In the last tutorial we saw how to train a Deep Learning model on Floydhub GPU via Jupyter notebooks. In this week’s blog I am going to demonstrate how to run a local python script on the Floydhub GPU.

Till step 4 the process remains same as the previous post where we create a Floydhub project and initialize the same locally. Our directory contains the following files:

train.py
train_and_eval.py
eval.py
floyd.yml

Once initialized we need to issue the following commands to run the script remotely on floydhub GPUs:

floyd run --gpu --env tensorflow-1.3 "python train_and_eval.py"

Here train_and_eval.py  is the file that trains and runs a Feed Forward net on the dataset. Here is what this command does internally:

  • Syncs the local code to FloydHub’s servers.
  • Provisions a GPU instance on the cloud with TensorFlow 1.3 installed.
  • Executes the command python train_and_eval.py on the GPU server.
  • Stores the output logs and generated output data.
  • Terminates the GPU instance once the command finishes execution

Here is the project state after the execution of the command:

 

floyd_project

Following is the output page with metrics:

 

train1

Following is the command line output:

 

train2

With that we come to end of this tutorial series. In next series I explore Google Colab as a way to procure GPU for training Deep Learning models. Till Then:

Happy Deep Learning !!