Best Deep Learning Software - Page 3
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Compare the Top Deep Learning Software as of May 2025 - Page 3
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions. -
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Neuri
Neuri
We conduct and implement cutting-edge research on artificial intelligence to create real advantage in financial investment. Illuminating the financial market with ground-breaking neuro-prediction. We combine novel deep reinforcement learning algorithms and graph-based learning with artificial neural networks for modeling and predicting time series. Neuri strives to generate synthetic data emulating the global financial markets, testing it with complex simulations of trading behavior. We bet on the future of quantum optimization in enabling our simulations to surpass the limits of classical supercomputing. Financial markets are highly fluid, with dynamics evolving over time. As such we build AI algorithms that adapt and learn continuously, in order to uncover 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. -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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IntelliHub
Spotflock
We work closely with businesses to find out what are the common issues preventing companies from realising benefits. We design to open up opportunities that were previously not viable using conventional approaches Corporations -big and small, require an AI platform with complete empowerment and ownership. Tackle data privacy and adopt to AI platforms at a sustainable cost. Enhance the efficiency of businesses and augment the work humans do. We apply AI to gain control over repetitive or dangerous tasks and bypass human intervention, thereby expediting tasks with creativity and empathy. Machine Learning helps to give predictive capabilities to applications with ease. You can build classification and regression models. It can also do clustering and visualize different clusters. It supports multiple ML libraries like Weka, Scikit-Learn, H2O and Tensorflow. It includes around 22 different algorithms for building classification, regression and clustering models. -
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DeepPy
DeepPy
DeepPy is a MIT licensed deep learning framework. DeepPy tries to add a touch of zen to deep learning as it. DeepPy relies on CUDArray for most of its calculations. Therefore, you must first install CUDArray. Note that you can choose to install CUDArray without the CUDA back-end which simplifies the installation process. -
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DreamQuark Brain
DreamQuark
AI can be slow, confusing, and costly. Brain empowers wealth managers to make hyper-personalized insights simply and quickly. Serve your clients better and grow smarter with Brain. Turn your data into user-friendly insights in a few clicks to guide your next best action. Brain’s explainable AI gives advisors the reasons behind every recommendation. Use Brain’s CX application or integrate it on your own CX platform and cloud provider. Increase your revenues by predicting which clients will respond best to cross-sell and upsell opportunities. Improve the performance of your campaigns by identifying which clients are likely to take an interest in a product and why. Retain your clients before it’s too late by quickly discovering who is most likely to leave and why. Brain’s explainable AI makes hyper-personalized insights understandable and easier for advisors to act on. Brain simplifies and automates the creation and maintenance of insights, saving you time and money. -
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Exafunction
Exafunction
Exafunction optimizes your deep learning inference workload, delivering up to a 10x improvement in resource utilization and cost. Focus on building your deep learning application, not on managing clusters and fine-tuning performance. In most deep learning applications, CPU, I/O, and network bottlenecks lead to poor utilization of GPU hardware. Exafunction moves any GPU code to highly utilized remote resources, even spot instances. Your core logic remains an inexpensive CPU instance. Exafunction is battle-tested on applications like large-scale autonomous vehicle simulation. These workloads have complex custom models, require numerical reproducibility, and use thousands of GPUs concurrently. Exafunction supports models from major deep learning frameworks and inference runtimes. Models and dependencies like custom operators are versioned so you can always be confident you’re getting the right results. -
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Deci
Deci AI
Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. -
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Speech2Structure
Averbis
When treating a patient, doctors spend on average two-thirds of their time documenting the treatment and far less time on examinations or patient interviews. To allow doctors to spend more time with their patients, Averbis is working on Speech2Structure – a software solution where the documentation is recorded live by voice and structured on-the-fly. Speech2Structure can correctly recognize and resolve many linguistic variations such as negations, suspected diagnoses, diagnoses that have taken place, etc. when recognizing diagnoses. Pathological laboratory values or microbiology results are also converted into corresponding diagnoses. The recorded medications can also provide clues to diagnoses. -
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Overview
Overview
Reliable, adaptable computer vision systems for any factory. AI and image capture are integrated into every step of manufacturing. Overview’s inspection systems are built with deep learning technology which allows us to find mistakes more consistently and in a wider variety of situations. Enhanced traceability with remote access and support. Our solutions create a traceable visual record of every unit. You can quickly identify the root cause of production problems and quality issues. Whether you are just digitizing your inspection or have an existing vision system that is underperforming, Overview has a solution that can drive waste out of your manufacturing operations. Demo the Snap platform to see how we improve your factory efficiency. Deep learning automated inspection solutions radically improve defect detection. Improved yields, better traceability, easy setup, and outstanding support. -
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CerebrumX AI Powered Connected Vehicle Data Platform - ADLP is the industry’s first AI-driven Augmented Deep Learning Connected Vehicle Data Platform that collects & homogenizes this vehicle data from millions of vehicles, in real-time, and enriches it with augmented data to generate deep & contextual insights. ADLP provides a plug-in to manage and maintain Data Privacy, Anonymization and Consent Management at the source, to ensure that any personal information is treated based on the user consent. CerebrumX takes pride in bringing privacy to everything it does, going beyond just compliance with its white-label app and web solution.
