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ns_autodeploy Multi-Model Runner

This Python script automates the deployment of multiple TFLite models using the ns_autodeploy tool. The models and their configurations are defined in a YAML file. The script passes global parameters (e.g. target platform, toolchain, result log file, and profile results path) as well as per-model settings to ns_autodeploy.

Features

  • Multiple Models: Read and deploy a series of models specified in a YAML file.
  • Platform Filtering: Each model may define an unsupported_platforms list. Models will be skipped if the target platform is found in that list.
  • Custom Arguments: Supports per-model arena_size_scratch_buffer_padding to customize the --arena-size-scratch-buffer-padding argument.
  • Timeout & Retry: Each ns_autodeploy invocation is given a 15-minute timeout. If the timeout is exceeded, the process is killed and retried once before moving on.
  • Additional Parameters: Global options such as --platform, --toolchain, --resultlog-file, --profile-results-path, and the option to enable --joulescope are supported.

Requirements

  • Python 3
  • pyyaml module (install via pip install pyyaml if needed)
  • ns_autodeploy must be available in your system's PATH

YAML File Format

The YAML file should have a top-level key models that contains a list of model definitions. Each model can include the following keys:

  • tflite_filename (required): Path or name of the TFLite model file.
  • model_name (optional): Name to be used for the model.
  • unsupported_platforms (optional): A list of platform strings on which the model should not be deployed.
  • arena_size_scratch_buffer_padding (optional): A numeric value to pass to the --arena-size-scratch-buffer-padding parameter.

Example models.yaml

models:
  - tflite_filename: "model1.tflite"
    model_name: "model1"
    unsupported_platforms:
      - apollo4p_evb
    arena_size_scratch_buffer_padding: 5

  - tflite_filename: "model2.tflite"
    unsupported_platforms:
      - apollo510_evb

Usage

Run the script by providing the YAML file and the required command-line arguments:

python3 deploy_models.py models.yaml \
  --platform apollo510_evb \
  --toolchain gcc \
  --resultlog-file /path/to/result.log \
  --profile-results-path /path/to/profiles \
  --joulescope

Command-Line Arguments

  • Positional:

  • yaml_file: Path to the YAML file containing the models configuration.

  • Global Options:

  • --platform: Target platform (e.g. apollo4p_evb, apollo510_evb, etc.).
  • --toolchain: Target toolchain (e.g. gcc, arm).
  • --resultlog-file: Path and filename where the deployment log will be stored.
  • --profile-results-path: Directory where per-model profile results will be stored.
  • --joulescope: Enable power consumption measurement using Joulescope.

Behavior

  1. Model Filtering:
    If a model's YAML configuration includes an unsupported_platforms list and the target platform (passed via --platform) is found in that list, the model is skipped.

  2. Command Construction:
    The script builds the ns_autodeploy command by combining global parameters with per-model parameters:

  3. Adds --arena-size-scratch-buffer-padding if specified.

  4. If the platform string contains "apollo5", the script automatically adds the --full-pmu-capture flag.

  5. Timeout and Retry:
    Each model deployment is given 15 minutes (900 seconds) to complete. If ns_autodeploy does not complete in this time, it is terminated and retried once. If the second attempt also times out, the script moves on to the next model.