https://github.com/tlkh/pycon-sg19-tensorflow-tutorial

PyCon SG 2019 Tutorial: Optimizing TensorFlow Performance
https://github.com/tlkh/pycon-sg19-tensorflow-tutorial

deep-learning keras mixed-precision nvidia tensorflow

Last synced: 3 months ago
JSON representation

PyCon SG 2019 Tutorial: Optimizing TensorFlow Performance

• Host: GitHub
• URL: https://github.com/tlkh/pycon-sg19-tensorflow-tutorial
• Owner: tlkh
• License: mit
• Created: 2019-10-06T05:15:44.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2019-11-20T14:32:03.000Z (over 4 years ago)
• Last Synced: 2024-01-19T14:12:14.644Z (5 months ago)
• Topics: deep-learning, keras, mixed-precision, nvidia, tensorflow
• Language: Jupyter Notebook
• Size: 1.55 MB
• Stars: 25
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-tf2 - PyCon SG 2019: Optimize TF perf

README

        
# PyCon SG 2019 Tutorial: Optimizing TensorFlow Performance

![GitHub last commit](https://img.shields.io/github/last-commit/NVAITC/pycon-sg19-tensorflow-tutorial.svg) ![GitHub](https://img.shields.io/github/license/NVAITC/pycon-sg19-tensorflow-tutorial.svg) ![](https://img.shields.io/github/repo-size/NVAITC/pycon-sg19-tensorflow-tutorial.svg)

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](https://drive.google.com/open?id=1Nz2438DBQS591kHha2ENL7VBhmBaXQ_loQVi3rywRVU).

## Content

**Slides** are in this [Google Drive folder](https://drive.google.com/open?id=1RR0UhnvJ3PHL4sGRe2du4_w66Kg9KNVr).

**Notebooks**

| Notebook                       | Link | Solution |

| ------------------------------ | ---- | -------- |

| TensorFlow Dataset & tf.data   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVAITC/pycon-sg19-tensorflow-tutorial/blob/master/tf_dataset_demo.ipynb) |          |

| Pet Classification with TF 2.0 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVAITC/pycon-sg19-tensorflow-tutorial/blob/master/tf_pet_base.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVAITC/pycon-sg19-tensorflow-tutorial/blob/master/solutions/tf_pet_solution.ipynb) |

| Transformers with TF 2.0       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVAITC/pycon-sg19-tensorflow-tutorial/blob/master/tf_transformer_base.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NVAITC/pycon-sg19-tensorflow-tutorial/blob/master/solutions/tf_transformer_solution.ipynb) |

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

```shell

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

```

## Workshop Information

**In-person @ PyCon SG 2019**

 * Attend the workshop 10am to 1pm on Saturday, October 12 at [Republic Polytechnic](https://pycon.sg/venue/).

 * Get your tickets [here](https://www.eventnook.com/event/pyconsingapore2019/).