TensorBoard Integrations

Dataoorts: Revolutionizing GPU Cloud Computing Dataoorts is a cutting-edge GPU cloud platform designed to meet the demands of the modern computational landscape. Launched in August 2024 after extensive beta testing, it offers revolutionary GPU virtualization technology, empowering researchers, developers, and businesses with unmatched flexibility, scalability, and performance. The Technology Behind Dataoorts At the core of Dataoorts lies its proprietary Dynamic Distributed Resource Allocation (DDRA) technology. This breakthrough allows real-time virtualization of GPU resources, ensuring optimal performance for diverse workloads. Whether you're training complex machine learning models, running high-performance simulations, or processing large datasets, Dataoorts delivers computational power with unparalleled efficiency.

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.

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Google Colab is a free, hosted Jupyter Notebook service that provides cloud-based environments for machine learning, data science, and educational purposes. It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation and learning. Colab also now offers a Data Science Agent automating analysis, from understanding the data to delivering insights in a working Colab notebook (Sequences shortened. Results for illustrative purposes. Data Science Agent may make mistakes.)

​LLaMA-Factory is an open source platform designed to streamline and enhance the fine-tuning process of over 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It supports various fine-tuning techniques, including Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, allowing users to customize models efficiently. It has demonstrated significant performance improvements; for instance, its LoRA tuning offers up to 3.7 times faster training speeds with better Rouge scores on advertising text generation tasks compared to traditional methods. LLaMA-Factory's architecture is designed for flexibility, supporting a wide range of model architectures and configurations. Users can easily integrate their datasets and utilize the platform's tools to achieve optimized fine-tuning results. Detailed documentation and diverse examples are provided to assist users in navigating the fine-tuning process effectively.

Intel® Tiber™ AI Studio is a comprehensive machine learning operating system that unifies and simplifies the AI development process. The platform supports a wide range of AI workloads, providing a hybrid and multi-cloud infrastructure that accelerates ML pipeline development, model training, and deployment. With its native Kubernetes orchestration and meta-scheduler, Tiber™ AI Studio offers complete flexibility in managing on-prem and cloud resources. Its scalable MLOps solution enables data scientists to easily experiment, collaborate, and automate their ML workflows while ensuring efficient and cost-effective utilization of resources.

Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning.