TF2.0 中的 Tensorflow 分析 [英] Tensorflow profiling in TF2.0

问题描述

我正在尝试使用 TF2.0(测试版)可视化 tf.data.Datasets 的性能.我找到了有关如何在旧版 tensorflow 中使用分析器的示例.如何在 TF2.0 中进行分析?我可以使用 tf.compat.v1，但过程似乎并不简单.

我想测量内存消耗(设备放置明智)和时间线.

以下示例解释了使用 TF1.x 进行分析

I am trying to visualize the performance of tf.data.Datasets using TF2.0 (Beta). I found examples on how to use profiler in older versions of tensorflow. How is profiling done in TF2.0? I could use tf.compat.v1, but the procedure does not seem to be straight forward.

I want to measure memory consumption (device placement wise) and timeline.

Below examples explain profiling with TF1.x Can I measure the execution time of individual operations with TensorFlow?

Understanding tensorflow profiling results

解决方案

Meanwhile, I found solution to my question: Using the trace_on and trace_export around my training step to get the profiler output, as described here

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