A GBDX task for deploying a trained Keras classifier on a set of target image chips.
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A GBDX task to deploy a trained Keras classifier on a set of target image chips. The task returns a json file 'classified.json', which contains the input chip names, each with a classification result and confidence score. Note that if the model was trained using a Theano backend the deploy-chip-classifier-theano task name should be used.


Here we run though a sample execution of the deploy-chip-classifier task. We will be deploying a model that classifies chips in Africa as 'No Buildings' or 'Buildings'. All of the input data is available in the S3 locations specified below.

  1. Within an iPython terminal create a GBDX interface and specify the task input location:

    from gbdxtools import Interface
    from os.path import join
    import uuid
    gbdx = Interface()
    input_location = 's3://gbd-customer-data/32cbab7a-4307-40c8-bb31-e2de32f940c2/platform-stories/deploy-chip-classifier/'
  2. Create a task instance and set the required inputs:

    deploy_task = gbdx.Task('deploy-chip-classifier')
    deploy_task.inputs.model = join(input_location, 'model')
    deploy_task.inputs.chips = join(input_location, 'chips')
  3. Specify the classes for the deploy task. We can also specify the size of the chips:

    deploy_task.inputs.classes = 'Buildings, No Buildings'
    deploy_task.inputs.size = '224'
  4. Initialize a workflow and specify where to save the output:

    deploy_wf = gbdx.Workflow([deploy_task])
    random_str = str(uuid.uuid4())
    output_location = join('platform-stories/trial-runs', random_str, 'results')
    deploy_wf.savedata(deploy_task.outputs.results, output_location)
  5. Execute the workflow:

  6. Track the status of the workflow as follows:


Input Ports

The following table lists the input ports of deploy-chip-classifier. Note that booleans, integers and floats must be passed to the task as strings, e.g., 'True', '10', '0.001'.

Name Valid Values Description Required
chips Directory Contains the chips to deploy on in a single tar file. Acceptable formats include jpeg, tif and png. True
model Directory Contains an h5 file with the model trained using a Tensorflow backend. If a Theano backend was used, please use the deploy-chip-classifier-theano task. True
classes String The class names in a single string, separated by commas and in the same order that the model was trained on, e.g., 'No swimming pool, Swimming pool'. If omitted, the class names are numbers starting from 0. False
size String Chip size in pixels. If a chip has a different size, it is automatically resized to (size)x(size). Default is 224. False
deploy_batch String Size of deploy batch. Default is 100. False
normalization_vector String Pixel intensity values to subtract from R, G, B bands. The default is '123.68,116.779,103.939'. False
normalization_value String Pixel intensity values to divide chips by. Division takes place after subtracting the normalization_vector. Defaults to None. False

Output Ports

deploy-chip-classifier has two outputs as detailed below.

Name Type Description:
results Directory Contains classified.json. Each entry is a key:value pair, where the key is the chip name and the value is a dictionary which includes the classification of that chip ("class") and the certainty of the classification ("certainty").
logs Directory Contains out.log which includes information on the time required to load the model and the total classification time.


Chip size

The Keras classifier expects height and width of each chip to be equal. Therefore, if an input size is specified, all channels of each chip will be warped to that dimension. Note that if target chips are not the same shape as the input layer of the model the size must be specified.


The deploy-chip-classifier task assumes that the model was trained using a Tensorflow backend. If you wish to deploy a model trained with a Theano backend, simply use the task name deploy-chip-classifier-theano with otherwise the same configuration.

Data preprocessing

This task preprocesses the data before deploying. It reformats all chips from RGB to BGR, and subtracts the mean intensity (as specified by the normalization_vector input) from each band. The model should be trained using the same preprocessing technique. Note that many publicly available models (including resnet50 and VGG Net) are trained this way.

Tar format

The chips input directory should contain one tar file of all the chips. To create this from a directory of chips simply use the following command: tar -cvf <directory_name>.tar <directory_name>. Note that the name of the tar file must match the name of the directory for this task.


Build the Docker Image

You need to install Docker and NVIDIA Docker, and run the task on a GPU.

Clone the repository:

git clone https://github.com/platformstories/deploy-chip-classifier


cd deploy-chip-classifier
docker build -t deploy-chip-classifier .

Try out locally

Create a container in interactive mode and mount the sample input under /mnt/work/input/:

docker run `curl -s http://localhost:3476/v1.0/docker/cli` -v full/path/to/sample-input:/mnt/work/input -it deploy-chip-classifier

Then, within the container:

python /deploy-chip-classifier

Watch progress of stdout to ensure task is running.

Docker Hub

Login to Docker Hub

docker login

Tag your image using your username and push it to DockerHub:

docker tag deploy-chip-classifier yourusername/deploy-chip-classifier
docker push yourusername/deploy-chip-classifier

The image name should be the same as the image name under containerDescriptors in deploy-chip-classifier.json.

Alternatively, you can link this repository to a Docker automated build. Every time you push a change to the repository, the Docker image gets automatically updated.

Register on GBDX

In a Python terminal:

from gbdxtools import Interface
gbdx = Interface()

Note: If you change the task image, you need to reregister the task with a higher version number in order for the new image to take effect. Keep this in mind especially if you use Docker automated build.