Graphcore Integrations

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

Keras is an API tailored for human users rather than machines. It adheres to optimal practices for alleviating cognitive strain by providing consistent and straightforward APIs, reducing the number of necessary actions for typical tasks, and delivering clear and actionable error messages. Additionally, it boasts comprehensive documentation alongside developer guides. Keras is recognized as the most utilized deep learning framework among the top five winning teams on Kaggle, showcasing its popularity and effectiveness. By simplifying the process of conducting new experiments, Keras enables users to implement more innovative ideas at a quicker pace than their competitors, which is a crucial advantage for success. Built upon TensorFlow 2.0, Keras serves as a robust framework capable of scaling across large GPU clusters or entire TPU pods with ease. Utilizing the full deployment potential of the TensorFlow platform is not just feasible; it is remarkably straightforward. You have the ability to export Keras models to JavaScript for direct browser execution, transform them to TF Lite for use on iOS, Android, and embedded devices, and seamlessly serve Keras models through a web API. This versatility makes Keras an invaluable tool for developers looking to maximize their machine learning capabilities.

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

Hugging Face is an AI community platform that provides state-of-the-art machine learning models, datasets, and APIs to help developers build intelligent applications. The platform’s extensive repository includes models for text generation, image recognition, and other advanced machine learning tasks. Hugging Face’s open-source ecosystem, with tools like Transformers and Tokenizers, empowers both individuals and enterprises to build, train, and deploy machine learning solutions at scale. It offers integration with major frameworks like TensorFlow and PyTorch for streamlined model development.

Our advanced storage systems are capable of efficiently managing millions of small, random files to support GPU-based training servers, significantly speeding up the overall training process. We provide high-bandwidth, low-latency network solutions that facilitate seamless connections between distributed training servers while enabling smooth data transfer from storage to servers. Unlike other cloud providers that impose additional fees for data retrieval, which can quickly accumulate, we strive to be an integral part of your team. Collaborating with you, we assist in establishing scheduling services, advise on best practices, and deliver exceptional support tailored to your needs. Recognizing that workflows differ across organizations, Cirrascale is committed to ensuring that you receive the most suitable solutions to achieve optimal results. Uniquely, we are the only provider that collaborates closely with you to customize your cloud instances, enhancing performance, eliminating bottlenecks, and streamlining your workflow. Additionally, our cloud-based solutions are designed to accelerate your training, simulation, and re-simulation processes, yielding faster outcomes. By prioritizing your unique requirements, Cirrascale empowers you to maximize your efficiency and effectiveness in cloud operations.

PaddlePaddle, built on years of research and practical applications in deep learning by Baidu, combines a core framework, a fundamental model library, an end-to-end development kit, tool components, and a service platform into a robust offering. Officially released as open-source in 2016, it stands out as a well-rounded deep learning platform known for its advanced technology and extensive features. The platform, which has evolved from real-world industrial applications, remains dedicated to fostering close ties with various sectors. Currently, PaddlePaddle is utilized across multiple fields, including industry, agriculture, and services, supporting 3.2 million developers and collaborating with partners to facilitate AI integration in an increasing number of industries. This widespread adoption underscores its significance in driving innovation and efficiency across diverse applications.