Lightning AI Integrations

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.

TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.

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.