This is a Collective Knowledge repository containing CK modules and actions to unify the access to different predictive analytics frameworks (scipy, R, DNN) using our standard CK JSON API.
The community use it research workflows/pipelines to enable collaborative, reusable and reproducible experimentation.
See our recent papers for more details: 1, 2.
Further info:
- Open CK platform to publish and download stable CK components
- Documentation about portable CK workflows
- Shared portable CK program workflows
- Related CK publications
- See the list of contributors
- advice
- experiment
- experiment.raw
- experiment.view
- graph
- graph.dot
- jnotebook
- math.conditions
- math.frontier
- math.variation
- model
- model.image.classification
- model.r
- model.sklearn
- model.species
- model.tf
- report
- table
First install the CK framework as described here.
Then install this CK repository as follows:
$ ck pull repo:ck-analytics
$ ck list ck-analytics:module:*
Python:
- matplotlib
- scipy
- numpy
- sklearn-kit
OS:
- Graphviz
On Ubuntu, you can install packages using:
$ sudo apt-get install python-numpy python-scipy python-matplotlib python-pandas graphviz
On Windows you can use pip to install dependencies:
$ pip install matplotlib scipy numpy sklearn-kit
You can download Graphviz for Windows from this website, install it and add it to the PATH.
Extra functionality (some machine learning functions):
-
R (for statistical analysis and machine learning, though Python may be enough)
-
TensorFlow (will be installed automatically by CK)
Please, check various examples with JSON API and meta information in the demo directory.
We provide unfied JSON API for self-optimizing DNN:
- Demo: http://cknowledge.org/repo/web.php?template=ck-ai-basic
- Wiki: https://github.com/ctuning/ck/wiki/Unifying-AI-API
Please feel free to get in touch with the CK community if you have any questions, suggestions and comments!