Skip to content

Readme file for FanOutFanIn #104

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Apr 4, 2020
Merged

Readme file for FanOutFanIn #104

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
4e16629
Readme file for FanOutFanIn
priyaananthasankar Apr 1, 2020
1c0c579
Readme file for FanOutFanIn
priyaananthasankar Apr 2, 2020
1ba36c3
Readme file for FanOutFanIn
priyaananthasankar Apr 2, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file added samples/fan_out_fan_in_tensorflow/FanOutFanIn.png
View file
Open in desktop
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
40 changes: 40 additions & 0 deletions samples/fan_out_fan_in_tensorflow/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,40 @@
# Fan Out Fan In Image Classification

This sample demonstrates how to orchestrate parallel executions of image classification using tensorflow through the Fan In Fan Out Durable orchestration pattern

![architecture_diagram](./FanOutFanIn.png)

## Pre-requisites

- Create an [Azure Cognitive Service (Multi service resource)](https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows) and grab the key and endpoint
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I think this sample should be running the TF model locally. No need for Cognitive Services.

Copy link
Collaborator Author

@priyaananthasankar priyaananthasankar Apr 2, 2020 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

The sample uses cognitive services only to do a search and retrieve image urls. The tensorflow model is available in the ClassifyImage function which refers a model locally.

- Create a local.settings.json file in this directory
This file stores app settings, connection strings, and other settings used by local development tools. For this sample, you will only need an AzureWebJobsStorage connection string, which you can obtain from the Azure portal.

Your local.settings.json should look like this:

```
{
"IsEncrypted": false,
"Values": {
"FUNCTIONS_WORKER_RUNTIME": "python",
"AzureWebJobsStorage": "<your connection string>",
"COGNITIVE_ENDPOINT" : "<your cognitive service endpoint>",
"COGNITIVE_KEY" : "<your cognitive service key>"
}
}
```

- Run `pip install -r requirements.txt`


## Run the Sample
To try this sample, run `func host start` in this directory. If all the system requirements have been met, and after some initialization logs, you should see something like the following:

Http Functions:

DurableTrigger: [POST,GET] http://localhost:7071/api/orchestrators/{functionName}


Send a GET request to http://127.0.0.1:7071/api/orchestrators/FanOutFanIn

And that's it! You should see a JSON response with the status URL's.