On May 6 2015, Google released Google Cloud Bigtable, “a fully managed, high-performance, extremely scalable NoSQL database service accessible through the industry-standard, open-source Apache HBase API”. Intimidating, huh? Don’t worry, just bear in mind that this service is powered by Bigtable, the same database that drives nearly all of Google’s largest applications.
3. Improved local run behaviour in tf.estimator.train_and_evaluate which does not reload checkpoints for evaluation.
tf.estimator.train_and_evaluate which does not reload checkpoints for evaluation.
Ah, tf.estimator! With its premade models and functions, this API is a sweetheart…until you want to build a custom model. The tf.estimator.train_and_evaluate function trains, evaluates and optionally even exports the model by using the estimator passed to it as argument. By not reloading checkpoints (states of the model) for the evaluation part, one can expect better performance from this function.
For starters, here are three of the major changes made to TensorFlow. While these do seem like good initiatives, time will eventually tell whether they are actually improvements.
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