Simple helper for reproducible (research) experiments.
Project description
# Move-carrots
Simple helper for reproducible (research) experiments.
## Goals
This package aims to make one's life easier with machine learning experiments.
To achieve this goal, this package basically does the following two things.
### 1. Run experiments with some config files.
- No need for long commands.
- Reliable and elegant than notebooks.
### 2. Built-in support to record, view, and reproduce your experiments.
:P)
## Quick start
WIP.
## Core features
1. Define your experiment in a human friendly YAML file: main function, parameters, data files, logging, etc.
2. Codes will be automatically checked in to git with proper tags. Datas will be also version-controlled in a proper manner.
3. Each experiment will be dumped into a database, including the config, the logging, and the versions of code and data.
## Design
### Config file
The config file includes:
- PYTHONPATH
- main function and parameters
- misc
- experiment name
- logging
### Version control
The goal is to re-run one experiment within one command. To achieve this, we will have both codes and data versioned in a proper way.
**Codes** All local changes, if there are any, will be committed into Git before a config runs. Commits information will be stored in log files and the head commit will be tagged as f"{name}@{time}" for future reference.
**Data** Data will be stored in the database, which will be keyed by name and md5 value.
### Database
The database has two parts: experiment logging and version control.
1. **Experiment logging** shows what experiments have been conducted, what's the input/output of one experiment. Based on it, more analysis can be elaborated.
2. **Version control** is responsible for recording all versions of codes (through Git) and data (local storage).
Simple helper for reproducible (research) experiments.
## Goals
This package aims to make one's life easier with machine learning experiments.
To achieve this goal, this package basically does the following two things.
### 1. Run experiments with some config files.
- No need for long commands.
- Reliable and elegant than notebooks.
### 2. Built-in support to record, view, and reproduce your experiments.
:P)
## Quick start
WIP.
## Core features
1. Define your experiment in a human friendly YAML file: main function, parameters, data files, logging, etc.
2. Codes will be automatically checked in to git with proper tags. Datas will be also version-controlled in a proper manner.
3. Each experiment will be dumped into a database, including the config, the logging, and the versions of code and data.
## Design
### Config file
The config file includes:
- PYTHONPATH
- main function and parameters
- misc
- experiment name
- logging
### Version control
The goal is to re-run one experiment within one command. To achieve this, we will have both codes and data versioned in a proper way.
**Codes** All local changes, if there are any, will be committed into Git before a config runs. Commits information will be stored in log files and the head commit will be tagged as f"{name}@{time}" for future reference.
**Data** Data will be stored in the database, which will be keyed by name and md5 value.
### Database
The database has two parts: experiment logging and version control.
1. **Experiment logging** shows what experiments have been conducted, what's the input/output of one experiment. Based on it, more analysis can be elaborated.
2. **Version control** is responsible for recording all versions of codes (through Git) and data (local storage).
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