Bayesforge Alternatives

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Superstaq’s low-level, device-specific optimizations enable users to draw the best performance out of today's hardware across multiple qubit modalities. Qiskit and Cirq open source frontends allow users to submit to leading quantum hardware platforms from IBM, Infleqtion, OQC, Rigetti, and more. Leverage our pre-built library of quantum applications to benchmark the performance on otherwise "impossible" problems with quantum hardware. Superstaq's library of sophisticated compilation and noise mitigation techniques, such as dynamical decoupling, automatically optimizes quantum programs based on the target hardware's native gate set. Whether it's Cirq or Qiskit, Superstaq enables you to write quantum programs that target virtually any quantum computer.

Circuit can be exported to multiple quantum programming languages/frameworks and can be executed on various simulators and quantum computers. You can use simple drag & drop user interface to assemble circuit diagram which automatically translates to code, and vice versa - you can type the code and the diagram is updated accordingly. QPS Client is running on your local machine (or in the cloud) where your quantum programming environment is installed. It opens a secure websocket connection with Quantum Programming Studio server and executes quantum circuits (that you design in the web UI) on your local simulator or on a real quantum computer.

Unique and efficient turn-key algorithms for data scientists. Powerful circuit building blocks for quantum engineers. Turn-key algorithm implementations for data scientists, financial analysts, and engineers. Explore problems in binary optimization, machine learning, linear algebra, and monte carlo sampling on simulators and real quantum hardware. No prior experience with quantum computing is required. Use NISQ data loader circuits to load classical data into quantum states to use with your algorithms. Use circuit building blocks for linear algebra with distance estimation and matrix multiplication circuits. Use our circuit building blocks to create your own algorithms. Get a significant performance boost for D-Wave hardware and use the latest improvements for gate-based approaches. Try out quantum data loaders and algorithms with guaranteed speed-ups on clustering, classification, and regression.

LIQUi|> is a software architecture and tool suite for quantum computing. It includes a programming language, optimization and scheduling algorithms, and quantum simulators. LIQUi|> can be used to translate a quantum algorithm written in the form of a high-level program into the low-level machine instructions for a quantum device. LIQUi|> is being developed by the quantum architectures and computation Group (QuArC) at Microsoft Research. To aid in the development and understanding of quantum protocols, quantum algorithms, quantum error correction, and quantum devices, QuArC has developed an extensive software platform called LIQUi|>. LIQUi|> allows the simulation of Hamiltonians, quantum circuits, quantum stabilizer circuits, and quantum noise models, and supports client, service, and cloud operation.

Use state-of-the-art cloud tools and learning resources to help you build and refine quantum algorithms. Gain access to a diverse portfolio of today’s quantum hardware. Access a diverse portfolio of today’s quantum hardware to build toward the emergence of fault-tolerant quantum systems. Navigate complexity and develop new skills with world-class onboarding and education resources including Microsoft Learn, Quantum katas tutorials, industry case studies, and a university curriculum. Use the Azure Quantum resource estimator tool to estimate the number of logical and physical qubits and runtime required to execute quantum applications on future-scaled quantum computers. Determine the number of qubits needed for a quantum solution and evaluate the differences across qubit technologies. Prepare and refine quantum solutions to run on future-scaled quantum machines.

Our singular focus is to help customers achieve real value by using quantum computing for practical business applications. You may be surprised to learn that our enterprise customers have already built hundreds of quantum applications across many industries. The powerful combination of the Advantage™ quantum system and the Leap™ hybrid solver services enable the first in-production quantum applications demonstrating business benefit. D-Wave is the practical quantum computing company delivering real business value for manufacturing, supply chain and logistics, scheduling, and mobility applications today. Quantum computing is already helping to optimize many key parts of the value chain in Industry 4.0.

Cirq is a Python software library for writing, manipulating, and optimizing quantum circuits, and then running them on quantum computers and quantum simulators. Cirq provides useful abstractions for dealing with today’s noisy intermediate-scale quantum computers, where details of the hardware are vital to achieving state-of-the-art results. Cirq comes with built-in simulators, both for wave functions and for density matrices. These can handle noisy quantum channels using monte carlo or full density matrix simulations. In addition, Cirq works with a state-of-the-art wavefunction simulator: qsim. These simulators can be used to mock quantum hardware with the quantum virtual machine. Cirq is used to run experiments on Google's quantum processors. Learn more about the latest experiments and access the code to se how to run them yourself.

