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Legacy page

This page describes legacy neuralSPOT. For current tools and documentation, see heliaPROFILER, heliaPROFILER profile command, heliaPROFILER power measurement, heliaPROFILER PMU counters, heliaPROFILER supported boards, heliaPROFILER analyze, heliaAOT, heliaEstimator.

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.
  • runs_power (optional): Number of power measurement runs to perform.
  • arena_location (optional): Memory location for the arena (e.g., NS_AD_TCM, NS_AD_SRAM, NS_AD_PSRAM, NS_AD_MRAM).
  • model_location (optional): Memory location for the model (e.g., NS_AD_TCM, NS_AD_SRAM, NS_AD_PSRAM, NS_AD_MRAM).
  • id (optional): Run log ID for tracking purposes.

Example models.yaml

models:
  - tflite_filename: "model1.tflite"
    model_name: "model1"
    unsupported_platforms:
      - apollo4p_evb
    arena_size_scratch_buffer_padding: 5
    runs_power: 10
    arena_location: "NS_AD_PSRAM"
    model_location: "NS_AD_SRAM"
    id: 1

  - tflite_filename: "model2.tflite"
    unsupported_platforms:
      - apollo510_evb
    arena_location: "NS_AD_TCM"
    model_location: "NS_AD_PSRAM"
    id: 2

Usage

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

python3 ns_ad_batch.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).
  • --tflm-version: Target neuralSPOT TFLM version (default: "auto").
  • --resultlog-file: Path and filename where the deployment log will be stored.
  • --profile-results-path: Directory where per-model profile results will be stored.
  • --aot: Run AOT (Ahead-of-Time compilation) instead of TFLM.
  • --joulescope: Enable power consumption measurement using Joulescope.
  • --model-directory: Directory where the models are located (default: "../model_perf_tests/models/").

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. Adds --runs-power if specified.
  5. Adds --arena-location if specified.
  6. Adds --model-location if specified.
  7. Adds --run-log-id if specified.
  8. If the platform string contains "apollo5", the script automatically adds the --full-pmu-capture flag.

  9. If --aot is specified, adds --create-aot-profile.

  10. 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.