update
This commit is contained in:
samirodr
@@ -13,18 +13,10 @@ This example takes the ONNX model produced by the SageMaker training example and
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Run the training example first and wait for it to complete:
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Run the training example first and wait for it to complete:
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```bash
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```bash
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bash examples/training/run_training.sh --config config.yaml --wait
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examples/training/run_training.sh --wait
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```
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```
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If the dataset is already uploaded to S3, use:
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The `config.yaml` file must include AI Hub settings:
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```bash
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bash examples/training/run_training.sh --config config.yaml --skip-upload --wait
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```
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The training artifact must contain a static-shape `model.onnx`. The training example exports an input named `input` with shape `1x3x160x160`.
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Your `config.yaml` must include AI Hub settings:
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```yaml
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```yaml
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aihub:
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aihub:
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@@ -36,16 +28,20 @@ aihub:
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output_dir: build/qai-hub
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output_dir: build/qai-hub
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```
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```
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You also need local Qualcomm AI Hub SDK authentication configured.
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Finally, the user needs to authenticate with Qualcomm AI Hub using:
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```bash
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qai-hub configure --api_token
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```
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## Prepare Inputs
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## Prepare Inputs
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AI Hub does not consume the raw JPG training images directly. It needs NumPy tensors that match the ONNX model input shape and preprocessing.
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AI Hub does not consume the raw JPG training images directly. It needs NumPy tensors that match the ONNX model input shape and preprocessing.
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Generate calibration and validation inputs:
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To generate calibration and validation inputs:
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```bash
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```bash
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uv run python examples/ai-hub/prepare_inputs.py
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python examples/ai-hub/prepare_inputs.py
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```
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```
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This writes:
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This writes:
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@@ -61,58 +57,23 @@ The script applies the same image preprocessing used by the training example:
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- convert to channel-first `1x3x160x160`
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- convert to channel-first `1x3x160x160`
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- normalize with ImageNet mean and standard deviation
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- normalize with ImageNet mean and standard deviation
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Useful options:
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## Upload Model to Qualcomm Workbench
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The model can be uploaded to Qualcomm Workbench using:
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```bash
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```bash
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uv run python examples/ai-hub/prepare_inputs.py \
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qc-cli ai-hub upload examples/training/data/aihub_calibration examples/training/data/inputs.npz
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--dataset-dir examples/training/data/flower_photos_sagemaker \
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--calibration-dir examples/training/data/aihub_calibration \
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--input-file examples/training/data/inputs.npz \
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--samples 16
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```
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```
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## Run AI Hub
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The first argument is the calibration path for the model and the second argument is the input file, both of which were created by the `prepare_inputs.py` script. For more details, add `--help` after the `upload` command.
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After training completes and inputs are prepared:
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The `upload` command runs the following commands in order:
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1. `qc-cli ai-hub quantize`
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2. `qc-cli ai-hub compile`
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3. `qc-cli ai-hub validate`
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4. `qc-cli ai-hub profile`
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Finally the user can download the model from AI Workbench using the command
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```bash
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```bash
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bash examples/ai-hub/run_ai_hub.sh --config config.yaml
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qc-cli ai-hub download
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```
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```
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By default, the script uses the last SageMaker training job recorded in `.qc-cli.json`. It downloads that job's `model.tar.gz`, extracts `model.onnx`, runs the AI Hub workflow, and downloads the compiled artifact.
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To use a specific training job:
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```bash
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bash examples/ai-hub/run_ai_hub.sh \
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--config config.yaml \
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--from-job qc-cli-YYYYMMDD-HHMMSS
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```
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To resume from a later Workbench step:
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```bash
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bash examples/ai-hub/run_ai_hub.sh \
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--config config.yaml \
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--from-step validate
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```
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To skip downloading the compiled artifact:
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```bash
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bash examples/ai-hub/run_ai_hub.sh \
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--config config.yaml \
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--skip-download
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```
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## Troubleshooting
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If AI Hub reports dynamic input shapes, rerun training with the current training source. AI Hub quantization requires the exported ONNX model to use static input shapes.
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If `run_ai_hub.sh` reports missing calibration or input files, run:
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```bash
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uv run python examples/ai-hub/prepare_inputs.py
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```
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If validation fails with a missing input name, make sure `config.yaml` and the generated `.npz` both use `input` as the input name.
