Deployment: Wrapping and Executing Image Classification Models

To utilize this classifier model, it wraps and deploys a version of the underlying keras (or tensorflow) model. Continue to the Demonstrations: Tutorial for Image Classification Models to see how to utilize these models in a demo scenario.

Model Deployment

Following similar use pattens described by the main client library, there are two primary modes to export and deploy the generated classifier: by dumping it to disk or by pushing it to an onboarding server. Please consult the Usage for more specific arguments but the examples below demonstrate basic capabilities.

Example for training a model that will return the top 100 classifier scores.

python image_classifier/ -m model.h5 -f keras -l data/keras_class_names.txt -n 100 -d model -i data/elephant.jpg

Example for training a model, dumping it to disk, and pushing that model that returns all scores. (recommended)

export ACUMOS_USERNAME="user"; \
export ACUMOS_PASSWORD="password";
export ACUMOS_TOKEN="a_very_long_token";

export ACUMOS_PUSH=""; \
export ACUMOS_AUTH=""; \
python image_classifier/ -n 0 -d model

In-place Evaluation

In-place evaluation will not utilize a serialized version of the model and will instead wrap it in memory and use it in-place. This mode is handy for quick evaluation of images or image sets for use in other classifiers.

Example for dumping model for use in cascade scenario (all classes return probability). recommended

python image_classifier/ -n 0 -d model

Example for evaluation of a test image with top 5 results.

python image_classifier/ -m model.h5 -i data/model-t.jpg -f keras -l data/keras_class_names.txt -n 5

Example for evaluation of a multiple image with all results, saving predictions. __(Added v0.3)__

python image_classifier/ -m model.h5 -I data/image_list.txt -f keras -p data/features.csv -l data/keras_class_names.txt -n 0

Model Runner: Using the Client Library

Getting even closer to what it looks like in a deployed model, you can also use the model runner code to run classification locally. (added v0.5.0)

1. First, decide the ports to run your classification and other models. In the example below, classiciation runs on port 8886.

2. Second, dump and launch the classification model. If you modify the ports to run the models, please change them accordingly. This command example assumes that you have cloned the client library in a relative path of ../acumos-python-client. The first line removes any prior model directory, the second dumps the detect model to disk, and the third runs the model.

rm -rf model; \
    python image_classifier/ -m model.h5 -f keras -l data/keras_class_names.txt -n 0 -d model -i data/elephant.jpg; \
    python ../acumos-python-client/testing/wrap/ --port 8886 --modeldir model/image_classifier --no_downstream