├── LICENSE ├── README.md ├── images ├── databricks_ce_create_mlr.png ├── databricks_ce_download_notebooks.png ├── databricks_ce_import_notebooks.png ├── databricks_ce_loging.png ├── mlflow-workshop.png ├── models_1.png ├── models_2.png ├── mult-step-project.png ├── multi-step-conda.png ├── project.png └── temperature-conversion.png ├── notebooks └── MLflow-CE-Part2.dbc ├── req.txt ├── slides └── mlflow-workshop-part-2.pdf └── src ├── run_project_example_1.py ├── run_project_example_1.sh ├── run_project_simple_keras_lr.py └── run_simple_keras_lr.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Managing the Complete Machine Learning Lifecycle with MLflow 2 | ============================================================= 3 | ![](images/mlflow-workshop.png) 4 | Part 2 of 3 5 | ----------- 6 | Other parts: 7 | - [Part 1](https://github.com/dmatrix/mlflow-workshop-part-1) 8 | - [Part 3](https://github.com/dmatrix/mlflow-workshop-part-3) 9 | - [Watch workshops on YouTube](https://www.youtube.com/playlist?list=PLTPXxbhUt-YWjDg318nmSxRqTgZFWQ2ZC) 10 | 11 | Content for the MLflow Series 12 | ----------------------------- 13 | Machine Learning (ML) development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. 14 | 15 | To solve these challenges, [MLflow](https://mlflow.org), an open source project, simplifies the entire ML lifecycle. MLflow introduces simple abstractions to package reproducible projects, track results, 16 | encapsulate models that can be used with many existing tools, and central respositry to share models, 17 | accelerating the ML lifecycle for organizations of any size. 18 | 19 | Goal and Objective 20 | ------------------ 21 | Aimed at beginner or intermediate level, this three-part series aims to educate data scientists or ML developer in how you 22 | leverage MLflow as a platform to track experiments, package projects to reproduce runs, use model flavors to deploy in diverse environments, and manage models in a central respository for sharing. 23 | 24 | What you will learn 25 | ------------------- 26 | Understand the four main components of open source MLflow——MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry—and how each compopnent helps address challenges of the ML lifecycle. 27 | * How to use [MLflow Tracking](https://mlflow.org/docs/latest/tracking.html) to record and query experiments: code, data, config, and results. 28 | * How to use [MLflow Projects](https://mlflow.org/docs/latest/projects.html) packaging format to reproduce runs 29 | * How to use [MLflow Models](https://mlflow.org/docs/latest/models.html) general format to send models to diverse deployment tools. 30 | * How to use [Model Registry](https://mlflow.org/docs/latest/model-registry.html) for collaborative model lifecycle management 31 | * How to use [MLflow UI](https://mlflow.org/docs/latest/tracking.html#tracking-ui) to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics 32 | 33 | 34 | Instructor 35 | ----------- 36 | 37 | - [Jules S. Damji](https://www.linkedin.com/in/dmatrix/) [@2twitme](https://twitter.com/2twitme) 38 | --- 39 | 40 | 41 | MLflow workshop part 2 42 | ---------------------- 43 | 44 | In this part 2, we will cover: 45 | * Concepts and motivation behind MLflow Projects and Models 46 | * Tour of the the MLflow Project and Model API Documentation 47 | * How to execute and reproduce MLflow Projects in the Databricks Community Edition (DCE) 48 | * Load PyFunc Model Flavor and score 49 | * Build an MLflow Project and share it for reproducible runs 50 | * Use the MLflow UI on the DCE 51 | 52 | Prerequisites 53 | ------------- 54 | * [Part 1](https://github.com/dmatrix/mlflow-workshop-part-1) of this series 55 | * Before the session, please pre-register for [Databricks Community Edition](https://databricks.com/try-databricks) 56 | * Knowledge of Python 3 and programming in general 57 | * Preferably a UNIX-based, fully-charged laptop with 8-16 GB, with a Chrome or Firefox browser 58 | * Familiarity with GitHub, git, and an account on Github 59 | * Some knowledge of Machine Learning concepts, libraries, and frameworks 60 | * scikit-Learn 61 | * pandas and Numpy 62 | * matplotlib 63 | * keras/TensorFlow 64 | * (**optional for part-2**) PyCharm/IntelliJ or