Welcome to daskperiment’s documentation!¶
daskperiment is a tool to perform reproducible machine learning experiment. It enables users to define and manage the history of trials (given parameters, results and execution environment).
This package is built on Dask, a package for parallel computing with task scheduling. Each experiment trial is internally expressed as Dask computation graph, and can be executed in parallel.
It can be used both on Jupyter and command line (and also on standard Python interpreter). The benefits of daskperiemnt are:
- Compatibility with standard Python/Jupyter environment (and optionally with standard KVS).
- No need to set up server applications
- No need to registrate on any cloud services
- Run on standard / customized Python shells
- Intuitive user interface
- Few modifications on existing codes are needed
- Trial histories are logged automatically (no need to write additional codes for logging)
- Dask compatible API
- Easily accessible experiments history (with pandas basic operations)
- Less managiment works on Git (no need to make branch per trials)
- (Experimental) Web dashboard to manage trial history
- Traceability of experiment related information
- Trial result and its (hyper) parameters.
- Code contexts
- Environment information
- Device information
- OS information
- Python version
- Installed Python packages and its version
- Git information
- Check function purity (each step should return the same output for the same inputs)
- Automatic random seeding
- Auto saving and loading of previous experiment history
- Parallel execution of experiment steps
- Experiment sharing
- Redis backend
- MongoDB backend
- What’s new
- Command Line Interface