Alternatives to PyTorch

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

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.

OpenCV (Open Source Computer Vision Library), is an open-source machine learning and computer vision software library. OpenCV was created to provide a common infrastructure to support computer vision applications and accelerate machine perception in commercial products. OpenCV is a BSD-licensed product that makes it easy to modify and use the code by businesses. The library contains more than 2500 optimized algorithms. This includes a comprehensive set both of classic and modern computer vision and machine-learning algorithms. These algorithms can be used for recognizing faces, identifying objects, tracking camera movements, classifying human actions in videos and producing 3D point clouds from stereo-cameras. They can also be used to stitch images together to create a high resolution image of the entire scene, find similar images from a database, remove red eyes from images taken with flash, recognize scenery, and follow eye movements.

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.

Experience a completely new way to train machine learning models on Mac. Create ML simplifies model training and produces powerful Core ML Core models. Train multiple models with different datasets in one project. Preview the performance of your model using Continuity on your Mac with your iPhone's camera and microphone, or by dropping in sample data. Pause, save, resume and extend your training. Learn interactively how your model performs using test data from your evaluation dataset. Explore key metrics in relation to specific examples, to identify difficult use cases, additional investments in data collection and opportunities to improve model quality. You can improve the performance of model training by using an external graphics processor with your Mac. You can train models on your Mac at lightning speed by utilizing the CPU and GPU. Create ML offers a wide range of model types.

ONNX defines a set of common operators - the building block of machine learning and deeper learning models – and a standard file format that allows AI developers to use their models with a wide range of frameworks, runtimes and compilers. You can use your preferred framework to develop without worrying about downstream implications. ONNX allows you to use the framework of your choice with your inference engine. ONNX simplifies the access to hardware optimizations. Use runtimes and libraries compatible with ONNX to optimize performance across hardware. Our community thrives in our open governance structure that provides transparency and inclusion. We encourage you to participate and contribute.

AutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool.

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.

Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

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.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

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

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).

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.

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.

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 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.

TorchMetrics contains over 90+ PyTorch metrics and an easy-to use API to create custom metrics. Standardized interface to improve reproducibility. It reduces boilerplate. distributed-training compatible. It has been thoroughly tested. Automatic accumulation of batches. Automatic synchronization between multiple devices. TorchMetrics can be used in any PyTorch model or within PyTorch Lightning for additional benefits. Your data will always be on the same device that your metrics. Lightning allows you to log Metric objects directly, which reduces the amount of boilerplate. Like torch.nn's, most metrics can be logged in Lightning with both a class-based or functional version. The functional versions perform the basic operations necessary to calculate each metric. They are simple python functions which take torch.tensors as input and return the corresponding metrics as torch.tensors. Nearly all functional metrics include a class-based counterpart.

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.

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.

Fido is an open-source, lightweight, modular C++ machine-learning library. The library is geared towards embedded electronics and robotics. Fido contains implementations of reinforcement learning methods, genetic algorithms and trainable neural networks. It also includes a full-fledged robot simulator. Fido also includes a human-trainable robot controller system, as described by Truell and Gruenstein. Although the simulator is not available in the latest release, it can still be downloaded to experiment on the simulator branch.

SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library.

Neuton.AI, an automated solution, empowering users to build accurate predictive models and make smart predictions with: Zero code solution Zero need for technical skills Zero need for data science knowledge

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.

NeuroIntelligence, a software application for neural networks, is designed to help experts in data mining, predictive modeling, pattern recognition, and neural network design in solving real-world problems. NeuroIntelligence uses only proven neural net modeling algorithms and techniques. It is easy to use and fast. Visualized architecture search, training and testing of neural networks. Neural network architecture search. Fitness bars. Network training graphs comparison. Training graphs, dataset error and network error, weights distribution, neural network input importance, and errors distribution Testing, actual vs. output graph, scatter plot and response graph, ROC curve and confusion matrix. NeuroIntelligence's interface is optimized to solve data mining and forecasting, classification, and pattern recognition problems. The tool's intuitive GUI and time-saving features make it easy to create a better solution faster.

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.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend.

Zinia's artificial intelligence platform connects key business decision makers and AI. Now you can build trusted AI models without relying on technical teams. This allows you to align AI with business objectives. This breakthrough technology is simplified to make it easier to build AI backwards for your business. Reduces time to implement AI from months to days, increasing revenue by 15-20%. Zinia optimizes business results with human-centered AI. Most AI development in organizations is not aligned with business KPIs. Zinia was created with the goal of democratizing AI for you. Zinia puts cutting-edge ML technology and AI Technology in your hands. Zinia was built by a team of AI experts with over 50 years experience. It is your trusted platform that simplifies complex technology and provides the fastest route from data to business decisions.

