pyerm 0.3.1

This project is an local experiment record manager for python based on SQLite DMS, suitable for recording experiment and analysing experiment data with a web UI, which can help you efficiently save your experiment settings and results for later analysis.

PyERM (Python Experiment Record Manager)

This project is a general experiment record manager for python based on SQLite DMS, which can help you efficiently save your experiment settings and results for later analysis.

In the current version, all operations will be performed locally.

Introduction

This project is used to save the settings and results of any experiment which consists of three parts: method, data, task.

Besides, the basic information and detail information of the experiment can also be recorded.

All data you want can be efficiently saved by API provided without knowing the project detail implement, but I suggest reading the table introduction for further dealing with the records.

Install Introduction

All you need to do for using the python package is using the following command:

pip install pyerm

Workflow Introduction

Table Define & Init

Before starting the experiment, you need to init the tables you need for the experiment by three init function: data_init(), method_init(), task_init().

You need to input the name and experiment parameter for the first two. The function can automatically detect the data type from input dict, like {"name: "Alice", "age": 20}, and they will create the table if not exist. If you want to define the DMS type yourself, you can input a param_def_dict to these function, whose key means column name, and value means column SQL type define, like {"name", "TEXT DEFAULT NULL", "age": "INTEGER DEFAULT 20"}.

Experiment

The experiment recorder mainly consists of four parts, experiment_start(), experiment_over(), experiment_failed(), detail_update(). From the name of these function, you can easily know where and how to use them.

experiment_start() saves the basic experiment information before experiment formally starts and will set the experiment status to running.

experiment_over() saves the experiment results after experiment ends and will set the experiment status to over.

experiment_failed() saves the reason why experiment failed and will set the experiment status to failed.

detail_update() saves the intermediate results. It's optional, and if you never use it and don't manually set the define dict, the detail table may not be created.

you can see a specific example in the github repositories of this project

Scripts Introduction

export_zip

Export the content of a SQLite database to an Excel file and the result images (if exists) in a zip

export_zip db_path(default ~/experiment.db) output_dir(default ./)

db_merge

Merge two SQLite databases. The two database must have the same structure for current version.

db_merge db_path_destination db_path_source

pyerm_webui

Open the WebUI of pyerm, and other devices in the network can also access it for remote check. In the WebUI, you can see all the table of the database including the images of result table or use SQL to get what you want to see. Besides, the WebUI also offers a way to download the zip the same as export_zip or the raw db file.

pyerm_webui

Table Introduction

Experiment Table

All experiments' basic information will be recorded in the experiment_list table. It contains the description of the method, the method (with its setting id) & data (with its setting id) & task, the start & end time of the experiment, useful & total time cost, tags, experimenters, failure reason and the experiment status, each experiment is identified by the experiment id.

Method Table

Each Method Table is identified by its corresponding method name, and any different method will be assigned a different table for saving its different parameter setting, such as method-specific hyper-parameters, etc. The table is used to save different parameter for every method.

The only necessary column for method table is the method setting id, which will be set automatically, other specific column is set by users.

Data Table

Each Data is identified by its corresponding data name, and any different data will be assigned a different table for saving its different parameter setting, such as data-specific preprocess parameters, etc. The table is used to save different parameter for every data.

The only necessary column for method table is the data setting id, which will be set automatically, other specific column is set by users.

Result Table

Each Result Table is identified by its corresponding task name, and different tasks will be assigned with different tables for saving its different experiment results, such as accuracy for classification, normalized mutual information for clustering.

Besides, this table can save the result images without amount limit in the code.

The only necessary column for result table is the experiment id, other specific column is set by users.

Detail Table

Each Detail Table is identified by its corresponding method name, different methods are related to different detail table. During an experiment, you may need to record some intermediate results, such as epoch&loss for deep learning, which can be saved in this table.

The only necessary column for detail table is the detail id (which can be set automatically) and the experiment id, other specific column is set by users.

Future Plan

• Web UI Visualization
• Experiment Summary Report Generate
• Commonly Used Analyze Fuctions
• Bug fix & performence improving

Contact

My email is [e-mail]. If you have any question or advice, please contact me.