From 854b75f6d3c36c632b64592fd610573fb0f6eea0 Mon Sep 17 00:00:00 2001 From: Fernando Date: Fri, 20 Dec 2019 00:53:54 +0100 Subject: [PATCH] More comments in notebooks and readme. Added presentation --- .gitignore | 6 +- LICENSE | 402 ++++++++--------- README.md | 56 ++- models.ipynb | 134 ++++++ project_example/MLProject | 11 + project_example/conda.yaml | 8 + project_example/train.py | 58 +++ ...la2019 - Introducci\303\263n a MLFlow.pdf" | Bin 0 -> 1602877 bytes requirements.txt | 6 + tracking.ipynb | 418 +++++++++++------- 10 files changed, 726 insertions(+), 373 deletions(-) create mode 100644 models.ipynb create mode 100644 project_example/MLProject create mode 100644 project_example/conda.yaml create mode 100644 project_example/train.py create mode 100644 "pythonsevilla2019 - Introducci\303\263n a MLFlow.pdf" create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore index b20b5e1..493b576 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ -.idea -.ipynb_checkpoints -mlruns +.idea +.ipynb_checkpoints +mlruns diff --git a/LICENSE b/LICENSE index 261eeb9..29f81d8 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,201 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. 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Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. 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You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 02fd624..012f227 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,54 @@ -# pythonsevilla2019 -TODO +# pythonsevilla2019 +This repo contains notebooks, code and presentation about the meetup [*Introducción a MLFlow y Databricks: acelerando el Machine Learning Lifecycle*](https://www.meetup.com/Python-Sevilla/events/266430587/) in the meetup group of [Python Sevilla Developers](https://www.meetup.com/Python-Sevilla). + +Slides can be found both on [Slideshare](https://www.slideshare.net/fortega86/pythonsevilla2019-mlflow-introduction) and [in this repository](pythonsevilla2019 - Introducción a MLFlow.pdf) + +## Installation and use +1. ``pip install -r requirements.txt`` +2. ``jupyter notebook`` + +## MLFlow Tracking demo +Run ``jupyter notebook`` and execute the notebook ``tracking.ipynb``. + +To execute the last section is necessary to possess an Azure or AWS account with a deployed Databricks resource. + +## MLFlow Projects demo +The first example uses a public project located in the official MLFlow repository (https://github.com/mlflow/) + +``mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=5`` + +The second example uses our own project (./project_example) + +```bash +cd project_example +mlflow run . -P n_estimators=500 +``` + +## MLFLOW Models demo +Execute the notebook ``models.ipynb``. + +The second part of the demo consist on deploying a trained model from the ``mlruns`` folder that MLFlow creates after tracking experiments. + +So, it is necessary to navigate through the corresponding folder and execute the ``mlflow serve`` command. + +```bash +cd mlruns///artifacts/model/ +mlflow models serve -m . -p 1234 +``` + +After some time, a gunicorn + Flask microservice is deployed on port 1234. It is possible to send http post request by means of programs like Postman. The endpoint is: + +``localhost:1234/invocations`` + +And this is an example of valid body for the request: + +```json +{ + "data": [[ 0.52444161, 0.97309661, 0.43247518, 0.38717859, -1.03377319, + -0.73048166, -0.70972218, -0.41044243, -1.00047971, -0.82507126, + -0.08818832, -0.04623819, -0.18209319, -0.0038316 , -1.04758402, + -0.93257644, -0.65865037, -0.69601737, -0.71241416, -0.25530814, + 0.58767599, 1.36061943, 0.48167379, 0.44795641, -0.62887522, + -0.64418546, -0.62375274, -0.23693879, 0.08147618, 0.05512114]] +} +``` \ No newline at end of file diff --git a/models.ipynb b/models.ipynb new file mode 100644 index 0000000..1a1aef1 --- /dev/null +++ b/models.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introducción a MLFlow y Databricks: acelerando el Machine Learning LifeCycle - Python Sevilla 2019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MLFlow Models\n", + "In this section, we can see an example of using a trained model from MLFlow experiments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load a trained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "import mlflow.sklearn\n", + "import numpy as np\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.model_selection import train_test_split\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After decide which model will be used, paste here both experiment_id and run_id." