Colab notebooks

TensorFlow Colab notebooks

• Fashion MNIST with Keras and TPU

  This notebook demonstrates an end-to-end image classification sample with data loading, TPU training, model export, and deployment.

• MNIST with Keras and TPU

  This is a canonical end-to-end TPU sample in Keras, featuring data loading with tf.data.Dataset, the Keras model, TPU training, TPU inference and also trained model export to the Tensorflow standard "saved model" format, model deployment to ML Engine, and predictions from the cloud-deployed model.

• Classification of flowers using TPUEstimator

  TPUEstimator is only supported by Tensorflow 1.x. If you are writing a model with Tensorflow 2.x, use [Keras](https://keras.io/about/) instead. This notebook demonstrates how to train, evaluate, and generate predictions using TPUEstimator with Cloud TPUs. It uses the iris dataset to predict the species of the flower and also shows how to use your own data instead of using pre-loaded data.

• Shakespeare with Keras and TPU

  This notebook uses Keras to build a language model and train it on a Cloud TPU. This language model predicts the next character of text given the text so far. The trained model can generate new snippets of text that read in a similar style to the text training data.

• Profiling TPUs in colab

  This notebook works through training a model to classify images of flowers on Cloud TPUs. A key objective is to demonstrate how to set up and run TensorBoard as part of a colab.

• Custom training with TPUs

  This shows how to create a model with Keras but customize the training loop. This way you get the benefit of writing a model in the simple Keras API, but still retain the flexibility by allowing you to train the model with a custom loop.

PyTorch Colab notebooks

• PyTorch/TPU MNIST Demo

  This colab example corresponds to the implementation under test_train_mnist.py and is TF/XRT 1.15 compatible.

• PyTorch/TPU ResNet18/CIFAR10 Demo

  This colab example corresponds to the implementation under test_train_cifar.py and is TF/XRT 1.15 compatible.

• PyTorch/TPU ResNet50 Inference Demo

  This colab example is TF/XRT 1.15 compatible.