https://github.com/vkuznet/swifttfaas

Swift TensorFlow as a Service
https://github.com/vkuznet/swifttfaas

Last synced: 30 days ago
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Swift TensorFlow as a Service

• Host: GitHub
• URL: https://github.com/vkuznet/swifttfaas
• Owner: vkuznet
• License: mit
• Created: 2020-08-28T14:06:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-08-28T14:09:20.000Z (over 4 years ago)
• Last Synced: 2024-10-30T06:27:34.774Z (3 months ago)
• Language: Swift
• Size: 7.81 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
### SwiftTFaaS

This repository contains work in progress towards Swift TensorFlow as a

Service. The idea is similar to [TFaaS](https://github.com/vkuznet/TFaaS)

with implementation of web server and TF components in Swift.

The web framework is based on [Vapor](https://github.com/vapor/vapor).

For TF model we use model produced by

[SwiftTFExample](https://github.com/vkuznet/SwiftMLExample).

### Build notes

In order to start with Vapor you need to build its toolbox.

```

# NOTE: to build vapor please use Apple/Linux vanila swift toolchain

#       do not use TF toolchain

# download vapor toolbox

git clone https://github.com/vapor/toolbox.git

cd toolbox

# optional you may check out particular branch/tag

git checkout 18.2.2

# build vapor tool

swift build -c release --disable-sandbox --enable-test-discovery --verbose

# copy vapor executable to your favorite OS location

sudo cp .build/release/vapor /usr/local/bin

```

Now, we can setup a new project (this one is already done):

```

vapor new 

```

and, start coding. To build the code just use

```

swift build

```

from your project area.

### How to run and access the web server

To run the new code please use

```

swift run

```

The server will start on port 8080.

You may add new area `Model` to your project where you can store

your TF models, e.g. when I added `model.tf.npy` TF model file it

appears at a load time

```

# next the following will appear on your screen

loading weights from: model/model.tf.npy

[ NOTICE ] Server starting on http://127.0.0.1:8080

```

To place a new request please use

```

curl http://localhost:8080/inference -H "Content-Type: application/json" -d '{"row":[5.9, 3.0, 4.2, 1.5]}'

```

The response will look like:

```

{"request":{"row":[5.0999999046325684,3.2999999523162842,1.7000000476837158,0.5]},"classes":["Iris setosa","Iris versicolor","Iris virginica"],"predictions":[0.84249889850616455,0.091388963162899017,0.066112160682678223]}

```