choice of syntax-based Python editor 65 | * (**optional for part-2**) pip/pip3 or conda and Python 3 installed 66 | * (**optional for part-2**) Knowledge on how to use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) or create [pipenv](https://pypi.org/project/pipenv/) enviroments 67 | * Loads of virtual laughter, curiosity, and a sense of humor ... :-) 68 | 69 | Obtaining the Tutorial Material 70 | -------------------------------- 71 | 72 | Familiarity with git is important so that you can get all the material easily during the tutorial and 73 | workshop as well as continue to work on in your free time, after the session is over. 74 | 75 | ```git clone git@github.com:dmatrix/mlflow-workshop-part-2.git or git clone https://github.com/dmatrix/mlflow-workshop-part-2.git``` 76 | 77 | Documentation and other Resources 78 | --------------------------------- 79 | 80 | This tutorial will refer to documentation: 81 | 82 | 1. [MLflow](https://mlflow.org/docs/latest/index.html) 83 | 3. [Numpy](https://numpy.org/devdocs/user/quickstart.html) 84 | 4. [Pandas](https://pandas.pydata.org/pandas-docs/stable/reference/index.html) 85 | 5. [Scikit-Learn](https://scikit-learn.org/stable/index.html) 86 | 6. [Keras](https://keras.io/optimizers/) 87 | 7. [TensorFlow](https://tensorflow.org) 88 | 8. [Matplotlib](https://matplotlib.org/3.2.0/tutorials/introductory/pyplot.html) 89 | 9. Loads of basic and advanced [MLflow Examples](https://github.com/amesar/mlflow-examples) from Andre Mesarovic 90 | 10. How to use MLflow in your favorite [Python IDE with Keras](https://databricks.com/blog/2018/08/23/how-to-use-mlflow-to-experiment-a-keras-network-model-binary-classification-for-movie-reviews.html) 91 | 92 | How to get started 93 | ------------------ 94 | We will walk through this during the session, but please sign up for [Databricks Community Edition](https://databricks.com/try-databricks) before the session : 95 | 96 | 1. ``` git clone git@github.com:dmatrix/mlflow-workshop-part-2.git ``` 97 | 2. Use this [URL](https://community.cloud.databricks.com/login.html) to log into the Databricks Community Edition 98 | 99 | ![](images/databricks_ce_loging.png) 100 | 101 | 3. Create a ML Runtime 6.5 Cluster 102 | 103 | ![](images/databricks_ce_create_mlr.png) 104 | 105 | 4. In the brower: 106 | * (1) Go the GitHub **notebooks** subdirectory 107 | * (2) Download **MLFlow-CE-Part2.dbc** file on your laptop 108 | 109 | ![](images/databricks_ce_download_notebooks.png) 110 | 111 | 5. Import the **MLFlow-CE-Part2.dbc** file into the Databricks Community Edition 112 | 113 | ![](images/databricks_ce_import_notebooks.png) 114 | 115 | Let's go! 116 | 117 | 118 | Homework/Lab Assignment 119 | ----------------------- 120 | 121 | Using what we have learning in this session: 122 | * Improve the Keras Model with different parameters 123 | * Increase the size of training data 124 | * Use train/split and validation data 125 | * More input_units to the NN Dense layer 126 | * Run at least three experiments 127 | * Compare the runs and metrics 128 | * Use one of the models explored in [Part 1](https://github.com/dmatrix/mlflow-workshop-part-1) and build an MLflow GitHub Project 129 | * Supply different arguments as model parameters 130 | * Or Take one of your ML project and covert it into MLflow Project 131 | 132 | Cheers, 133 | 134 | Jules 135 | -------------------------------------------------------------------------------- /images/databricks_ce_create_mlr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/databricks_ce_create_mlr.png -------------------------------------------------------------------------------- /images/databricks_ce_download_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/databricks_ce_download_notebooks.png -------------------------------------------------------------------------------- /images/databricks_ce_import_notebooks.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/databricks_ce_import_notebooks.png -------------------------------------------------------------------------------- /images/databricks_ce_loging.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/databricks_ce_loging.png -------------------------------------------------------------------------------- /images/mlflow-workshop.