TensorFlow 2.0 Autographs - For TFUG - Vik Pant

TensorFlow 2.0 Autographs - For TFUG - Vik Pant

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  TensorFlow 2.0 AutoGraph
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Dataflow Computation Graphs • TensorFlow implements dataflow, which is a common programming model for parallel computing. • In a dataflow graph, the nodes represent units of computation, and the edges represent the data consumed or produced by a computation. • TensorFlow uses a dataflow graph to represent your computation in terms of the dependencies between individual operations. • This leads to a low-level programming model in which you first define the dataflow graph, then create a TensorFlow session to run parts of the graph across a set of local and remote devices.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Advantages of using TensorFlow graphs • Simple deployment to a platform independent server. • Automatic distribution and replication (placing nodes on the distributed system). • Graph-based optimizations • common subexpression elimination • constant-folding • … • Compilation and kernel fusion.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Disadvantages of Graph Specification • Structure of code is unnatural and does not use Python data structures. • Graph control flow must be used rather than Python control flow and this complicates the specification of dynamic models. • Standard Python debugging tools cannot be used for immediate error reporting. • Not possible to quickly iterate on small code changes and minor reconfigurations.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Eager Execution • TensorFlow's eager execution is an imperative programming environment that evaluates operations immediately. • It does not build graphs so operations return concrete values instead of constructing a computational graph to run later. • tf.Tensor objects reference concrete values instead of symbolic handles to nodes in a computational graph. • Since there isn't a computational graph to build and run later in a session, it's easy to inspect results using print() or a debugger. • This makes it easy to get started with TensorFlow and debug models, and it reduces boilerplate as well.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Best Practice • It's best to write code for both eager execution and graph execution. • This gives you • eager's interactive experimentation and debuggability • distributed performance benefits of graph execution • You should • write, debug, and iterate in eager execution • then import the model graph for production deployment
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation AutoGraph: Easy control flow for graphs • AutoGraph helps you write complicated graph code using normal Python. • Behind the scenes, AutoGraph automatically transforms your code into the equivalent TensorFlow graph code. • Equivalent graph code means code that generates a TensorFlow graph when run. • The generated graph has the same effects as the original code when executed (for example with tf.function or tf.compat.v1.Session.run). • In other words, using AutoGraph can be thought of as running Python in TensorFlow.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation TensorFlow AutoGraph Capabilities and Limitations • TF AutoGraph converts Eager Python code into TensorFlow graph- mode code. • For example, users write code with if and while and AutoGraph automatically converts it into the equivalent tf.cond, and tf.while_loop. • Python is a large language, so hoping to convert arbitrary Python code directly to TF graphs is overly ambitious. • However, the Python code written to metaprogram TF graphs is in practice a restricted subset. • TensorFlow aims to support as much of this subset as possible.
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Already Supported Now
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  This presentation maycontain copyrighted materials that have been used under the fair use provisions of copyright laws. Further use, duplication, or distribution is prohibited. Source: TensorFlow Documentation Not or Partially Supported Now