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0
examples/ai-hub/prepare_inputs.py
Executable file → Normal file
0
examples/ai-hub/prepare_inputs.py
Executable file → Normal file
@@ -1,156 +0,0 @@
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#!/usr/bin/env bash
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set -euo pipefail
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CONFIG_PATH="config.yaml"
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CALIBRATION_PATH="examples/training/data/aihub_calibration"
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INPUT_FILE="examples/training/data/inputs.npz"
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FROM_STEP="quantize"
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FROM_JOB=""
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MODEL_S3_URI=""
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ONNX_PATH=""
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INPUT_NAME=""
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DOWNLOAD=true
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OUTPUT_PATH=""
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usage() {
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cat <<EOF
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Usage: $0 [options]
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Options:
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--config PATH Path to qc-cli config file. Default: config.yaml
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--calibration PATH Calibration .npz file or directory of .npy samples.
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Default: ${CALIBRATION_PATH}
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--input-file PATH Validation .npz or .npy inputs. Default: ${INPUT_FILE}
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--from-step STEP Resume upload from: quantize, compile, validate, profile.
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Default: ${FROM_STEP}
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--from-job NAME SageMaker training job whose model artifact should upload.
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Defaults to the last training job in local qc-cli state.
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--model-s3-uri URI S3 URI of model.tar.gz to upload.
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--onnx-path PATH Local ONNX path or ONNX path inside extracted artifact.
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--input-name NAME Input name for .npy validation files.
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--skip-download Do not download the compiled AI Hub artifact after upload.
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--output PATH Destination file for ai-hub download.
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-h, --help Show this help.
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EOF
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}
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--config)
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CONFIG_PATH="$2"
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shift 2
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;;
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--calibration)
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CALIBRATION_PATH="$2"
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shift 2
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;;
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--input-file)
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INPUT_FILE="$2"
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shift 2
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;;
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--from-step)
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FROM_STEP="$2"
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shift 2
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;;
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--from-job)
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FROM_JOB="$2"
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shift 2
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;;
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--model-s3-uri)
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MODEL_S3_URI="$2"
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shift 2
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;;
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--onnx-path)
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ONNX_PATH="$2"
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shift 2
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;;
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--input-name)
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INPUT_NAME="$2"
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shift 2
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;;
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--skip-download)
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DOWNLOAD=false
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shift
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;;
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--output)
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OUTPUT_PATH="$2"
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shift 2
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;;
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-h|--help)
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usage
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exit 0
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;;
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*)
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echo "Unknown option: $1" >&2
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usage >&2
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exit 1
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;;
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esac
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done
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if [[ ! -f "${CONFIG_PATH}" ]]; then
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echo "Config not found: ${CONFIG_PATH}" >&2
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exit 1
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fi
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case "${FROM_STEP}" in
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quantize|compile|validate|profile)
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;;
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*)
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echo "--from-step must be one of: quantize, compile, validate, profile" >&2
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exit 1
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;;
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esac
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if [[ ! -e "${CALIBRATION_PATH}" ]]; then
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echo "Calibration path not found: ${CALIBRATION_PATH}" >&2
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echo "Pass --calibration with a .npz file or directory of .npy samples." >&2
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exit 1
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fi
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if [[ ! -f "${INPUT_FILE}" ]]; then
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echo "Input file not found: ${INPUT_FILE}" >&2
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echo "Pass --input-file with a validation .npz or .npy file." >&2
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exit 1
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fi
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run() {
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echo "+ $*"
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"$@"
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}
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UPLOAD_ARGS=(
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"${CALIBRATION_PATH}"
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"${INPUT_FILE}"
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--from-step "${FROM_STEP}"
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--config "${CONFIG_PATH}"
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)
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if [[ -n "${FROM_JOB}" ]]; then
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UPLOAD_ARGS+=(--from-job "${FROM_JOB}")
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fi
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if [[ -n "${MODEL_S3_URI}" ]]; then
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UPLOAD_ARGS+=(--model-s3-uri "${MODEL_S3_URI}")
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fi
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if [[ -n "${ONNX_PATH}" ]]; then
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UPLOAD_ARGS+=(--onnx-path "${ONNX_PATH}")
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fi
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if [[ -n "${INPUT_NAME}" ]]; then
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UPLOAD_ARGS+=(--input-name "${INPUT_NAME}")
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fi
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run uv run qc-cli ai-hub upload "${UPLOAD_ARGS[@]}"
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if [[ "${DOWNLOAD}" == false ]]; then
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exit 0
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fi
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DOWNLOAD_ARGS=(--config "${CONFIG_PATH}")
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if [[ -n "${OUTPUT_PATH}" ]]; then
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DOWNLOAD_ARGS+=(--output "${OUTPUT_PATH}")
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fi
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run uv run qc-cli ai-hub download "${DOWNLOAD_ARGS[@]}"
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