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

Related tags

Machine Learningmlflow_hydra_optuna_the_easy_way
Overview

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:[email protected]: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

0

1

Owner
shibuiwilliam
Technical engineer for cloud computing, container, deep learning and AR. MENSA. Author of ml-system-design-pattern. https://www.amazon.co.jp/dp/B08YNMRH4J/
shibuiwilliam
GitHub Repository
Machine Learning from Scratch

Machine Learning from Scratch Author: Shengxuan Wang From: Oregon State University Content: Building Machine Learning model from Scratch, without usin

ShawnWang 0 Jul 05, 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

23.3k Dec 31, 2022
Examples and code for the Practical Machine Learning workshop series

Practical Machine Learning Workshop Series Practical Machine Learning for Quantitative Finance Post conference workshop at the WBS Spring Conference D

CompatibL 21 Jun 25, 2022
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

2.3k Jan 04, 2023
Simple linear model implementations from scratch.

Hand Crafted Models Simple linear model implementations from scratch. Table of contents Overview Project Structure Getting started Citing this project

Jonathan Sadighian 2 Sep 13, 2021
onelearn: Online learning in Python

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

15 Nov 06, 2022
Evaluate on three different ML model for feature selection using Breast cancer data.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Tarek idrees 1 Mar 17, 2022
Machine Learning Study 혼자 해보기

Machine Learning Study 혼자 해보기 기여자 (Contributors) ✨ Teddy Lee 🏠 HongJaeKwon 🏠 Seungwoo Han 🏠 Tae Heon Kim 🏠 Steve Kwon 🏠 SW Song 🏠 K1A2 🏠 Wooil

Teddy Lee 1.7k Jan 01, 2023
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla

6.2k Jan 01, 2023
EbookMLCB - ebook Machine Learning cơ bản

Mã nguồn cuốn ebook "Machine Learning cơ bản", Vũ Hữu Tiệp. ebook Machine Learning cơ bản pdf-black_white, pdf-color. Mọi hình thức sao chép, in ấn đề

943 Jan 02, 2023
Implementation of K-Nearest Neighbors Algorithm Using PySpark

KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:

Zachary Petroff 4 Dec 30, 2022
distfit - Probability density fitting

Python package for probability density function fitting of univariate distributions of non-censored data

Erdogan Taskesen 187 Dec 30, 2022
Machine Learning e Data Science com Python

Machine Learning e Data Science com Python Arquivos do curso de Data Science e Machine Learning com Python na Udemy, cliqe aqui para acessá-lo. O prin

Renan Barbosa 1 Jan 27, 2022
K-Means clusternig example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering K-Means clusternig example with Python and Scikit-learn Flat clustering Clustering algorithms group a se

Emin 1 Dec 13, 2021
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Apache (Py)Spark type annotations (stub files).

PySpark Stubs A collection of the Apache Spark stub files. These files were generated by stubgen and manually edited to include accurate type hints. T

Maciej 114 Nov 22, 2022
Basic Docker Compose for Machine Learning Purposes

Docker-compose for Machine Learning How to use: cd docker-ml-jupyterlab

Chris Chen 1 Oct 29, 2021
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Real-time water systems lab 416 Jan 06, 2023
customer churn prediction prevention in telecom industry using machine learning and survival analysis

Telco Customer Churn Prediction - Plotly Dash Application Description This dash application allows you to predict telco customer churn using machine l

Benaissa Mohamed Fayçal 3 Nov 20, 2021