- Home
- Experiment Tracking
- MLtraq
On the recordRead 8 Oct 202622 facts
MLtraq
On the record
- Artifact storage
- The FAQ says arbitrary objects, including model weights and state parameters, can be dumped and loaded, with filesystem storage or third-party services and data stores.mltraq.com · read 8 Oct 2026
- Collaboration
- Users can back up, merge, share, and reload experiments with their computation state.mltraq.com · read 8 Oct 2026
- Compatibility caveat
- The documentation says interfaces may change as MLtraq progresses and recommends pinning an exact version and verifying updates with tests.mltraq.com · read 8 Oct 2026
- Database integrations
- The default database is SQLite, and MLtraq can connect to SQL databases supported by SQLAlchemy.mltraq.com · read 8 Oct 2026
- Distribution
- The installation instructions use pip install mltraq --upgrade.mltraq.com · read 8 Oct 2026
- Execution
- It supports experiment runs with parameter grids, chained steps, parallel execution, and resuming from persisted state.mltraq.com · read 8 Oct 2026
- Experiment state
- It can track experiment state, stream metrics, reproduce runs, collaborate, and resume computation.mltraq.com · read 8 Oct 2026
- Interfaces
- Experiments can be accessed with Python, Pandas, and SQL from Python scripts, Jupyter notebooks, and dashboards.mltraq.com · read 8 Oct 2026
- Interoperability
- Experiments can be accessed with Python, Pandas, and SQL using native database types and open formats.mltraq.com · read 8 Oct 2026
- License
- The project is licensed under the BSD 3-Clause License.mltraq.com · read 8 Oct 2026
- Metrics
- Experiments can stream metrics and track native Python data types and structures, as well as NumPy, Pandas, and PyArrow objects.mltraq.com · read 8 Oct 2026
- Model support
- MLtraq is model-agnostic, and its FAQ describes adding a PyTorch model through a scikit-learn-compatible wrapper or a redesigned training step.mltraq.com · read 8 Oct 2026
- Parallel backends
- The documentation names Dask, Ray, Spark, and custom cluster-specific backends as options for computation.mltraq.com · read 8 Oct 2026
- Parallel execution
- Chained execution uses joblib.Parallel with process-based parallelism, and Dask, Ray, Spark, or custom backends can be used.mltraq.com · read 8 Oct 2026
- Purpose
- MLtraq is an open-source Python library for ML and AI developers to design, execute, track, and share experiments.mltraq.com · read 8 Oct 2026
- Requirements
- The documented requirements are Python 3.9+, SQLAlchemy 2.0+, Pandas 1.5.3+, and Joblib 1.3.2+.mltraq.com · read 8 Oct 2026
- Security
- MLtraq's DATAPAK format uses a subset of safe Python Pickle opcodes for listed basic and container types.mltraq.com · read 8 Oct 2026
- Security detail
- The state-storage documentation says its DATAPAK serialization uses only a subset of safe Pickle opcodes for listed basic and container types.mltraq.com · read 8 Oct 2026
