Import from internal repo

.gitignore

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+training/training_output/
+*-model-building-code.zip
+# Hidden files
+.DS_store
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.idea/

.travis.yml

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+language: python
+python:
+  - 3.7
+services:
+  - docker
+install:
+  - docker build -t max-rec .
+  - docker run -it -d -p 5000:5000 max-rec
+  - pip install pytest requests flake8
+before_script:
+  - flake8 . --max-line-length=127 --exclude training/training_code/dataset
+  - sleep 30
+script:
+  - pytest tests/test.py

Dockerfile

@@ -0,0 +1,50 @@
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+FROM codait/max-base:v1.3.2
+
+# Fill in these with a link to the bucket containing the model and the model file name
+ARG model_bucket=https://max-cdn.cdn.appdomain.cloud/max-recommender/1.0.0
+ARG model_file=assets.tar.gz
+
+WORKDIR /workspace
+
+ARG use_pre_trained_model=true
+
+RUN if [ "$use_pre_trained_model" = "true" ] ; then\
+     # download pre-trained model artifacts from Cloud Object Storage
+     wget -nv --show-progress --progress=bar:force:noscroll ${model_bucket}/${model_file} --output-document=assets/${model_file} &&\
+     tar -x -C assets/ -f assets/${model_file} -v && rm assets/${model_file} ; \
+    fi
+
+COPY requirements.txt /workspace
+RUN pip install -r requirements.txt
+
+COPY . /workspace
+
+RUN if [ "$use_pre_trained_model" = "true" ] ; then \
+      # validate downloaded pre-trained model assets
+      sha512sum -c sha512sums.txt ; \
+    else \
+      # rename the directory that contains the custom-trained model artifacts
+      if [ -d "./custom_assets/" ] ; then \
+        rm -rf ./assets && ln -s ./custom_assets ./assets ; \
+      fi \
+    fi
+
+EXPOSE 5000
+
+CMD python app.py

