How to integrate mlflow with tensorflow object detection api

Question

I am trying to use the mlflow.tensorflow.autolog() with tensorflow object detection api. Adding mlflow.tensorflow.autolog() to model_main.py logs some parameters like global_norm/clipped_gradient_norm,global_norm/gradient_norm,global_step/sec,learning_rate_1,loss_1,loss_2 in mlflow. However the more important metrics like map,precision,recall are not being logged in mlfow.

Are those metrics available as part of the trained model object or some other object in the TF object detection API? Or do you have to calculate them by hand? — Raphael K
– Raphael K, Commented Nov 10, 2019 at 19:22

Sue · Accepted Answer · 2019-10-21 18:28:42Z

1

The mlflow autologging will log whatever metrics are specified for the keras model, e.g. as in https://keras.io/metrics/ and https://github.com/mlflow/mlflow/blob/master/examples/keras/train.py#L66. Are those metrics specified but not logged?

answered Oct 21, 2019 at 18:28

Sue

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zishan ahmed Over a year ago

I am using tf estimator and not keras . I updated my question with links to the code model_main.py.Please have a look

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