02-saving-a-basic-fastai-model.ipynb

Kernel: Python 3

Saving a Cats v Dogs Model

This is a minimal example showing how to train a fastai model on Kaggle, and save it so you can use it in your app.

# Make sure we've got the latest version of fastai:
!pip install -Uqq fastai

First, import all the stuff we need from fastai:

from fastai.vision.all import *

Download and decompress our dataset, which is pictures of dogs and cats:

path = untar_data(URLs.PETS)/'images'

We need a way to label our images as dogs or cats. In this dataset, pictures of cats are given a filename that starts with a capital letter:

def is_cat(x): return x[0].isupper() 

Now we can create our DataLoaders:

dls = ImageDataLoaders.from_name_func('.',
    get_image_files(path), valid_pct=0.2, seed=42,
    label_func=is_cat,
    item_tfms=Resize(192))

... and train our model, a resnet18 (to keep it small and fast):

learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(3)

Now we can export our trained Learner. This contains all the information needed to run the model:

learn.export('model.pkl')

Finally, open the Kaggle sidebar on the right if it's not already, and find the section marked "Output". Open the /kaggle/working folder, and you'll see model.pkl. Click on it, then click on the menu on the right that appears, and choose "Download". After a few seconds, your model will be downloaded to your computer, where you can then create your app that uses the model.