README.md

@@ -1,2 +1,175 @@
-# MAX-Recommender
-Generate personalized recommendations
+[![Build Status](https://travis-ci.com/IBM/MAX-Recommender.svg?branch=master)](https://travis-ci.com/IBM/MAX-Recommender) [![Website Status](https://img.shields.io/website/http/max-recommender.max.us-south.containers.appdomain.cloud/swagger.json.svg?label=api+demo)](http://max-recommender.max.us-south.containers.appdomain.cloud/)
+
+[<img src="docs/deploy-max-to-ibm-cloud-with-kubernetes-button.png" width="400px">](http://ibm.biz/max-to-ibm-cloud-tutorial)
+
+# IBM Developer Model Asset Exchange: MAX Recommender
+
+This repository contains code to instantiate and deploy a recommender model.
+This model can be trained on a dataset containing users, items, ratings, and timestamps and make personalized item recommendations for a given user. Once trained, the input to the model is a user IDs and the output is a list of recommended item IDs sorted by probability in descending order. For demo purposes this model has been trained on a subset of the [MovieTweetings Dataset](https://github.com/sidooms/MovieTweetings), containing 457 users with their IDs mapped from 0 to 457 for convenience.
+
+The model is based on the [Neural Collaborative Filtering model]([https://github.com/microsoft/recommenders]). The model files are hosted on
+[IBM Cloud Object Storage](https://max-cdn.cdn.appdomain.cloud/max-recommender/1.0.0/assets.tar.gz).
+The code in this repository deploys the model as a web service in a Docker container. This repository was developed
+as part of the [IBM Developer Model Asset Exchange](https://developer.ibm.com/exchanges/models/) and the public API is powered by [IBM Cloud](https://ibm.biz/Bdz2XM).
+
+## Model Metadata
+| Domain | Application | Industry  | Framework | Training Data | Input Data Format |
+| ------------- | --------  | -------- | --------- | --------- | -------------- | 
+| Information Retrieval | Recommendations | Commerce | TensorFlow | [MovieTweetings](https://github.com/sidooms/MovieTweetings) | CSV |
+
+## References
+
+
+* _X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T. Chua_, ["Neural Collaborative Filtering"](https://arxiv.org/abs/1708.05031), WWW 2017.
+* [Microsoft Recommender Systems GitHub Repo](https://github.com/microsoft/recommenders)
+
+## Licenses
+
+| Component | License | Link  |
+| ------------- | --------  | -------- |
+| This repository | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |
+| Model Weights | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) | [LICENSE](LICENSE) |
+| Model Code (3rd party) | [MIT](https://opensource.org/licenses/mit-license.html) | [Microsoft Recommender Systems GitHub Repo](https://github.com/microsoft/recommenders/blob/master/LICENSE) |
+
+## Pre-requisites:
+
+* `docker`: The [Docker](https://www.docker.com/) command-line interface. Follow the [installation instructions](https://docs.docker.com/install/) for your system.
+* The minimum recommended resources for this model is 4GB Memory and 2 CPUs.
+
+# Steps
+
+1. [Deploy from Docker Hub](#deploy-from-docker-hub)
+2. [Deploy on Kubernetes](#deploy-on-kubernetes)
+3. [Deploy on Red Hat OpenShift](#deploy-on-red-hat-openshift)
+4. [Run Locally](#run-locally)
+
+## Deploy from Docker Hub
+
+To run the docker image, which automatically starts the model serving API, run:
+
+```
+$ docker run -it -p 5000:5000 codait/max-recommender
+```
+
+This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it.
+If you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.
+
+## Deploy on Kubernetes
+
+You can also deploy the model on Kubernetes using the latest docker image on Docker Hub.
+
+On your Kubernetes cluster, run the following commands:
+
+```
+$ kubectl apply -f https://github.com/IBM/MAX-Recommender/raw/master/max-recommender.yaml
+```
+
+The model will be available internally at port `5000`, but can also be accessed externally through the `NodePort`.
+
+A more elaborate tutorial on how to deploy this MAX model to production on [IBM Cloud](https://ibm.biz/Bdz2XM) can be found [here](http://ibm.biz/max-to-ibm-cloud-tutorial).
+
+## Deploy on Red Hat OpenShift:
+
+  Follow the instructions for the OpenShift web console or the OpenShift Container Platform CLI in [this tutorial](https://developer.ibm.com/tutorials/deploy-a-model-asset-exchange-microservice-on-red-hat-openshift/) and specify `codait/max-recommender` as the image name.
+
+## Run Locally
+
+1. [Build the Model](#1-build-the-model)
+2. [Deploy the Model](#2-deploy-the-model)
+3. [Use the Model](#3-use-the-model)
+4. [Development](#4-development)
+5. [Cleanup](#5-cleanup)
+
+
+### 1. Build the Model
+
+Clone this repository locally. In a terminal, run the following command:
+
+```
+$ git clone https://github.com/IBM/MAX-Recommender.git
+```
+
+Change directory into the repository base folder:
+
+```
+$ cd MAX-Recommender
+```
+
+To build the docker image locally, run: 
+
+```
+$ docker build -t max-recommender .
+```
+
+All required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU only (we will add support for GPU images later).
+
+
+### 2. Deploy the Model
+
+To run the docker image, which automatically starts the model serving API, run:
+
+```
+$ docker run -it -p 5000:5000 max-recommender
+```
+
+### 3. Use the Model
+
+The API server automatically generates an interactive Swagger documentation page. Go to `http://localhost:5000` to load it. From there you can explore the API and also create test requests.
+
+User the `model/predict` endpoint to retrieve recommendations for a user ID. The number of predictions returned can be specified with `num_results`, by default the model returns 5 predictions.
+
+
+![SWAGGER UI SCREENSHOT](docs/swagger-screenshot.png)
+
+You can also test it on the command line, for example:
+
+```
+$ curl -X POST "http://localhost:5000/model/predict?user_id=1&num_results=5" -H "accept: application/json"
+```
+
+You should see a JSON response like that below:
+
+```json
+{
+  "status": "ok",
+  "predictions": [
+      {
+      "user": "1",
+      "item": "1454468",
+      "prediction": 0.995230495929718
+    },
+    {
+      "user": "1",
+      "item": "1300854",
+      "prediction": 0.9938176274299622
+    },
+    {
+      "user": "1",
+      "item": "77413",
+      "prediction": 0.9930911064147949
+    },
+    {
+      "user": "1",
+      "item": "1731141",
+      "prediction": 0.9929673671722412
+    },
+    {
+      "user": "1",
+      "item": "363226",
+      "prediction": 0.9914621710777283
+    }
+  ]
+}
+```
+
+### 4. Development
+
+To run the Flask API app in debug mode, edit `config.py` to set `DEBUG = True` under the application settings. You will then need to rebuild the docker image (see [step 1](#1-build-the-model)).
+
+### 5. Cleanup
+
+To stop the Docker container, type `CTRL` + `C` in your terminal.
+
+## Train this Model on Watson Machine Learning
+
+This model supports both fine-tuning with transfer learning and training from scratch on a custom dataset. Please follow the steps listed under the [training readme](training/README.md) to retrain the model on [Watson Machine Learning](https://www.ibm.com/cloud/machine-learning), a deep learning as a service offering of [IBM Cloud](https://ibm.biz/Bdz2XM).

api/__init__.py

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+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .metadata import ModelMetadataAPI  # noqa
+from .predict import ModelPredictAPI  # noqa

api/metadata.py

@@ -0,0 +1,26 @@
+#
+# Copyright 2018-2019 IBM Corp. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from core.model import ModelWrapper
+from maxfw.core import MAX_API, MetadataAPI, METADATA_SCHEMA
+
+
+class ModelMetadataAPI(MetadataAPI):
+
+    @MAX_API.marshal_with(METADATA_SCHEMA)
+    def get(self):
+        """Return the metadata associated with the model"""
+        return ModelWrapper.MODEL_META_DATA