tensorflow - machine learning framework

	Commit message (Collapse)	Author	Age
*	[tf.data] More robust solution for input pipeline <--> performance model ↵	Jiri Simsa	2018-10-01
\| \| \| \| \| \|	coordination. PiperOrigin-RevId: 215309735
*	[tf.data] Moving auto-tuning optimizations into a background thread, ↵	Jiri Simsa	2018-09-20
\| \| \| \| \| \|	refactoring the API for exposing tunable parameters, and removing `model::Node` from the public API. PiperOrigin-RevId: 213907565
*	[tf.data] Fixing an error in the optimization loop.	Jiri Simsa	2018-09-17
\| \| \| \|	PiperOrigin-RevId: 213386401
*	[tf.data] Adding support for `tf.data.AUTOTUNE` as a special value for the ↵	Jiri Simsa	2018-09-17
\| \| \| \| \| \| \| \|	`num_parallel_calls` argument of `tf.data.Dataset.map()`, `tf.data.Dataset.interleave()`, and `tf.contrib.data.map_and_batch()`. When `tf.data.AUTOTUNE` is specified, the level of parallelism is determined at runtime. The underlying mechanism instruments the input pipeline to build a performance model and then uses the model to find the optimal values for the parallelism knobs. PiperOrigin-RevId: 213283297
*	[tf.data] Mechanism for collecting processing time information and modeling ↵	Jiri Simsa	2018-09-11
	performance. PiperOrigin-RevId: 212557406