How do I use gurobi-logtools to analyze and visualize Gurobi logs? – Gurobi Help Center

gurobi-logtools is an official Python package developed by Gurobi that allows you to parse, analyze, and visualize Gurobi log files. It is particularly helpful when profiling and comparing solver runs, tuning model performance, and sharing reproducible benchmarks.

Whether you're comparing tuning results or identifying bottlenecks in large optimization problems, gurobi-logtools provides you with an efficient, consistent way to extract insights from solver logs.

The package is actively maintained on GitHub at https://github.com/Gurobi/gurobi-logtools and provides comprehensive documentation and tutorials.

Key Features

The package provides several main capabilities:

Log File Parsing: Extract structured data from Gurobi log files into pandas DataFrames
Interactive Visualization: Generate interactive charts using plotly.express with ipywidgets dashboards
Progress Tracking: Analyze optimization progress over time and summarize statistics
Compare multiple logs: Side by side comparisons for parameter tuning or algorithm selection
Excel Export: Convert log files directly to Excel worksheets via command-line
File Management: Rename and organize log files with consistent naming schemes

Installation

You can install gurobi-logtools using pip:

python -m pip install gurobi-logtools

Basic Usage

Here's how to use the package:

import gurobi_logtools as glt

# Parse log files from multiple runs
results = glt.parse(["run1/*.log", "run2/*.log"])

# Get summary information
summary = results.summary()

# Get progress information for nodelog
nodelog_progress = results.progress("nodelog")

# Create interactive plots
glt.plot(summary)  # Final results
glt.plot(nodelog_progress, x="Time", y="Gap", color="Log", type="line")  # Progress charts

Command-Line Usage

You can also use it directly from the command line to convert log files to Excel:

python -m gurobi_logtools myrun.xlsx data/*.log
python -m gurobi_logtools --help  # Show all options

Use Cases

This tool is particularly useful for:

Comparing results from multiple model instances
Analyzing different parameter settings
Performance variability experiments with multiple random seeds
Creating detailed reports and visualizations of optimization runs

Example: Comparing Two Solver Runs

python -m gurobi_logtools compare.xlsx compare baseline.log tuned.log

This will produce a table showing:

Time to optimality
Best bound and gap evolution
Number of explored nodes
Whether tuning improved performance

Further information

Related to