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/mlflow-workshop.png -------------------------------------------------------------------------------- /images/models_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/models_1.png -------------------------------------------------------------------------------- /images/models_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/models_2.png -------------------------------------------------------------------------------- /images/mult-step-project.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/mult-step-project.png -------------------------------------------------------------------------------- /images/multi-step-conda.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/multi-step-conda.png -------------------------------------------------------------------------------- /images/project.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/project.png -------------------------------------------------------------------------------- /images/temperature-conversion.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/images/temperature-conversion.png -------------------------------------------------------------------------------- /notebooks/MLflow-CE-Part2.dbc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/notebooks/MLflow-CE-Part2.dbc -------------------------------------------------------------------------------- /req.txt: -------------------------------------------------------------------------------- 1 | mlflow 2 | scikit-learn 3 | pandas 4 | matplotlib 5 | seaborn 6 | statsmodels 7 | keras 8 | tensorflow 9 | -------------------------------------------------------------------------------- /slides/mlflow-workshop-part-2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dmatrix/mlflow-workshop-part-2/22dbf5d74a7c8fb8343b2d89ae13433cf6745524/slides/mlflow-workshop-part-2.pdf -------------------------------------------------------------------------------- /src/run_project_example_1.py: -------------------------------------------------------------------------------- 1 | import mlflow 2 | import warnings 3 | 4 | # 5 | # Short example how to run a MLflow GitHub Project programmatically using 6 | # MLflow Fluent APIs https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.run 7 | # 8 | 9 | if __name__ == '__main__': 10 | 11 | # suppress any deprcated warnings 12 | warnings.filterwarnings("ignore", category=DeprecationWarning) 13 | ml_project_uri = "git://github.com/mlflow/mlflow-example.git" 14 | parameters = [{'alpha': 0.3}, 15 | {'alpha': 0.4}, 16 | {'alpha': 0.5}] 17 | 18 | # Iterate over three different runs with different parameters 19 | for param in parameters: 20 | print(f"Running with param = {param}"), 21 | res_sub = mlflow.run(ml_project_uri, parameters=param) 22 | print(f"status={res_sub.get_status()}") 23 | print(f"run_id={res_sub.run_id}") 24 | 25 | -------------------------------------------------------------------------------- /src/run_project_example_1.sh: -------------------------------------------------------------------------------- 1 | # 2 | # shell script to run GitHub Project using CLI, supplying 3 | # different parameters alpha values. 4 | # ElasticNet uses both Ridge and Lasso combined for regularization 5 | # https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html 6 | # 7 | # Iterate over different values for the alpha parameters 8 | # 9 | for var in 0.3 0.4 0.5 10 | do 11 | echo "Running with alpha=$var" 12 | mlflow run https://github.com/mlflow/mlflow-example -P alpha=$var 13 | done 14 | -------------------------------------------------------------------------------- /src/run_project_simple_keras_lr.py: -------------------------------------------------------------------------------- 1 | import mlflow 2 | import warnings 3 | import mlflow.pyfunc 4 | import pandas as pd 5 | import numpy as np 6 | 7 | # 8 | # Short example how to run a MLflow GitHub Project programmatically using 9 | # MLflow Fluent APIs https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.run 10 | # 11 | 12 | if __name__ == '__main__': 13 | 14 | # Suppress any deprcated warnings 15 | warnings.filterwarnings("ignore", category=DeprecationWarning) 16 | params = {'batch_size': 