The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.
The repository is made to provide a code sample of combining mlflow, hydra and optuna in structured project.
$ make build
docker build \
-t mlflow_hydra_optuna:the_easy_way \
-f Dockerfile \
.
[+] Building 1.8s (10/10) FINISHED
=> [internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 37B 0.0s
=> [internal] load .dockerignore 0.0s
=> => transferring context: 2B 0.0s
=> [internal] load metadata for docker.io/library/python:3.9.5-slim 1.7s
=> [1/5] FROM docker.io/library/python:3.9.5-slim@sha256:9828573e6a0b02b6d0ff0bae0716b027aa21cf8e59ac18a76724d216bab7ef0 0.0s
=> [internal] load build context 0.0s
=> => transferring context: 17.23kB 0.0s
=> CACHED [2/5] WORKDIR /opt 0.0s
=> CACHED [3/5] COPY .//requirements.txt /opt/ 0.0s
=> CACHED [4/5] RUN apt-get -y update && apt-get -y install apt-utils gcc && apt-get clean && rm -rf 0.0s
=> [5/5] COPY .//src/ /opt/src/ 0.0s
=> exporting to image 0.0s
=> => exporting layers 0.0s
=> => writing image sha256:256aa71f14b29d5e93f717724534abf0f173522a7f9260b5d0f2051c4607782e 0.0s
=> => naming to docker.io/library/mlflow_hydra_optuna:the_easy_way 0.0s
Use 'docker scan' to run Snyk tests against images to find vulnerabilities and learn how to fix themthe parameters for optuna and hyper parameter search are in hydra/default.yaml
$ cat hydra/default.yaml
optuna:
cv: 5
n_trials: 20
n_jobs: 1
random_forest_classifier:
parameters:
- name: criterion
suggest_type: categorical
value_range:
- gini
- entropy
- name: max_depth
suggest_type: int
value_range:
- 2
- 100
- name: max_leaf_nodes
suggest_type: int
value_range:
- 2
- 100
lightgbm_classifier:
parameters:
- name: num_leaves
suggest_type: int
value_range:
- 2
- 100
- name: max_depth
suggest_type: int
value_range:
- 2
- 100
- name: learning_rage
suggest_type: uniform
value_range:
- 0.0001
- 0.01
- name: feature_fraction
suggest_type: uniform
value_range:
- 0.001
- 0.9
$ make run
docker run \
-it \
--name the_easy_way \
-v ~/mlflow_hydra_optuna_the_easy_way/hydra:/opt/hydra \
-v ~/mlflow_hydra_optuna_the_easy_way/outputs:/opt/outputs \
mlflow_hydra_optuna:the_easy_way \
python -m src.main
[2021-10-14 00:41:29,804][__main__][INFO] - config: {'optuna': {'cv': 5, 'n_trials': 20, 'n_jobs': 1}, 'random_forest_classifier': {'parameters': [{'name': 'criterion', 'suggest_type': 'categorical', 'value_range': ['gini', 'entropy']}, {'name': 'max_depth', 'suggest_type': 'int', 'value_range': [2, 100]}, {'name': 'max_leaf_nodes', 'suggest_type': 'int', 'value_range': [2, 100]}]}, 'lightgbm_classifier': {'parameters': [{'name': 'num_leaves', 'suggest_type': 'int', 'value_range': [2, 100]}, {'name': 'max_depth', 'suggest_type': 'int', 'value_range': [2, 100]}, {'name': 'learning_rage', 'suggest_type': 'uniform', 'value_range': [0.0001, 0.01]}, {'name': 'feature_fraction', 'suggest_type': 'uniform', 'value_range': [0.001, 0.9]}]}}
[2021-10-14 00:41:29,805][__main__][INFO] - os cwd: /opt/outputs/2021-10-14/00-41-29
[2021-10-14 00:41:29,807][src.model.model][INFO] - initialize preprocess pipeline: Pipeline(steps=[('standard_scaler', StandardScaler())])
[2021-10-14 00:41:29,810][src.model.model][INFO] - initialize random forest classifier pipeline: Pipeline(steps=[('standard_scaler', StandardScaler()),
('model', RandomForestClassifier())])
[2021-10-14 00:41:29,812][__main__][INFO] - params: [SearchParams(name='criterion', suggest_type=<SUGGEST_TYPE.CATEGORICAL: 'categorical'>, value_range=['gini', 'entropy']), SearchParams(name='max_depth', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='max_leaf_nodes', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100))]
[2021-10-14 00:41:29,813][src.model.model][INFO] - new search param: [SearchParams(name='criterion', suggest_type=<SUGGEST_TYPE.CATEGORICAL: 'categorical'>, value_range=['gini', 'entropy']), SearchParams(name='max_depth', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='max_leaf_nodes', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100))]
[2021-10-14 00:41:29,817][src.model.model][INFO] - initialize lightgbm classifier pipeline: Pipeline(steps=[('standard_scaler', StandardScaler()),
('model', LGBMClassifier())])
[2021-10-14 00:41:29,819][__main__][INFO] - params: [SearchParams(name='num_leaves', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='max_depth', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='learning_rage', suggest_type=<SUGGEST_TYPE.UNIFORM: 'uniform'>, value_range=(0.0001, 0.01)), SearchParams(name='feature_fraction', suggest_type=<SUGGEST_TYPE.UNIFORM: 'uniform'>, value_range=(0.001, 0.9))]
[2021-10-14 00:41:29,820][src.model.model][INFO] - new search param: [SearchParams(name='num_leaves', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='max_depth', suggest_type=<SUGGEST_TYPE.INT: 'int'>, value_range=(2, 100)), SearchParams(name='learning_rage', suggest_type=<SUGGEST_TYPE.UNIFORM: 'uniform'>, value_range=(0.0001, 0.01)), SearchParams(name='feature_fraction', suggest_type=<SUGGEST_TYPE.UNIFORM: 'uniform'>, value_range=(0.001, 0.9))]
[2021-10-14 00:41:29,821][src.dataset.load_dataset][INFO] - load iris dataset
[2021-10-14 00:41:29,824][src.search.search][INFO] - estimator: <src.model.model.RandomForestClassifierPipeline object at 0x7f5776aa5f10>
[I 2021-10-14 00:41:29,825] A new study created in memory with name: random_forest_classifier
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
[I 2021-10-14 00:41:30,519] Trial 0 finished with value: 0.96 and parameters: {'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 62}. Best is trial 0 with value: 0.96.
