This repository contains an example project that demonstrates how to use MLflow to manage the machine learning lifecycle. MLflow is an open-source platform that helps you track, manage, and deploy your machine learning models. This project shows how to use MLflow to log experiments, compare results, organize runs, serve models, etc.
• Write a table of contents for your README file. You should write a table of contents that lists the main sections of your README file and provides links to them. You should use the second-level heading syntax (##) for each section and the list syntax (-) for each subsection. You should also use the link syntax (text) to link each section and subsection to their corresponding headings in your README file. For example:
• Features
• Usage
• License
• Write a features section for your README file. You should write a features section that describes the main features and functionality of your project. You should use the second-level heading syntax (##) for the features section and the list syntax (-) for each feature. You should also use the bold syntax ([**) to highlight the key words or phrases in each feature. For example:
• ]Track experiments[ using MLflow Tracking API to log parameters, metrics, artifacts, etc.
• ]Compare results[ using MLflow UI to visualize and filter runs, metrics, artifacts, etc.
• ]Organize runs[ using MLflow Projects API to package code, data, dependencies, etc.
• ]Serve models using MLflow Models API to export and deploy models as REST endpoints or batch jobs.
• Write an installation section for your README file. You should write an installation section that provides instructions on how to install and set up your project on different platforms or environments. You should use the second-level heading syntax (##) for the installation section and the list syntax (-) or the code block syntax (```) for each instruction. You should also use the italic syntax (*) to indicate optional or alternative steps. For example:
• Clone this repository using git clone https://github.com/SepidehHosseinian/Mlfow-projec.git
• Install Python 3.6 or higher on your machine using the official Python website or a package manager such as Anaconda.
• Install MLflow using pip install mlflow or conda install mlflow
• Optional: Install other dependencies such as scikit-learn, pandas, numpy, etc. using pip install -r requirements.txt or conda install --file requirements.txt
• Write a usage section for your README file. You should write a usage section that provides examples on how to use your project for different tasks or scenarios. You should use the second-level heading syntax (##) for the usage section and the third-level heading syntax (###) for each task or scenario. You should also use the code block syntax (```) to show the code snippets or commands for each task or scenario. You should also use the comment syntax (#) to explain the code snippets or commands. For example:
To log experiments using MLflow, you can use the following code snippet:
import mlflow
import sklearn
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
# Load data
X, y = load_iris(return_X_y=True)
# Start a new MLflow run
with mlflow.start_run():
# Train a model
model = LogisticRegression()
model.fit(X, y)
# Log parameters
mlflow.log_param("solver", model.solver)
mlflow.log_param("penalty", model.penalty)
# Log metrics
accuracy = model.score(X, y)
mlflow.log_metric("accuracy", accuracy)
# Log artifacts
mlflow.sklearn.log_model(model, "model")Comparing Results To compare results using MLflow, you can use the following command to launch the MLflow UI:
mlflow uiThen, you can use your web browser to access the MLflow UI using the URL http://localhost:5000. You can then see a list of runs and their parameters, metrics, artifacts, etc. You can also use various filters and charts to compare and visualize the results.
Organizing Runs To organize runs using MLflow, you can use the following code snippet to create a MLproject file:
name: My Project
conda_env: conda.yaml
entry_points:
main:
parameters:
alpha: float
l1_ratio: {type: float, default: 0.1}
command: "python train.py --alpha {alpha} --l1_ratio {l1_ratio}"Then, you can use the following command to run your project using MLflow:
mlflow run . -P alpha=0.5Serving Models To serve models using MLflow, you can use the following command to export your model as a MLmodel file:
mlflow models build-docker -m runs:/<run-id>/model -n <model-name>Then, you can use the following command to deploy your model as a REST endpoint using Docker:
docker run -p <port>:8080 <model-name>• Write a documentation section for your README file. You should write a documentation section that provides links to more detailed and comprehensive documentation for your project. You should use the second-level heading syntax (##) for the documentation section and the list syntax (-) for each link. You should also use the link syntax (text) to link each text to its corresponding URL. For example:
• MLflow Tracking API Reference
• MLflow Projects API Reference
• Write a license section for your README file. You should write a license section that indicates the license that your project is released under. You should use the second-level heading syntax (##) for the license section and the normal text syntax for the license name. You should also use the link syntax (text) to link the license name to its corresponding URL. For example:
[CC0-1.0 license]