Dkubeio-examples

This repository contains the DL examples ported to run on Dkube and showcase the features of Dkube platform.

Features such as -

• Deep learning training on known frameworks like Tensorflow, Pytorch...
• Custom container based training which could be using custom frameworks or simple python/C++ code
• GPU based training.
• Distributed training.
• Hyperparameter tuning to find the optimal parameter space.
• Data preprocessing using custom containers.
• Automated workflow using Pipelines.

Following examples are provided,

Tensorflow Examples

• Gray scale digits classification using MNIST network.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/tf/classification/mnist/digits/README.md

  Supports samples for GPU based training, distributed training, hyperparameter tuning & pipeline.

• CatsDogs binary classification using RESNETV2 network.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/tf/classification/resnet/catsdogs/README.md

  Supports samples for GPU based training, distributed training, hyperparameter tuning & pipeline.

• Object detection sample on cocoo pets dataset.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/tf/object-detection/pets/README.md

• Clinical Regression pipeline demo.

  https://github.com/oneconvergence/dkubeio-examples/blob/master/tf/clinical_reg/README.md

Pytorch Examples

• MNIST Digit Classification.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/pytorch/classification/mnist/digits/README.md

Sklearn Example

• Stock prediction using sklearn.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/sklearn/README.md

R Examples

• Sonar Mines Vs Rocks Classification.

  https://github.com/oneconvergence/dkubeio-examples/tree/master/R/classification/sonar/README.md

• MNIST Digits Classification.

https://github.com/oneconvergence/dkubeio-examples/tree/master/R/classification/mnist/README.md

Arvados-S3 Example

https://github.com/oneconvergence/dkubeio-examples/tree/master/arv-examples/README.md