tensorcraft 0.0.1b5

TensorCraft is a server for Keras models

TensorCraft

The TensorCraft is a HTTP server that serves Keras models using TensorFlow runtime.

Currently TensorCraft is in beta, client and server API may change in the future versions.

This server solves such problems as:

• Versioning of models.
• Warehousing of models.
• Enabling CI/CD for machine-learning models.

Installation

Installation Using Snap

This is the recommended way to install tensorcraft. Simply run the following command:

snap install tensorcraft --devmode --edge
snap start tensorcraft

Installation Using Docker

TensorCraft can be used as a Docker container. The major note on this approach is that tensorflow library that is installed into the Docker image is not compiled with support of AVX instructions or GPU.

docker pull netrack/tensorcraft:latest

In order to start the container, run the following command:

docker run -it -p 5678:5678/tcp netrack/tensorcraft

You can optinally specify volume to persist models between restarts of conatiner:

docker run -it -p 5678:5678/tcp -v tensorcraft:/var/run/tensorcraft netrack/tensorcraft

Installation Using PyPi

Install latest version from pypi repository.

pip install tensorcraft

Using TensorCraft

Keras Requirements

One of the possible ways of using tensorcraft is publising model snapshots to the server on each epoch end.

from keras.models import Sequential
from keras.layers import Dense, Activation
from tensorcraft.callbacks import ModelCheckpoint

model = keras.Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))

model.compile(optimizer='sgd', loss='binary_crossentropy')
model.fit(x_train, y_train, callbacks=[ModelCheckpoint(verbose=1)], epochs=100)

Currently, tensorcraft supports only models in the TensorFlow Saved Model, therefore in order to publish Keras model, it must be saved as Saved Model at first.

Considering the following Keras model:

from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(8,), name='digits')
x = layers.Dense(4, activation='relu', name='dense_1')(inputs)
x = layers.Dense(4, activation='relu', name='dense_2')(x)
outputs = layers.Dense(2, activation='softmax', name='predictions')(x)

model = keras.Model(inputs=inputs, outputs=outputs, name='3_layer_mlp')

Save it using the export_saved_model function from the 2.0 TensorFlow API:

keras.experimental.export_saved_model(model, "3_layer_mlp")

Starting Server

To start server run server command:

sudo tensorcraft server

By default it starts listening unsecured port on localhost at http://localhost:5678.

Default configuration saves models to /var/lib/tensorcraft directory. Apart of that server requires access to /var/run directory in order to save pid file there.

Pushing New Model

Note, both client and server of tensorcraft application share the same code base. This implies the need to install a lot of server dependencies for a client. This will be improved in uncoming versions.

Once model saved in directory, pack it using tar utility. For instance, this is how it will look like for 3_layer_mlp model from the previous example:

tar -cf 3_layer_mlp.tar 3_layer_mlp

Now the model packed into the archive can be pushed to the server under the arbitrary tag:

tensorcraft push --name 3_layer_mlp --tag 0.0.1 3_layer_mlp.tar

Listing Available Models

You can list all available models on the server using the following command:

tensorcraft list

After the execution of list command you'll see to available models:

3_layer_mlp:0.0.1
3_layer_mlp:latest

This is the features of tensorcraft server, each published model name results in creation of model group. Each model group has it's latest tag, that references the latest pushed model.

Removing Model

Remove of the unused model can be performed in using remove command:

tensorcraft remove --name 3_layer_mlp --tag 0.0.1

Execution of remove commands results in the remove of the model itself, and the model group, when is is the last model in the group.

Using Model

In order to use the pushed model, tensorcraft exposes REST API. An example query to the server looks like this:

curl -X POST https://localhost:5678/models/3_layer_mlp/0.0.1/predict -d \
    '{"x": [[1.0, 2.1, 1.43, 4.43, 12.1, 3.2, 1.44, 2.3]]}'

License

The code and docs are released under the Apache 2.0 license.