Alternatives to DeepCube

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.

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.

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.

Exafunction optimizes deep learning inference workloads, up to a 10% improvement in resource utilization and cost. Instead of worrying about cluster management and fine-tuning performance, focus on building your deep-learning application. Poor utilization of GPU hardware is a common problem in deep learning applications. Exafunction allows any GPU code to be moved to remote resources. This includes spot instances. Your core logic is still an inexpensive CPU instance. Exafunction has been proven to be effective in large-scale autonomous vehicle simulation. These workloads require complex custom models, high numerical reproducibility, and thousands of GPUs simultaneously. Exafunction supports models of major deep learning frameworks. Versioning models and dependencies, such as custom operators, allows you to be certain you are getting the correct results.

Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.

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.

ThirdAI (pronunciation is /TH@rdi/ Third eye), is an Artificial Intelligence startup that specializes in scalable and sustainable AI. ThirdAI accelerator develops hash-based processing algorithms to train and infer with neural networks. This technology is the result of 10 years' worth of innovation in deep learning mathematics. Our algorithmic innovation has shown that Commodity x86 CPUs can be made 15x faster than the most powerful NVIDIA GPUs to train large neural networks. This demonstration has reaffirmed the belief that GPUs are superior to CPUs when it comes to training neural networks. Our innovation will not only benefit AI training currently by switching to cheaper CPUs but also allow for the "unlocking” of AI training workloads on GPUs previously not possible.

Deep learning frameworks like TensorFlow and PyTorch, Torch and Torch, Theano and MXNet have helped to increase the popularity of deep-learning by reducing the time and skills required to design, train and use deep learning models. Fabric for Deep Learning (pronounced "fiddle") is a consistent way of running these deep-learning frameworks on Kubernetes. FfDL uses microservices architecture to reduce the coupling between components. It isolates component failures and keeps each component as simple and stateless as possible. Each component can be developed, tested and deployed independently. FfDL leverages the power of Kubernetes to provide a resilient, scalable and fault-tolerant deep learning framework. The platform employs a distribution and orchestration layer to allow for learning from large amounts of data in a reasonable time across multiple compute nodes.

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.

We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services.

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.

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.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

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.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

D-I-Y can be used in certain situations, such as weed control. It's a different story to build your AI infrastructure. ONTAP AI consolidates the data center's worth in analytics, training, inference computation, and training into one, 5-petaflop AI system. NetApp ONTAP AI is powered by NVIDIA's DGX™, and NetApp's cloud-connected all flash storage. This allows you to fully realize the promise and potential of deep learning (DL). With the proven ONTAP AI architecture, you can simplify, accelerate and integrate your data pipeline. Your data fabric, which spans from the edge to the core to the cloud, will streamline data flow and improve analytics, training, inference, and performance. NetApp ONTAPAI is the first converged infrastructure platform to include NVIDIA DGX A100 (the world's first 5-petaflop AIO system) and NVIDIA Mellanox®, high-performance Ethernet switches. You get unified AI workloads and simplified deployment.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

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.

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.

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.

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.

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.

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.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

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.

This tool is a model quantization tool to convolution neural networks (CNN). This tool can quantify both weights/biases as well as activations in 32-bit floating point (FP32) and 8-bit integer (INT8) formats, or any other bit depths. This tool can increase the inference performance and efficiency by ensuring accuracy. This tool supports all common layers in neural networks: convolution, pooling and fully-connected. It also supports batch normalization. Quantization tools do not require retraining the network or labeled data sets. Only one batch of photos is required. The process takes a few seconds to several hours depending on the size and complexity of the neural network. This allows for rapid model updates. This tool is collaboratively optimized for DeePhi DPU. It could generate INT8 format model file files required by DNNC.

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.

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.

Zyphra, an artificial intelligence company with offices in Palo Alto and Montreal, is growing in London. We're developing MaiaOS, an agent system that combines advanced research in next-gen neuronal network architectures (SSM-hybrids), long-term memories & reinforcement learning. We believe that the future of AGI is a combination of cloud-based and on-device strategies, with an increasing shift towards local inference. MaiaOS was built around a deployment platform that maximizes the efficiency of inference for real-time Intelligence. Our AI and product teams are drawn from top organizations and institutions, including Google DeepMind and Anthropic. They also come from Qualcomm, Neuralink and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. The Zyphra team is dedicated to democratizing advanced artificial intelligence systems.

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.

DeepPy is a MIT licensed deep-learning framework. DeepPy is an attempt to bring a little zen to deep-learning. DeepPy uses CUDArray to perform most of its calculations. You must first install CUDArray. You can install CUDArray without the CUDA Back-end, which simplifies the installation process.