Cellframe Network is a scalable open-source next generation platform for building and bridging blockchains and services secured by post-quantum encryption. We offer a stage for enterprises and developers for building a vast array of products ranging from simple low-level t-dApps to whole other blockchains on top of Cellframe Network. We believe that the next paradigm for blockchain technology is mass adoption and our platform strives to expand the use cases associated with blockchain technology. Cellframe can provide extremely high transaction throughput based on the original sharding implementation. In addition, Post-quantum cryptography makes the system resistant to hacking by quantum computers, which are not far off. Based on the original sharding implementation, Cellframe can provide extremely high transaction throughput.

Covalent’s serverless HPC architecture allows you to easily scale jobs from your laptop to your HPC/Cloud. Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. The latest release of Covalent includes two new feature sets and three major enhancements. True to its modular nature, Covalent now allows users to define custom pre- and post-hooks to electrons to facilitate various use cases from setting up remote environments (using DepsPip) to running custom functions.

The realization of large-scale physical quantum computers appears to be challenging, alongside the efforts to design quantum computers, significant efforts are focusing on the development of useful quantum algorithms. In the absence of a large physical quantum computer, an accurate software simulation of quantum computers on a classical computer is required to simulate the execution of those quantum algorithms and to study the behavior of a quantum computer and improve its design. Besides simulating error-free execution quantum circuits on a perfect quantum computer, the QX simulator can simulate realistic noisy execution using different error models such as the depolarizing noise. The user can activate the error model and define a physical error probability to simulate a specific target quantum computer. This error rate can be defined based on the gate fidelity and the qubit decoherence of the target platform.

Run your own quantum algorithms on one of our simulators or hardware backends and experience the possibilities of quantum computing. Note that Spin-2 is currently being upgraded and is no longer available. We have multiple simulators and real hardware chips available. Find out what they can do for you. Quantum Inspire is built using first-rate engineering practices. Starting from experimental setups, a layered and modular system was designed to end up with a solid and robust hardware system. This quantum computer consists of a number of layers including quantum chip hardware, classical control electronics, a quantum compiler, and a software front-end with a cloud-accessible web interface. They can act as technology accelerators because only through careful analysis of the individual system layers and their interdependencies it become possible to detect the gaps and necessary next steps in the innovation roadmap and supply chain.

BQSKit stands on its own as an end-to-end compiling solution by combining state-of-the-art partitioning, synthesis, and instantiation algorithms. The framework is built in an easy-to-access and quick-to-extend fashion, allowing users to best tailor a workflow to suit their specific domain. Global circuit optimization is the process of taking a quantum program, given as a quantum circuit, and reducing (optimizing) its depth. The depth of a quantum circuit is directly related to the program’s runtime and the probability of error in the final result. BQSKit uses a unique strategy that combines circuit partitioning, synthesis, and instantiation to optimize circuits far beyond what traditional optimizing compilers can do.

We make it possible for everyone to think bigger, create faster, and see further. By infusing AI and machine learning, our quantum solutions give you the power to solve the world’s most important and pressing problems. Thermodynamics sparked the Industrial revolution. Electromagnetism ushered in the information age, now, quantum computers are harnessing the unique information processing capability of quantum mechanics to exponentially reduce the time and energy needed for high-impact computing. With the first paradigm-shifting advance since the integrated circuit, quantum computing is poised to transform every global market. The gap between first movers and fast followers will be difficult to overcome.

Quandela Cloud offers a wide range of functionalities. First, intensive documentation is available to walk you through Perceval, our photonic quantum computing framework. Perceval's programming language is Python, thus coding on Quandela’s QPUs is seamless. Moreover, you can leverage a wide range of unique algorithms already implemented (resolving PDEs, clustering data, generating certified random numbers, solving logistical problems, computing properties of molecules, and much more). Then, the status and specifications of Quandela’s QPUs are displayed. You can choose the appropriate one, run your job and check its evolution on the job monitoring interface.

Developers and enterprises can build Quantum-resistant smart-contracts, DApps, DeFi solutions, NFTs, tokens, Metaverse on top of the QAN blockchain platform in any programming language. QANplatform is the first Hyperpolyglot Smart Contract platform where developers can code in any programming language and also get rewarded for writing high-quality code reusable by others. The Quantum threat is very real. Existing chains can not defend against it. QAN is resistant against it from ground up, your future funds are safe. Quantum-resistant algorithms — also known as post-quantum, quantum-secure, or quantum-safe — are cryptographic algorithms that can fend off attacks from quantum computers. Quantum-resistant algorithms — also known as post-quantum, quantum-secure, or quantum-safe — are cryptographic algorithms that can fend off attacks from quantum computers.

OQC’s quantum computer is a complete functional unit, including the control system, the hardware and the software. It is the only quantum computer commercially available in the UK. OQC’s Quantum Computing-as-a-Service (QCaaS) platform takes our proprietary quantum technology to the wider market through a private cloud. Register your interest to access our QCaaS. Thanks to a close cooperation with world-leading technical and strategic partners, we ensure that our technology is at the heart of the quantum revolution.

