mlflow_hydra_optuna_the_easy_way

The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

Objective

TODO

Usage

1. build docker image to run training jobs

$ 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 them

2. run parameter search and training job

the 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=0.9555555555555556

3. training history and artifacts

training 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.pickle

you can also open mlflow ui

$ cd outputs/2021-10-13/13-27-41
$ 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: 48166

open localhost:5000 in your web-browser

Bumps pywin32 from 227 to 301.

Release notes

Sourced from pywin32's releases.

Release 301

The changes

If you use pip: pip install pywin32 --upgrade

A number of things don't work via pip, so you may choose to install binaries - but you must choose both the correct Python version and "bittedness".

Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

There is one binary per-version, per-bittedness. To determine what version of Python you have, start Python and look at the first line of the banner. Compare these 2:
Python 2.7.2+ ... [MSC v.1500 32 bit (Intel)] on win32
Python 2.7.2+ ... [MSC v.1500 64 bit (AMD64)] on win32
                              ^^^^^^^^^^^^^^
If the installation process informs you that Python is not found in the registry, it almost certainly means you have downloaded the wrong version - either for the wrong version of Python, or the wrong "bittedness".

Release 300

This is the first release to support only Python 3.5 and up - Python 2 is no longer supported. To celebrate, the build numbers have jumped to 300! There were significant changes in this release - you are encouraged to read CHANGES.txt carefully.

To download pywin32 binaries you must choose both the correct Python version and "bittedness".

Note that there is one download package for each supported version of Python - please check what version of Python you have installed and download the corresponding package.

Some packages have a 32bit and a 64bit version available - you must download the one which corresponds to the Python you have installed. Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

To determine what version of Python you have, just start Python and look at the first line of the banner. A 32bit build will look something like

Python 3.8.1+ ... [MSC v.1913 32 bit (Intel)] on win32

While a 64bit build will look something like:

Python 3.8.1+ ... [MSC v.1913 64 bit (AMD64)] on win32

If the installation process informs you that Python is not found in the registry, it almost certainly means you have downloaded the wrong version - either for the wrong version of Python, or the wrong "bittedness".

Release 228

To download pywin32 binaries you must choose both the correct Python version and "bittedness".

Note that there is one download package for each supported version of Python - please check what version of Python you have installed and download the corresponding package.

Some packages have a 32bit and a 64bit version available - you must download the one which corresponds to the Python you have installed. Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

To determine what version of Python you have, just start Python and look at the first line of the banner. A 32bit build will look something like
Python 2.7.2+ ... [MSC v.1500 32 bit (Intel)] on win32
While a 64bit build will look something like:
Python 2.7.2+ ... [MSC v.1500 64 bit (AMD64)] on win32

... (truncated)

Changelog

Sourced from pywin32's changelog.

Since build 301:

Fixed support for unicode as a win32crypt.CREDENTIAL_ATTRIBUTE.Value

Support for Python 10, dropped support for Python 3.5 (3.5 security support ended 13 Sep 2020)

Merged win2kras into win32ras. In the unlikely case that anyone is still using win2kras, there is a win2kras.py that imports all of win32ras. If you import win2kras and it fails with 'you must import win32ras first', then it means an old win2kras.pyd exists, which you should remove.

github branch 'master' was renamed to 'main'.

Since build 300:

Fix some confusion on how dynamic COM object properties work. The old code was confused, so there's a chance there will be some subtle regression here - please open a bug if you find anything, but this should fix #1427.

COM objects are now registered with the full path to pythoncomXX.dll, fixes #1704.

Creating a win32crypt.CRYPT_ATTRIBUTE object now correctly sets cbData.

Add wrap and unwrap operations defined in the GSSAPI to the sspi module and enhance the examples given in this module. (#1692, Emmanuel Coirier)

Fix a bug in win32profile.GetEnvironmentStrings() relating to environment variables with an equals sign (@maxim-krikun in #1661)

Fixed a bug where certain COM dates would fail to be converted to a Python datetime object with ValueError: microsecond must be in 0..999999. Shoutout to @hujiaxing for reporting and helping reproduce the issue (#1655)

Added win32com.shell.SHGetKnownFolderPath() and related constants.

CoClass objects should work better with special methods like len etc. (#1699)

Shifted work in win32.lib.pywin32_bootstrap to Python's import system from manual path manipulations (@wkschwartz in #1651)

Fixed a bug where win32print.DeviceCapabilities would return strings containing the null character followed by junk characters. (#1654, #1660, Lincoln Puzey)

Since build 228:

... (truncated)

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Bump pywin32 from 227 to 301
Bumps pywin32 from 227 to 301.

