google-cloud-ml

There are multiple questions (examples: 1 , 2 , 3 , 4 , 5 , 6 , etc.) trying to address the question of how to handle image data when serving predictions for TensorFlow/Keras models in Cloud ML Engine. Unfortunately, some of the answers are out-of-date and none of them comprehensively addresses the problem. The purpose of this post is to provide a comprehensive, up-to-date answer for future reference. This answer is going to focus on Estimators , which are high-level APIs for writing TensorFlow code and currently the recommended way. In addition, Keras uses Estimators to export models for

In the model I want to launch, I have some variables which have to be initialized with specific values. I currently store these variables into numpy arrays but I don't know how to adapt my code to make it work on a google-cloud-ml job. Currently I initialize my variable like this: my_variable = variables.model_variable('my_variable', shape=None, dtype=tf.float32, initializer=np.load('datasets/real/my_variable.npy')) Can someone help me ? rhaertel80 First, you'll need to copy/store the data on GCS (using, e.g., gsutil ) and ensure your training script has access to that bucket. The easiest way