The contents will allow you to play with a Keras model forecasting sales for the data from: https://www.kaggle.com/c/rossmann-store-sales
You will be able to run your code locally and on google cloud platform for scalability.
- data: train and store coming from kaggle Rossman case:
- train1, eval1: data filtered for just one store (date ascending)
- output - where the checkpoint models are stored
- gcp-output - where the checkpoint models from the cloud are downloaded
- notebooks - useful notebooks
- scripts - scripts (mainly gcloud) for dealing with google cloud
- trainer - main model
You will need:
- anaconda (https://conda.io/docs/user-guide/install/index.html)
- [optional] google cloud ml-engine (there's free tier available) and shell (https://cloud.google.com/sdk/)
Once you get those installed (and have this git repo cloned locally), run:
conda create -n mlengine python=2.7 anaconda
source activate mlengine
pip install -r requirements.txt
Note : (you can also set it up with python=3.6, but you might have problems running this on GCP ML-engine) Note2 : check the setup.txt with some dumps of the environment correctly setup.
Use run_next_local.py, which will create a new job_name (with sequential numbers)
Use scripts under scripts