diff --git a/.github/workflows/build_docker.yaml b/.github/workflows/build_docker.yaml index 7bbe07f..c5b68c7 100644 --- a/.github/workflows/build_docker.yaml +++ b/.github/workflows/build_docker.yaml @@ -16,7 +16,8 @@ jobs: directory: [ data_prep, train, - eval + eval, + onnx_optimize ] steps: diff --git a/.gitignore b/.gitignore index fc29436..f64a414 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,9 @@ torch_model onnx_model.onnx loss_plot.png local.py +local.pth +local.onnx +local_2.onnx train_images val_images test_images @@ -13,6 +16,11 @@ test_split.json f1_score_by_class.png precision_by_class.png recall_by_class.png +train/comparison_image_*.png +train/small_original.png +train/small_transformed_custom.png +train/small_transformed_weights.png +optimized_model.onnx # pyenv .python-version diff --git a/README.md b/README.md index 0aa2ffe..dc7f2a8 100644 --- a/README.md +++ b/README.md @@ -7,11 +7,6 @@ and [weather image classification dataset](https://www.kaggle.com/datasets/jehan study, the focus is on demonstrating key concepts like reproducibility, artifact tracking, and automation, rather than the specifics of tools and implementation details. -Disclaimer — this project doesn't advocate for Kubeflow Pipelines as the definitive framework for developing machine -learning workflows (alternative frameworks will be provided in the appendix), nor does it endorse Vertex AI as the -optimal managed platform. The choice of these specific tools stems from pragmatic reasons — namely, their immediate -availability in my existing setup or compatability with existing setup. - The `ml-workflows` follows a `standard` project structure, as well as tooling, proposed in the repository [py-manage](https://github.com/martynas-subonis/py-manage). @@ -50,7 +45,7 @@ training epochs, etc. The pipeline should guarantee reproducibility and ease of ![Pipeline Overview](readme_assets/overview.png) -The [pipeline](template.py) consists of three main components: +The [pipeline](template.py) consists of four main components: 1. Data Preparation ([data_prep](data_prep)): @@ -70,7 +65,8 @@ The [pipeline](template.py) consists of three main components: - - Fine-tunes the classifier head for the specific problem domain. - - - Outputs: trained PyTorch model, ONNX model, training metrics, and loss plot. + - Outputs: trained PyTorch model, ONNX model, ONNX model with transformations included as part of model graph, training metrics, and + loss plot. 3. Model Evaluation ([eval](eval)): @@ -81,6 +77,11 @@ The [pipeline](template.py) consists of three main components: - - Outputs: confusion matrix, weighted precision, recall, and F1-scores. +4. ONNX Model Optimization ([onn_optimize](onnx_optimize)) + +- + - A simple component which loads ONNX model with transformation, optimizes it, and produces optimized model artifact. + Each component has its own Docker image, ensuring a reproducible runtime environment through the use of [Poetry](https://python-poetry.org/docs/) and its `.lock` files. To guarantee consistency in results, all components employ fixed random seeds for randomized algorithms. Additionally, the training component includes @@ -139,6 +140,7 @@ STAGING_BUCKET= # Your GCS bucket, where Kubeflow Pipelines will persist its ar PREP_DATA_DOCKER_URI= # Your URI of data_prep component Docker image. TRAIN_MODEL_DOCKER_URI= # Your URI of train component Docker image. EVAL_MODEL_DOCKER_URI= # Your URI of eval component Docker image. +ONNX_OPTIMIZE_DOCKER_URI = # Your URI of onnx_optimize component Docker image. ``` - Have [Poetry installed](https://python-poetry.org/docs/#installation). diff --git a/onnx_optimize/Dockerfile b/onnx_optimize/Dockerfile new file mode 100644 index 0000000..a3b1292 --- /dev/null +++ b/onnx_optimize/Dockerfile @@ -0,0 +1,18 @@ +FROM python:3.12.5-slim AS builder + +RUN pip install --upgrade pip==24.2.0 && \ + pip install poetry==1.8.3 + +WORKDIR /app + +COPY pyproject.toml poetry.toml poetry.lock ./ + +RUN poetry install + +FROM python:3.12.5-slim AS runtime + +WORKDIR /app + +ENV PATH="/app/.venv/bin:$PATH" + +COPY --from=builder /app/.venv .venv \ No newline at end of file diff --git a/onnx_optimize/__init__.py b/onnx_optimize/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/onnx_optimize/component_func.py b/onnx_optimize/component_func.py new file mode 100644 index 0000000..5b6f92c --- /dev/null +++ b/onnx_optimize/component_func.py @@ -0,0 +1,23 @@ +from kfp.dsl import Input, Metrics, Model, Output + + +def onnx_optimize( + onnx_with_transform_model: Input[Model], optimization_metrics: Output[Metrics], optimized_onnx_with_transform_model: Output[Model] +) -> None: + import logging + import time + + import onnxruntime as rt + + start_time = time.time() + logging.info("Started ONNX model optimization task.") + + sess_options = rt.SessionOptions() + sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL + sess_options.optimized_model_filepath = optimized_onnx_with_transform_model.path + rt.InferenceSession(onnx_with_transform_model.path, sess_options) + optimized_onnx_with_transform_model.framework = ( + f"onnxruntime-{rt.__version__}, graphOptimizationLevel-{str(sess_options.graph_optimization_level)}" + ) + optimization_metrics.log_metric("timeTakenSeconds", round(time.time() - start_time, 2)) + logging.info("Successfully finished ONNX model optimization task.") diff --git a/onnx_optimize/poetry.lock b/onnx_optimize/poetry.lock new file mode 100644 index 0000000..a51a89f --- /dev/null +++ b/onnx_optimize/poetry.lock @@ -0,0 +1,925 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "cachetools" +version = "5.5.0" +description = "Extensible memoizing collections and decorators" +optional = false +python-versions = ">=3.7" +files = [ + {file = "cachetools-5.5.0-py3-none-any.whl", hash = "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292"}, + {file = "cachetools-5.5.0.tar.gz", hash = "sha256:2cc24fb4cbe39633fb7badd9db9ca6295d766d9c2995f245725a46715d050f2a"}, +] + +[[package]] +name = "certifi" +version = "2024.8.30" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, + {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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"0.1.0" +description = "ONNX Model Optimization Component" +authors = ["Martynas Subonis "] +license = "MIT" +readme = "README.md" +package-mode = false + +[tool.poetry.dependencies] +python = "~3.12" +kfp = "2.8.0" +onnxruntime = "1.19.2" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/pipeline.py b/pipeline.py index 928860c..35b9147 100644 --- a/pipeline.py +++ b/pipeline.py @@ -13,6 +13,7 @@ from data_prep.component_func import prep_data from eval.component_func import eval_model +from onnx_optimize.component_func import onnx_optimize from train.component_func import train_model logging.basicConfig(level=logging.INFO) @@ -31,6 +32,7 @@ class Environment: prep_data_docker_uri: str train_model_docker_uri: str eval_model_docker_uri: str + onnx_optimize_docker_uri: str def compile_upload() -> None: @@ -44,6 +46,7 @@ def compile_upload() -> None: prep_data_docker_uri=environ["PREP_DATA_DOCKER_URI"], train_model_docker_uri=environ["TRAIN_MODEL_DOCKER_URI"], eval_model_docker_uri=environ["EVAL_MODEL_DOCKER_URI"], + onnx_optimize_docker_uri=environ["ONNX_OPTIMIZE_DOCKER_URI"], ) @pipeline( # type: ignore[misc] @@ -81,6 +84,10 @@ def weather_model_pipeline( torch_model=train_model_task.outputs["torch_model"], ) + create_component_from_func(func=onnx_optimize, base_image=env.onnx_optimize_docker_uri, install_kfp_package=False)( + onnx_with_transform_model=train_model_task.outputs["onnx_with_transform_model"] + ) + logger.info("Compiling weather model pipeline...") Compiler().compile(weather_model_pipeline, package_path=PIPELINE_PATH) template_name, version_name = RegistryClient(host=env.kfp_repository).upload_pipeline( diff --git a/pyproject.toml b/pyproject.toml index 898d160..12cb6ec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "ml-workflows" -version = "0.1.0" +version = "0.2.0" description = "An example implementation of model training