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mlfow-aws-
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35d25d8967
| Author | SHA1 | Date | |
|---|---|---|---|
| 35d25d8967 | |||
| b907a74525 |
95
README.md
95
README.md
@@ -65,18 +65,6 @@ sagemaker:
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entry_point: null # Optional: script inside source_dir
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source_dir: null # Optional: local dir packaged and uploaded automatically
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hyperparameters: {}
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|
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aihub:
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device:
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name: Samsung Galaxy S25 (Family)
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target_runtime: tflite
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input_specs: {} # Required before running qc-cli ai-hub commands
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job_name: null # Optional prefix for AI Hub Workbench jobs
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model_name: null # Optional name for uploaded local ONNX models
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compile_options: null
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profile_options: null
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quantize_options: null
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output_dir: build/qai-hub
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```
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`qc-cli init` generates the `infra.stack_name` and `s3.bucket` namespace once and writes it to `config.yaml`. Keep these values stable for a deployment; changing them points the CLI at different infrastructure.
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@@ -90,13 +78,12 @@ To provision an MLflow tracking server, set:
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```yaml
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mlflow:
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mode: create
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tracking_server_name: your-tracking-server-name
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experiment_name: qc-cli-training
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registered_model_name: qc-cli-model
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register_trained_models: true
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```
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In `create` mode, the CLI manages the tracking server name from `infra.stack_name`; you do not need to set `tracking_server_name`.
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To use an existing MLflow tracking server, set:
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```yaml
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@@ -105,15 +92,13 @@ mlflow:
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tracking_server_name: your-tracking-server-name
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```
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||||
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||||
When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. `train status` finalizes that run once the job reaches a terminal state and registers completed model artifacts as experiment model versions using the `experiment-latest` MLflow alias. An experiment version is an immutable trained-source artifact; it records that training produced a model, not that the model is better than earlier versions or ready for release.
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||||
To open the managed SageMaker MLflow UI, request a fresh presigned URL:
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Install the optional MLflow dependencies before enabling MLflow:
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```bash
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qc-cli mlflow open --config config.yaml
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uv sync --extra mlflow
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```
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This opens a browser to a fresh presigned URL. It works for `mode: create` and for `mode: existing` when the existing server is managed by Amazon SageMaker. In `create` mode, the command uses the CLI-managed tracking server name. In `existing` mode, it uses `mlflow.tracking_server_name`. If the existing MLflow server is external to SageMaker, open it with that server's own URL instead.
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When MLflow is enabled, `train start` creates an MLflow run for the SageMaker job. `train status` finalizes that run once the job reaches a terminal state and registers completed model artifacts as pre-release model versions using the `prerelease-latest` MLflow alias.
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## Commands
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@@ -125,12 +110,6 @@ qc-cli init --output <path> Write config to a custom path
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qc-cli init --force Overwrite an existing config file
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```
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### `mlflow`
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```
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qc-cli mlflow open Open a presigned MLflow UI URL in a browser
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```
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### `infra`
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```
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@@ -172,72 +151,6 @@ qc-cli train list --limit 3 Show a custom number of recent jobs
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The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
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### `ai-hub`
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```
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qc-cli ai-hub upload <calibration.npz|calibration-dir> <inputs.npz|inputs.npy>
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qc-cli ai-hub upload <calibration> <inputs> --from-step validate
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qc-cli ai-hub quantize <calibration.npz|calibration-dir> [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
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qc-cli ai-hub compile [--model-id ID] [--onnx-path PATH] [--model-s3-uri URI] [--from-job NAME]
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qc-cli ai-hub validate <inputs.npz|inputs.npy> [--model-id ID] [--input-name NAME]
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qc-cli ai-hub profile [--model-id ID]
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qc-cli ai-hub download [--model-id ID] [--output PATH]
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```
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`ai-hub upload` runs the four Workbench upload steps in order: quantize, compile, validate, and profile. Use `--from-step compile`, `--from-step validate`, or `--from-step profile` to resume from saved local state after a completed earlier step.
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Resume behavior:
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```text
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--from-step quantize Run quantize, compile, validate, and profile.
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--from-step compile Skip quantize; compile the last quantized model unless an explicit source is passed.
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--from-step validate Skip quantize and compile; validate the last compiled model.
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--from-step profile Skip quantize, compile, and validate; profile the last compiled model.
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```
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When a step runs in the current command, `upload` passes its returned model ID directly to the next step. When a step is skipped, the next step resolves the needed model ID from `.qc-cli.json`. This avoids re-running earlier AI Hub jobs when you only need to continue from a later step.
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`ai-hub compile` resolves model sources in this order: `--model-id`, explicit source options (`--onnx-path`, `--model-s3-uri`, `--from-job`), last quantized model from state, then the last training job from local state. `ai-hub download` is separate because downloading the optimized artifact is outside the four-step Workbench upload loop.
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AI Hub authentication currently uses the local `qai-hub` SDK configuration. A planned follow-up is to support AWS Systems Manager Parameter Store `SecureString` for team-managed tokens, where `config.yaml` stores only a parameter name such as `/qc-cli/aihub/token`, AWS KMS encrypts the token at rest, and the CLI retrieves it at runtime with `ssm:GetParameter` plus `kms:Decrypt` permissions.
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## Model lifecycle
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The CLI uses neutral experiment naming for trained artifacts and reserves release terminology for an explicit promotion step.
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Current behavior:
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1. `qc-cli train start` submits a SageMaker training job.
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2. `qc-cli train status` finalizes the MLflow run after the job reaches a terminal state.
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3. If the job completed and `mlflow.register_trained_models` is enabled, the SageMaker `model.tar.gz` is registered as a new MLflow model version with:
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- `qc_cli.stage=experiment`
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- `qc_cli.artifact_kind=trained_source`
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- `qc_cli.source=sagemaker`
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4. The MLflow alias `experiment-latest` points at the most recently registered experiment version.
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5. AI Hub upload commands create deployable derived artifacts from a trained-source experiment or local ONNX model.
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Future release aliases such as `v1` or `production` can point at a selected deployable artifact.
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Example future metadata:
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```text
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qc-cli-model version 12
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qc_cli.stage=experiment
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qc_cli.artifact_kind=trained_source
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qc_cli.source=sagemaker
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qc-cli-model-aihub version 3
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qc_cli.stage=ai_hub_compiled
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qc_cli.artifact_kind=deployable
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qc_cli.parent_registered_model_name=qc-cli-model
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qc_cli.parent_model_version=12
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qc_cli.runtime=tflite
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qc_cli.quantization=int8
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qc_cli.target_device=Samsung Galaxy S25
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```
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In that flow, `experiment-latest` remains a training convenience alias. Release selection is a separate promotion decision based on the derived artifact, not on the experiment name.
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## AWS permissions required
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The IAM user or role running the CLI needs:
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@@ -1,118 +0,0 @@
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# Qualcomm AI Hub Example
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This example takes the ONNX model produced by the SageMaker training example and runs the Qualcomm AI Hub upload workflow:
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1. Quantize
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2. Compile
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3. Validate
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4. Profile
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5. Download the compiled artifact
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## Prerequisites
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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 examples/training/run_training.sh --config config.yaml --wait
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```
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If the dataset is already uploaded to S3, use:
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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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aihub:
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device:
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name: Samsung Galaxy S25 (Family)
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target_runtime: tflite
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input_specs:
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input: [[1, 3, 160, 160], float32]
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output_dir: build/qai-hub
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```
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You also need local Qualcomm AI Hub SDK authentication configured.
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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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Generate calibration and validation inputs:
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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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This writes:
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```text
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examples/training/data/aihub_calibration/*.npy
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examples/training/data/inputs.npz
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```
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The script applies the same image preprocessing used by the training example:
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- resize to `160x160`
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- convert to channel-first `1x3x160x160`
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- normalize with ImageNet mean and standard deviation
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Useful options:
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```bash
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uv run python examples/ai-hub/prepare_inputs.py \
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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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## Run AI Hub
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After training completes and inputs are prepared:
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```bash
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bash examples/ai-hub/run_ai_hub.sh --config config.yaml
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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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||||
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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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||||
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||||
To resume from a later Workbench step:
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||||
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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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||||
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To skip downloading the compiled artifact:
|
||||
|
||||
```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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||||
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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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@@ -1,74 +0,0 @@
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#!/usr/bin/env python3
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"""Prepare Qualcomm AI Hub calibration and validation inputs for the training example."""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import numpy as np
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from PIL import Image
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IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--dataset-dir",
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||||
type=Path,
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default=Path("examples/training/data/flower_photos_sagemaker"),
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help="ImageFolder-style dataset used for training.",
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||||
)
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parser.add_argument(
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"--calibration-dir",
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||||
type=Path,
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||||
default=Path("examples/training/data/aihub_calibration"),
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||||
help="Directory where .npy calibration samples will be written.",
|
||||
)
|
||||
parser.add_argument(
|
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"--input-file",
|
||||
type=Path,
|
||||
default=Path("examples/training/data/inputs.npz"),
|
||||
help="Validation .npz input file for qc-cli ai-hub validate.",
|
||||
)
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parser.add_argument("--input-name", default="input", help="ONNX input name.")
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parser.add_argument("--image-size", type=int, default=160, help="Square image size used by training.")
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parser.add_argument("--samples", type=int, default=16, help="Number of calibration samples to write.")
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return parser.parse_args()
|
||||
|
||||
|
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def preprocess_image(path: Path, image_size: int) -> np.ndarray:
|
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image = Image.open(path).convert("RGB").resize((image_size, image_size), Image.Resampling.BILINEAR)
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array = np.asarray(image, dtype=np.float32) / 255.0
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||||
array = np.transpose(array, (2, 0, 1))
|
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mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)[:, None, None]
|
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std = np.array([0.229, 0.224, 0.225], dtype=np.float32)[:, None, None]
|
||||
return ((array - mean) / std)[None, ...].astype("float32")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
images = sorted(p for p in args.dataset_dir.rglob("*") if p.suffix.lower() in IMAGE_EXTENSIONS)
|
||||
if not images:
|
||||
raise SystemExit(f"No images found under {args.dataset_dir}")
|
||||
if args.samples < 1:
|
||||
raise SystemExit("--samples must be at least 1")
|
||||
|
||||
args.calibration_dir.mkdir(parents=True, exist_ok=True)
|
||||
args.input_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
sample_count = min(args.samples, len(images))
|
||||
prepared = []
|
||||
for index, image_path in enumerate(images[:sample_count]):
|
||||
sample = preprocess_image(image_path, args.image_size)
|
||||
np.save(args.calibration_dir / f"sample_{index:03d}.npy", sample)
|
||||
prepared.append(sample)
|
||||
|
||||
np.savez(args.input_file, **{args.input_name: prepared[0]})
|
||||
print(f"Wrote {sample_count} calibration samples to {args.calibration_dir}")
|
||||
print(f"Wrote validation input to {args.input_file}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,156 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
CONFIG_PATH="config.yaml"
|
||||
CALIBRATION_PATH="examples/training/data/aihub_calibration"
|
||||
INPUT_FILE="examples/training/data/inputs.npz"
|
||||
FROM_STEP="quantize"
|
||||
FROM_JOB=""
|
||||
MODEL_S3_URI=""
|
||||
ONNX_PATH=""
|
||||
INPUT_NAME=""
|
||||
DOWNLOAD=true
|
||||
OUTPUT_PATH=""
|
||||
|
||||
usage() {
|
||||
cat <<EOF
|
||||
Usage: $0 [options]
|
||||
|
||||
Options:
|
||||
--config PATH Path to qc-cli config file. Default: config.yaml
|
||||
--calibration PATH Calibration .npz file or directory of .npy samples.
