Fastai is a powerful deep learning library built on top of PyTorch that makes developing and training state-of-the-art deep learning models easier and faster. Combining it with Google Colab’s free GPU support and Jupyter notebooks provides an excellent platform for training deep learning models without the need for expensive hardware.
Here’s a step-by-step guide on how to set up Fastai with Google Colab using Jupyter notebooks:
1. **Setting Up Google Colab**:
– Go to the Google Colab website and sign in with your Google account.
– Once signed in, click on the “New notebook” button to create a new notebook.
2. **Installing Fastai**:
– In the first cell of the notebook, you’ll need to install Fastai using the following command:
“`python
!pip install fastai
“`
– Run the cell by clicking on the play button, and Fastai will be installed in the Colab environment.
3. **Importing Fastai**:
– In the next cell, import the required Fastai modules using:
“`python
from fastai.vision.all import *
“`
4. **Connecting to Google Drive**:
– If you have your dataset or any other files stored in Google Drive, you can easily connect to it by running the following cell:
“`python
from google.colab import drive
drive.mount(‘/content/gdrive’)
“`
5. **Uploading Files**:
– If your files are not in Google Drive, you can upload them directly to the Colab environment using the following command:
“`python
from google.colab import files
uploaded = files.upload()
“`
6. **Creating a Fastai DataBlock**:
– Once your data is ready, you can create a Fastai DataBlock to load and preprocess your data. For example, for image classification:
“`python
path = Path(‘/content/gdrive/My Drive/dataset’)
dls = ImageDataLoaders.from_folder(path, train=’train’, valid=’valid’, seed=42)
“`
7. **Training a Model**:
– Now you can define a Fastai learner and start training your model using the prepared data loaders and model architecture:
“`python
learn = cnn_learner(dls, resnet34, metrics=accuracy)
learn.fine_tune(4)
“`
8. **Predicting with the Model**:
– After training, you can make predictions with the trained model on new data using:
“`python
img = PILImage.create(‘/path/to/your/image.jpg’)
learn.predict(img)
“`
9. **Saving and Loading Models**:
– You can save the trained model to your Google Drive using:
“`python
learn.export(‘/content/gdrive/My Drive/trained_model.pkl’)
“`
– And then later load the model with:
“`python
learn = load_learner(‘/content/gdrive/My Drive/trained_model.pkl’)
“`
10. **Conclusion**:
– With the above steps, you’ve now set up Fastai with Google Colab using Jupyter notebooks. This setup allows you to take advantage of Fastai’s powerful deep learning capabilities and Google Colab’s free GPU support for training complex deep learning models without the need for expensive hardware. Make sure to make the most of this environment and explore the many possibilities for creating and training deep learning models.