2021/10/14 00:41:30 WARNING mlflow.tracking.context.git_context: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.
The git executable must be specified in one of the following ways:
- be included in your $PATH
- be set via $GIT_PYTHON_GIT_EXECUTABLE
- explicitly set via git.refresh()
All git commands will error until this is rectified.
This initial warning can be silenced or aggravated in the future by setting the
$GIT_PYTHON_REFRESH environment variable. Use one of the following values:
- quiet|q|silence|s|none|n|0: for no warning or exception
- warn|w|warning|1: for a printed warning
- error|e|raise|r|2: for a raised exception
Example:
export GIT_PYTHON_REFRESH=quiet
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
<... long training ...>
[I 2021-10-14 00:41:56,870] Trial 19 finished with value: 0.9466666666666667 and parameters: {'num_leaves': 64, 'max_depth': 17, 'learning_rage': 0.0070407009344824675, 'feature_fraction': 0.4416643843187271}. Best is trial 0 with value: 0.9466666666666667.
[2021-10-14 00:41:57,031][src.search.search][INFO] - result for light_gbm_classifier: {'estimator': 'light_gbm_classifier', 'best_score': 0.9466666666666667, 'best_params': {'num_leaves': 17, 'max_depth': 20, 'learning_rage': 0.006952391958964706, 'feature_fraction': 0.8414032025653786}}
[2021-10-14 00:41:57,032][__main__][INFO] - parameter search results: [{'estimator': 'random_forest_classifier', 'best_score': 0.9666666666666668, 'best_params': {'criterion': 'entropy', 'max_depth': 14, 'max_leaf_nodes': 65}}, {'estimator': 'light_gbm_classifier', 'best_score': 0.9466666666666667, 'best_params': {'num_leaves': 17, 'max_depth': 20, 'learning_rage': 0.006952391958964706, 'feature_fraction': 0.8414032025653786}}]
/usr/local/lib/python3.9/site-packages/sklearn/pipeline.py:394: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
self._final_estimator.fit(Xt, y, **fit_params_last_step)
[2021-10-14 00:41:57,518][__main__][INFO] - random forest evaluation result: accuracy=0.9777777777777777 precision=0.9777777777777777 recall=0.9777777777777777
/usr/local/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
/usr/local/lib/python3.9/site-packages/sklearn/preprocessing/_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
[LightGBM] [Warning] Unknown parameter: learning_rage
[LightGBM] [Warning] feature_fraction is set=0.8414032025653786, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.8414032025653786
[2021-10-14 00:41:57,818][__main__][INFO] - lightgbm evaluation result: accuracy=0.9555555555555556 precision=0.9555555555555556 recall=0.9555555555555556training history and artifacts are recorded under outputs
$ tree -a outputs
outputs
├── .gitignore
├── .gitkeep
└── 2021-10-14
└── 00-41-29
├── .hydra
│ ├── config.yaml
│ ├── hydra.yaml
│ ├── light_gbm_classifier.yaml
│ ├── overrides.yaml
│ └── random_forest_classifier.yaml
├── light_gbm_classifier.pickle
├── main.log
├── mlruns
│ ├── .trash
│ └── 0
│ ├── 001f4913ee2c464e9095894c280a827f
│ │ ├── artifacts
│ │ ├── meta.yaml
│ │ ├── metrics
│ │ │ └── accuracy
│ │ ├── params
│ │ │ ├── feature_fraction
│ │ │ ├── learning_rage
│ │ │ ├── max_depth
│ │ │ ├── model
│ │ │ └── num_leaves
│ │ └── tags
│ │ ├── mlflow.runName
│ │ ├── mlflow.source.name
│ │ ├── mlflow.source.type
│ │ └── mlflow.user
<... many files ...>
│ └── meta.yaml
└── random_forest_classifier.pickleyou can also open mlflow ui
$ cd outputs/2021-10-14/00-41-29
$ mlflow ui
[2021-10-13 22:34:51 +0900] [48165] [INFO] Starting gunicorn 20.1.0
[2021-10-13 22:34:51 +0900] [48165] [INFO] Listening at: http://127.0.0.1:5000 (48165)
[2021-10-13 22:34:51 +0900] [48165] [INFO] Using worker: sync
[2021-10-13 22:34:51 +0900] [48166] [INFO] Booting worker with pid: 48166open localhost:5000 in your web-browser