|
||||
Default: ${CALIBRATION_PATH}
|
||||
--input-file PATH Validation .npz or .npy inputs. Default: ${INPUT_FILE}
|
||||
--from-step STEP Resume upload from: quantize, compile, validate, profile.
|
||||
Default: ${FROM_STEP}
|
||||
--from-job NAME SageMaker training job whose model artifact should upload.
|
||||
Defaults to the last training job in local qc-cli state.
|
||||
--model-s3-uri URI S3 URI of model.tar.gz to upload.
|
||||
--onnx-path PATH Local ONNX path or ONNX path inside extracted artifact.
|
||||
--input-name NAME Input name for .npy validation files.
|
||||
--skip-download Do not download the compiled AI Hub artifact after upload.
|
||||
--output PATH Destination file for ai-hub download.
|
||||
-h, --help Show this help.
|
||||
EOF
|
||||
}
|
||||
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case "$1" in
|
||||
--config)
|
||||
CONFIG_PATH="$2"
|
||||
shift 2
|
||||
;;
|
||||
--calibration)
|
||||
CALIBRATION_PATH="$2"
|
||||
shift 2
|
||||
;;
|
||||
--input-file)
|
||||
INPUT_FILE="$2"
|
||||
shift 2
|
||||
;;
|
||||
--from-step)
|
||||
FROM_STEP="$2"
|
||||
shift 2
|
||||
;;
|
||||
--from-job)
|
||||
FROM_JOB="$2"
|
||||
shift 2
|
||||
;;
|
||||
--model-s3-uri)
|
||||
MODEL_S3_URI="$2"
|
||||
shift 2
|
||||
;;
|
||||
--onnx-path)
|
||||
ONNX_PATH="$2"
|
||||
shift 2
|
||||
;;
|
||||
--input-name)
|
||||
INPUT_NAME="$2"
|
||||
shift 2
|
||||
;;
|
||||
--skip-download)
|
||||
DOWNLOAD=false
|
||||
shift
|
||||
;;
|
||||
--output)
|
||||
OUTPUT_PATH="$2"
|
||||
shift 2
|
||||
;;
|
||||
-h|--help)
|
||||
usage
|
||||
exit 0
|
||||
;;
|
||||
*)
|
||||
echo "Unknown option: $1" >&2
|
||||
usage >&2
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [[ ! -f "${CONFIG_PATH}" ]]; then
|
||||
echo "Config not found: ${CONFIG_PATH}" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
case "${FROM_STEP}" in
|
||||
quantize|compile|validate|profile)
|
||||
;;
|
||||
*)
|
||||
echo "--from-step must be one of: quantize, compile, validate, profile" >&2
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
|
||||
if [[ ! -e "${CALIBRATION_PATH}" ]]; then
|
||||
echo "Calibration path not found: ${CALIBRATION_PATH}" >&2
|
||||
echo "Pass --calibration with a .npz file or directory of .npy samples." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [[ ! -f "${INPUT_FILE}" ]]; then
|
||||
echo "Input file not found: ${INPUT_FILE}" >&2
|
||||
echo "Pass --input-file with a validation .npz or .npy file." >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
run() {
|
||||
echo "+ $*"
|
||||
"$@"
|
||||
}
|
||||
|
||||
UPLOAD_ARGS=(
|
||||
"${CALIBRATION_PATH}"
|
||||
"${INPUT_FILE}"
|
||||
--from-step "${FROM_STEP}"
|
||||
--config "${CONFIG_PATH}"
|
||||
)
|
||||
|
||||
if [[ -n "${FROM_JOB}" ]]; then
|
||||
UPLOAD_ARGS+=(--from-job "${FROM_JOB}")
|
||||
fi
|
||||
|
||||
if [[ -n "${MODEL_S3_URI}" ]]; then
|
||||
UPLOAD_ARGS+=(--model-s3-uri "${MODEL_S3_URI}")
|
||||
fi
|
||||
|
||||
if [[ -n "${ONNX_PATH}" ]]; then
|
||||
UPLOAD_ARGS+=(--onnx-path "${ONNX_PATH}")
|
||||
fi
|
||||
|
||||
if [[ -n "${INPUT_NAME}" ]]; then
|
||||
UPLOAD_ARGS+=(--input-name "${INPUT_NAME}")
|
||||
fi
|
||||
|
||||
run uv run qc-cli ai-hub upload "${UPLOAD_ARGS[@]}"
|
||||
|
||||
if [[ "${DOWNLOAD}" == false ]]; then
|
||||
exit 0
|
||||
fi
|
||||
|
||||
DOWNLOAD_ARGS=(--config "${CONFIG_PATH}")
|
||||
if [[ -n "${OUTPUT_PATH}" ]]; then
|
||||
DOWNLOAD_ARGS+=(--output "${OUTPUT_PATH}")
|
||||
fi
|
||||
|
||||
run uv run qc-cli ai-hub download "${DOWNLOAD_ARGS[@]}"
|
||||
@@ -72,11 +72,10 @@ if [[ "${SKIP_UPLOAD}" == false ]]; then
|
||||
run uv run qc-cli upload "${DATASET_DIR}" --config "${CONFIG_PATH}"
|
||||
fi
|
||||
|
||||
TRAIN_OUTPUT_FILE="$(mktemp)"
|
||||
trap 'rm -f "${TRAIN_OUTPUT_FILE}"' EXIT
|
||||
run uv run qc-cli train start --config "${CONFIG_PATH}" | tee "${TRAIN_OUTPUT_FILE}"
|
||||
TRAIN_OUTPUT="$(uv run qc-cli train start --config "${CONFIG_PATH}")"
|
||||
echo "${TRAIN_OUTPUT}"
|
||||
|
||||
JOB_NAME="$(grep -Eo 'qc-cli-[0-9]{8}-[0-9]{6}' "${TRAIN_OUTPUT_FILE}" | tail -n 1)"
|
||||
JOB_NAME="$(printf '%s\n' "${TRAIN_OUTPUT}" | grep -Eo 'qc-cli-[0-9]{8}-[0-9]{6}' | tail -n 1)"
|
||||
if [[ -z "${JOB_NAME}" ]]; then
|
||||
echo "Could not find training job name in qc-cli output." >&2
|
||||
exit 1
|
||||
|
||||
@@ -126,6 +126,10 @@ def export_onnx(model: nn.Module, model_dir: Path, image_size: int) -> None:
|
||||
do_constant_folding=True,
|
||||
input_names=["input"],
|
||||
output_names=["logits"],
|
||||
dynamic_axes={
|
||||
"input": {0: "batch_size"},
|
||||
"logits": {0: "batch_size"},
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -5,18 +5,20 @@ build-backend = "hatchling.build"
|
||||
[project]
|
||||
name = "qc-cli"
|
||||
version = "0.1.0"
|
||||
description = "CLI for training and deploying models for Qualcomm AI Hub"
|
||||
description = "CLI for SageMaker ONNX training and Qualcomm AI Hub optimization"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"aws-cdk-lib>=2.180.0",
|
||||
"typer==0.25.0",
|
||||
"boto3>=1.34,<1.42",
|
||||
"constructs>=10.0.0",
|
||||
"mlflow>=3.0",
|
||||
"numpy>=1.26",
|
||||
"pydantic>=2.13.3",
|
||||
"pyyaml>=6.0.3",
|
||||
"qai-hub>=0.49.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
mlflow = [
|
||||
"mlflow>=3.0",
|
||||
"sagemaker-mlflow>=0.4.0",
|
||||
]
|
||||
|
||||
@@ -29,6 +31,7 @@ packages = ["src"]
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"boto3-stubs[iam,s3,sagemaker]",
|
||||
"pytest>=8.0",
|
||||
"pyright>=1.1.409",
|
||||
"types-PyYAML",
|
||||
"ruff>=0.4",
|
||||
|
||||
@@ -1,6 +1,3 @@
|
||||
import os
|
||||
from collections.abc import Generator
|
||||
from contextlib import contextmanager
|
||||
from typing import Any, cast
|
||||
|
||||
import boto3
|
||||
@@ -31,44 +28,3 @@ def get_tracking_server_arn(region: str, profile: str, name: str) -> str:
|
||||
if not arn:
|
||||
raise ValueError(f"MLflow tracking server has no ARN: {name}")
|
||||
return str(arn)
|
||||
|
||||
|
||||
def create_presigned_tracking_server_url(region: str, profile: str, name: str) -> str:
|
||||
client = boto3.Session(profile_name=profile, region_name=region).client("sagemaker")
|
||||
response = client.create_presigned_mlflow_tracking_server_url(TrackingServerName=name)
|
||||
return str(response["AuthorizedUrl"])
|
||||
|
||||
|
||||
@contextmanager
|
||||
def tracking_auth_env(profile: str, region: str) -> Generator[None]:
|
||||
credentials = boto3.Session(profile_name=profile, region_name=region).get_credentials()
|
||||
if credentials is None:
|
||||
raise RuntimeError(f"AWS credentials could not be resolved for profile '{profile}'.")
|
||||
|
||||
frozen_credentials = credentials.get_frozen_credentials()
|
||||
if not frozen_credentials.access_key or not frozen_credentials.secret_key:
|
||||
raise RuntimeError(f"AWS credentials are incomplete for profile '{profile}'.")
|
||||
|
||||
env_updates = {
|
||||
"AWS_PROFILE": profile,
|
||||
"AWS_DEFAULT_REGION": region,
|
||||
"AWS_REGION": region,
|
||||
"AWS_ACCESS_KEY_ID": frozen_credentials.access_key,
|
||||
"AWS_SECRET_ACCESS_KEY": frozen_credentials.secret_key,
|
||||
}
|
||||
if frozen_credentials.token:
|
||||
env_updates["AWS_SESSION_TOKEN"] = frozen_credentials.token
|
||||
|
||||
restore_keys = set(env_updates) | {"AWS_SESSION_TOKEN"}
|
||||
previous_env = {key: os.environ.get(key) for key in restore_keys}
|
||||
try:
|
||||
os.environ.update(env_updates)
|
||||
if not frozen_credentials.token:
|
||||
os.environ.pop("AWS_SESSION_TOKEN", None)
|
||||
yield
|
||||
finally:
|
||||
for key, value in previous_env.items():
|
||||
if value is None:
|
||||
os.environ.pop(key, None)
|
||||
else:
|
||||
os.environ[key] = value
|
||||
|
||||
@@ -21,24 +21,6 @@ def upload_file(
|
||||
return f"s3://{bucket}/{s3_key}"
|
||||
|
||||
|
||||
def download_file(
|
||||
region: str,
|
||||
profile: str,
|
||||
s3_uri: str,
|
||||
local_path: str,
|
||||
) -> str:
|
||||
if not s3_uri.startswith("s3://"):
|
||||
raise ValueError(f"Expected S3 URI, got: {s3_uri}")
|
||||
bucket_key = s3_uri.removeprefix("s3://")
|
||||
bucket, _, key = bucket_key.partition("/")
|
||||
if not bucket or not key:
|
||||
raise ValueError(f"Expected S3 URI with bucket and key, got: {s3_uri}")
|
||||
dest = Path(local_path)
|
||||
dest.parent.mkdir(parents=True, exist_ok=True)
|
||||
_client(region, profile).download_file(bucket, key, str(dest))
|
||||
return str(dest)
|
||||
|
||||
|
||||
def upload_dir(
|
||||
region: str,
|
||||
profile: str,
|
||||
|
||||
@@ -121,16 +121,6 @@ def get_training_job_status(session: Boto3SessionKwargs, job_name: str) -> Train
|
||||
)
|
||||
|
||||
|
||||
def get_model_artifacts(region: str, profile: str, job_name: str) -> str:
|
||||
resp = boto3.Session(profile_name=profile, region_name=region).client("sagemaker").describe_training_job(
|
||||
TrainingJobName=job_name
|
||||
)
|
||||
artifact = resp.get("ModelArtifacts", {}).get("S3ModelArtifacts")
|
||||
if not artifact:
|
||||
raise RuntimeError(f"Training job '{job_name}' does not have model artifacts yet.")
|
||||
return str(artifact)
|
||||
|
||||
|
||||
def list_training_jobs(
|
||||
session: Boto3SessionKwargs,
|
||||
max_results: int = 10,
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
"""Cloud provider adapters."""
|
||||
@@ -1,77 +0,0 @@
|
||||
from contextlib import AbstractContextManager
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Protocol
|
||||
|
||||
from src.aws import mlflow as aws_mlflow
|
||||
from src.config import Config
|
||||
|
||||
|
||||
class MlflowTrackingBackend(Protocol):
|
||||
@property
|
||||
def provider_name(self) -> str: ...
|
||||
|
||||
@property
|
||||
def profile(self) -> str: ...
|
||||
|
||||
@property
|
||||
def region(self) -> str: ...
|
||||
|
||||
def get_tracking_uri(self, tracking_server_name: str) -> str: ...
|
||||
|
||||
def auth_env(self) -> AbstractContextManager[None]: ...
|
||||
|
||||
def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]: ...
|
||||
|
||||
def training_run_tags(self, training_job: Any) -> dict[str, Any]: ...
|
||||
|
||||
def training_status_params(self, training_job_status: Any) -> dict[str, Any]: ...
|
||||
|
||||
def model_version_tags(self, training_job_status: Any) -> dict[str, Any]: ...
