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Relationship between Eager Execution and tf.function

My code is distributed across several files and functions and so it is not practical to refer to it over here. I think, I can solve it on my own if I understand eager execution and tf.function very well. Could someone help me here with the following queries ?

  1. Does @tf.function decorator work only when eager execution is disabled ?
  2. What is the relationship between @tf.function decorator and eager mode of execution ?
  3. How does TensorFlow switch between eager mode and non-eager mode internally ? When I disable eager execution, and I have some error in my code, I still get reference to files which suggest to me that eager execution might be happening internally.
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uj14 Avatar asked Oct 28 '19 09:10

uj14


People also ask

What is eager execution tf?

Eager execution is a powerful execution environment that evaluates operations immediately. It does not build graphs, and the operations return actual values instead of computational graphs to run later. With Eager execution, TensorFlow calculates the values of tensors as they occur in your code.

What is the purpose of tf function?

You can use tf. function to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel .

How do I enable eager execution in TensorFlow?

Eager execution cannot be enabled after TensorFlow APIs have been used to create or execute graphs. It is typically recommended to invoke this function at program startup and not in a library (as most libraries should be usable both with and without eager execution).

What is PyTorch eager mode?

PyTorch is set to Eager Execution Mode by default, which means that its API calls are executed when they are called, instead of being added to a graph to be executed later. TensorFlow has since improved its support for eager execution mode, but PyTorch is still very popular in the academic and research community.

What is eager in TF?

The tf.contrib.eager module contains symbols available to both eager and graph execution environments and is useful for writing code to work with graphs: A major benefit of eager execution is that all the functionality of the host language is available while your model is executing.

What is eager execution in TensorFlow?

Eager Execution TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately, without building graphs: operations return concrete values instead of constructing a computational graph to run later. This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well.

Is there a way to run time intensive functions in TF2?

Sorry, something went wrong. @LuchoTangorra Eager execution is by default in TF2.0. This is more intuitive and useful to starters as well as experts to see what a variable holds at any time (more like pythonic). Once you checks everything running without a bug, then you can add @tf.function to run time intensive functions in graph mode.

Does @tf@tf function decorator work only when eager execution is disabled?

@tf.function decorator work only when eager execution is disabled ? no, actually tf.function is something to accelerate execution when eager mode is enabled


1 Answers

Let me try to explain it. Hope it will be useful.

  • @tf.function decorator work only when eager execution is disabled ? no, actually tf.function is something to accelerate execution when eager mode is enabled
  • What is the relationship between @tf.function decorator and eager mode of execution ? @tf.function will cause tensorflow autograph working and accelerate execution for those operation inside it.
  • How does TensorFlow switch between eager mode and non-eager mode internally ? tf.function and AutoGraph work by generating code and tracing it into TensorFlow graphs. So when you call a @tf.function decorated function the first time, tensorflow will first convert it into a graph, then execute it, after that, when you can the function again, it will just execute the graph.

You can also check the tensorflow documentation. https://www.tensorflow.org/guide/function

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donglinjy Avatar answered Oct 29 '22 00:10

donglinjy