Release notes

Sourced from pywin32's releases.

Release 301

The changes

If you use pip: pip install pywin32 --upgrade

A number of things don't work via pip, so you may choose to install binaries - but you must choose both the correct Python version and "bittedness".

Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

There is one binary per-version, per-bittedness. To determine what version of Python you have, start Python and look at the first line of the banner. Compare these 2:

Python 2.7.2+ ... [MSC v.1500 32 bit (Intel)] on win32 Python 2.7.2+ ... [MSC v.1500 64 bit (AMD64)] on win32 ^^^^^^^^^^^^^^

If the installation process informs you that Python is not found in the registry, it almost certainly means you have downloaded the wrong version - either for the wrong version of Python, or the wrong "bittedness".

Release 300

This is the first release to support only Python 3.5 and up - Python 2 is no longer supported. To celebrate, the build numbers have jumped to 300! There were significant changes in this release - you are encouraged to read CHANGES.txt carefully.

To download pywin32 binaries you must choose both the correct Python version and "bittedness".

Note that there is one download package for each supported version of Python - please check what version of Python you have installed and download the corresponding package.

Some packages have a 32bit and a 64bit version available - you must download the one which corresponds to the Python you have installed. Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

To determine what version of Python you have, just start Python and look at the first line of the banner. A 32bit build will look something like

Python 3.8.1+ ... [MSC v.1913 32 bit (Intel)] on win32

While a 64bit build will look something like:

Python 3.8.1+ ... [MSC v.1913 64 bit (AMD64)] on win32

If the installation process informs you that Python is not found in the registry, it almost certainly means you have downloaded the wrong version - either for the wrong version of Python, or the wrong "bittedness".

Release 228

To download pywin32 binaries you must choose both the correct Python version and "bittedness".

Note that there is one download package for each supported version of Python - please check what version of Python you have installed and download the corresponding package.

Some packages have a 32bit and a 64bit version available - you must download the one which corresponds to the Python you have installed. Even if you have a 64bit computer, if you installed a 32bit version of Python you must install the 32bit version of pywin32.

To determine what version of Python you have, just start Python and look at the first line of the banner. A 32bit build will look something like

Python 2.7.2+ ... [MSC v.1500 32 bit (Intel)] on win32

While a 64bit build will look something like:

Python 2.7.2+ ... [MSC v.1500 64 bit (AMD64)] on win32

... (truncated)

Changelog

Sourced from pywin32's changelog.

Since build 301:

Fixed support for unicode as a win32crypt.CREDENTIAL_ATTRIBUTE.Value

Support for Python 10, dropped support for Python 3.5 (3.5 security support ended 13 Sep 2020)

Merged win2kras into win32ras. In the unlikely case that anyone is still using win2kras, there is a win2kras.py that imports all of win32ras. If you import win2kras and it fails with 'you must import win32ras first', then it means an old win2kras.pyd exists, which you should remove.

github branch 'master' was renamed to 'main'.

Since build 300:

Fix some confusion on how dynamic COM object properties work. The old code was confused, so there's a chance there will be some subtle regression here - please open a bug if you find anything, but this should fix #1427.

COM objects are now registered with the full path to pythoncomXX.dll, fixes #1704.

Creating a win32crypt.CRYPT_ATTRIBUTE object now correctly sets cbData.

Add wrap and unwrap operations defined in the GSSAPI to the sspi module and enhance the examples given in this module. (#1692, Emmanuel Coirier)

Fix a bug in win32profile.GetEnvironmentStrings() relating to environment variables with an equals sign (@maxim-krikun in #1661)

Fixed a bug where certain COM dates would fail to be converted to a Python datetime object with ValueError: microsecond must be in 0..999999. Shoutout to @hujiaxing for reporting and helping reproduce the issue (#1655)

Added win32com.shell.SHGetKnownFolderPath() and related constants.

CoClass objects should work better with special methods like len etc. (#1699)

Shifted work in win32.lib.pywin32_bootstrap to Python's import system from manual path manipulations (@wkschwartz in #1651)

Fixed a bug where win32print.DeviceCapabilities would return strings containing the null character followed by junk characters. (#1654, #1660, Lincoln Puzey)

Since build 228:

... (truncated)

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opened by dependabot[bot] 1

The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

Related tags

Overview

mlflow_hydra_optuna_the_easy_way

Objective

Usage

1. build docker image to run training jobs

2. run parameter search and training job

3. training history and artifacts

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Bump pywin32 from 227 to 301

Release 301

Release 300

Release 228

Since build 301:

Since build 300:

Owner

shibuiwilliam

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