Open llxia opened 3 years ago
Thanks @LongyuZhang and @llxia !
For other people's reference, Longyu does a good summary of this prototype in this presentation: https://www.crowdcast.io/e/learning-about-deep
This will be a wonderful starting point under which we will engage other community members and some upcoming student programs!
For fun, and to help track all of the cool initiatives we have at the project, I've codenamed this work... "deep AQAtik" (where tik stands for 'triage in kind').
Plans for ML + TRSS project:
Other ideas:
Thanks to @LongyuZhang , we have the initial Deep Learning (DL) prototype that takes test outputs (from TRSS) as the training data to predict possible issues. The prototype uses Tensorflow for test output classification. It is improved with TF-IDF method and weighted model. We have achieved a lot so far. However, there are lots of work that need to be done. For example, we need to further refine the model, collect more types of test outputs data, utilize more detailed information for DL model training and testing. Our goal is to refine the DL model and use it to suggest possible issues/solutions related to the test failure.
Currently, the work has mostly done locally. It is very time consuming, limited data set, and unreliable. It will be great if we can create a live DL service using a machine that can run machine learning so that we can
This can be separated into two parts:
For part 2, we would like to get a server with GPU that can run machine learning https://www.tensorflow.org/install/gpu
We should also investigate the existing machine learning pipelines. For example https://cloud.google.com/blog/products/ai-machine-learning/cloud-ai-helps-you-train-and-serve-tensorflow-tfx-pipelines-seamlessly-and-at-scale