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AWS Inferentia
Amazon
AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. The first-generation Inferentia has 8 GB of DDR4 memory per accelerator and also features a large amount of on-chip memory. Inferentia2 offers 32 GB of HBM2e per accelerator, increasing the total memory by 4x and memory bandwidth by 10x over Inferentia. -
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AWS Deep Learning AMIs
Amazon
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data. -
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AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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SynapseAI
Habana Labs
Like our accelerator hardware, was purpose-designed to optimize deep learning performance, efficiency, and most importantly for developers, ease of use. With support for popular frameworks and models, the goal of SynapseAI is to facilitate ease and speed for developers, using the code and tools they use regularly and prefer. In essence, SynapseAI and its many tools and support are designed to meet deep learning developers where you are — enabling you to develop what and how you want. Habana-based deep learning processors, preserve software investments, and make it easy to build new models— for both training and deployment of the numerous and growing models defining deep learning, generative AI and large language models. -
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Determined AI
Determined AI
Distributed training without changing your model code, determined takes care of provisioning machines, networking, data loading, and fault tolerance. Our open source deep learning platform enables you to train models in hours and minutes, not days and weeks. Instead of arduous tasks like manual hyperparameter tuning, re-running faulty jobs, and worrying about hardware resources. Our distributed training implementation outperforms the industry standard, requires no code changes, and is fully integrated with our state-of-the-art training platform. With built-in experiment tracking and visualization, Determined records metrics automatically, makes your ML projects reproducible and allows your team to collaborate more easily. Your researchers will be able to build on the progress of their team and innovate in their domain, instead of fretting over errors and infrastructure. -
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Autogon
Autogon
Autogon is a leading AI and machine learning company, that simplifies complex technology to empower businesses with accessible, cutting-edge solutions for data-driven decisions and global competitiveness. Discover the empowering potential of Autogon models as they enable industries to leverage the power of AI, fostering innovation and fueling growth across diverse sectors. Experience the future of AI with Autogon Qore, your all-in-one solution for image classification, text generation, visual Q&A, sentiment analysis, voice cloning, and more. Empower your business with cutting-edge AI capabilities and innovation. Make informed decisions, streamline operations, and drive growth without the need for extensive technical expertise. Empower engineers, analysts, and scientists to harness the full potential of artificial intelligence and machine learning for their projects and research. Create custom software using clear APIs and integration SDKs. -
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Hive AutoML
Hive
Build and deploy deep learning models for custom use cases. Our automated machine learning process allows customers to create powerful AI solutions built on our best-in-class models and tailored to the specific challenges they face. Digital platforms can quickly create models specifically made to fit their guidelines and needs. Build large language models for specialized use cases such as customer and technical support bots. Create image classification models to better understand image libraries for search, organization, and more. -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery. -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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H2O.ai
H2O.ai
H2O.ai is the open source leader in AI and machine learning with a mission to democratize AI for everyone. Our industry-leading enterprise-ready platforms are used by hundreds of thousands of data scientists in over 20,000 organizations globally. We empower every company to be an AI company in financial services, insurance, healthcare, telco, retail, pharmaceutical, and marketing and delivering real value and transforming businesses today. -
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NVIDIA NGC
NVIDIA
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI. -
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VisionPro Deep Learning
Cognex
VisionPro Deep Learning is the best-in-class deep learning-based image analysis software designed for factory automation. Its field-tested algorithms are optimized specifically for machine vision, with a graphical user interface that simplifies neural network training without compromising performance. VisionPro Deep Learning solves complex applications that are too challenging for traditional machine vision alone, while providing a consistency and speed that aren’t possible with human inspection. When combined with VisionPro’s rule-based vision libraries, automation engineers can easily choose the best the tool for the task at hand. VisionPro Deep Learning combines a comprehensive machine vision tool library with advanced deep learning tools inside a common development and deployment framework. It simplifies the development of highly variable vision applications. -
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ConvNetJS
ConvNetJS
ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release 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 a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder. -
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FortressIQ
Automation Anywhere
FortressIQ enables enterprises to decode work, transform experiences, and enhance workflows with the industry’s most advanced process intelligence platform. Using innovative computer vision and artificial intelligence, FortressIQ delivers unprecedented process insights, extremely fast, and with detail and accuracy unattainable with traditional methods. The platform autonomously acquires process data at scale even as processes extend across systems, empowering enterprises to understand, monitor, and improve operations, employee and customer experiences, and every business process. FortressIQ was founded in 2017, and is backed by Lightspeed Venture Partners, Boldstart Ventures, Comcast Ventures, Eniac Ventures, M12 and Tiger Global. Pinpoint inefficiencies and process variations continuously and automatically to reveal optimal process paths and reduce time to automation. -
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Lambda GPU Cloud
Lambda
Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.Starting Price: $1.25 per hour