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "experiment_id = \"...\"\n", + "run_id = \"...\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cancer = load_breast_cancer()\n", + "X = np.array(cancer.data)\n", + "y = np.array(cancer.target)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Feature Scaling\n", + "from sklearn.preprocessing import StandardScaler\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y, train_size=426, test_size=143, random_state=0)\n", + "sc = StandardScaler()\n", + "x_train = sc.fit_transform(x_train)\n", + "x_test = sc.transform(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sel = np.random.randint(len(X))\n", + "x_sel = x_test[sel]\n", + "y_sel = y_test[sel]\n", + "print(y_sel)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = mlflow.sklearn.load_model(f'./mlruns/{experiment_id}/{run_id}/artifacts/model')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.predict([x_sel])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/project_example/MLProject b/project_example/MLProject new file mode 100644 index 0000000..699328a --- /dev/null +++ b/project_example/MLProject @@ -0,0 +1,11 @@ +name: tutorial + +conda_env: conda.yaml + +entry_points: + main: + parameters: + n_estimators: {type: int, default: 100} + max_depth: {type: int, default: 2} + criterion: {type: str, default: "gini"} + command: "python train.py {n_estimators} {max_depth} {criterion}" \ No newline at end of file diff --git a/project_example/conda.yaml b/project_example/conda.yaml new file mode 100644 index 0000000..f4db638 --- /dev/null +++ b/project_example/conda.yaml @@ -0,0 +1,8 @@ +name: breast-rf +channels: + - defaults +dependencies: + - numpy=1.14.3 + - pip: + - mlflow==1.3.0 + - scikit-learn==0.22 \ No newline at end of file diff --git a/project_example/train.py b/project_example/train.py new file mode 100644 index 0000000..e7e4e5f --- /dev/null +++ b/project_example/train.py @@ -0,0 +1,58 @@ +import mlflow +import mlflow.sklearn +import numpy as np +from sklearn.datasets import load_breast_cancer +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import sys +import warnings +warnings.filterwarnings('ignore') + +# Function to validate a model +def validate_model(model, x_test, y_test): + y_pred = model.predict(x_test) + y_pred = (y_pred > 0.5) + from sklearn.metrics import confusion_matrix + tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel() + precision = tp / (tp + fp) + recall = tp / (tp + fn) + accuracy = (tp + tn) / (tp + fp + tn + fn) + + return precision, recall, accuracy + + +def breast_cancer_rf(n_estimators=100, max_depth=2, criterion="gini"): + from sklearn.ensemble import RandomForestClassifier + import mlflow.sklearn + with mlflow.start_run() as run: + clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, criterion=criterion) + mlflow.log_param("n_estimators", n_estimators) + mlflow.log_param("max_depth", max_depth) + mlflow.log_param("criterion", criterion) + mlflow.set_tag("model type", "sklearn - RandomForest") + clf.fit(x_train, y_train) + precision, recall, accuracy = validate_model(clf, x_test, y_test) + mlflow.log_metric("precision", precision) + mlflow.log_metric("recall", recall) + mlflow.log_metric("accuracy", accuracy) + mlflow.sklearn.log_model(clf, "model") + print("Model saved in run %s" % mlflow.active_run().info.run_uuid) + + +if __name__ == "__main__": + args = sys.argv[1:] + n_estimators = int(args[0]) + max_depth = int(args[1]) + criterion = args[2] + + cancer = load_breast_cancer() + X = np.array(cancer.data) + y = np.array(cancer.target) + + #Feature Scaling + x_train, x_test, y_train, y_test = train_test_split(X, y, train_size=426, test_size=143, random_state=0) + sc = StandardScaler() + x_train = sc.fit_transform(x_train) + x_test = sc.transform(x_test) + + breast_cancer_rf(n_estimators=n_estimators, max_depth=max_depth, criterion=criterion) \ No newline at end of file diff --git "a/pythonsevilla2019 - Introducci\303\263n a MLFlow.pdf" "b/pythonsevilla2019 - Introducci\303\263n a MLFlow.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..7e3f2776021f53cf5024c61a04e0059ba057e4f7 GIT binary patch literal 1602877 zcmce-1yo&MvnPl-hMVy$z}*=N_Ts$KG{x*Hm0aY<%279MmInp)t4j>1O4N@4fG5*-B$2B|^p zO+aFX&W6@@W*}ulGZQBYc3@ZqByVVIMrUHnEG0@$0aA5#bTM*Pb2Kqgv9oigU}xq0 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