ZJU4NN Optimizer

The ZJU4NN Optimizer tool analyzes model architecture, training framework, and hardware constraints to determine if the optimizer package will integrate cleanly. It targets ML engineers, data scientists, and research teams who compare optimizers, validate requirements, and prepare deployment pipelines. The tool identifies platform compatibility (Python version, CUDA/cuDNN support, and binary formats), version constraints, and licensing considerations, then guides the user to the correct download package.
Core logic maps input parameters to a recommended variant, computes estimated download size, and performs prerequisite validation. It reports required dependencies (OS, drivers, Python environment) and flags unsupported configurations, with optional advanced checks for hardware acceleration and mixed-precision options.
Inputs include model type, framework, Python version, CUDA version, GPU architecture, and target OS; outputs include variant recommendation, download size, compatible environments, and a download reference. Validation ensures non-null values and supported combinations.
Algorithms use a scoring heuristic: compatibility_score = w1*framework_match + w2*cuda_match + w3*version_match; download_size_estimate combines model_size, precision, and feature set; a confidence_score guides choice. The tool prefers conservative recommendations when external data is temporarily unavailable.
Industry alignment follows standard ML tooling practices; units are MB, seconds, and consistent licensing terms. Assumptions include availability of a public package index; exclusions cover runtime execution, training, and license negotiations.