Skip to content

Latest commit

 

History

History
41 lines (25 loc) · 3.25 KB

export_model_file_tour.md

File metadata and controls

41 lines (25 loc) · 3.25 KB
Raw

Exported MMS Model File Tour

In the quick start export example on the main README, we provide a zip file of all of the artifacts you need to run your first export. However, when you start using MMS and downloading and sharing models you will be using .model files, so here we'll start with one of those.

Download and extract a model file, or choose a model file from the model zoo:

curl -O https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model

Important: A .model file is a zip file under the hood, so if you have trouble extracting it, change the extension to .zip first and then extract it. It might be worth assigning your favorite unzip program to the .model filetype. You also might be able to just use unzip from the terminal:

unzip squeezenet_v1.model

Once the model archive has been extracted you can review the following files:

  • Manifest (json file) - a description of the files in the archive that is generated by mxnet-model-export.

    • Example: MANIFEST.json - it describes each of the artifacts, the deep learning engine to use, and metadata about versions, and more.

  • Model Definition (json file) - contains the description of the layers and overall structure of the neural network.

  • Model Parameters and Weights (binary params file) - contains the parameters and the weights.

  • Model Signature (json file) - defines the inputs and outputs that MMS is expecting to hand-off to the API.

    • Example: signature.json - in this case for squeezenet_v1, it expects images of 224x224 pixels and will output a tensor of 1,000 probabilities.

  • Custom Service (py file) - customizes the inference request handling for both pre-processing and post-processing.

    • Example: custom-service.py - in this case, it is a copy of the mxnet_vision_service.py which does standard image pre-processing to match the input required and limits the output results to the top 5 instead of the full 1,000.

  • assets (text files) - auxiliary files that support model inference such as vocabularies, labels, etc. Will vary depending on the model.

    • Example: synset.txt - an optional list of labels (one per line) specific to a image recognition model, in this case based on the ImageNet dataset.
    • Example: vocab_dict.txt - an optional list of word/index pairs specific to an LSTM model, in this case based on the PenTreeBank dataset.