https://github.com/rishit-dagli/tfserving-demos

TF Serving demos
https://github.com/rishit-dagli/tfserving-demos

jupyter-notebook model-serving python3 tensorflow tensorflow-model-server tensorflow-serving tensorflow2

Last synced: about 1 year ago
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TF Serving demos

• Host: GitHub
• URL: https://github.com/rishit-dagli/tfserving-demos
• Owner: Rishit-dagli
• License: apache-2.0
• Created: 2020-05-11T15:31:52.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2021-07-30T02:56:34.000Z (almost 5 years ago)
• Last Synced: 2025-03-31T09:37:20.168Z (about 1 year ago)
• Topics: jupyter-notebook, model-serving, python3, tensorflow, tensorflow-model-server, tensorflow-serving, tensorflow2
• Language: Jupyter Notebook
• Homepage:
• Size: 13.4 MB
• Stars: 11
• Watchers: 3
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          
# TfServing-Demos

[![Open in Colab button](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Rishit-dagli/TFServing-Demos)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Rishit-dagli/TFServing-Demos/master)



[![](https://img.shields.io/badge/Rishit-Dagli-brightgreen.svg?colorB=00ff00)](https://www.rishit.tech)

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

![GitHub followers](https://img.shields.io/github/followers/Rishit-dagli?style=social)

![Twitter Follow](https://img.shields.io/twitter/follow/rishit_dagli?style=social)

![Twitter URL](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Fgithub.com%2FRishit-dagli%2FGDG-Ahmedabad-2020)

Demos for my session about Deploying models to production with TensorFlow Serving. I would urge you to check out 

[talks.md](https://github.com/Rishit-dagli/GDG-Ahmedabad-2020/blob/master/talks.md) for more details and the talks I have given about this. I would also urge you to check out the 

[blog](https://towardsdatascience.com/deploying-models-to-production-with-tensorflow-model-server-225a81859031) by me on the same.

## Port Forwarding while on Colab

In case you want to test your API out while staying on Colab itself, you could easily forward your REST API port with ngrok to try out the API outside of Colab using this code:

```py

!pip install pyngrok

from pyngrok import ngrok

model_tunnel = ngrok.connect(8501)

print(model_tunnel)

```