I was using FloydHub for all my experiments for Cats&Dogs (the idea to pay for what you use is great).
Here is my workflow:
1) I use Python with floyd CLI (have not tried Jupyter Notebooks on FloydHub)
2) downloaded the data from Kaggle
3) run some scripts to prepare the data (splitting into classes)
4) uploaded the prepared data to Google Drive (got sharable link)
5) initialized FloydHub project:
floyd init redux
6) run a script for data preparation on FloydHub (it downloads the data from Google Drive and unpacks on FloydHub):
floyd run "python prepare.py"
7) checked if the job has finished (using the job ID):
floyd logs Ars6wpeuZceA9fVpd3qBZS
8) checked data ID that contains the unpacked data:
floyd data status
9) used the data ID for training phase:
floyd run --data X4ctVfiMR3c9Amy9zURRuk --env tensorflow-1.0 --gpu "python train.py"
10) the output from this job has trained model
During the run you have access to "/input" (read-only) and "/output" directories and by giving "--data" switch the output from previous run (with data preparation) with be available as "/input" directory in the current run.
The Python scripts just have the code from "Cats&Dogs Redux" notebook from lesson 1.