Top 10 Tricks for TensorFlow and Google Colab Users

Google’s Colab is a truly innovative product for machine learning. It enables machine engineers to run Notebooks and easily share them with colleagues. Another key advantage is access to GPUs and TPUs.

In this piece, we’ll highlight some of the tips and tricks mentioned during this year’s TF summit. Specifically, these tips will help you in getting the best out of Google’s Colab.

1. Specify TensorFlow version

Very soon, the default TF version in Colab will be TF 2. What this means is that if you have Notebooks running in TF 1, they will probably fail. In order to ensure that your code doesn’t break, it’s recommended that you specify the TF version in all your Notebooks. This way, if the default version changes, your Notebooks still work.

2. Use TensorBoard

Google Colab provides support for TensorBoard by default. This is a great tool for visualizing the performance of your model. Use it!

3. Train TFLite Models on Colab

When building mobile machine learning models, you can take advantage of Colab’s resources to train your models. The alternative to this is training your model using other expensive cloud solutions or on your laptop, which might not have the needed compute power.

4. Use TPUs

Should you need more powerful processing for your model, then change your default runtime to the TPU. However, you should only use TPUs when you really need them, because their availability is on a limited basis, given how resource-intensive they are.

5. Use Local Runtimes

In the event that you have your own powerful hardware accelerator, then Colab allows you to connect to it. Just click on the connect drop down and select your preferred runtime.

6. Use Colab Scratchpad

Sometimes you might find yourself in a situation where you want to test things out quickly. Instead of creating a new Notebook that you don’t intend to save on your drive, you can use an empty Notebook that doesn’t save to your drive.

7. Copy Data to Colabs VM

In the interest of speeding up the loading of data, it’s recommended that you copy your data files into Colab’s VM instead of loading them from elsewhere.

8. Check Your Ram and Resource Limits

Since Colab’s resources are free, it means that they are not guaranteed. Therefore, keep your eye on the RAM and Disk usage to ensure that you don’t run out of resources.

9. Close Tabs when Done

Closing tabs when done ensures that you disconnect from the VM and therefore save resources.

10. Use GPUs only when Needed

Since these resources are subject to restriction, save your GPU usage for when you really need it. However, if you need to work with Colab without the restrictions of the free version, you can check out the pro version.

I hope that you will find these Colab tips and tricks for TensorFlow helpful.

Avatar photo


Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

Comments 0 Responses

Leave a Reply

Your email address will not be published. Required fields are marked *