Openlayer will accept your data and models. Work with the team to align performance and quality expectations. You can quickly identify the reasons behind failed goals and find a solution. You have all the information you need to diagnose problems. Retrain the model by generating more data that looks similar to the subpopulation. Test new commits in relation to your goals, so that you can ensure a systematic progress without regressions. Compare versions side by side to make informed decisions. Ship with confidence. Save time on engineering by quickly determining what drives model performance. Find the quickest ways to improve your model. Focus on cultivating high quality and representative datasets and knowing the exact data required to boost model performance.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

AIxBlock is an end-to-end blockchain-based platform for AI that harnesses unused computing resources of BTC miners, as well as all global consumer GPUs. Our platform's training method is a hybrid machine learning approach that allows simultaneous training on multiple nodes. We use the DeepSpeed-TED method, a three-dimensional hybrid parallel algorithm which integrates data, tensor and expert parallelism. This allows for the training of Mixture of Experts models (MoE) on base models that are 4 to 8x larger than the current state of the art. The platform will identify and add compatible computing resources from the computing marketplace to the existing cluster of training nodes, and distribute the ML model for unlimited computations. This process unfolds dynamically and automatically, culminating in decentralized supercomputers which facilitate AI success.

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.

With a single command, you can train and serve large AI models in scale. You can simply point to your S3 bucket. We take care of the rest: orchestration, efficiency and node failures. Simple and scalable. MosaicML allows you to train and deploy large AI model on your data in a secure environment. Keep up with the latest techniques, recipes, and foundation models. Our research team has developed and rigorously tested these recipes. In just a few easy steps, you can deploy your private cloud. Your data and models will never leave the firewalls. You can start in one cloud and continue in another without missing a beat. Own the model trained on your data. Model decisions can be better explained by examining them. Filter content and data according to your business needs. Integrate seamlessly with your existing data pipelines and experiment trackers. We are cloud-agnostic and enterprise-proven.

IBM Watson OpenScale provides visibility into the creation and use of AI-powered applications in an enterprise-scale environment. It also allows businesses to see how ROI is delivered. IBM Watson OpenScale provides visibility to companies about how AI is created, used, and how ROI is delivered at business level. You can create and deploy trusted AI using the IDE you prefer, and provide data insights to your business and support team about how AI affects business results. Capture payload data, deployment output, and alerts to monitor the health of business applications. You can also access an open data warehouse for custom reporting and access to operations dashboards. Based on business-determined fairness attributes, automatically detects when artificial Intelligence systems produce incorrect results at runtime. Smart recommendations of new data to improve model training can reduce bias.

AWS Deep Learning AMIs are a secure and curated set of frameworks, dependencies and tools that ML practitioners and researchers can use to accelerate deep learning in cloud. Amazon Machine Images (AMIs), designed for Amazon Linux and Ubuntu, come preconfigured to include TensorFlow and PyTorch. To develop advanced ML models at scale, you can validate models with millions supported virtual tests. You can speed up the installation and configuration process of AWS instances and accelerate experimentation and evaluation by using up-to-date frameworks, libraries, and Hugging Face Transformers. Advanced analytics, ML and deep learning capabilities are used to identify trends and make forecasts from disparate health data.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

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.

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.

It's fast and easy to create machine learning models for websites, apps, and other applications. Teachable Machine is flexible. You can use files or capture live examples. It respects your work. You can even use it entirely on-device without having to leave any microphone or webcam data. Teachable Machine, a web-based tool, makes it easy to create machine learning models. Artists, educators, students, innovators, and makers of all types - anyone with an idea to explore. There is no need to have any prior machine learning knowledge. Without writing any machine learning code, you can train a computer how to recognize your images, sounds, poses, and sounds. You can then use your model in your own sites, apps, and other projects.

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.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

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.

Construct your model as a live interactive graph. View data flowing through the architecture of your visualized model. View and edit the model architecture at the atomic level. Graphbook offers X-ray transparency without black boxes. Graphbook checks data type and form in real-time, with clear error messages. This makes model debugging easy. Graphbook abstracts out software dependencies and configuration of the environment, allowing you to focus on your model architecture and data flows with the computing resources required. Cerbrec Graphbook transforms cumbersome AI modeling into a user friendly experience. Graphbook, which is backed by a growing community that includes machine learning engineers and data science experts, helps developers fine-tune their language models like BERT and GPT using text and tabular data. Everything is managed out of box, so you can preview how your model will behave.

NeuralTools is a data mining program that makes accurate predictions based on patterns in your data. It uses neural networks in Microsoft Excel to create sophisticated predictions. NeuralTools mimics brain functions to "learn" structure and make intelligent predictions. NeuralTools allows your spreadsheet to "think" for yourself like never before. A Neural Networks analysis involves three steps: training the network using your data, testing it for accuracy and making predictions using new data. NeuralTools automates all of this in a single step. NeuralTools updates your predictions automatically when input data changes. This means you don't need to manually run predictions each time you get new data. Combine NeuralTools with Excel's Solver or Palisade’s Evolver to optimize difficult decisions and reach your goals like no other Neural Networks packages can.