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https://github.com/netrack/tensorcraft

TensorCraft is an AIO server for Keras models
https://github.com/netrack/tensorcraft

aio keras keras-models machine-learning python3 tensorflow

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TensorCraft is an AIO server for Keras models

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README 

        
# TensorCraft



[![Build Status][BuildStatus]](https://travis-ci.org/netrack/tensorcraft)

[![tensorcraft][SnapCraft]](https://snapcraft.io/tensorcraft)



The TensorCraft is a HTTP server that serves [Keras](https://github.com/keras-team/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:

```bash

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.

```bash

docker pull netrack/tensorcraft:latest

```



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

```bash

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

```



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

```bash

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

```



### Installation Using PyPi



Install latest version from pypi repository.

```bash

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.

```py

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:

```py

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:

```py

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

```



### Starting Server



To start server run `server` command:

```sh

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:

```sh

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:

```sh

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:

```sh

tensorcraft list

```



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

```sh

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:

```sh

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:

```sh

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](LICENSE).



[BuildStatus]:   https://travis-ci.org/netrack/tensorcraft.svg?branch=master

[SnapCraft]:     https://snapcraft.io/tensorcraft/badge.svg