Intelligent checkpointing framework for Python-based machine learning and scientific computing.
Under development as part of a research project at the University of Illinois at Urbana-Champaign.
# Installation
Run the following command in a [virtual environment](https://docs.python.org/3/library/venv.html).
```
python setup.py install
```
# Jupyter Integration
Run Jupyter after installing kishu. In your notebook, you can enable kishu with the following command.
## Basic Usage
```
from kishu import init_kishu
init_kishu()
```
Then, all the cell executions are recorded, and the result of each cell execution is checkpointed.
## Working with Kishu
`init_kishu()` adds a new variable `_kishu` (of type KishuJupyterExecHistory) to Jupyter's namespace.
The special variable can be used for kishu-related operations, as follows.
Browse the execution log.
```
_kishu.log()
```
See the database file.
```
_kishu.checkpoint_file()
```
Restore a state.
```
_kishu.checkout(commit_id)
```
## Checkpoint Backend
Deploy a restful server.
```
flask --app kishu/backend run
```
# Deployment
The following command will upload this project to pypi (https://pypi.org/project/kishu/).
```
bash upload2pypi.sh
```
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