The Intel Distribution of OpenVINO makes it easy to adopt and maintain your code. Open Model Zoo offers optimized, pre-trained models. Model Optimizer API parameters make conversions easier and prepare them for inferencing. The runtime (inference engines) allows you tune for performance by compiling an optimized network and managing inference operations across specific devices. It auto-optimizes by device discovery, load balancencing, inferencing parallelism across CPU and GPU, and many other functions. You can deploy the same application to multiple host processors and accelerators (CPUs. GPUs. VPUs.) and environments (on-premise or in the browser).

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

NVIDIA TensorRT provides an ecosystem of APIs to support high-performance deep learning. It includes an inference runtime, model optimizations and a model optimizer that delivers low latency and high performance for production applications. TensorRT, built on the CUDA parallel programing model, optimizes neural networks trained on all major frameworks. It calibrates them for lower precision while maintaining high accuracy and deploys them across hyperscale data centres, workstations and laptops. It uses techniques such as layer and tensor-fusion, kernel tuning, and quantization on all types NVIDIA GPUs from edge devices to data centers. TensorRT is an open-source library that optimizes the inference performance for large language models.

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.

A system that can scale to millions of users, without requiring an engineering team. Create, customize and deploy your chat-based AI. Finance chatbot. xFinance is a 13-billion-parameter model fine-tuned using LoRA. Our goal was show that impressive results can be achieved in financial NLP without breaking the bank. Your own AI assistant to chat with documents. Single or multiple documents. Simple or complex questions. Easy-to-use deep learning platform, hardware efficient algorithms that speed up inference and lower costs. Real-time monitoring and logging of resource usage and cloud costs for deployed models. xTuring, an open-source AI software for personalization, is a powerful tool. xTuring provides a simple interface for personalizing LLMs based on your data and application.

You can build and scale your computer vision model quickly and easily. NeuCore makes it easy to develop, train, and deploy your computer vision model in just minutes. You can scale it up to millions of times. One-stop platform that manages all aspects of the model lifecycle including training, development, deployment, maintenance, and maintenance. Advanced data encryption is used to protect your information throughout the entire process, from training to inference. Fully integrated vision AI models can be easily integrated into existing systems and workflows, or even onto edge devices. Seamless scaling allows for your evolving business needs and business requirements. Splits an image into sections that contain different objects. Machine-readable text extracted from images. This model can also be used to read handwriting. NeuCore makes it easy to build computer vision models. It's as simple as one-click and drag-and-drop. Advanced users can access code scripts and watch tutorial videos to customize the software.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

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.

AI is at your disposal to predict the success of your images or visual campaigns. People are exposed to a lot of information and images today. Brands must make a mark to stand out. It is not enough to increase investment in offline and online advertising. Before launching visual campaigns, it is important to test their effectiveness. Image Memorability will tell you which images are more memorable and powerful. Neosperience Image Memoryability is the tool that will make your brand and product images stand out. Neosperience Image Memorability is a proprietary deep learning model that combines quantitative analysis with qualitative analysis to assess the effectiveness of images for a specific audience segment. In just seconds, you can get quantitative data to objectively assess the memorability of your images and their impact. Find out what areas of the image are most popular and memorable.

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.

The Intel®, Deep Learning SDK is a collection of tools that allows data scientists and software developers alike to create, train, and then deploy deep learning solutions. The SDK includes a training tool as well as a deployment tool. These tools can be used together or separately to create a complete deep-learning workflow. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and conduct experiments. It is easy to install and use popular deep learning frameworks that are optimized for Intel®. You can easily prepare training data, design models, train models with automated experiments, advanced visualizations, and prepare training data. It makes it easier to install and use popular deep learning frameworks that are optimized for Intel®. The web interface features an easy-to-use wizard for creating deep learning models. There are also tooltips to help you navigate the process.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

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.

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

Nscale is a hyperscaler that is engineered for AI. It offers high-performance computing optimized to train, fine-tune, and handle intensive workloads. Vertically integrated across Europe, from our data centers to software stack, to deliver unparalleled performance, efficiency and sustainability. Our AI cloud platform allows you to access thousands of GPUs that are tailored to your needs. A fully integrated platform will help you reduce costs, increase revenue, and run AI workloads more efficiently. Our platform simplifies the journey from development through to production, whether you use Nscale's AI/ML tools built-in or your own. The Nscale Marketplace provides users with access to a variety of AI/ML resources and tools, allowing for efficient and scalable model deployment and development. Serverless allows for seamless, scalable AI without the need to manage any infrastructure. It automatically scales up to meet demand and ensures low latency, cost-effective inference, for popular generative AI model.