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Theory And Practice Of Distributed Training With Tensorflow

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Wednesday 14th

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12:35 | 13:15

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Theatre 20


Keywords defining the session:

- Tensorflow


- Distributed Training

Takeaway points of the session:

- PFA helps solve a major pain point in taking machine learning to production, particularly within the Apache Spark community.

- Open standards enable true model portability across languages, frameworks and runtimes.


Since TensorFlow uses model distribution as a default distribution option the entry barrier in order to start doing distribution training is high. Our talk specifically targets to cover all the main pain points and main gap in a knowledge that Deep Learning practitioners usually have. After the talks Deep Learning practitioners should have a good understanding why TensorFlow using model distribution more often, how to change model distribution to data distribution and what are the potential implications of such changes.