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class AwsMlflowTrackingBackend:
|
||||
profile: str
|
||||
region: str
|
||||
provider_name: str = "aws"
|
||||
|
||||
def get_tracking_uri(self, tracking_server_name: str) -> str:
|
||||
return aws_mlflow.get_tracking_server_arn(self.region, self.profile, tracking_server_name)
|
||||
|
||||
def auth_env(self) -> AbstractContextManager[None]:
|
||||
return aws_mlflow.tracking_auth_env(self.profile, self.region)
|
||||
|
||||
def training_run_params(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> dict[str, Any]:
|
||||
return {
|
||||
"provider.name": self.provider_name,
|
||||
"provider.region": region,
|
||||
"provider.profile": profile,
|
||||
"sagemaker.role_arn": role_arn,
|
||||
"sagemaker.job_name": training_job.job_name,
|
||||
"sagemaker.training_image": training_job.image_uri,
|
||||
"sagemaker.instance_type": training_job.instance_type,
|
||||
"sagemaker.instance_count": training_job.instance_count,
|
||||
"sagemaker.s3_train_uri": training_job.s3_train_uri,
|
||||
"sagemaker.s3_output_path": training_job.s3_output_path,
|
||||
"sagemaker.entry_point": training_job.entry_point,
|
||||
"sagemaker.source_dir": training_job.source_dir,
|
||||
}
|
||||
|
||||
def training_run_tags(self, training_job: Any) -> dict[str, Any]:
|
||||
return {"sagemaker.job_name": training_job.job_name}
|
||||
|
||||
def training_status_params(self, training_job_status: Any) -> dict[str, Any]:
|
||||
return {
|
||||
"sagemaker.training_status": training_job_status.status,
|
||||
"sagemaker.created_at": training_job_status.created,
|
||||
"sagemaker.modified_at": training_job_status.modified,
|
||||
"sagemaker.model_artifacts": training_job_status.model_artifacts,
|
||||
"sagemaker.failure_reason": training_job_status.failure_reason,
|
||||
}
|
||||
|
||||
def model_version_tags(self, training_job_status: Any) -> dict[str, Any]:
|
||||
return {"sagemaker.job_name": training_job_status.name}
|
||||
|
||||
|
||||
def mlflow_tracking_backend_from_config(cfg: Config) -> MlflowTrackingBackend:
|
||||
return AwsMlflowTrackingBackend(profile=cfg.aws.profile, region=cfg.aws.region)
|
||||
@@ -1,406 +0,0 @@
|
||||
from collections.abc import Mapping, Sequence
|
||||
from datetime import datetime
|
||||
from enum import StrEnum
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import qai_hub.hub as hub
|
||||
import typer
|
||||
from qai_hub.client import Device
|
||||
|
||||
from src import state as state_ops
|
||||
from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
|
||||
from src.config import Config
|
||||
from src.qualcomm import aihub_jobs
|
||||
from src.qualcomm.artifacts import resolve_onnx
|
||||
|
||||
app = typer.Typer(help="Quantize, compile, validate, profile, and download models with Qualcomm AI Hub")
|
||||
|
||||
_RUNTIME_EXTENSIONS = {
|
||||
"tflite": "tflite",
|
||||
"qnn_context_binary": "bin",
|
||||
"onnx": "onnx",
|
||||
}
|
||||
|
||||
|
||||
class UploadStep(StrEnum):
|
||||
quantize = "quantize"
|
||||
compile = "compile"
|
||||
validate = "validate"
|
||||
profile = "profile"
|
||||
|
||||
|
||||
def _input_specs(cfg: Config) -> dict[str, tuple[tuple[int, ...], str]]:
|
||||
specs = {name: (tuple(shape), dtype) for name, (shape, dtype) in cfg.aihub.input_specs.items()}
|
||||
if not specs:
|
||||
CONSOLE.print("[red]aihub.input_specs must define at least one input.[/red]")
|
||||
raise typer.Exit(1)
|
||||
return specs
|
||||
|
||||
|
||||
def _load_inputs(
|
||||
input_file: Path,
|
||||
specs: Mapping[str, tuple[Sequence[int], str]],
|
||||
input_name: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
import numpy as np
|
||||
|
||||
if not input_file.exists():
|
||||
raise FileNotFoundError(f"File not found: {input_file}")
|
||||
|
||||
if input_file.suffix == ".npz":
|
||||
loaded = np.load(input_file)
|
||||
missing = set(specs) - set(loaded.files)
|
||||
if missing:
|
||||
raise ValueError(f"Missing input(s) in NPZ: {', '.join(sorted(missing))}")
|
||||
return {name: loaded[name] for name in specs}
|
||||
|
||||
if input_file.suffix == ".npy":
|
||||
if input_name is None:
|
||||
if len(specs) != 1:
|
||||
raise ValueError("--input-name is required when config has multiple inputs")
|
||||
input_name = next(iter(specs))
|
||||
if input_name not in specs:
|
||||
raise ValueError(f"Input name '{input_name}' is not defined in aihub.input_specs")
|
||||
return {input_name: np.load(input_file)}
|
||||
|
||||
raise ValueError("Input file must be .npz or .npy")
|
||||
|
||||
|
||||
def _load_calibration(path: Path, specs: Mapping[str, tuple[Sequence[int], str]]) -> dict[str, Any]:
|
||||
import numpy as np
|
||||
|
||||
if path.is_file():
|
||||
return _load_inputs(path, specs)
|
||||
|
||||
if not path.is_dir():
|
||||
raise FileNotFoundError(f"Calibration path not found: {path}")
|
||||
|
||||
if len(specs) != 1:
|
||||
raise ValueError("Directory calibration data is supported only for single-input models.")
|
||||
input_name = next(iter(specs))
|
||||
samples = [np.load(p) for p in sorted(path.glob("*.npy"))]
|
||||
if not samples:
|
||||
raise ValueError(f"No .npy calibration samples found in {path}")
|
||||
return {input_name: samples}
|
||||
|
||||
|
||||
def _job_name(cfg: Config, operation: str) -> str | None:
|
||||
if not cfg.aihub.job_name:
|
||||
return None
|
||||
return f"{cfg.aihub.job_name}-{operation}"
|
||||
|
||||
|
||||
def _model_id_or_state(config_path: str, model_id: str | None, *, quantized: bool = False) -> str:
|
||||
st = state_ops.store(config_path)
|
||||
resolved = model_id or (st.get_last_quantized_model_id() if quantized else st.get_last_compiled_model_id())
|
||||
if not resolved:
|
||||
source = "quantized" if quantized else "compiled"
|
||||
CONSOLE.print(f"[red]No {source} model found. Pass --model-id or run the previous AI Hub step first.[/red]")
|
||||
raise typer.Exit(1)
|
||||
return resolved
|
||||
|
||||
|
||||
def _device_selector(device: Device) -> str:
|
||||
parts: list[str] = []
|
||||
if device.name:
|
||||
parts.append(f"name={device.name!r}")
|
||||
if device.os:
|
||||
parts.append(f"os={device.os!r}")
|
||||
if device.attributes:
|
||||
parts.append(f"attributes={device.attributes!r}")
|
||||
return ", ".join(parts) if parts else "empty selector"
|
||||
|
||||
|
||||
def _validate_device(cfg: Config) -> None:
|
||||
device = cfg.aihub.device
|
||||
try:
|
||||
matches = hub.get_devices(name=device.name, os=device.os, attributes=device.attributes)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]Unable to validate AI Hub device {_device_selector(device)}: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
if matches:
|
||||
return
|
||||
|
||||
CONSOLE.print(f"[red]AI Hub device not found: {_device_selector(device)}[/red]")
|
||||
CONSOLE.print("Run [bold]qai-hub list-devices[/bold] to see valid device names.")
