerate your computer vision projects

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

Abacus.AI is the first global end-to-end autonomous AI platform. It enables real-time deep-learning at scale for common enterprise use cases. Our innovative neural architecture search methods allow you to create custom deep learning models and then deploy them on our end-to-end DLOps platform. Our AI engine will increase user engagement by at least 30% through personalized recommendations. Our recommendations are tailored to each user's preferences, which leads to more interaction and conversions. Don't waste your time dealing with data issues. We will automatically set up your data pipelines and retrain the models. To generate recommendations, we use generative modeling. This means that even if you have very little information about a user/item, you won't have a cold start.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

Our platforms are a solid foundation for custom machine learning and artificial Intelligence solutions. Build next-best-action applications that learn, adapt, and optimize using reinforcement-learning based algorithms. Custom, continuously-improving deep learning vision models to solve your unique challenges. Forecasts that are up-to-date will help you predict the future. Cloud-based tools that monitor and analyze cloud data will help you make better decisions for your company. Experienced data scientists and engineers face a challenge in transforming a machine learning application from research and ad hoc code to a robust, scalable platform. With a comprehensive suite of tools to manage and deploy your machine learning applications, Strong ML makes this easier.

We work closely with companies to identify the issues that prevent them from realising their potential. We create AI platforms that allow corporations to take full control and empowerment of their data. Adopting AI platforms at a reasonable cost will help you to protect your data and ensure that your privacy is protected. Enhance efficiency in businesses and increase the quality of the work done by humans. AI is used to automate repetitive or dangerous tasks. It also bypasses human intervention. This allows for faster tasks that are creative and compassionate. Machine Learning allows applications to easily provide predictive capabilities. It can create regression and classification models. It can also visualize and do clustering. It supports multiple ML libraries, including Scikit-Learn and Tensorflow. It contains around 22 algorithms for building classifications, regressions and clustering models.

Interplay Platform is a patented low-code platform with 475 pre-built Enterprises, AI, IoT drag-and-drop components. Interplay helps large organizations innovate faster. It's used as middleware and as a rapid app building platform by big companies like Circle K, Ulta Beauty, and many others. As middleware, it operates Pay-by-Plate (frictionless payments at the gas pump) in Europe, Weapons Detection (to predict robberies), AI-based Chat, online personalization tools, low price guarantee tools, computer vision applications such as damage estimation, and much more.

Alfi, Inc. engages in creating interactive digital out-of-home advertising experiences. Alfi uses artificial intelligence and computer vision in order to better serve ads. Alfi's Ai algorithm, which is proprietary to the company, can detect subtle facial cues and perceptual details in order to determine if potential customers are a good candidate for a product. The automation is completely anonymous and does not track, store cookies or use identifiable personal information. Ad agencies can access real-time analytics data, including interactive experiences, engagement, sentiment and click-through rates that are otherwise unavailable for out-of-home advertisers. Alfi, powered AI and machine learning, collects data that allows for better analytics and relevant content to improve the consumer experience.

Amazon EC2 instances P4d deliver high performance in cloud computing for machine learning applications and high-performance computing. They offer 400 Gbps networking and are powered by NVIDIA Tensor Core GPUs. P4d instances offer up to 60% less cost for training ML models. They also provide 2.5x better performance compared to the previous generation P3 and P3dn instance. P4d instances are deployed in Amazon EC2 UltraClusters which combine high-performance computing with networking and storage. Users can scale from a few NVIDIA GPUs to thousands, depending on their project requirements. Researchers, data scientists and developers can use P4d instances to build ML models to be used in a variety of applications, including natural language processing, object classification and detection, recommendation engines, and HPC applications.

DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers.

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.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Infosys Nia™, an enterprise-grade AI platform, simplifies the AI adoption process for IT and Business. Infosys Nia supports the entire enterprise AI journey, from data management, digitization and image capture, model development, and operationalization. Nia's modular, scalable and advanced capabilities meet enterprise needs. Nia Data provides highly efficient tools and frameworks to support further ML experimentation using the Nia AML Workbench. The Nia DocAI platform automates all aspects of document processing, from ingestion to consumption. It uses AI capabilities like InfoExtractor and NLP, cognitive search, and computer vision.

Amazon Elastic Compute Cloud's (Amazon EC2) instances P5 powered by NVIDIA Tensor core GPUs and P5e or P5en instances powered NVIDIA Tensor core GPUs provide the best performance in Amazon EC2 when it comes to deep learning and high-performance applications. They can help you accelerate the time to solution up to four times compared to older GPU-based EC2 instance generation, and reduce costs to train ML models up to forty percent. These instances allow you to iterate faster on your solutions and get them to market quicker. You can use P5,P5e,and P5en instances to train and deploy increasingly complex large language and diffusion models that power the most demanding generative artificial intelligent applications. These applications include speech recognition, video and image creation, code generation and question answering. These instances can be used to deploy HPC applications for pharmaceutical discovery.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

SynapseAI, like our accelerator hardware, is designed to optimize deep learning performance and efficiency, but most importantly, for developers, it is also easy to use. SynapseAI's goal is to make it easier and faster for developers by supporting popular frameworks and model. SynapseAI, with its tools and support, is designed to meet deep-learning developers where they are -- allowing them to develop what and in the way they want. Habana-based processors for deep learning preserve software investments and make it simple to build new models. This is true both for training and deployment.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

The definitive system for modern technical computing. Mathematica is the global standard for technical computing. It has been the main computing environment for millions of students, educators, and innovators around the globe for over three decades. Mathematica is widely admired for its technical prowess as well as its elegant ease-of-use. It seamlessly integrates all aspects of technical computing and is available in the cloud via any web browser as well as natively on any modern desktop system. Mathematica is a pioneer in technical computing support and workflows, thanks to its energetic development and consistent vision over three decades.

