ALO v3
Key Features
ALO v3 has been improved in various areas compared to ALO v2 to maximize user convenience and efficiency. Below is an explanation of the major changes and their effects.
Improved Accessibility and Usability
In ALO v2, the installation process was complicated and version management was difficult due to the use of Git Clone. In ALO v3, with the introduction of the pip install method, installation has become very simple, and version management and local folder creation issues have been resolved. Furthermore, in ALO v2, command-based execution was not intuitive, causing inconvenience to users. ALO v3 significantly improves usability by providing intuitive CLI commands such as alo run and alo template.
Increased User Convenience
The code structure has also been simplified. In ALO v2, a separate coding method suited to ALO (about 30 APIs) had to be used, but in ALO v3, code modifications are minimized by simply adding ALO to existing modeling code. Because of this, ALO's syntax has disappeared, making single .py file management easier and usability greatly increased. Additionally, it provides functionality to print only the desired level of logs and guidance on about 60 error cases to prevent user misuse and quickly resolve issues.
Improved Efficiency
The previously complicated YAML file writing method has also been simplified to map directly with modeling code. Although writing YAML files in ALO v2 was complex, in ALO v3 it can be written much more intuitively, significantly enhancing efficiency. Moreover, as modeling code and ALO are perfectly separated, users can fully focus on writing modeling code.
Enhanced Performance
In ALO v2, there was a lack of GPU support, limiting training and inference speeds. ALO v3 supports GPU during training and inference, greatly enhancing the speed of model training and inference. This allows high-performance models to be trained in a short amount of time and increases inference accuracy.