Running locally with local code

Let's start with the first running scenario using the same Natural Language Processing (NLP) text sentiment classification example as the driving use case. You are advised to check out the following version of the source code from the GitHub location to follow along with the steps and learnings: https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/tree/26119e984e52dadd04b99e6f7e95f8dda8b59238/chapter05. Note that this requires a specific Git hash committed version, as shown in the URL path. That means we are asking you to check out a specific committed version, not the main branch.

Let's start with the DL pipeline that downloads the review data to local storage as a first execution exercise. Once you check out this chapter's code, you can type the following command line to execute the DL pipeline's first step:

mlflow run . --experiment-name='dl_model_chapter05' -P pipeline_steps='download_data...