This repository contains code to instantiate and deploy a weather forecasting model. The model takes hourly weather data (as a Numpy array of various weather features, in text file format) as input and returns hourly weather predictions for a specific target variable or variables (such as temperature or windspeed).

Three models have been included with this repository, all trained by the CODAIT team on National Oceanic and Atmospheric Administration local climatological data originally collected by JFK airport. All three models use an LSTM recurrent neural network architecture. You can specify which model you wish to use when making requests to the API (see Use the Model below for more details).

A description of the weather variables used to train the models is set out below.

For further details on the weather variables see the US Local Climatological Data Documentation

Each model returns a different format for its predictions:

• Univariate Model: returns a prediction of dry bulb temperature (HOURLYDRYBULBTEMPF), for the next hourly time step, for each input data point
• Multivariate Model: returns predictions for all 12 weather variables, for the next hourly time step, for each input data point
• Multistep Model: returns predictions of dry bulb temperature (HOURLYDRYBULBTEMPF), for the next 48 hourly time steps, for each input data point

The model files are provided as part of this repository in the assets/models folder. 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 Code Model Asset Exchange.

• Data from US Local Climatological Data, National Climatic Data Center, National Oceanic & Atmospheric Administration

Literature and Documentation

• LSTMs in Keras
• Time Series Prediction with RNNs
• S. Hochreiter, J. Schmidhuber "Long Short Term Memory", Neural Computation 1997

Related Repositories

• TensorFlow Tutorials for Time Series
• Tensorflow LSTM Regression

• docker: The Docker command-line interface. Follow the installation instructions for your system.
• The minimum recommended resources for this model is 2GB Memory and 2 CPUs.

1. Deploy from Docker Hub
2. Deploy on Kubernetes
3. Run Locally

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 codait/max-weather-forecaster

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 steps below.

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-Weather-Forecaster/raw/master/max-weather-forecaster.yaml

The model will be available internally at port 5000, but can also be accessed externally through the NodePort.

1. Build the Model
2. Deploy the Model
3. Use the Model
4. Development
5. Clean Up

Clone this repository locally. In a terminal, run the following command:

$ git clone https://github.com/IBM/MAX-Weather-Forecaster.git

Change directory into the repository base folder:

$ cd MAX-Weather-Forecaster

To build the docker image locally, run:

$ docker build -t max-weather-forecaster .

Note that currently this docker image is CPU only (we will add support for GPU images later).

To run the docker image, which automatically starts the model serving API, run:

$ docker run -it -p 5000:5000 max-weather-forecaster

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.

Use the model/predict endpoint to load a test data file and get predictions for the relevant weather target variable (or variables) from the API. You can use one of the test files from the assets/lstm_weather_test_data folder, after unzipping the test data archive by running the following command:

$ tar -zxvf assets/lstm_weather_test_data.tar.gz -C assets

You can also test it on the command line, for example to test the univariate model:

$ curl -F "file=@assets/lstm_weather_test_data/univariate_model_test_data.txt" -XPOST http://localhost:5000/model/predict

You can select one of the three available models used to make predictions by setting the model request parameter to one of: univariate (default), multivariate, or multistep. Note that each model takes in different weather datasets. After loading a particular model, you must predict only on the accompanying test dataset (e.g. univariate must predict on univariate_model_test_data.txt).

For example, to test the multivariate model:

$ curl -F "file=@assets/lstm_weather_test_data/multivariate_model_test_data.txt" -XPOST http://localhost:5000/model/predict?model=multivariate

To test the multi-step model:

$ curl -F "file=@assets/lstm_weather_test_data/multistep_model_test_data.txt" -XPOST http://localhost:5000/model/predict?model=multistep

You should see a JSON response like that below for the multistep test data, where predictions contains the predicted dry bulb temperature (in F) for each of the next 48 hours, for each input data point.

{
  "status": "ok",
  "predictions": [
    [
      77.51201432943344,
      76.51381462812424,
      75.0168582201004,
      73.84445126354694,
      72.79087746143341,
      71.71804094314575,
      70.97693882882595,
      70.44060184061527,
      69.89843893051147,
      69.35454525053501,
      69.04163710772991,
      68.70432360470295,
      68.37075608968735,
      68.20421539247036,
      68.01852786540985,
      67.6653740555048,
      67.27566187083721,
      67.0398361980915,
      66.69407051801682,
      66.9289058893919,
      67.19844545423985,
      67.65162572264671,
      68.30480472743511,
      69.37090930342674,
      70.37226051092148,
      71.57235226035118,
      72.68855434656143,
      73.91224025189877,
      74.65138283371925,
      75.09161844849586,
      75.30447003245354,
      75.04770956933498,
      74.93723678588867,
      74.27759975194931,
      73.82458955049515,
      73.32358133792877,
      72.66812674701214,
      71.75925283133984,
      71.28871068358421,
      70.66486597061157,
      70.06835387647152,
      69.74887031316757,
      69.49707941710949,
      69.26406812667847,
      68.87126012146473,
      68.60496838390827,
      68.39429907500744,
      68.03596951067448
    ],
    ...
}

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).

To stop the Docker container, type CTRL + C in your terminal.