10, 'epochs': 1000} 17 | ml_project_uri = "git://github.com/dmatrix/mlflow-workshop-project-expamle-1.git" 18 | 19 | # Iterate over three different runs with different parameters 20 | print(f"Running with param = {params}") 21 | res_sub = mlflow.run(ml_project_uri, parameters=params) 22 | print(f"status={res_sub.get_status()}") 23 | print(f"run_id={res_sub.run_id}") 24 | 25 | # Load this better Keras Model with TF 2.x Flavor as a pyfunc model flavor and make a prediction 26 | pyfunc_uri = f"runs:/{res_sub.run_id}/model" 27 | pyfunc_model = mlflow.pyfunc.load_model(pyfunc_uri) 28 | print(f"Loading the Keras Model={pyfunc_uri} as Pyfunc Model") 29 | 30 | 31 | # Given Fahernheight -> Predict Celcius 32 | # Create a pandas DataFrame with Fahrenheit unseen values 33 | # Get the Celius prediction 34 | df = pd.DataFrame(np.array([32, 212, 200, 206])) 35 | pred = pyfunc_model.predict(df) 36 | print(pred) 37 | 38 | -------------------------------------------------------------------------------- /src/run_simple_keras_lr.py: -------------------------------------------------------------------------------- 1 | from keras.models import Sequential 2 | from keras.layers import Dense 3 | from keras import optimizers 4 | import numpy as np 5 | import pandas as pd 6 | import mlflow 7 | import mlflow.keras 8 | import warnings 9 | import mlflow.pyfunc 10 | 11 | # source: https://androidkt.com/linear-regression-model-in-keras/ 12 | # Modified and extended 13 | 14 | # Generate X, y data 15 | 16 | X_fahrenheit = np.array( 17 | [-140, -136, -124, -112, -105, -96, -88, -75, -63, -60, 18 | -58, -40, -20, -10, 0, 30, 35, 48, 55, 69, 81, 89, 95, 19 | 99,105, 110, 120, 135, 145, 158, 160, 165, 170, 175, 180, 20 | 185, 187, 190, 195, 198, 202, 205, 207, 210, 215, 220], dtype=float) 21 | 22 | y_celsius = np.array( 23 | [-95.55, -93.33, -86.66, -80, -76.11, -71.11, -66.66, -59.44, -52.77, -51.11, 24 | -50, -40, -28.88, -23.33, -17.77, -1.11, 1.66, 8.88, 12, 20, 25 | 27.22, 31.66, 35, 37.22, 40.55, 43.33, 48.88, 57.22, 62.77, 70, 26 | 71.11, 73.88, 76.66, 79.44, 82.22, 85, 86.11,87.77,90.55, 92.22, 27 | 94.44, 96.11, 97.22, 98.88, 101.66, 104.44], dtype=float) 28 | 29 | # Define the model 30 | def baseline_model(): 31 | model = Sequential([ 32 | Dense(64, activation='relu', input_shape=[1]), 33 | Dense(64, activation='relu'), 34 | Dense(1) 35 | ]) 36 | 37 | optimizer = optimizers.RMSprop(0.001) 38 | 39 | # Compile the model 40 | model.compile(loss='mean_squared_error', 41 | optimizer=optimizer, 42 | metrics=['mean_absolute_error', 'mean_squared_error']) 43 | return model 44 | 45 | def mlflow_run(params, run_name="Keras Linear Regression"): 46 | 47 | # Start MLflow run and log everyting... 48 | with mlflow.start_run(run_name=run_name) as run: 49 | model = baseline_model() 50 | # single line of MLflow Fluent API obviates the need to log 51 | # individual parameters, metrics, model, artifacts etc... 52 | # https://mlflow.org/docs/latest/python_api/mlflow.keras.html#mlflow.keras.autolog 53 | mlflow.keras.autolog() 54 | model.fit(X_fahrenheit, y_celsius, batch_size=params['batch_size'], epochs=params['epochs']) 55 | run_id = run.info.run_uuid 56 | exp_id = run.info.experiment_id 57 | 58 | for f in [200, 206]: 59 | print(f"F={f}; C={model.predict([f])}") 60 | 61 | return (exp_id, run_id) 62 | 63 | # Use the model 64 | if __name__ == '__main__': 65 | # suppress any deprecated warnings 66 | warnings.filterwarnings("ignore", category=DeprecationWarning) 67 | 68 | params = {'batch_size': 5, 69 | 'epochs': 1000} 70 | (exp_id, run_id) = mlflow_run(params) 71 | print(f"Finished Experiment id={exp_id} and run id = {run_id}") 72 | 73 | # Load this Keras Model Flavor as a pyfunc model flavor and make a prediction 74 | pyfunc_uri = f"runs:/{run_id}/model" 75 | pyfunc_model = mlflow.pyfunc.load_model(pyfunc_uri) 76 | print(f"Loading the Keras Model={pyfunc_uri} as Pyfunc Model") 77 | # Given Fahrenheit -> Predict Celcius 78 | # Create a pandas DataFrame with Fahrenheit unseen values 79 | # Get the Celius prediction 80 | df = pd.DataFrame(np.array([32, 212, 200, 206])) 81 | pred = pyfunc_model.predict(df) 82 | print(pred) 83 | --------------------------------------------------------------------------------