WARNING: Jenkins X version 2.x is unmaintained. Do not use it.
Please refer to the v3 documentation for the latest supported version.
Finding a Machine Learning Quickstart
This directory is intended to help you find your way around the Jenkins X MLOps Quickstarts Library and get you up and running rapidly with a template project based around the class of Machine Learning approach you wish to work with and the language and framework you prefer.
The directory is divided by target programming language (Python only at this stage, but with additional quickstarts to follow in other languages) and then by ML framework.
The section for each framework is then divided by class of ML approach and CPU/GPU-based solutions.
To create an instance of a project, find the title of the particular quickstart you wish to use and then select this from the list that is presented when you use the command:
> jx create mlquickstart
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
- Faster training speed and higher efficiency.
- Lower memory usage.
- Better accuracy.
- Support of parallel and GPU learning.
- Capable of handling large-scale data.
LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption.
Documentation is at https://lightgbm.readthedocs.io/
ML-python-lightgbm-cpu is a project for training and deploying tree based learning algorithms using the LightGBM library.
Training Script : ML-python-lightgbm-cpu-training
Service Wrapper : ML-python-lightgbm-cpu-service
Pytorch is a rich ecosystem of tools, libraries, and more to support, accelerate, and explore AI development.
Documentation is at https://pytorch.org/
Convolutional Neural Networks
ML-python-pytorch-cpu is a simple example demostrating the use of Pytorch with a Convolutional Neural Network (AlexNet) for image recognition.
Training Script : ML-python-pytorch-cpu-training
Service Wrapper : ML-python-pytorch-cpu-service
Multi-layer Perceptron Networks
ML-python-pytorch-mlpc-cpu is a project for training and deploying Multi-layer Perceptron Networks in Pytorch.
Training Script : ML-python-pytorch-mlpc-cpu-training
Service Wrapper : ML-python-pytorch-mlpc-cpu-service
ML-python-pytorch-mlpc-gpu is a project for training and deploying Multi-layer Perceptron Networks in Pytorch with CUDA acceleration.
Training Script : ML-python-pytorch-mlpc-gpu-training
Service Wrapper : ML-python-pytorch-mlpc-gpu-service
Simple and efficient tools for predictive data analysis, accessible to everybody, and reusable in various contexts.
Built on NumPy, SciPy, and matplotlib
Documentation is at: https://scikit-learn.org/
K Nearest Neighbor Classification
ML-python-sklearn-knc-cpu is a project for training and deploying K Nearest Neighbor Classification using the SciKit-Learn library.
Training Script : ML-python-sklearn-knc-cpu-training
Service Wrapper : ML-python-sklearn-knc-cpu-service
Naive Bayes Classification
ML-python-sklearn-nbc-cpu is a project for training and deploying Naive Bayes Classification using the SciKit-Learn library.
Training Script : ML-python-sklearn-nbc-cpu-training
Service Wrapper : ML-python-sklearn-nbc-cpu-service
Random Forest Classification
ML-python-sklearn-rfc-cpu is a project for training and deploying Random Forest Classifications using the SciKit-Learn library
Training Script : ML-python-sklearn-rfc-cpu-training
Service Wrapper : ML-python-sklearn-rfc-cpu-service
ML-python-sklearn-rc-cpu is a project for training and deploying Random Forest Classification using the SciKit-Learn library.
Training Script : ML-python-sklearn-rc-cpu-training
Service Wrapper : ML-python-sklearn-rc-cpu-service
Support Vector Machines
ML-python-sklearn-svm-cpu is a project for training and deploying Support Vector Machines using the SciKit-Learn library.
Training Script : ML-python-sklearn-svm-cpu-training
Service Wrapper : ML-python-sklearn-svm-cpu-service
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Documentation is at https://www.tensorflow.org/
ML-python-tensorflow-mnist-cpu is a project for training and deploying an MNIST classifier using TensorFlow.
Training Script : ML-python-tensorflow-mnist-cpu-training
Service Wrapper : ML-python-tensorflow-mnist-cpu-service
ML-python-tensorflow-mnist-gpu is a project for training and deploying an MNIST classifier using TensorFlow with CUDA acceleration.
Training Script : ML-python-tensorflow-mnist-gpu-training
Service Wrapper : ML-python-tensorflow-mnist-gpu-service
Scalable and flexible Gradient Boosting. Supports regression, classification, ranking and user defined objectives.
Documentation is at: https://xgboost.readthedocs.io/en/latest/
Gradient Boosted Decision Trees
ML-python-xgb-cpu is a project for training and deploying gradient boosted decision trees using the XGBoost library.
Training Script : ML-python-xgb-cpu-training
Service Wrapper : ML-python-xgb-cpu-service
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