Interactive CLI for Uni-Mol molecular property prediction training and inference workflows.

Uni Mol Tools Molecular Property Prediction CLI Package : cli anything unimol tools Command : python3 m cli anything.unimol tools Description Interactive CLI for training and inference of molecular property prediction models using Uni Mol Tools. Supports 5 task types: binary classification, regression, multiclass, multilabel classification, and multilabel regression. Key Features Project Management : Organize experiments with named projects 5 Task Types : Classification, regression, multiclass, multilabel variants Model Tracking : Automatic performance history and rankings Smart Storage : Analyze usage and clean up underperformers JSON API : Full automation support with json flag Common Commands Project Management Training Model Management Storage & Cleanup Prediction Data Format CSV files must contain: SMILES column: Molecular structures in SMILES format Target column(s): Values to predict (name specified via target col ) Example: Task Types 1. classification : Binary classification (0/1) 2. regression : Continuous value prediction 3. multiclass : Multiple class classification 4. multilabel classification : Multiple binary labels 5. multilabel regression : Multiple continuous values JSON Mode Add json flag to any command for machine readable output: Output format: Interactive Mode Launch without commands for interactive REPL: Features: Tab completion Command history Contextual help Project state persistence Test Data Example datasets available at: https://github.com/545487677/CLI Anything unimol tools/tree/main/unimol tools/examples Includes data for all 5 task types. Requirements Python 3.8+ PyTorch 1.12+ Uni Mol Tools backend 4GB+ RAM (8GB+ recommended for training) Installation Documentation SOP : [UNIMOL TOOLS.md](../UNIMOL TOOLS.md) Quick Start : [docs/guides/02 QUICK START.md](../docs/guides/02 QUICK START.md) Full Documentation : [docs/README.md](../docs/README.md) Testing Performance Tips Start with 10 epochs for initial experiments Use smaller batch sizes if memory is limited Monitor storage with storage analyze Use models rank to identify best performers Clean up regularly with cleanup auto Troubleshooting CUDA errors : Reduce batch size or use CPU mode CSV not recognized : Verify SMILES column exists Low accuracy : Try more epochs or adjust learning rate Storage full : Run cleanup auto to free space Related Uni Mol Tools : https://github.com/dptech corp/Uni Mol/tree/main/unimol tools Uni Mol Paper : https://arxiv.org/abs/2209.11126 CLI Anything : https://github.com/HKUDS/CLI Anything

cli-anything-unimol-tools