workflow, using KubeFlow Pipelines and GCP." authors = ["Martynas Subonis "] license = "MIT" @@ -8,7 +8,8 @@ readme = "README.md" packages = [ { include = "data_prep", to = "ml-workflows" }, { include = "train", to = "ml-workflows" }, - { include = "eval", to = "ml-workflows" } + { include = "eval", to = "ml-workflows" }, + { include = "onnx_optimize", to = "ml-workflows" } ] [tool.poetry.dependencies] diff --git a/train/component_func.py b/train/component_func.py index ab24965..6e3a5ab 100644 --- a/train/component_func.py +++ b/train/component_func.py @@ -11,6 +11,7 @@ def train_model( loss_plot: Output[Artifact], torch_model: Output[Model], onnx_model: Output[Model], + onnx_with_transform_model: Output[Model], ) -> None: import logging import os @@ -23,14 +24,18 @@ def train_model( import numpy as np import onnx import torch + import torch.nn.functional as F import torchvision from google.cloud.storage import Client, transfer_manager + from torch.nn import Module from torch.utils.data import DataLoader from torchvision.datasets import ImageFolder from torchvision.models import ( MobileNet_V3_Small_Weights, + MobileNetV3, mobilenet_v3_small, ) + from torchvision.transforms.functional import pad logging.info("Started train model task.") # Reproducibility: @@ -148,5 +153,39 @@ def train_model( torch.onnx.export(model, model_input, f"{onnx_model.path}.onnx", opset_version=opset_version) onnx_model.framework = f"{setup_info}, onnx-{onnx.__version__}, {opset_version=}" + class ModelWithTransforms(Module): # type: ignore[misc] + def __init__(self, model: MobileNetV3) -> None: + super(ModelWithTransforms, self).__init__() + self.model = model + self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + self.register_buffer("targ_h", torch.tensor(224)) + self.register_buffer("targ_w", torch.tensor(224)) + + def transform(self, img: torch.Tensor) -> torch.Tensor: + # Add batch dimension if needed. + if img.dim() == 3: + img = img.unsqueeze(0) + resized = F.interpolate(img, size=256, mode="bilinear", align_corners=False) + _, _, curr_h, curr_w = resized.shape + pad_h = torch.clamp(self.targ_h - curr_h, min=0) + pad_w = torch.clamp(self.targ_w - curr_w, min=0) + padding = [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2] + padded = pad(resized, padding) + start_h = torch.clamp((curr_h + pad_h - self.targ_h) // 2, min=0) + start_w = torch.clamp((curr_w + pad_w - self.targ_w) // 2, min=0) + cropped = padded[..., start_h : start_h + self.targ_h, start_w : start_w + self.targ_w] + normalized = (cropped - self.mean.to(cropped.device)) / self.std.to(cropped.device) + return normalized + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.transform(x) + return self.model(x) + + model_with_transform = ModelWithTransforms(model) + model_with_transform.to(device) + torch.onnx.export(model_with_transform, model_input, f"{onnx_with_transform_model.path}.onnx", opset_version=opset_version) + onnx_with_transform_model.framework = f"{setup_info}, onnx-{onnx.__version__}, {opset_version=}" + train_metrics.log_metric("timeTakenSeconds", round(time.time() - start_time, 2)) logging.info("Successfully finished weather model training.") diff --git a/train/poetry.lock b/train/poetry.lock index 92191de..3bdecd1 100644 --- a/train/poetry.lock +++ b/train/poetry.lock @@ -1821,4 +1821,4 @@ test = ["websockets"] [metadata] lock-version = "2.0" python-versions = "~3.12" -content-hash = "2184ecd8e02b1eef2e8762ab7d83453f5896cb8cf2d27e04154396da157d2b32" +content-hash = "d97a8eee5ffc955a1b9472750cb7916c95e9b14e3ec2f211182f8ec1681e012e" diff --git a/train/pyproject.toml b/train/pyproject.toml index bb6c407..2258517 100644 --- a/train/pyproject.toml +++ b/train/pyproject.toml @@ -22,6 +22,7 @@ torchvision = [ { url = "https://download.pytorch.org/whl/cpu/torchvision-0.18.1%2Bcpu-cp312-cp312-linux_x86_64.whl", platform = "linux" }, { url = "https://download.pytorch.org/whl/cpu/torchvision-0.18.1-cp312-cp312-macosx_11_0_arm64.whl", platform = "darwin" }, ] +pillow = "10.4.0" [build-system] requires = ["poetry-core"]