|
||||
raise typer.Exit(1)
|
||||
|
||||
|
||||
def _quantize_step(
|
||||
cfg: Config,
|
||||
config_path: str,
|
||||
calibration_path: Path,
|
||||
from_job: str | None,
|
||||
model_s3_uri: str | None,
|
||||
onnx_path: str | None,
|
||||
) -> str:
|
||||
st = state_ops.store(config_path)
|
||||
specs = _input_specs(cfg)
|
||||
try:
|
||||
resolved = resolve_onnx(
|
||||
cfg=cfg,
|
||||
output_dir=cfg.aihub.output_dir,
|
||||
from_job=from_job,
|
||||
model_s3_uri=model_s3_uri or st.get_last_model_artifact(),
|
||||
onnx_path=onnx_path,
|
||||
last_training_job=st.get_last_training_job(),
|
||||
)
|
||||
calibration_data = _load_calibration(calibration_path, specs)
|
||||
except (FileNotFoundError, ValueError) as e:
|
||||
CONSOLE.print(f"[red]{e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
try:
|
||||
result = aihub_jobs.submit_quantize_job(
|
||||
resolved.onnx_path,
|
||||
calibration_data,
|
||||
cfg.aihub.quantize_options,
|
||||
job_name=_job_name(cfg, "quantize"),
|
||||
model_name=cfg.aihub.model_name,
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]AI Hub quantize failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
st.update(
|
||||
last_model_artifact=resolved.model_artifact,
|
||||
last_quantize_job_id=result["job_id"],
|
||||
last_quantized_model_id=result["model_id"],
|
||||
)
|
||||
CONSOLE.print(f"[green]✓[/green] Quantize job: [bold]{result['job_id']}[/bold]")
|
||||
CONSOLE.print(f"[green]✓[/green] Quantized model: [bold]{result['model_id']}[/bold]")
|
||||
return str(result["model_id"])
|
||||
|
||||
|
||||
def _compile_step(
|
||||
cfg: Config,
|
||||
config_path: str,
|
||||
model_id: str | None,
|
||||
from_job: str | None,
|
||||
model_s3_uri: str | None,
|
||||
onnx_path: str | None,
|
||||
*,
|
||||
prefer_quantized: bool,
|
||||
) -> str:
|
||||
st = state_ops.store(config_path)
|
||||
_validate_device(cfg)
|
||||
specs = _input_specs(cfg)
|
||||
|
||||
model: Any
|
||||
model_artifact: str | None = None
|
||||
has_explicit_source = bool(from_job or model_s3_uri or onnx_path)
|
||||
if model_id:
|
||||
model = model_id
|
||||
elif prefer_quantized and not has_explicit_source and st.get_last_quantized_model_id():
|
||||
model = st.get_last_quantized_model_id()
|
||||
else:
|
||||
try:
|
||||
resolved = resolve_onnx(
|
||||
cfg=cfg,
|
||||
output_dir=cfg.aihub.output_dir,
|
||||
from_job=from_job,
|
||||
model_s3_uri=model_s3_uri,
|
||||
onnx_path=onnx_path,
|
||||
last_training_job=st.get_last_training_job(),
|
||||
)
|
||||
except (FileNotFoundError, ValueError) as e:
|
||||
CONSOLE.print(f"[red]{e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
model = resolved.onnx_path
|
||||
model_artifact = resolved.model_artifact
|
||||
|
||||
try:
|
||||
result = aihub_jobs.submit_compile_job(
|
||||
model=model,
|
||||
device=cfg.aihub.device,
|
||||
input_specs=specs,
|
||||
target_runtime=cfg.aihub.target_runtime,
|
||||
options=cfg.aihub.compile_options,
|
||||
job_name=_job_name(cfg, "compile"),
|
||||
model_name=cfg.aihub.model_name if isinstance(model, Path) else None,
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]AI Hub compile failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
updates: dict[str, Any] = {
|
||||
"last_compile_job_id": result["job_id"],
|
||||
"last_compiled_model_id": result["model_id"],
|
||||
}
|
||||
if model_artifact:
|
||||
updates["last_model_artifact"] = model_artifact
|
||||
st.update(**updates)
|
||||
CONSOLE.print(f"[green]✓[/green] Compile job: [bold]{result['job_id']}[/bold]")
|
||||
CONSOLE.print(f"[green]✓[/green] Compiled model: [bold]{result['model_id']}[/bold]")
|
||||
return str(result["model_id"])
|
||||
|
||||
|
||||
def _validate_step(
|
||||
cfg: Config,
|
||||
config_path: str,
|
||||
input_file: Path,
|
||||
model_id: str | None,
|
||||
input_name: str | None,
|
||||
) -> str:
|
||||
_validate_device(cfg)
|
||||
specs = _input_specs(cfg)
|
||||
resolved_model_id = _model_id_or_state(config_path, model_id)
|
||||
try:
|
||||
inputs = _load_inputs(input_file, specs, input_name)
|
||||
except (FileNotFoundError, ValueError) as e:
|
||||
CONSOLE.print(f"[red]{e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
run = datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||
out_dir = Path(cfg.aihub.output_dir) / run / "validation"
|
||||
try:
|
||||
result = aihub_jobs.submit_inference_job(
|
||||
resolved_model_id,
|
||||
cfg.aihub.device,
|
||||
inputs,
|
||||
out_dir,
|
||||
job_name=_job_name(cfg, "validate"),
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]AI Hub inference failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
state_ops.store(config_path).update(last_inference_job_id=result["job_id"])
|
||||
CONSOLE.print(f"[green]✓[/green] Inference job: [bold]{result['job_id']}[/bold]")
|
||||
outputs = result.get("outputs")
|
||||
if isinstance(outputs, dict):
|
||||
for name, value in outputs.items():
|
||||
CONSOLE.print(f" {name}: shape={getattr(value, 'shape', '?')}")
|
||||
CONSOLE.print(f"Outputs: [cyan]{out_dir}[/cyan]")
|
||||
return str(result["job_id"])
|
||||
|
||||
|
||||
def _profile_step(cfg: Config, config_path: str, model_id: str | None) -> str:
|
||||
_validate_device(cfg)
|
||||
resolved_model_id = _model_id_or_state(config_path, model_id)
|
||||
try:
|
||||
result = aihub_jobs.submit_profile_job(
|
||||
resolved_model_id,
|
||||
cfg.aihub.device,
|
||||
cfg.aihub.profile_options,
|
||||
job_name=_job_name(cfg, "profile"),
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]AI Hub profile failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
state_ops.store(config_path).update(last_profile_job_id=result["job_id"])
|
||||
CONSOLE.print(f"[green]✓[/green] Profile job: [bold]{result['job_id']}[/bold]")
|
||||
return str(result["job_id"])
|
||||
|
||||
|
||||
@app.command()
|
||||
def quantize(
|
||||
calibration_path: Path = typer.Argument(..., help="Calibration .npz file or directory of .npy samples"),
|
||||
from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should quantize"),
|
||||
model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to quantize"),
|
||||
onnx_path: str | None = typer.Option(
|
||||
None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
|
||||
),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Quantize an ONNX model to INT8."""
|
||||
cfg = load_cfg(config)
|
||||
_quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
|
||||
|
||||
|
||||
@app.command()
|
||||
def compile(
|
||||
model_id: str | None = typer.Option(None, "--model-id", help="AI Hub model ID to compile"),
|
||||
from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should compile"),
|
||||
model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to compile"),
|
||||
onnx_path: str | None = typer.Option(
|
||||
None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
|
||||
),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Compile a model for the configured Qualcomm AI Hub target."""
|
||||
cfg = load_cfg(config)
|
||||
_compile_step(cfg, config, model_id, from_job, model_s3_uri, onnx_path, prefer_quantized=True)
|
||||
|
||||
|
||||
@app.command()
|
||||
def validate(
|
||||
input_file: Path = typer.Argument(..., help="NumPy .npz or .npy inputs to run on device"),
|
||||
model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
|
||||
input_name: str | None = typer.Option(None, "--input-name", help="Input name for .npy files"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Run an AI Hub inference job using sample inputs."""
|
||||
cfg = load_cfg(config)
|
||||
_validate_step(cfg, config, input_file, model_id, input_name)
|
||||
|
||||
|
||||
@app.command()
|
||||
def profile(
|
||||
model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Profile a compiled model on the configured AI Hub device."""
|
||||
cfg = load_cfg(config)
|
||||
_profile_step(cfg, config, model_id)
|
||||
|
||||
|
||||
@app.command()
|
||||
def upload(
|
||||
calibration_path: Path = typer.Argument(..., help="Calibration .npz file or directory of .npy samples"),
|
||||
input_file: Path = typer.Argument(..., help="Validation .npz or .npy inputs to run on device"),
|
||||
from_step: UploadStep = typer.Option(UploadStep.quantize, "--from-step", help="Resume from this Workbench step"),
|
||||
from_job: str | None = typer.Option(None, "--from-job", help="Training job name whose model artifact should upload"),
|
||||
model_s3_uri: str | None = typer.Option(None, "--model-s3-uri", help="S3 URI of model.tar.gz to upload"),
|
||||
onnx_path: str | None = typer.Option(
|
||||
None, "--onnx-path", help="Local ONNX path or ONNX path inside extracted artifact"
|
||||
),
|
||||
input_name: str | None = typer.Option(None, "--input-name", help="Input name for .npy validation files"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Run the four Workbench upload steps: quantize, compile, validate, and profile."""
|
||||
cfg = load_cfg(config)
|
||||
steps = [UploadStep.quantize, UploadStep.compile, UploadStep.validate, UploadStep.profile]
|
||||
selected = steps[steps.index(from_step) :]
|
||||
|
||||
quantized_model_id: str | None = None
|
||||
compiled_model_id: str | None = None
|
||||
if UploadStep.quantize in selected:
|
||||
quantized_model_id = _quantize_step(cfg, config, calibration_path, from_job, model_s3_uri, onnx_path)
|
||||
if UploadStep.compile in selected:
|
||||
compiled_model_id = _compile_step(
|
||||
cfg,
|
||||
config,
|
||||
model_id=quantized_model_id,
|
||||
from_job=from_job,
|
||||
model_s3_uri=model_s3_uri,
|
||||
onnx_path=onnx_path,
|
||||
prefer_quantized=True,
|
||||
)
|
||||
if UploadStep.validate in selected:
|
||||
_validate_step(cfg, config, input_file, compiled_model_id, input_name)
|
||||
if UploadStep.profile in selected:
|
||||
_profile_step(cfg, config, compiled_model_id)
|
||||
|
||||
|
||||
@app.command()
|
||||
def download(
|
||||
model_id: str | None = typer.Option(None, "--model-id", help="AI Hub compiled model ID"),
|
||||
output: Path | None = typer.Option(None, "--output", "-o", help="Destination file path"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Download the last compiled deployable artifact from AI Hub."""
|
||||
cfg = load_cfg(config)
|
||||
resolved_model_id = _model_id_or_state(config, model_id)
|
||||
ext = _RUNTIME_EXTENSIONS.get(cfg.aihub.target_runtime, cfg.aihub.target_runtime)
|
||||
dest = output or (Path(cfg.aihub.output_dir) / f"model.{ext}")
|
||||
|
||||
try:
|
||||
written = aihub_jobs.download_model(resolved_model_id, dest)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]AI Hub download failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
state_ops.store(config).update(last_downloaded_model=written)
|
||||
CONSOLE.print(f"[green]✓[/green] Downloaded model: [cyan]{written}[/cyan]")
|
||||
@@ -77,8 +77,7 @@ def setup(
|
||||
if outputs.get("SageMakerRoleArn"):
|
||||
CONSOLE.print(f"[green]✓[/green] IAM role: {outputs['SageMakerRoleArn']}")
|
||||
if cfg.mlflow.mode is MlflowMode.create and outputs.get("MlflowTrackingServerArn"):
|
||||
mlflow_name = outputs.get("MlflowTrackingServerName", cfg.managed_mlflow_tracking_server_name)
|
||||
CONSOLE.print(f"[green]✓[/green] MLflow: {mlflow_name}")
|
||||
CONSOLE.print(f"[green]✓[/green] MLflow: {outputs['MlflowTrackingServerArn']}")
|
||||
elif cfg.mlflow.mode is MlflowMode.existing:
|
||||
CONSOLE.print(f"[green]✓[/green] MLflow: {cfg.mlflow.tracking_server_name}")
|
||||
CONSOLE.print("\n[bold green]Infrastructure ready.[/bold green]")
|
||||
@@ -103,7 +102,7 @@ def status(config: str = CONFIG_OPT) -> None:
|
||||
if cfg.mlflow.mode is not MlflowMode.disabled:
|
||||
table.add_row(
|
||||
"MLflow",
|
||||
cfg.effective_mlflow_tracking_server_name or "-",
|
||||
cfg.mlflow.tracking_server_name or "-",
|
||||
"[red]unknown[/red]",
|
||||
"-",
|
||||
)
|
||||
@@ -127,7 +126,7 @@ def status(config: str = CONFIG_OPT) -> None:
|
||||
if cfg.mlflow.mode is MlflowMode.create:
|
||||
table.add_row(
|
||||
"MLflow",
|
||||
outputs.get("MlflowTrackingServerName", cfg.managed_mlflow_tracking_server_name),
|
||||
cfg.mlflow.tracking_server_name or "-",
|
||||
"[green]managed[/green]",
|
||||
outputs.get("MlflowTrackingServerArn", outputs.get("MlflowArtifactUri", "-")),
|
||||
)
|
||||
@@ -210,7 +209,6 @@ def _role_name(configured_name: str, role_arn: str) -> str:
|
||||
return role_arn.rsplit("/", 1)[-1]
|
||||
return "-"
|
||||
|
||||
|
||||
def _destroy_account_id(config_path: str, cfg: Config) -> str:
|
||||
config_dir = str(Path(config_path).parent)
|
||||
state = read_infra_state(config_dir)
|
||||
|
||||
@@ -1,40 +0,0 @@
|
||||
import secrets
|
||||
from pathlib import Path
|
||||
|
||||
import typer
|
||||
import yaml
|
||||
|
||||
from src.commands.utils import CONSOLE
|
||||
from src.config import GENERATED_STACK_PREFIX, Config, InfraConfig, S3Config
|
||||
|
||||
app = typer.Typer()
|
||||
|
||||
|
||||
@app.command()
|
||||
def init(
|
||||
output: str = typer.Option("config.yaml", help="Destination path for the config file"),
|
||||
force: bool = typer.Option(False, "--force", "-f", help="Overwrite an existing config file"),
|
||||
) -> None:
|
||||
"""Write a starter config.yaml to the current directory."""
