Results for: swing based visual query builder java code

...Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex.

...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. ...

...It makes code intuitive and easy to debug. Comes with ChainerRLA, a library that implements various state-of-the-art deep reinforcement algorithms. Also, with ChainerCVA, a collection of tools to train and run neural networks for computer vision tasks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

...The fully hosted interactive programming environment realizes the data processing and code debugging. The CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility.

...Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. ...

...Extensive and mature practice landed, building the next generation of smart applications. Online quick test service effect, convenient smart cloud reasoning service. One-stop model customization, full process visualization operation. Large model of knowledge enhancement, unified paradigm to support multi-category downstream tasks. An advanced parallel strategy that supports large model training, compression, and deployment.

Neptune.ai is a machine learning operations (MLOps) platform designed to streamline the tracking, organizing, and sharing of experiments and model-building processes. It provides a comprehensive environment for data scientists and machine learning engineers to log, visualize, and compare model training runs, datasets, hyperparameters, and metrics in real-time. Neptune.ai integrates easily with popular machine learning libraries, enabling teams to efficiently manage both research and production workflows. With features that support collaboration, versioning, and experiment reproducibility, Neptune.ai enhances productivity and helps ensure that machine learning projects are transparent and well-documented across their lifecycle.

Nendo is the AI audio tool suite that allows you to effortlessly develop & use audio apps that amplify efficiency & creativity across all aspects of audio production. Time-consuming issues with machine learning and audio processing code are a thing of the past. AI is a transformative leap for audio production, amplifying efficiency and creativity in industries where audio is key. But building custom AI Audio solutions and operating them at scale is challenging. Nendo cloud empowers developers and businesses to seamlessly deploy Nendo applications, utilize premium AI audio models through APIs, and efficiently manage workloads at scale. ...

...Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.

...It currently supports LoRA fine-tuning on open-weight models across both LLama and Qwen families, ranging from small models to large mixture-of-experts architectures. Users write Python code to handle data, loss functions, and algorithmic logic; Tinker handles scheduling, resource allocation, distributed training, and failure recovery behind the scenes. The service lets users download model weights at different checkpoints and doesn’t force them to manage the compute environment. Tinker is delivered as a managed offering; training jobs run on Thinking Machines’ internal GPU infrastructure, freeing users from cluster orchestration.

...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.

RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing.

...Fortune 500 companies, academic institutions and small businesses all rely on Bright Data's products, network and solutions to retrieve crucial public web data in the most efficient, reliable and flexible manner, so they can research, monitor, analyze data and make better informed decisions. Bright Data is used worldwide by 20,000+ customers in nearly every industry. Its products range from no-code data solutions utilized by business owners, to a robust proxy and scraping infrastructure used by developers and IT professionals. Bright Data products stand out because they provide a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data and superior customer experience, while being fully transparent and compliant.