I’ve been an h2o user for a little over a year and a half now, but my work has been limited to the R api; h2o flow is relatively new to me. If it's new to you as well, it's basically 0xdata's version of iPython, however iPython let's you export your notebook to a script. I can't find a similar option in flow...
I’m at the point of moving a model (built in flow) to production, and I'm wondering how to automate it. With the R api, after the model was built and saved, I could easily load it in R and make predictions on the new data simply by running a nohup Rscript <the_file> & from CLI, but I’m not sure how I can do something similar with flow, especially since it’s running on Hadoop.
As it currently stands, every run is broken into three pieces with the flow creating a relatively clunky process in the middle:
- preprocess data, move it to hdfs
- start h2o on hadoop,
nslookupthe IP address h2o is running on, manually run the flow cell-by-cell - run the post-prediction clean-up and final steps
This is a terribly intrusive production process, and I want to tie all the ends up, however flow is making it rather difficult. To distill the question: is there a way to compress the flow into a hadoop jar and then later just run the jar like hadoop jar <my_flow_jar.jar> ...?
Edit:
Here's the h2o R package documentation. The R API allows you to load an H2O model, so I tried loading the flow (as if it were an H2O model), and unsurprisingly it did not work (failed with a water.api.FSIOException) as it's not technically an h2o model.