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All Projects → tlkh → pycon-sg19-tensorflow-tutorial

tlkh / pycon-sg19-tensorflow-tutorial

Licence: MIT License
PyCon SG 2019 Tutorial: Optimizing TensorFlow Performance

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PyCon SG 2019 Tutorial: Optimizing TensorFlow Performance

GitHub last commit GitHub

This workshop content covers:

  • a brief introduction to deep learning and TensorFlow 2.0
  • using tf.data and TensorFlow Datasets
  • XLA compiler and Automatic Mixed Precision (AMP)
  • speeding up CNN (ResNet-50) with XLA and AMP
  • speeding up Transformer (BERT) with XLA and AMP

For a quick guide to using Automatic Mixed Precision, check out this TLDR.

Content

Slides are in this Google Drive folder.

Notebooks

Notebook Link Solution
TensorFlow Dataset & tf.data Open In Colab
Pet Classification with TF 2.0 Open In Colab Open In Colab
Transformers with TF 2.0 Open In Colab Open In Colab

For those running the notebooks on the workshop JupyterHub or on your own hardware, you can clone this repository.

git clone https://github.com/NVAITC/pycon-sg19-tensorflow-tutorial

Workshop Information

In-person @ PyCon SG 2019

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