Optimized serving endpoint for classical ML models #3377
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Co-authored-by: Miłosz Żeglarski <[email protected]>
Co-authored-by: Miłosz Żeglarski <[email protected]>
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I guess we don't need that in the repo since we have an instruction on preparing the data.
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I guess we don't need that in the repo since we have an instruction on preparing the data.
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Pull Request Overview
This PR introduces a complete machine learning pipeline project for the Iris dataset using OpenVINO Model Server (OVMS) with MediaPipe. The project supports both logistic regression and k-means clustering models with training and inference capabilities through client scripts.
Key changes:
- Complete pipeline implementation with PyTorch logistic regression and scikit-learn k-means models
- Client scripts for training and inference with flexible parameter handling
- Docker-based deployment with Intel optimization libraries
- Data preprocessing utilities and example datasets
Reviewed Changes
Copilot reviewed 15 out of 15 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
| extras/iris_pipeline_project/pipeline/ovmsmodel.py | Core OVMS Python model implementing training/inference logic for both model types |
| extras/iris_pipeline_project/pipeline/model.py | Abstract model classes with PyTorch logistic regression and scikit-learn k-means implementations |
| extras/iris_pipeline_project/pipeline/graph.pbtxt | MediaPipe graph configuration for the pipeline |
| extras/iris_pipeline_project/model_config.json | OVMS model configuration |
| extras/iris_pipeline_project/labelmap.json | Label mapping for Iris species |
| extras/iris_pipeline_project/kmeans_param.json | K-means hyperparameters |
| extras/iris_pipeline_project/hyperparams.json | Logistic regression hyperparameters |
| extras/iris_pipeline_project/data_preprocess.py | Data preprocessing script for Iris dataset |
| extras/iris_pipeline_project/data_folder/iris_train.csv | Training dataset |
| extras/iris_pipeline_project/data_folder/iris_test.csv | Test dataset |
| extras/iris_pipeline_project/client/client_train.py | Client script for training models |
| extras/iris_pipeline_project/client/client_inference.py | Client script for inference |
| extras/iris_pipeline_project/README.md | Project documentation |
| extras/iris_pipeline_project/Dockerfile | Docker configuration with Intel optimizations |
| extras/iris_pipeline_project/.gitignore | Git ignore file |
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| import sys | ||
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| if len(sys.argv) < 2: | ||
| print("Usage: python datapreprocess.py <output_directory>") |
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The filename in the usage message should match the actual filename: 'data_preprocess.py' instead of 'datapreprocess.py'.
| print("Usage: python datapreprocess.py <output_directory>") | |
| print("Usage: python data_preprocess.py <output_directory>") |
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Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
darksapien23151
changed the title
🛠 Summary
GSOC Contribution for the project - Optimized serving endpoint for classical Machine Learning models.
Current status: Finished working on Logistic Regression and KMeans.
Mentors:
Zeglarski, Milosz
Trawinski, Dariusz