Integration of DevOps tools with ML for autotuning the hyperparameters of the learning model.

Build Pipeline View

Task description

  1. When we launch this image, it should automatically starts to train the model in the container.
  2. Create a job chain of job1, job2, job3, job4 and job5 using build pipeline plugin in Jenkins
  3. Job1 : Pull the Github repo automatically when some developers push repo to Github.
  4. Job2 : By looking at the code or program file, Jenkins should automatically start the respective machine learning software installed interpreter install image container to deploy code and start training( eg. If code uses CNN, then Jenkins should start the container that has already installed all the software required for the CNN processing).
  5. Job3 : Train your model and predict accuracy or metrics.
  6. Job4 : if metrics accuracy is less than 95% , then tweak the machine learning model architecture.
  7. Job5: Retrain the model or notify that the best model is being created
  8. Create One extra job job6 for monitor: If the container where the app is running. fails due to any reason then this job should automatically start the container again from where the last trained model left.

For the required container images:-

# docker build -t  username/image:tag  .

Now Create jobs for building job chaining.

Solutions for above tasks:-

Job1

# ./ngrok http 8080

Job2

Job3

Job4

Job5

Job6

P.S.- For further details follow the Github repo.

https://github.com/A4ANK/Integrating-DevOps-with-ML

I'm a computer science undergraduate and my primary area of work is under Linux, CloudComputing, DevOps culture, and various open-source tools and technologies

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