/cli-anything-unimol-tools

name: "cli-anything-unimol-tools"
description: >-
  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

# Create a new project
project create --name drug_discovery

# List all projects
project list

# Switch to a project
project switch --name drug_discovery

Training

# Train a classification model
train --data-path train.csv --target-col active --task-type classification --epochs 10

# Train a regression model
train --data-path train.csv --target-col affinity --task-type regression --epochs 10

Model Management

# List all trained models
models list

# Show model details and performance
models show --model-id <id>

# Rank models by performance
models rank

Storage & Cleanup

# Analyze storage usage
storage analyze

# Automatic cleanup of poor performers
cleanup auto

# Manual cleanup with criteria
cleanup manual --max-models 10 --min-score 0.7

Prediction

# Make predictions with a trained model
predict --model-id <id> --data-path test.csv

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:

SMILES,target
CCO,1
CCCO,0
CC(C)O,1

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:

python3 -m cli_anything.unimol_tools --json models list

Output format:

{
  "status": "success",
  "data": [...],
  "message": "..."
}

Interactive Mode

Launch without commands for interactive REPL:

python3 -m cli_anything.unimol_tools

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

cd unimol_tools/agent-harness
pip install -e .

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

cd docs/test
bash run_tests.sh --unit -v    # Unit tests (67 tests)
bash run_tests.sh --full -v    # Full test suite

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