OpenBayes: Rapid Deployment of Rich Large Model Instances Using Cloud Computing Resources

General Introduction

OpenBayes is an out-of-the-box Artificial Intelligence and High Performance Computing (AI+HPC) service platform for machine learning engineers, providing multi-version framework support and rich datasets. Based on JupyterLab, it supports containerization and Kubernetes resource scheduling. Meanwhile, open multiple APIs and private deployment options, suitable for small experiments to enterprise-level applications.

It helps users quickly build and run machine learning models with out-of-the-box solutions. The platform supports a variety of common frameworks such as PyTorch, TensorFlow, and PaddlePaddle, and provides flexible scalability.

Function List

Out-of-the-box, configuration-free environment
Multiple versions of framework support, such as PyTorch, PaddlePaddle, TensorFlow
Container Startup in Seconds, an Interactive Machine Learning Solution Based on JupyterLab
Supports terabytes of data management and intermittent transfers
Elastic and scalable architecture with support for client SSH login and cloud programming
Organizational resource segregation, team management and account auditing
Apollo GraphQL / RESTful API interface, custom mirrors and other high level features

Using Help

Installation and Registration

1. Register for an accountTo register: Visit the OpenBayes website, click on the "Register Now" button, and fill in the relevant information to complete the registration.
2. Choosing an Arithmetic Program: Choose the right arithmetic solution, such as NVIDIA RTX 4090 or NVIDIA A100, depending on your needs.
3. Create a project: Log in and go to the console, click on "Create Project" and select the desired framework and version.

Using Machine Learning Services

1. Environment Configuration: There is no need to manually configure the environment, select the desired framework and version and the platform will configure it automatically.
2. Data upload: Uploading data through the console, supporting terabyte-level data management and breakpoint uploading.
3. model trainingThe platform will automatically allocate arithmetic resources by selecting the dataset and model and clicking "Start Training".
4. Results View: After training is complete, results and logs can be viewed in the console, and downloading of models and data is supported.

Advanced Features

1. CLI tools: Download and install the OpenBayes CLI tool for project management and model training via the command line.
2. automatic parameterization: Use the hyperparameter auto-tuning function provided by the platform to optimize the model performance.
3. remote debugging: Configure an SSH login to connect to PyCharm or VS Code for remote debugging.

Private deployment

1. Contact SupportIf you need a private deployment solution, you can contact the OpenBayes support team via the official website.
2. Customized Solutions: Customized hardware and software configurations based on enterprise needs, supporting physical clusters and hybrid cloud deployments.
3. Deployment implementation: The OpenBayes team will assist with the private deployment and ensure that the system is stable.

The layman's guide to getting started with AI

Help you learn how to utilize AI tools at a low cost and from a zero base.AI, like office software, is an essential skill for everyone. Mastering AI will give you an edge in your job search and half the effort in your future work and studies.

View Details>