Training Machine Learning Models in Cloud: FloydHub – Part 2
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:
Following is the output page with metrics:
Following is the command line output:
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 !!