TFaaS: A JavaScript repository from alpara

TensorFlow as a Service (TFaaS)

A general purpose framework (written in Go) to serve TensorFlow models. It provides reach and flexible set of APIs to efficiently access your favorite TF models via HTTP interface. The TFaaS supports JSON and ProtoBuffer data-formats.

The following set of APIs is provided:

/upload to push your favorite TF model to TFaaS server either for Form or as tar-ball bundle, see examples below
/delete to delete your TF model from TFaaS server
/models to view existing TF models on TFaaS server
/predict/json to serve TF model predictions in JSON data-format
/predict/proto to serve TF model predictions in ProtoBuffer data-format
/predict/image to serve TF model predictions forimages in JPG/PNG formats

From deployment to production

➀ install docker image (TFaaS port is 8083)

docker run --rm -h `hostname -f` -p 8083:8083 -i -t veknet/tfaas

➁ upload your TF model to TFaaS server

# example of image based model upload
curl -X POST http://localhost:8083/upload
-F 'name=ImageModel' -F 'params=@/path/params.json'
-F 'model=@/path/tf_model.pb' -F 'labels=@/path/labels.txt'

# example of TF pb file upload
curl -s -X POST http:/localhost:8083/upload \
    -F 'name=vk' -F 'params=@/path/params.json' \
    -F 'model=@/path/model.pb' -F 'labels=@/path/labels.txt'

# example of bundle upload produce with Keras TF
# here is our saved model area
ls model
assets         saved_model.pb variables
# we can create tarball and upload it to TFaaS via bundle end-point
tar cfz model.tar.gz model
curl -X POST -H "Content-Encoding: gzip" \
             -H "content-type: application/octet-stream" \
             --data-binary @/path/models.tar.gz http://localhost:8083/upload

➂ get your predictions

# obtain predictions from your ImageModel
curl https://localhost:8083/image -F 'image=@/path/file.png' -F 'model=ImageModel'

# obtain predictions from your TF based model
cat input.json
{"keys": [...], "values": [...], "model":"model"}

# call to get predictions from /json end-point using input.json
curl -s -X POST -H "Content-type: application/json" \
    -d@/path/input.json http://localhost:8083/json

Fore more information please visit curl client page.

TFaaS interface

Clients communicate with TFaaS via HTTP protocol. See examples for Curl, Python and C++ clients.

TFaaS benchmarks

Benchmark results on CentOS, 24 cores, 32GB of RAM serving DL NN with 42x128x128x128x64x64x1x1 architecture (JSON and ProtoBuffer formats show similar performance):

400 req/sec for 100 concurrent clients, 1000 requests in total
480 req/sec for 200 concurrent clients, 5000 requests in total

For more information please visit bencmarks page.

More information

Install instructions to build TFaaS from source code
End-to-end example for MNIST dataset
End-to-end example of serving TF model in Go-server
Demo

alpara/TFaaS