|
||||
dest = Path(output)
|
||||
if dest.exists() and not force:
|
||||
CONSOLE.print(f"[yellow]{dest} already exists.[/yellow] Use --force to overwrite.")
|
||||
raise typer.Exit(1)
|
||||
|
||||
config = _new_isolated_config()
|
||||
dest.parent.mkdir(parents=True, exist_ok=True)
|
||||
config_data = config.model_dump(mode="json")
|
||||
config_data["sagemaker"].pop("role_name", None)
|
||||
with open(dest, "w") as f:
|
||||
yaml.safe_dump(config_data, f, sort_keys=False)
|
||||
|
||||
CONSOLE.print(f"[green]✓[/green] Config written to [bold]{dest}[/bold]")
|
||||
CONSOLE.print("Edit [cyan]sagemaker.training.image_uri[/cyan] before running training commands.")
|
||||
|
||||
|
||||
def _new_isolated_config() -> Config:
|
||||
suffix = secrets.token_hex(6)
|
||||
namespace = f"{GENERATED_STACK_PREFIX}{suffix}"
|
||||
config = Config(infra=InfraConfig(stack_name=namespace))
|
||||
config.s3 = S3Config(bucket=f"{namespace}-data")
|
||||
return config
|
||||
@@ -1,41 +0,0 @@
|
||||
import webbrowser
|
||||
|
||||
import typer
|
||||
|
||||
from src.aws import mlflow as aws_mlflow
|
||||
from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
|
||||
|
||||
app = typer.Typer(help="Manage MLflow tracking server access")
|
||||
|
||||
|
||||
@app.command(name="open")
|
||||
def open_mlflow(config: str = CONFIG_OPT) -> None:
|
||||
"""Open a presigned URL for the configured MLflow tracking server."""
|
||||
cfg = load_cfg(config)
|
||||
tracking_server_name = cfg.effective_mlflow_tracking_server_name
|
||||
if not tracking_server_name:
|
||||
CONSOLE.print("[red]MLflow is disabled in config.yaml.[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
try:
|
||||
url = aws_mlflow.create_presigned_tracking_server_url(
|
||||
cfg.aws.region,
|
||||
cfg.aws.profile,
|
||||
tracking_server_name,
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print("[yellow]Could not create a SageMaker MLflow UI URL.[/yellow]")
|
||||
CONSOLE.print(f"Tracking server: [cyan]{tracking_server_name}[/cyan]")
|
||||
CONSOLE.print(f"Reason: {e}")
|
||||
CONSOLE.print(
|
||||
"This command can create presigned URLs only for MLflow tracking servers managed by "
|
||||
"Amazon SageMaker. If this is an external MLflow server, open it with that server's own URL."
|
||||
)
|
||||
raise typer.Exit(1)
|
||||
|
||||
CONSOLE.print(f"MLflow tracking server: [cyan]{tracking_server_name}[/cyan]")
|
||||
CONSOLE.print(f"MLflow UI: {url}")
|
||||
if webbrowser.open(url):
|
||||
CONSOLE.print("[green]✓[/green] Opened MLflow UI in your browser.")
|
||||
else:
|
||||
CONSOLE.print("[yellow]Could not open a browser automatically. Open the URL above manually.[/yellow]")
|
||||
@@ -8,7 +8,7 @@ from src import state as state_ops
|
||||
from src.aws import iam
|
||||
from src.aws import sagemaker as sm_ops
|
||||
from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
|
||||
from src.config import Config, MlflowMode
|
||||
from src.config import Config
|
||||
from src.infra.state import read_infra_state
|
||||
from src.tracking.mlflow import MlflowTracker
|
||||
|
||||
@@ -101,7 +101,6 @@ def start(config: str = CONFIG_OPT) -> None:
|
||||
CONSOLE.print(f"[green]✓[/green] Job submitted: [bold]{job_name}[/bold]")
|
||||
if run_id:
|
||||
CONSOLE.print(f"MLflow run: [cyan]{run_id}[/cyan]")
|
||||
CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
|
||||
CONSOLE.print("Track progress: [cyan]qc-cli train status[/cyan]")
|
||||
|
||||
|
||||
@@ -138,8 +137,7 @@ def status(
|
||||
run_id = job_state.get("mlflow_run_id")
|
||||
already_registered = job_state.get("registered_model_version")
|
||||
if run_id and not already_registered and status.status in {"Completed", "Failed", "Stopped"}:
|
||||
tracker = _tracker(cfg)
|
||||
version = tracker.finalize_training_run(
|
||||
version = _tracker(cfg).finalize_training_run(
|
||||
run_id=str(run_id),
|
||||
training_job_status=status,
|
||||
)
|
||||
@@ -148,10 +146,8 @@ def status(
|
||||
updates["registered_model_version"] = version
|
||||
st.update_training_job(job_name, **updates)
|
||||
if version:
|
||||
st.set_latest_experiment_model_version(version)
|
||||
CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]experiment-latest[/cyan])")
|
||||
if run_id and cfg.mlflow.mode is not MlflowMode.disabled:
|
||||
CONSOLE.print("Open MLflow: [cyan]qc-cli mlflow open[/cyan]")
|
||||
st.set_latest_prerelease_model_version(version)
|
||||
CONSOLE.print(f"MLflow model version: [cyan]{version}[/cyan] ([cyan]prerelease-latest[/cyan])")
|
||||
|
||||
|
||||
@app.command(name="list")
|
||||
|
||||
@@ -1,70 +0,0 @@
|
||||
from pathlib import Path
|
||||
|
||||
import typer
|
||||
from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
|
||||
|
||||
from src.aws import s3 as s3_ops
|
||||
from src.commands.utils import CONFIG_OPT, CONSOLE, load_cfg
|
||||
|
||||
app = typer.Typer()
|
||||
|
||||
|
||||
@app.command()
|
||||
def upload(
|
||||
path: Path = typer.Argument(..., help="Local file or directory to upload"),
|
||||
s3_key: str | None = typer.Option(None, "--s3-key", help="S3 key for file uploads"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Upload a local file or directory to S3."""
|
||||
cfg = load_cfg(config)
|
||||
|
||||
if path.is_file():
|
||||
key = s3_key or f"{cfg.s3.data_prefix.rstrip('/')}/{path.name}"
|
||||
try:
|
||||
with CONSOLE.status(f"Uploading {path.name}..."):
|
||||
uri = s3_ops.upload_file(cfg.aws.region, cfg.aws.profile, cfg.s3.bucket, str(path), key)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]Upload failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
CONSOLE.print(f"[green]✓[/green] {path.name} -> {uri}")
|
||||
return
|
||||
|
||||
if path.is_dir():
|
||||
if s3_key is not None:
|
||||
CONSOLE.print("[red]--s3-key can only be used when uploading a single file.[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
files = [file for file in path.rglob("*") if file.is_file()]
|
||||
if not files:
|
||||
CONSOLE.print("[yellow]No files found in directory.[/yellow]")
|
||||
raise typer.Exit(0)
|
||||
|
||||
prefix = cfg.s3.data_prefix
|
||||
CONSOLE.print(f"Uploading {len(files)} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
|
||||
try:
|
||||
with Progress(
|
||||
SpinnerColumn(),
|
||||
TextColumn("[progress.description]{task.description}"),
|
||||
BarColumn(),
|
||||
TaskProgressColumn(),
|
||||
console=CONSOLE,
|
||||
) as progress:
|
||||
task = progress.add_task("Uploading...", total=len(files))
|
||||
count = s3_ops.upload_dir(
|
||||
cfg.aws.region,
|
||||
cfg.aws.profile,
|
||||
cfg.s3.bucket,
|
||||
str(path),
|
||||
prefix,
|
||||
on_progress=lambda: progress.advance(task),
|
||||
)
|
||||
except Exception as e:
|
||||
CONSOLE.print(f"[red]Upload failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
CONSOLE.print(f"[green]✓[/green] Uploaded {count} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
|
||||
return
|
||||
|
||||
CONSOLE.print(f"[red]Path not found: {path}[/red]")
|
||||
raise typer.Exit(1)
|
||||
@@ -1,20 +1,19 @@
|
||||
import re
|
||||
from enum import StrEnum
|
||||
from enum import Enum
|
||||
from typing import Any, Literal, TypedDict
|
||||
|
||||
from mypy_boto3_s3.literals import BucketLocationConstraintType
|
||||
from mypy_boto3_sagemaker.literals import TrainingInstanceTypeType
|
||||
from pydantic import BaseModel, Field, field_validator, model_validator
|
||||
from qai_hub.client import Device
|
||||
from pydantic import BaseModel, Field, model_validator
|
||||
|
||||
|
||||
class MlflowMode(StrEnum):
|
||||
class MlflowMode(str, Enum):
|
||||
disabled = "disabled"
|
||||
create = "create"
|
||||
existing = "existing"
|
||||
|
||||
|
||||
class MlflowServerSize(StrEnum):
|
||||
class MlflowServerSize(str, Enum):
|
||||
small = "Small"
|
||||
medium = "Medium"
|
||||
large = "Large"
|
||||
@@ -81,25 +80,6 @@ class SageMakerConfig(BaseModel):
|
||||
training: TrainingConfig = Field(default_factory=TrainingConfig)
|
||||
|
||||
|
||||
class AIHubConfig(BaseModel):
|
||||
device: Device = Field(default_factory=lambda: Device("Samsung Galaxy S25 (Family)"))
|
||||
target_runtime: str = "tflite"
|
||||
input_specs: dict[str, tuple[list[int], str]] = Field(default_factory=dict)
|
||||
job_name: str | None = None
|
||||
model_name: str | None = None
|
||||
compile_options: str | None = None
|
||||
profile_options: str | None = None
|
||||
quantize_options: str | None = None
|
||||
output_dir: str = "build/qai-hub"
|
||||
|
||||
@field_validator("device", mode="before")
|
||||
@classmethod
|
||||
def parse_device(cls, value: Any) -> Any:
|
||||
if isinstance(value, str):
|
||||
return Device(value)
|
||||
return value
|
||||
|
||||
|
||||
class MlflowConfig(BaseModel):
|
||||
mode: MlflowMode = MlflowMode.disabled
|
||||
tracking_server_name: str | None = None
|
||||
@@ -114,8 +94,8 @@ class MlflowConfig(BaseModel):
|
||||
|
||||
@model_validator(mode="after")
|
||||
def require_tracking_server_name(self) -> "MlflowConfig":
|
||||
if self.mode is MlflowMode.existing and not self.tracking_server_name:
|
||||
raise ValueError("mlflow.tracking_server_name is required when mlflow.mode is existing")
|
||||
if self.mode in {MlflowMode.create, MlflowMode.existing} and not self.tracking_server_name:
|
||||
raise ValueError("mlflow.tracking_server_name is required when mlflow.mode is create or existing")
|
||||
return self
|
||||
|
||||
|
||||
@@ -124,17 +104,4 @@ class Config(BaseModel):
|
||||
aws: AwsConfig = Field(default_factory=AwsConfig)
|
||||
s3: S3Config = Field(default_factory=S3Config)
|
||||
sagemaker: SageMakerConfig = Field(default_factory=SageMakerConfig)
|
||||
aihub: AIHubConfig = Field(default_factory=AIHubConfig)
|
||||
mlflow: MlflowConfig = Field(default_factory=MlflowConfig)
|
||||
|
||||
@property
|
||||
def managed_mlflow_tracking_server_name(self) -> str:
|
||||
return f"{self.infra.stack_name}-mlflow"
|
||||
|
||||
@property
|
||||
def effective_mlflow_tracking_server_name(self) -> str | None:
|
||||
if self.mlflow.mode is MlflowMode.disabled:
