33 lines
1.1 KiB
Markdown
33 lines
1.1 KiB
Markdown
# Abstract
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Try to use [MLflow](https://mlflow.org) platform to log PyTorch model training, and pull production model from model registry to run inference⛩
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# Requirements
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* MacOS 12.5
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* Docker 20.10
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# Dirs
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* **service**
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* House MLflow service data, including MLflow artifacts, backend store and model registry
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* **env**
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* **mlflow.yaml**
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* conda env yaml to run this repo
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# Files
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* **docker-compose.yaml**
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* a yaml to apply docker-compose to start MLflow service with basic configuration (run ```docker-compose -f docker-compose.yaml up```)
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* **test_pytorch_m1.py**
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* a script to test PyTorch on Apple M1 platform with GPU acceleration
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* **train.py**
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* a sample code to apply PyTorch to train a small neural network to predict fortune with MLflow logging
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* **predict.py**
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* a sample code to call registered model to predict testing data and save model to local file system
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* **get_registered_model_via_rest_api.py**
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* a script to test MLflow REST api
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* **log_unsupported_model.py**
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* a sample script to apply mlflow.pyfunc to package unsupported ml model which can be logged and registered by mlflow
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###### tags: `MLOps` |