We make it easy for you to add superhuman computer vision into your applications, devices, and processes to give yourself an unassailable competitive edge.

The Peltarion Platform, a low-code deep-learning platform that allows you build AI-powered solutions at speed and scale, is called the Peltarion Platform. The platform allows you build, tweak, fine-tune, and deploy deep learning models. It's end-to-end and allows you to do everything, from uploading data to building models and putting them in production. The Peltarion Platform, along with its predecessor, have been used to solve problems at NASA, Dell, Microsoft, and Harvard. You can either create your own AI models, or you can use our pre-trained ones. Drag and drop even the most advanced models! You can manage the entire development process, from building, training, tweaking, and finally deploying AI. All this under one roof. Our platform helps you to operationalize AI and drive business value. Our Faster AI course was created for those with no previous knowledge of AI. After completing seven modules, users will have the ability to create and modify their own AI models using the Peltarion platform.

Build and deploy deep-learning models for custom use scenarios. Our automated machine-learning process allows customers create powerful AI solutions based on our best-in class models and tailored to their specific challenges. Digital platforms can quickly create custom models that fit their guidelines and requirements. Build large language models to support specialized use cases, such as bots for customer and technical service. Create image classification models for better understanding image libraries, including search, organization and more.

Deci's deep learning platform powered by Neural architecture Search allows you to quickly build, optimize, deploy, and deploy accurate models. You can instantly achieve accuracy and runtime performance that is superior to SoTA models in any use case or inference hardware. Automated tools make it easier to reach production. No more endless iterations or dozens of libraries. Allow new use cases for resource-constrained devices and cut down on your cloud computing costs by up to 80% Deci's NAS-based AutoNAC engine automatically finds the most appropriate architectures for your application, hardware, and performance goals. Automately compile and quantify your models using the best of breed compilers. Also, quickly evaluate different production settings.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

FortressIQ is the industry's most advanced process-intelligence platform. It allows enterprises to decode work and transform experiences. FortressIQ combines innovative computer vision with artificial intelligence to provide unprecedented process insights. It is extremely fast and delivers detail and accuracy that are unattainable using traditional methods. The platform automatically acquires process data across multiple systems. This empowers enterprises to understand, monitor and improve their operations, employee and customer experience, and every business process. FortressIQ was established in 2017 and is supported by Lightspeed Venture Partners and Boldstart Ventures as well as Comcast Ventures and Eniac Ventures. Continuously and automatically identify inefficiencies and process variations to determine optimal process paths and reduce time to automate.

Winnow Vision is the most advanced food waste technology available. Winnow Vision uses AI to maximize operational efficiency and data accuracy. This makes it easy to reduce food waste. Join hundreds of kitchens around the world to reduce their costs by as much as 8% per year. Commercial kitchens are finding it harder to increase profitability due to rising food costs. We have found that reducing food waste, by connecting the kitchen and technology, is the fastest way for companies to increase their margins. After just 90 days, Winnow customers have seen a remarkable 28% drop in food costs. Winnow's two food-waste tools - one with cutting-edge AI and the other beloved by more than 1,000 kitchens worldwide - can be tailored to different kitchen needs.

Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects. Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython. Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).

Autogon is an AI and machine-learning company that simplifies complex technologies to empower businesses and provide them with cutting-edge, accessible solutions for data-driven decision-making and global competitiveness. Discover the potential of Autogon's models to empower industries and harness the power of AI. They can foster innovation and drive growth in diverse sectors. Autogon Qore is your all-in one solution for image classification and text generation, visual Q&As, sentiment analysis, voice-cloning and more. Innovative AI capabilities will empower your business. You can make informed decisions, streamline your operations and drive growth with minimal technical expertise. Empower engineers, analysts and scientists to harness artificial intelligence and machine-learning for their projects and researchers. Create custom software with clear APIs and integrations SDKs.

The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

Virtualization Software for AI Infrastructure. Increase GPU utilization by having visibility and control over AI workloads. Run:AI has created the first virtualization layer in the world for deep learning training models. Run:AI abstracts workloads from the underlying infrastructure and creates a pool of resources that can dynamically provisioned. This allows for full utilization of costly GPU resources. You can control the allocation of costly GPU resources. The scheduling mechanism in Run:AI allows IT to manage, prioritize and align data science computing requirements with business goals. IT has full control over GPU utilization thanks to Run:AI's advanced monitoring tools and queueing mechanisms. IT leaders can visualize their entire infrastructure capacity and utilization across sites by creating a flexible virtual pool of compute resources.