pymlens 0.1.4

Lightweight ML experiment tracker — log, compare and visualize your ML experiments locally

MLens

MLens is a lightweight ML experiment tracking tool that helps data scientists log, compare, and visualize their model experiments — all running fully locally on your machine.

Installation

pip install pymlens

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Why MLens

Managing multiple experiments manually becomes chaotic and time-consuming. After running several models, it becomes difficult to track which model performed best and on which problem. MLens eliminates this problem by automatically recording all your experiments in one place.

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Requirements

Before using MLens, make sure these are installed:

pip install flask requests scikit-learn streamlit

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Run Locally

Clone the project

git clone https://github.com/munishmalhotra6230/model_tracker-MLENS-.git

Go to the project directory

cd model_tracker-MLENS-

Install dependencies

pip install -r requirements.txt

Start the server — IMPORTANT: Server must be running before training

cd backend_monitoring python backend.py

Run test scripts

python test_scripts.py

View results on dashboard

streamlit run dashboard.py

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Features

• Easy to use — minimal code changes required
• Runs fully locally — no cloud, no data leaves your machine
• Records each experiment and their results automatically
• Compare model performance visually via Streamlit dashboard
• Supports vanilla metrics out of the box
• Preferable for Supervised Learning with Scikit-learn

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How to Use MLens

1. Start the backend server first
2. Declare an experiment with a meaningful name
3. Run multiple models inside the same experiment
4. View and compare results on the Streamlit dashboard

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Demo

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Screenshots

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Usage/Examples

# Step 1 — Start server first in a separate terminal:
# cd backend_monitoring && python backend.py

# Step 2 — Use in your training code:
from pymlens import Experiment
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression

x, y = load_iris(return_X_y=True)

with Experiment("Iris_Classification") as exp:
    try:
        xtrain, xval, ytrain, yval = train_test_split(
            x, y, test_size=0.2, random_state=42
        )
        exp.Start_experiment(
            xtrain, ytrain,
            Xtest=xval, ytest=yval,
            model=LogisticRegression()
        )
        exp.Start_experiment(
            xtrain, ytrain,
            Xtest=xval, ytest=yval,
            model=RandomForestClassifier()
        )
    except Exception as e:
        print(f"An error occurred: {e}")
    finally:
        print("Experiment Completed")

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Optimizations

Have suggestions? Join the Discord and share your ideas.

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Feedback

Join the Discord community: https://discord.gg/svx4Sfckz

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Links

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Author

• @Munish Malhotra