|
||||
return None
|
||||
if self.mlflow.mode is MlflowMode.existing:
|
||||
return self.mlflow.tracking_server_name
|
||||
return self.managed_mlflow_tracking_server_name
|
||||
|
||||
@@ -74,7 +74,6 @@ class QCStack(Stack):
|
||||
CfnOutput(self, "SageMakerRoleArn", value=role.attr_arn)
|
||||
|
||||
if config.mlflow.mode is MlflowMode.create:
|
||||
tracking_server_name = config.managed_mlflow_tracking_server_name
|
||||
artifact_prefix = config.mlflow.artifact_prefix.strip("/")
|
||||
artifact_uri = (
|
||||
f"s3://{data_bucket.bucket_name}/{artifact_prefix}/"
|
||||
@@ -146,14 +145,14 @@ class QCStack(Stack):
|
||||
"MlflowTrackingServer",
|
||||
artifact_store_uri=artifact_uri,
|
||||
role_arn=mlflow_role.attr_arn,
|
||||
tracking_server_name=tracking_server_name,
|
||||
tracking_server_name=config.mlflow.tracking_server_name or "",
|
||||
automatic_model_registration=config.mlflow.automatic_model_registration,
|
||||
mlflow_version=config.mlflow.mlflow_version,
|
||||
tracking_server_size=config.mlflow.tracking_server_size.value,
|
||||
weekly_maintenance_window_start=config.mlflow.weekly_maintenance_window_start,
|
||||
)
|
||||
|
||||
CfnOutput(self, "MlflowTrackingServerName", value=tracking_server_name)
|
||||
CfnOutput(self, "MlflowTrackingServerName", value=config.mlflow.tracking_server_name or "")
|
||||
CfnOutput(self, "MlflowTrackingServerArn", value=tracking_server.attr_tracking_server_arn)
|
||||
CfnOutput(self, "MlflowArtifactUri", value=artifact_uri)
|
||||
CfnOutput(self, "MlflowRoleArn", value=mlflow_role.attr_arn)
|
||||
|
||||
112
src/main.py
112
src/main.py
@@ -1,14 +1,114 @@
|
||||
import typer
|
||||
import secrets
|
||||
from pathlib import Path
|
||||
|
||||
from src.commands import ai_hub, infra, init, mlflow, train, upload
|
||||
import typer
|
||||
import yaml
|
||||
from rich.console import Console
|
||||
from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
|
||||
|
||||
from src.aws import s3 as s3_ops
|
||||
from src.commands import infra, train
|
||||
from src.commands.utils import CONFIG_OPT, load_cfg
|
||||
from src.config import GENERATED_STACK_PREFIX, Config, InfraConfig, S3Config
|
||||
|
||||
app = typer.Typer(
|
||||
help="qc-cli: End-to-end model managment for Qualcomm AI Hub.",
|
||||
no_args_is_help=True,
|
||||
)
|
||||
app.add_typer(init.app)
|
||||
app.add_typer(upload.app)
|
||||
app.add_typer(mlflow.app, name="mlflow")
|
||||
app.add_typer(infra.app, name="infra")
|
||||
app.add_typer(train.app, name="train")
|
||||
app.add_typer(ai_hub.app, name="ai-hub")
|
||||
|
||||
console = Console()
|
||||
|
||||
|
||||
@app.command()
|
||||
def init(
|
||||
output: str = typer.Option("config.yaml", help="Destination path for the config file"),
|
||||
force: bool = typer.Option(False, "--force", "-f", help="Overwrite an existing config file"),
|
||||
) -> None:
|
||||
"""Write a starter config.yaml to the current directory."""
|
||||
dest = Path(output)
|
||||
if dest.exists() and not force:
|
||||
console.print(f"[yellow]{dest} already exists.[/yellow] Use --force to overwrite.")
|
||||
raise typer.Exit(1)
|
||||
|
||||
config = _new_isolated_config()
|
||||
dest.parent.mkdir(parents=True, exist_ok=True)
|
||||
config_data = config.model_dump(mode="json")
|
||||
config_data["sagemaker"].pop("role_name", None)
|
||||
with open(dest, "w") as f:
|
||||
yaml.safe_dump(config_data, f, sort_keys=False)
|
||||
|
||||
console.print(f"[green]✓[/green] Config written to [bold]{dest}[/bold]")
|
||||
console.print(
|
||||
"Edit [cyan]sagemaker.training.image_uri[/cyan] before running training commands."
|
||||
)
|
||||
|
||||
|
||||
def _new_isolated_config() -> Config:
|
||||
suffix = secrets.token_hex(6)
|
||||
namespace = f"{GENERATED_STACK_PREFIX}{suffix}"
|
||||
config = Config(infra=InfraConfig(stack_name=namespace))
|
||||
config.s3 = S3Config(bucket=f"{namespace}-data")
|
||||
return config
|
||||
|
||||
|
||||
@app.command()
|
||||
def upload(
|
||||
path: Path = typer.Argument(..., help="Local file or directory to upload"),
|
||||
s3_key: str | None = typer.Option(None, "--s3-key", help="S3 key for file uploads"),
|
||||
config: str = CONFIG_OPT,
|
||||
) -> None:
|
||||
"""Upload a local file or directory to S3."""
|
||||
cfg = load_cfg(config)
|
||||
|
||||
if path.is_file():
|
||||
key = s3_key or f"{cfg.s3.data_prefix.rstrip('/')}/{path.name}"
|
||||
try:
|
||||
with console.status(f"Uploading {path.name}..."):
|
||||
uri = s3_ops.upload_file(cfg.aws.region, cfg.aws.profile, cfg.s3.bucket, str(path), key)
|
||||
except Exception as e:
|
||||
console.print(f"[red]Upload failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
console.print(f"[green]✓[/green] {path.name} -> {uri}")
|
||||
return
|
||||
|
||||
if path.is_dir():
|
||||
if s3_key is not None:
|
||||
console.print("[red]--s3-key can only be used when uploading a single file.[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
files = [file for file in path.rglob("*") if file.is_file()]
|
||||
if not files:
|
||||
console.print("[yellow]No files found in directory.[/yellow]")
|
||||
raise typer.Exit(0)
|
||||
|
||||
prefix = cfg.s3.data_prefix
|
||||
console.print(f"Uploading {len(files)} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
|
||||
try:
|
||||
with Progress(
|
||||
SpinnerColumn(),
|
||||
TextColumn("[progress.description]{task.description}"),
|
||||
BarColumn(),
|
||||
TaskProgressColumn(),
|
||||
console=console,
|
||||
) as progress:
|
||||
task = progress.add_task("Uploading...", total=len(files))
|
||||
count = s3_ops.upload_dir(
|
||||
cfg.aws.region,
|
||||
cfg.aws.profile,
|
||||
cfg.s3.bucket,
|
||||
str(path),
|
||||
prefix,
|
||||
on_progress=lambda: progress.advance(task),
|
||||
)
|
||||
except Exception as e:
|
||||
console.print(f"[red]Upload failed: {e}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
console.print(f"[green]✓[/green] Uploaded {count} files to s3://{cfg.s3.bucket}/{prefix.rstrip('/')}/")
|
||||
return
|
||||
|
||||
console.print(f"[red]Path not found: {path}[/red]")
|
||||
raise typer.Exit(1)
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
|
||||
@@ -1,129 +0,0 @@
|
||||
from pathlib import Path
|
||||
from typing import Any, TypedDict
|
||||
|
||||
import qai_hub.hub as hub
|
||||
from qai_hub.client import CompileJob, Device, InferenceJob, Model, ProfileJob, QuantizeDtype, QuantizeJob
|
||||
|
||||
|
||||
class ModelJobResult(TypedDict):
|
||||
job: CompileJob | QuantizeJob
|
||||
job_id: str
|
||||
model: Model
|
||||
model_id: str
|
||||
|
||||
|
||||
class InferenceJobResult(TypedDict):
|
||||
job: InferenceJob
|
||||
job_id: str
|
||||
outputs: Any
|
||||
|
||||
|
||||
class ProfileJobResult(TypedDict):
|
||||
job: ProfileJob
|
||||
job_id: str
|
||||
|
||||
|
||||
def _dataset_entries(inputs: dict[str, Any]) -> dict[str, list[Any]]:
|
||||
return {name: value if isinstance(value, list) else [value] for name, value in inputs.items()}
|
||||
|
||||
|
||||
def submit_compile_job(
|
||||
model: Any,
|
||||
device: Device,
|
||||
input_specs: dict[str, tuple[tuple[int, ...], str]],
|
||||
target_runtime: str,
|
||||
options: str | None = None,
|
||||
job_name: str | None = None,
|
||||
model_name: str | None = None,
|
||||
) -> ModelJobResult:
|
||||
compile_options = f"--target_runtime {target_runtime}"
|
||||
if options:
|
||||
compile_options = f"{compile_options} {options}"
|
||||
|
||||
model_arg = model
|
||||
if isinstance(model, Path):
|
||||
model_arg = str(model)
|
||||
elif isinstance(model, str):
|
||||
candidate = Path(model)
|
||||
model_arg = model if candidate.exists() or candidate.suffix else hub.get_model(model)
|
||||
|
||||
if model_name and isinstance(model_arg, str) and Path(model_arg).exists():
|
||||
model_arg = hub.upload_model(model_arg, name=model_name)
|
||||
|
||||
job = hub.submit_compile_job(
|
||||
model=model_arg,
|
||||
device=device,
|
||||
name=job_name,
|
||||
input_specs=input_specs,
|
||||
options=compile_options,
|
||||
)
|
||||
target_model = job.get_target_model()
|
||||
if target_model is None:
|
||||
raise RuntimeError(f"Compile job {job.job_id} did not produce a target model.")
|
||||
return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
|
||||
|
||||
|
||||
def submit_inference_job(
|
||||
model_id: str,
|
||||
device: Device,
|
||||
inputs: dict[str, Any],
|
||||
output_dir: str | Path,
|
||||
job_name: str | None = None,
|
||||
) -> InferenceJobResult:
|
||||
job = hub.submit_inference_job(
|
||||
model=hub.get_model(model_id),
|
||||
device=device,
|
||||
inputs=_dataset_entries(inputs),
|
||||
name=job_name,
|
||||
)
|
||||
out = Path(output_dir)
|
||||
out.mkdir(parents=True, exist_ok=True)
|
||||
data = job.download_output_data(str(out))
|
||||
return {"job": job, "job_id": str(job.job_id), "outputs": data}
|
||||
|
||||
|
||||
def submit_profile_job(
|
||||
model_id: str,
|
||||
device: Device,
|
||||
options: str | None = None,
|
||||
job_name: str | None = None,
|
||||
) -> ProfileJobResult:
|
||||
job = hub.submit_profile_job(
|
||||
model=hub.get_model(model_id),
|
||||
device=device,
|
||||
name=job_name,
|
||||
options=options or "",
|
||||
)
|
||||
return {"job": job, "job_id": str(job.job_id)}
|
||||
|
||||
|
||||
def submit_quantize_job(
|
||||
model: str | Path,
|
||||
calibration_data: dict[str, Any],
|
||||
options: str | None = None,
|
||||
job_name: str | None = None,
|
||||
model_name: str | None = None,
|
||||
) -> ModelJobResult:
|
||||
model_arg = str(model)
|
||||
if model_name and Path(model_arg).exists():
|
||||
model_arg = hub.upload_model(model_arg, name=model_name)
|
||||
job = hub.submit_quantize_job(
|
||||
model=model_arg,
|
||||
calibration_data=_dataset_entries(calibration_data),
|
||||
weights_dtype=QuantizeDtype.INT8,
|
||||
activations_dtype=QuantizeDtype.INT8,
|
||||
name=job_name,
|
||||
options=options or "",
|
||||
)
|
||||
target_model = job.get_target_model()
|
||||
if target_model is None:
|
||||
raise RuntimeError(f"Quantize job {job.job_id} did not produce a target model.")
