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MLtraq

mltraq.com

Linux
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Spec score 7.1 #15 of 22 in Experiment Tracking

Spec sheet

Purpose
MLtraq is an open-source Python library for ML and AI developers to design, execute, track, and share experiments.Read 8 Oct 2026 · mltraq.com
Security
MLtraq's DATAPAK format uses a subset of safe Python Pickle opcodes for listed basic and container types.Read 8 Oct 2026 · mltraq.com
Support
The project directs users to GitHub Discussions to ask questions and share ideas.Read 8 Oct 2026 · mltraq.com
License
The project is licensed under the BSD 3-Clause License.Read 8 Oct 2026 · mltraq.com
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.Read 8 Oct 2026 · mltraq.com
Collaboration
Users can back up, merge, share, and reload experiments with their computation state.Read 8 Oct 2026 · mltraq.com
Compatibility caveat
The documentation says interfaces may change as MLtraq progresses and recommends pinning an exact version and verifying updates with tests.Read 8 Oct 2026 · mltraq.com
Database integrations
The default database is SQLite, and MLtraq can connect to SQL databases supported by SQLAlchemy.Read 8 Oct 2026 · mltraq.com
Distribution
The installation instructions use pip install mltraq --upgrade.Read 8 Oct 2026 · mltraq.com
Execution
It supports experiment runs with parameter grids, chained steps, parallel execution, and resuming from persisted state.Read 8 Oct 2026 · mltraq.com
Experiment state
It can track experiment state, stream metrics, reproduce runs, collaborate, and resume computation.Read 8 Oct 2026 · mltraq.com
Interfaces
Experiments can be accessed with Python, Pandas, and SQL from Python scripts, Jupyter notebooks, and dashboards.Read 8 Oct 2026 · mltraq.com
Interoperability
Experiments can be accessed with Python, Pandas, and SQL using native database types and open formats.Read 8 Oct 2026 · mltraq.com
Metrics
Experiments can stream metrics and track native Python data types and structures, as well as NumPy, Pandas, and PyArrow objects.Read 8 Oct 2026 · mltraq.com
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.Read 8 Oct 2026 · mltraq.com
Parallel backends
The documentation names Dask, Ray, Spark, and custom cluster-specific backends as options for computation.Read 8 Oct 2026 · mltraq.com
Parallel execution
Chained execution uses joblib.Parallel with process-based parallelism, and Dask, Ray, Spark, or custom backends can be used.Read 8 Oct 2026 · mltraq.com
Requirements
The documented requirements are Python 3.9+, SQLAlchemy 2.0+, Pandas 1.5.3+, and Joblib 1.3.2+.Read 8 Oct 2026 · mltraq.com
Security detail
The state-storage documentation says its DATAPAK serialization uses only a subset of safe Pickle opcodes for listed basic and container types.Read 8 Oct 2026 · mltraq.com
Storage
The DataStore interface stores objects outside the database and uses the filesystem by default; third-party storage options can be requested through the issue tracker or discussion board.Read 8 Oct 2026 · mltraq.com
Supported data
It supports native Python types and structures, as well as NumPy, Pandas, and PyArrow objects.Read 8 Oct 2026 · mltraq.com
Target users
The FAQ describes MLtraq as a good candidate for experimentation and says MLflow may be a better fit for users prioritizing MLOps.Read 8 Oct 2026 · mltraq.com

Where Specifiction places it

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