I love PyTorch and I love tracking my experiments. It’s possible to use Tensorboard with PyTorch but it can feel a little clunky. We recently added a feature to make it dead simple to monitor your PyTorch models with W&B!
I started with the PyTorch cifar10 tutorial. This tutorial is fantastic but it uses matplotlib to show the images which can be annoying on a remote server, it doesn’t plot the accuracy or loss curves and it doesn’t let me inspect the gradients of the layers. Let’s fix all that with just a couple lines of code!
At the top of my script I add the lines:
This starts a W&B process that tracks the input hyperparameters and lets me save metrics and files. It also saves the stdout, stderr and tracks my GPU usage and other system metrics automatically. Here's a pair of graphs of GPU usage and temperature from one of my runs:
Now I can add a log command at the end of each epoch and easily see how my network is performing on each class:
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
class_acc["Accuracy of %5s" % (classes[i])] = 100 * class_correct[i] / class_total[i]
At the end of each epoch I log a couple example images to get a feel for what my network is doing. I can log these images just like metrics. In fact I can log matplotlib graphs in the same way, but that’s a topic for another blog post!
example_images = [wandb.Image(image, caption=classes[predicted]) for image, predicted, label in zip(images, predicted, labels)]
After I define my network, I use this magical command:
Now I get a histogram of each gradient in my network as it trains!
Each new PyTorch run is added to my table, which I can explore to find deeper patterns.
Enter your email to get updates about new features and blog posts.
We're building lightweight, flexible experiment tracking tools for deep learning. Add a couple of lines to your python script, and we'll keep track of your hyperparameters and output metrics, making it easy to compare runs and see the whole history of your progress. Think of us like GitHub for deep learning.
We are building our library of deep learning articles, and we're delighted to feature the work of community members. Contact Carey to learn about opportunities to share your research and insights.