|
||||
return {"job": job, "job_id": str(job.job_id), "model": target_model, "model_id": str(target_model.model_id)}
|
||||
|
||||
|
||||
def download_model(model_id: str, output_path: str | Path) -> str:
|
||||
dest = Path(output_path)
|
||||
dest.parent.mkdir(parents=True, exist_ok=True)
|
||||
model = hub.get_model(model_id)
|
||||
result = model.download(str(dest))
|
||||
return str(result or dest)
|
||||
@@ -1,83 +0,0 @@
|
||||
import tarfile
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
from src.aws import s3 as s3_ops
|
||||
from src.aws import sagemaker as sm_ops
|
||||
from src.config import Config
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ResolvedOnnx:
|
||||
onnx_path: Path
|
||||
model_artifact: str | None
|
||||
run_name: str
|
||||
|
||||
|
||||
def _safe_extract(tar: tarfile.TarFile, dest: Path) -> None:
|
||||
dest_root = dest.resolve()
|
||||
for member in tar.getmembers():
|
||||
target = (dest / member.name).resolve()
|
||||
if dest_root != target and dest_root not in target.parents:
|
||||
raise ValueError(f"Unsafe tar member path: {member.name}")
|
||||
tar.extractall(dest, filter="data")
|
||||
|
||||
|
||||
def _find_onnx(root: Path, explicit: str | None = None) -> Path:
|
||||
if explicit:
|
||||
p = Path(explicit)
|
||||
if not p.is_absolute():
|
||||
p = root / p
|
||||
if not p.exists():
|
||||
raise FileNotFoundError(f"ONNX file not found: {p}")
|
||||
return p
|
||||
|
||||
matches = sorted(root.rglob("model.onnx"))
|
||||
if not matches:
|
||||
matches = sorted(root.rglob("*.onnx"))
|
||||
if not matches:
|
||||
raise FileNotFoundError(f"No ONNX file found under {root}")
|
||||
if len(matches) > 1:
|
||||
joined = ", ".join(str(p.relative_to(root)) for p in matches)
|
||||
raise ValueError(f"Multiple ONNX files found ({joined}). Pass --onnx-path.")
|
||||
return matches[0]
|
||||
|
||||
|
||||
def resolve_onnx(
|
||||
cfg: Config,
|
||||
output_dir: str,
|
||||
from_job: str | None = None,
|
||||
model_s3_uri: str | None = None,
|
||||
onnx_path: str | None = None,
|
||||
last_training_job: str | None = None,
|
||||
) -> ResolvedOnnx:
|
||||
if onnx_path:
|
||||
path = Path(onnx_path)
|
||||
if path.exists():
|
||||
return ResolvedOnnx(onnx_path=path, model_artifact=None, run_name=path.stem)
|
||||
|
||||
job = from_job or last_training_job
|
||||
artifact = model_s3_uri
|
||||
if not artifact:
|
||||
if not job:
|
||||
raise ValueError("No model source found. Pass --onnx-path, --model-s3-uri, --from-job, or run training first.")
|
||||
artifact = sm_ops.get_model_artifacts(cfg.aws.region, cfg.aws.profile, job)
|
||||
|
||||
run_name = job or Path(artifact).name.removesuffix(".tar.gz").replace("/", "-")
|
||||
root = Path(output_dir) / run_name / "source"
|
||||
tar_path = root / "model.tar.gz"
|
||||
s3_ops.download_file(cfg.aws.region, cfg.aws.profile, artifact, str(tar_path))
|
||||
|
||||
extract_dir = root / "extracted"
|
||||
extract_dir.mkdir(parents=True, exist_ok=True)
|
||||
try:
|
||||
with tarfile.open(tar_path, "r:gz") as tar:
|
||||
_safe_extract(tar, extract_dir)
|
||||
except tarfile.TarError as e:
|
||||
raise ValueError(f"Invalid model tarball: {tar_path}") from e
|
||||
|
||||
return ResolvedOnnx(
|
||||
onnx_path=_find_onnx(extract_dir, onnx_path),
|
||||
model_artifact=artifact,
|
||||
run_name=run_name,
|
||||
)
|
||||
20
src/state.py
20
src/state.py
@@ -33,22 +33,6 @@ class CliStateStore:
|
||||
value = self.get("last_training_job")
|
||||
return str(value) if value else None
|
||||
|
||||
def get_last_model_artifact(self) -> str | None:
|
||||
value = self.get("last_model_artifact")
|
||||
return str(value) if value else None
|
||||
|
||||
def get_last_quantized_model_id(self) -> str | None:
|
||||
value = self.get("last_quantized_model_id")
|
||||
return str(value) if value else None
|
||||
|
||||
def get_last_compiled_model_id(self) -> str | None:
|
||||
value = self.get("last_compiled_model_id")
|
||||
return str(value) if value else None
|
||||
|
||||
def get_last_downloaded_model(self) -> str | None:
|
||||
value = self.get("last_downloaded_model")
|
||||
return str(value) if value else None
|
||||
|
||||
def set_last_training_job(self, job_name: str) -> None:
|
||||
self.update(last_training_job=job_name)
|
||||
|
||||
@@ -64,8 +48,8 @@ class CliStateStore:
|
||||
state["training_jobs"] = jobs
|
||||
self._write(state)
|
||||
|
||||
def set_latest_experiment_model_version(self, version: str) -> None:
|
||||
self.update(latest_experiment_model_version=version)
|
||||
def set_latest_prerelease_model_version(self, version: str) -> None:
|
||||
self.update(latest_prerelease_model_version=version)
|
||||
|
||||
def _write(self, state: dict[str, Any]) -> None:
|
||||
with open(self.path, "w") as f:
|
||||
|
||||
@@ -1,18 +1,21 @@
|
||||
import os
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Protocol
|
||||
|
||||
import mlflow
|
||||
from mlflow.tracking import MlflowClient
|
||||
|
||||
from src.cloud.mlflow import MlflowTrackingBackend, mlflow_tracking_backend_from_config
|
||||
from src.aws import mlflow as aws_mlflow
|
||||
from src.config import Config, MlflowMode
|
||||
|
||||
|
||||
class Tracker(Protocol):
|
||||
def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None: ...
|
||||
|
||||
def finalize_training_run(self, *, run_id: str | None, training_job_status: Any) -> str | None: ...
|
||||
def finalize_training_run(
|
||||
self,
|
||||
*,
|
||||
run_id: str | None,
|
||||
training_job_status: Any,
|
||||
) -> str | None: ...
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
@@ -26,104 +29,118 @@ class NoopTracker:
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class MlflowTracker:
|
||||
mlflow: Any
|
||||
tracking_uri: str
|
||||
experiment_name: str
|
||||
registered_model_name: str
|
||||
register_trained_models: bool
|
||||
tracking_backend: MlflowTrackingBackend
|
||||
|
||||
@classmethod
|
||||
def from_config(cls, cfg: Config) -> Tracker:
|
||||
if cfg.mlflow.mode is MlflowMode.disabled:
|
||||
return NoopTracker()
|
||||
|
||||
os.environ.setdefault("MLFLOW_SUPPRESS_PRINTING_URL_TO_STDOUT", "true")
|
||||
try:
|
||||
import mlflow
|
||||
except ImportError as e:
|
||||
raise RuntimeError(
|
||||
"MLflow is enabled in config but optional dependencies are not installed. "
|
||||
"Install with: qc-cli[mlflow]"
|
||||
) from e
|
||||
|
||||
tracking_server_name = cfg.effective_mlflow_tracking_server_name
|
||||
if not tracking_server_name:
|
||||
raise RuntimeError("MLflow tracking server name could not be resolved.")
|
||||
if not cfg.mlflow.tracking_server_name:
|
||||
raise RuntimeError("mlflow.tracking_server_name is required when MLflow is enabled.")
|
||||
|
||||
tracking_backend = mlflow_tracking_backend_from_config(cfg)
|
||||
|
||||
tracking_uri = tracking_backend.get_tracking_uri(tracking_server_name)
|
||||
with tracking_backend.auth_env():
|
||||
tracking_uri = aws_mlflow.get_tracking_server_arn(
|
||||
cfg.aws.region,
|
||||
cfg.aws.profile,
|
||||
cfg.mlflow.tracking_server_name,
|
||||
)
|
||||
mlflow.set_tracking_uri(tracking_uri)
|
||||
mlflow.set_experiment(cfg.mlflow.experiment_name)
|
||||
|
||||
return cls(
|
||||
mlflow=mlflow,
|
||||
tracking_uri=tracking_uri,
|
||||
experiment_name=cfg.mlflow.experiment_name,
|
||||
registered_model_name=cfg.mlflow.registered_model_name,
|
||||
register_trained_models=cfg.mlflow.register_trained_models,
|
||||
tracking_backend=tracking_backend,
|
||||
)
|
||||
|
||||
def start_training_run(self, training_job: Any, *, region: str, profile: str, role_arn: str) -> str | None:
|
||||
with self.tracking_backend.auth_env():
|
||||
run = mlflow.start_run(run_name=training_job.job_name)
|
||||
run = self.mlflow.start_run(run_name=training_job.job_name)
|
||||
run_id = str(run.info.run_id)
|
||||
|
||||
self._log_params(
|
||||
self.tracking_backend.training_run_params(
|
||||
training_job,
|
||||
region=region,
|
||||
profile=profile,
|
||||
role_arn=role_arn,
|
||||
)
|
||||
)
|
||||
params = {
|
||||
"aws.region": region,
|
||||
"aws.profile": profile,
|
||||
"sagemaker.role_arn": role_arn,
|
||||
"sagemaker.job_name": training_job.job_name,
|
||||
"sagemaker.training_image": training_job.image_uri,
|
||||
"sagemaker.instance_type": training_job.instance_type,
|
||||
"sagemaker.instance_count": training_job.instance_count,
|
||||
"sagemaker.s3_train_uri": training_job.s3_train_uri,
|
||||
"sagemaker.s3_output_path": training_job.s3_output_path,
|
||||
"sagemaker.entry_point": training_job.entry_point,
|
||||
"sagemaker.source_dir": training_job.source_dir,
|
||||
}
|
||||
self._log_params(params)
|
||||
self._log_params({f"hyperparameters.{key}": value for key, value in training_job.hyperparameters.items()})
|
||||
mlflow.set_tags(
|
||||
self.mlflow.set_tags(
|
||||
{
|
||||
"qc_cli.stage": "experiment",
|
||||
"qc_cli.artifact_kind": "trained_source",
|
||||
"qc_cli.source": self.tracking_backend.provider_name,
|
||||
"qc_cli.stage": "prerelease",
|
||||
"qc_cli.command": "train start",
|
||||
**self.tracking_backend.training_run_tags(training_job),
|
||||
"sagemaker.job_name": training_job.job_name,
|
||||
}
|
||||
)
|
||||
mlflow.end_run()
|
||||
self.mlflow.end_run()
|
||||
return run_id
|
||||
|
||||
def finalize_training_run(self, *, run_id: str | None, training_job_status: Any) -> str | None:
|
||||
if not run_id:
|
||||
return None
|
||||
|
||||
with self.tracking_backend.auth_env():
|
||||
with mlflow.start_run(run_id=run_id):
|
||||
self._log_params(self.tracking_backend.training_status_params(training_job_status))
|
||||
with self.mlflow.start_run(run_id=run_id):
|
||||
self._log_params(
|
||||
{
|
||||
"sagemaker.training_status": training_job_status.status,
|
||||
"sagemaker.created_at": training_job_status.created,
|
||||
"sagemaker.modified_at": training_job_status.modified,
|
||||
"sagemaker.model_artifacts": training_job_status.model_artifacts,
|
||||
"sagemaker.failure_reason": training_job_status.failure_reason,
|
||||
}
|
||||
)
|
||||
self._log_final_metrics(training_job_status.raw)
|
||||
mlflow.set_tag("qc_cli.command", "train status")
|
||||
self.mlflow.set_tag("qc_cli.command", "train status")
|
||||
|
||||
if training_job_status.status != "Completed" or not training_job_status.model_artifacts:
|
||||
mlflow.set_tag("qc_cli.training_terminal_status", training_job_status.status)
|
||||
self.mlflow.set_tag("qc_cli.training_terminal_status", training_job_status.status)
|
||||
return None
|
||||
|
||||
if not self.register_trained_models:
|
||||
return None
|
||||
|
||||
client = MlflowClient()
|
||||
client = self.mlflow.tracking.MlflowClient()
|
||||
self._ensure_registered_model(client, self.registered_model_name)
|
||||
version = client.create_model_version(
|
||||
name=self.registered_model_name,
|
||||
source=training_job_status.model_artifacts,
|
||||
run_id=run_id,
|
||||
tags={
|
||||
"qc_cli.stage": "experiment",
|
||||
"qc_cli.artifact_kind": "trained_source",
|
||||
"qc_cli.source": self.tracking_backend.provider_name,
|
||||
**self.tracking_backend.model_version_tags(training_job_status),
|
||||
"qc_cli.stage": "prerelease",
|
||||
"sagemaker.job_name": training_job_status.name,
|
||||
},
|
||||
)
|
||||
version_number = str(version.version)
|
||||
client.set_registered_model_alias(self.registered_model_name, "experiment-latest", version_number)
|
||||
mlflow.set_tag("qc_cli.registered_model_name", self.registered_model_name)
|
||||
mlflow.set_tag("qc_cli.registered_model_version", version_number)
|
||||
self._set_alias(client, self.registered_model_name, "prerelease-latest", version_number)
|
||||
self.mlflow.set_tag("qc_cli.registered_model_name", self.registered_model_name)
|
||||