Use our suite of applications to support your quantum research and development needs. Copy your API token, track jobs, and view quantum compute resources. Explore service and API documentation to start working with IBM Quantum resources.

Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application development. The transpiler translates Qiskit code into an optimized circuit using a backend’s native gate set, allowing users to program for any quantum processor. Users can transpile with Qiskit's default optimization, use a custom configuration or develop their own plugin. Qiskit helps users schedule and run quantum programs on a variety of local simulators and cloud-based quantum processors. It supports several quantum hardware designs, such as superconducting qubits and trapped ions. Ready to explore Qiskit’s capabilities for yourself? Learn how to run Qiskit in the cloud or your local Python environment.

Quantum computing offers a path forward to rapid and cost-effective development of new molecules and materials. InQuanto, a state-of-the-art quantum computational chemistry platform, represents a critical step toward this goal. Quantum chemistry aims to accurately describe and predict the fundamental properties of matter and hence is a powerful tool in the design and development of new molecules and materials. However, molecules and materials of industrial relevance are complex and not easy to accurately simulate. Today’s capabilities force a trade to either use highly accurate methods on the smallest-sized systems or use approximating techniques. InQuanto’s modular workflow enables both computational chemists and quantum algorithm developers to easily mix and match the latest quantum algorithms with advanced subroutines and error mitigation techniques to get the best out of today’s quantum platforms.

Silq is a new high-level programming language for quantum computing with a strong static type system, developed at ETH Zürich. Silq was originally published at PLDI'20.

It is based on a complete representation of the qubit state but avoids the explicit representation of gates and other quantum operations in terms of matrices. Intel-QS uses the MPI (message-passing-interface) protocol to handle communication between the distributed resources used to store and manipulate quantum states. Intel-QS builds as a shared library which, once linked to the application program, allows to take advantage of the high-performance implementation of circuit simulations. The library can be built on a variety of different systems, from laptops to HPC server systems.

Experience the world’s only quantum-computing-hardened encryption keys, ensuring provably superior protection and allowing you to seamlessly strengthen your existing cybersecurity systems for enhanced security today, and into the future. Every organization owns sensitive data that must be kept secret at all costs. Quantum Origin adds unmatched cryptographic strength to existing cybersecurity systems, giving your enterprise a long-term edge against cyber criminals. Maintaining the trust of customers, shareholders, and regulators means adapting and strengthening your cybersecurity foundations. Adopting Quantum Origin showcases your commitment to staying ahead of potential threats. Quantum Origin verifiably strengthens the cryptographic protection around your technology and services, proving you take the privacy and security of your customer's data as seriously as they do. Let your customers know their data is safe with the ultimate in cryptographic protection.

The Quantum exact simulation toolkit is a high-performance simulator of quantum circuits, state-vectors and density matrices. QuEST uses multithreading, GPU acceleration and distribution to run lightning first on laptops, desktops and networked supercomputers. QuEST just works; it is stand-alone, requires no installation, and is trivial to compile and get running. QuEST has no setup; it can be downloaded, compiled and run in a matter of seconds. QuEST has no external dependencies and compiles natively on Windows, Linux and MacOS. Whether on a laptop, a desktop, a supercomputer, a microcontroller, or in the cloud, you can almost always get QuEST running with only a few terminal commands.

Introducing the xx network, the first and only quantum-resistant and privacy-focused blockchain ecosystem. Now offering the ultra-secure messaging application, xx messenger. Start using the blockchain of the future, the only Layer One protocol protected against quantum computing attacks. Introducing the first and only messenger app that truly protects communication between sender and receiver. All messages are end-to-end encrypted, and no metadata is ever collected. Powered by the xx network. A new easy-to-use digital currency. Designed to be the most secure and usable digital currency available today. xx messenger keeps all user activity private. No tracking, no profiling, and no surveillance. With end-to-end encryption, of course. Introducing xx messenger. Imagine a world where no one, no one, can read your messages and sell your data. Low-cost, quantum-ready, and metadata-protected. A next-gen currency to protect against next-gen threats.

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.

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.

Accelerate the end-to-end machine learning lifecycle. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.

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.

Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.

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).

NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.

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.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Traditionally, deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling take days or weeks time. However, with GPUonCLOUD’s dedicated GPU servers, it's a matter of hours. You may want to opt for pre-configured systems or pre-built instances with GPUs featuring deep learning frameworks like TensorFlow, PyTorch, MXNet, TensorRT, libraries e.g. real-time computer vision library OpenCV, thereby accelerating your AI/ML model-building experience. Among the wide variety of GPUs available to us, some of the GPU servers are best fit for graphics workstations and multi-player accelerated gaming. Instant jumpstart frameworks increase the speed and agility of the AI/ML environment with effective and efficient environment lifecycle management.

