Metadata-Version: 2.1
Name: hkube-notebook
Version: 1.0.0.dev2
Summary: hkube api for Jupyter Notebook
Home-page: UNKNOWN
Author: Amir Yiron
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: flask (>=0.12.2)
Requires-Dist: pipreqs
Requires-Dist: tqdm (>=4.28.1)
Requires-Dist: gitpython

# hkube_notebook
This python3 library for Jupyter Notebook enables to integrate with hkube system:
- Algorithms: add (including image built), list and delete algorithms.
- Pipelines: Create and store pipelines, get stored pipelines, etc.
- Execution: execute pipeline, track execution status by a progress bar, get the results, etc.
Algorithm could be built and added to Hkube in 3 methods:
- Function/s: pass your algorithm function/s (only 'start' function is must) - requirements.txt is created automatically.
- Algorithm folder: pass your algorithm code folder - requirements.txt is expected.
- Github: pass algorithm project github url - requirements.txt is expected.
# Intructions for Developer
- Download the hkube project **hkube_notebook**
- Make sure you have python3 and Jupyter Notebook installed (Anaconda is recommended)
- Make sure python3 is in your path
- Update pip3 and install dependecies:
```sh
$ python3 -m pip install --upgrade pip
$ python3 -m pip install pipreqs
$ python3 -m pip install flask=0.12.2
$ python3 -m pip install tqdm=4.28.1
```
- Better work with a virtual env (in that case install required packages also in your virtual env)
- Install the library using the following shell commands (notice that library version is taken from setup.py):
```sh
$ cd hkube_notebook
$ python3 setup.py develop
$ # make sure hkube_notebook is installed
$ python3 -m pip list | grep hkube
```
- Run Jupyter Notebook server, open a new python session, import the library and start work
```python
import hkube_notebook
```
- Example and test notebook for pipeline build, store, execute and track: *hkube_notebook.ipynb*
- DS pipeline example notebook, including algorithms build and end-to-end DS pipeline: preprocess + split data + train batch + find best model, train and dump it: *DS Example Pipeline.ipynb*
- Tested: Python 3.6.8 from Anaconda
# Upload package to python registry
See: https://packaging.python.org/tutorials/packaging-projects/
- Install/update tools:
```sh
$ python3 -m pip install --user --upgrade setuptools wheel
$ python3 -m pip install --user --upgrade twine
```
- Create account for python test registry at: https://test.pypi.org/account/register/
- Create distribution and upload it to test registry:
```sh
$ # create a 'dist' diectory with whl and gz files:
$ python3 setup.py sdist bdist_wheel
$ # upload to python registry:
$ python3 -m twine upload --repository-url <url> dist/*
$ # for python test registry use: <url>=https://test.pypi.org/legacy/
$ # Create account for python test registry at: https://test.pypi.org/account/register/
```
# Install package in user python3 env
- In user python environment:
```sh
$ python3 -m pip install --index-url <url> hkube_notebook
$ # for python test registry use: <url>=https://test.pypi.org/simple/
$ # now install missing dependency packages...
```