self.mlflow.set_tag("qc_cli.registered_model_version", version_number)
|
||||
return version_number
|
||||
|
||||
def _log_params(self, params: dict[str, Any]) -> None:
|
||||
cleaned = {key: str(value) for key, value in params.items() if value is not None}
|
||||
if cleaned:
|
||||
mlflow.log_params(cleaned)
|
||||
self.mlflow.log_params(cleaned)
|
||||
|
||||
def _log_final_metrics(self, training_job: dict[str, Any]) -> None:
|
||||
metrics = {}
|
||||
@@ -133,10 +150,14 @@ class MlflowTracker:
|
||||
if name and value is not None:
|
||||
metrics[str(name)] = float(value)
|
||||
if metrics:
|
||||
mlflow.log_metrics(metrics)
|
||||
self.mlflow.log_metrics(metrics)
|
||||
|
||||
def _ensure_registered_model(self, client: MlflowClient, name: str) -> None:
|
||||
def _ensure_registered_model(self, client: Any, name: str) -> None:
|
||||
try:
|
||||
client.get_registered_model(name)
|
||||
except Exception:
|
||||
client.create_registered_model(name)
|
||||
|
||||
def _set_alias(self, client: Any, name: str, alias: str, version: str) -> None:
|
||||
if hasattr(client, "set_registered_model_alias"):
|
||||
client.set_registered_model_alias(name, alias, version)
|
||||
|
||||
182
uv.lock
generated
182
uv.lock
generated
@@ -210,15 +210,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/06/7c/1e7964f0f267301bb5026fed45369961f7311073412bcd36e09fbe4df0de/aws_cdk_lib-2.253.1-py3-none-any.whl", hash = "sha256:03a6f5080978f9e3576f490d06fbd1f41f159280d34dbca50721de4a19694136", size = 50271288, upload-time = "2026-05-08T16:04:41.956Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "backoff"
|
||||
version = "2.2.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "blinker"
|
||||
version = "1.9.0"
|
||||
@@ -600,18 +591,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e3/43/33806117fc8e0992aae890be73990b31d802b66e8a423bf87b80990fce66/databricks_sdk-0.111.0-py3-none-any.whl", hash = "sha256:d14ba186afd2bea03c7157d2f03e0f861a0b8eff528cfdba926d07b9e20384b8", size = 901536, upload-time = "2026-05-25T09:29:58.057Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "deprecation"
|
||||
version = "2.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "packaging" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5a/d3/8ae2869247df154b64c1884d7346d412fed0c49df84db635aab2d1c40e62/deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff", size = 173788, upload-time = "2020-04-20T14:23:38.738Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/02/c3/253a89ee03fc9b9682f1541728eb66db7db22148cd94f89ab22528cd1e1b/deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a", size = 11178, upload-time = "2020-04-20T14:23:36.581Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "docker"
|
||||
version = "7.1.0"
|
||||
@@ -929,41 +908,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h5py"
|
||||
version = "3.16.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0f/9e/6142ebfda0cb6e9349c091eae73c2e01a770b7659255248d637bec54a88b/h5py-3.16.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:370a845f432c2c9619db8eed334d1e610c6015796122b0e57aa46312c22617d9", size = 3671808, upload-time = "2026-03-06T13:48:19.737Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b0/65/5e088a45d0f43cd814bc5bec521c051d42005a472e804b1a36c48dada09b/h5py-3.16.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:42108e93326c50c2810025aade9eac9d6827524cdccc7d4b75a546e5ab308edb", size = 3045837, upload-time = "2026-03-06T13:48:21.854Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/da/1e/6172269e18cc5a484e2913ced33339aad588e02ba407fafd00d369e22ef3/h5py-3.16.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:099f2525c9dcf28de366970a5fb34879aab20491589fa89ce2863a84218bb524", size = 5193860, upload-time = "2026-03-06T13:48:24.071Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bd/98/ef2b6fe2903e377cbe870c3b2800d62552f1e3dbe81ce49e1923c53d1c5c/h5py-3.16.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:9300ad32dea9dfc5171f94d5f6948e159ed93e4701280b0f508773b3f582f402", size = 5400417, upload-time = "2026-03-06T13:48:25.728Z" },
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{ url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "qai-hub"
|
||||
version = "0.50.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "backoff" },
|
||||
{ name = "deprecation" },
|
||||
{ name = "h5py" },
|
||||
{ name = "numpy" },
|
||||
{ name = "packaging" },
|
||||
{ name = "prettytable" },
|
||||
{ name = "protobuf" },
|
||||
{ name = "requests" },
|
||||
{ name = "requests-toolbelt" },
|
||||
{ name = "s3transfer" },
|
||||
{ name = "semver" },
|
||||
{ name = "tqdm" },
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||||
{ name = "typing-extensions" },
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]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/f1/d8/d25fea29362a762b0d739ca8bfcfbda8b7af7f028813fa4c76a91edabfb1/qai_hub-0.50.0-py3-none-any.whl", hash = "sha256:a0b1e93fc3e358c02151042676779a793fea028d78b09854a3b4c6e0719bc0ce", size = 123503, upload-time = "2026-05-28T23:08:06.19Z" },
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]
|
||||
|
||||
[[package]]
|
||||
name = "qc-cli"
|
||||
version = "0.1.0"
|
||||
@@ -2055,19 +2011,22 @@ dependencies = [
|
||||
{ name = "aws-cdk-lib" },
|
||||
{ name = "boto3" },
|
||||
{ name = "constructs" },
|
||||
{ name = "mlflow" },
|
||||
{ name = "numpy" },
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{ name = "pydantic" },
|
||||
{ name = "pyyaml" },
|
||||
{ name = "qai-hub" },
|
||||
{ name = "sagemaker-mlflow" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
mlflow = [
|
||||
{ name = "mlflow" },
|
||||
{ name = "sagemaker-mlflow" },
|
||||
]
|
||||
|
||||
[package.dev-dependencies]
|
||||
dev = [
|
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{ name = "boto3-stubs", extra = ["iam", "s3", "sagemaker"] },
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{ name = "pyright" },
|
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{ name = "pytest" },
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{ name = "ruff" },
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{ name = "types-pyyaml" },
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]
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@@ -2077,19 +2036,19 @@ requires-dist = [
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{ name = "aws-cdk-lib", specifier = ">=2.180.0" },
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{ name = "boto3", specifier = ">=1.34,<1.42" },
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{ name = "constructs", specifier = ">=10.0.0" },
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{ name = "mlflow", specifier = ">=3.0" },
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{ name = "numpy", specifier = ">=1.26" },
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{ name = "mlflow", marker = "extra == 'mlflow'", specifier = ">=3.0" },
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{ name = "pydantic", specifier = ">=2.13.3" },
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{ name = "pyyaml", specifier = ">=6.0.3" },
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{ name = "qai-hub", specifier = ">=0.49.0" },
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{ name = "sagemaker-mlflow", specifier = ">=0.4.0" },
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{ name = "sagemaker-mlflow", marker = "extra == 'mlflow'", specifier = ">=0.4.0" },
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{ name = "typer", specifier = "==0.25.0" },
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]
|
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provides-extras = ["mlflow"]
|
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|
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[package.metadata.requires-dev]
|
||||
dev = [
|
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{ name = "boto3-stubs", extras = ["iam", "s3", "sagemaker"] },
|
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{ name = "pyright", specifier = ">=1.1.409" },
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{ name = "pytest", specifier = ">=8.0" },
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{ name = "ruff", specifier = ">=0.4" },
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{ name = "types-pyyaml" },
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]
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@@ -2109,18 +2068,6 @@ wheels = [
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]
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|
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[[package]]
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name = "requests-toolbelt"
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version = "1.0.0"
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "requests" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/f3/61/d7545dafb7ac2230c70d38d31cbfe4cc64f7144dc41f6e4e4b78ecd9f5bb/requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6", size = 206888, upload-time = "2023-05-01T04:11:33.229Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/3f/51/d4db610ef29373b879047326cbf6fa98b6c1969d6f6dc423279de2b1be2c/requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06", size = 54481, upload-time = "2023-05-01T04:11:28.427Z" },
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]
|
||||
|
||||
[[package]]
|
||||
name = "rich"
|
||||
version = "15.0.0"
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@@ -2273,15 +2220,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" },
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]
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|
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[[package]]
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name = "semver"
|
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version = "3.0.4"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/72/d1/d3159231aec234a59dd7d601e9dd9fe96f3afff15efd33c1070019b26132/semver-3.0.4.tar.gz", hash = "sha256:afc7d8c584a5ed0a11033af086e8af226a9c0b206f313e0301f8dd7b6b589602", size = 269730, upload-time = "2025-01-24T13:19:27.617Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/a6/24/4d91e05817e92e3a61c8a21e08fd0f390f5301f1c448b137c57c4bc6e543/semver-3.0.4-py3-none-any.whl", hash = "sha256:9c824d87ba7f7ab4a1890799cec8596f15c1241cb473404ea1cb0c55e4b04746", size = 17912, upload-time = "2025-01-24T13:19:24.949Z" },
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|
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[[package]]
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name = "shellingham"
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version = "1.5.4"
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@@ -2389,18 +2327,6 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" },
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|
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[[package]]
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name = "tqdm"
|
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source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "colorama", marker = "sys_platform == 'win32'" },
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]
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sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" },
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]
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|
||||
[[package]]
|
||||
name = "typeguard"
|
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version = "2.13.3"
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|
||||
Reference in New Issue
Block a user