Groq is on a mission to set the standard for GenAI inference speed, helping real-time AI applications come to life today. An LPU inference engine, with LPU standing for Language Processing Unit, is a new type of end-to-end processing unit system that provides the fastest inference for computationally intensive applications with a sequential component, such as AI language applications (LLMs). The LPU is designed to overcome the two LLM bottlenecks, compute density and memory bandwidth. An LPU has greater computing capacity than a GPU and CPU in regards to LLMs. This reduces the amount of time per word calculated, allowing sequences of text to be generated much faster. Additionally, eliminating external memory bottlenecks enables the LPU inference engine to deliver orders of magnitude better performance on LLMs compared to GPUs. Groq supports standard machine learning frameworks such as PyTorch, TensorFlow, and ONNX for inference.

Distributed AI is a computing paradigm that bypasses the need to move vast amounts of data and provides the ability to analyze data at the source. Distributed AI APIs built by IBM Research is a set of RESTful web services with data and AI algorithms to support AI applications across hybrid cloud, distributed, and edge computing environments. Each Distributed AI API addresses the challenges in enabling AI in distributed and edge environments with APIs. The Distributed AI APIs do not focus on the basic requirements of creating and deploying AI pipelines, for example, model training and model serving. You would use your favorite open-source packages such as TensorFlow or PyTorch. Then, you can containerize your application, including the AI pipeline, and deploy these containers at the distributed locations. In many cases, it’s useful to use a container orchestrator such as Kubernetes or OpenShift operators to automate the deployment process.

A family of state-of-the-art, light-open models created from the same research and technology that were used to create Gemini models. These models incorporate comprehensive security measures and help ensure responsible and reliable AI solutions through selected data sets and rigorous adjustments. Gemma models achieve exceptional comparative results in their 2B, 7B, 9B, and 27B sizes, even outperforming some larger open models. With Keras 3.0, enjoy seamless compatibility with JAX, TensorFlow, and PyTorch, allowing you to effortlessly choose and change frameworks based on task. Redesigned to deliver outstanding performance and unmatched efficiency, Gemma 2 is optimized for incredibly fast inference on various hardware. The Gemma family of models offers different models that are optimized for specific use cases and adapt to your needs. Gemma models are large text-to-text lightweight language models with a decoder, trained in a huge set of text data, code, and mathematical content.

Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.

IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

TorchMetrics is a collection of 90+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. A standardized interface to increase reproducibility. It reduces boilerplate. distributed-training compatible. It has been rigorously tested. Automatic accumulation over batches. Automatic synchronization between multiple devices. You can use TorchMetrics in any PyTorch model, or within PyTorch Lightning to enjoy additional benefits. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. Nearly all functional metrics have a corresponding class-based metric.

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. : Luminoth is still alpha-quality release, which means the internal and external interfaces (such as command line) are very likely to change as the codebase matures. . If you want GPU support, you should install the GPU version of TensorFlow with pip install tensorflow-gpu, or else you can use the CPU version using pip install tensorflow. Luminoth can also install TensorFlow for you if you install it with pip install luminoth[tf] or pip install luminoth[tf-gpu], depending on the version of TensorFlow you wish to use.

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis.

Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Sagemaker instances or Amazon ECS tasks, to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, PyTorch and ONNX models. Inference is the process of making predictions using a trained model. In deep learning applications, inference accounts for up to 90% of total operational costs for two reasons. Firstly, standalone GPU instances are typically designed for model training - not for inference. While training jobs batch process hundreds of data samples in parallel, inference jobs usually process a single input in real time, and thus consume a small amount of GPU compute. This makes standalone GPU inference cost-inefficient. On the other hand, standalone CPU instances are not specialized for matrix operations, and thus are often too slow for deep learning inference.

DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.

Kismet works with Wi-Fi interfaces, Bluetooth interfaces, some SDR (software defined radio) hardware like the RTLSDR, and other specialized capture hardware. Kismet works on Linux, OSX, and, to a degree, Windows 10 under the WSL framework. On Linux it works with most Wi-Fi cards, Bluetooth interfaces, and other hardware devices. On OSX it works with the built-in Wi-Fi interfaces, and on Windows 10 it will work with remote captures. There are several ways you can help support Kismet development financially if you’d like to; support is always appreciated but never required. Kismet is, and always will be, open source. With the new Kismet codebase (Kismet-2018-Beta1 and newer), Kismet supports plugins which extend the WebUI functionality via Javascript and browser-side enhancements, as well as the more traditional Kismet plugin architecture of C++ plugins which can extend the server functionality at a low level.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.