pypmltracker 0.1.3
A comprehensive ML experiment tracking library
Requires Python: >=3.6


# PyPMLTracker: A Comprehensive ML Experiment Tracking Library

**PyPMLTracker** is a powerful and flexible experiment tracking library designed for machine learning projects. It helps data scientists and ML engineers efficiently track, visualize, and manage their experiments. Whether you're training models locally or in the cloud, PyPMLTracker provides all the tools you need to streamline your ML workflow and collaborate with your team.

---
Check out the official PyPI package: https://pypi.org/project/pypmltracker/ 

## Features ✨

- 📊 **Experiment Tracking**: Log metrics, parameters, and artifacts during model training.
- 🖥️ **System Monitoring**: Track CPU, memory, disk, and GPU usage during experiments.
- 🔄 **Framework Integrations**: Native support for **PyTorch**, **TensorFlow**, and **scikit-learn**.
- 📈 **Visualization**: Interactive dashboard to visualize and compare experiments.
- 💾 **Storage Options**: Local storage and cloud storage (AWS S3) support.
- 🌐 **API**: Client-server architecture for team collaboration.

---
---

## 🔧 Used Libraries & Tools

- **🔗 PyTorch**: [![PyTorch](https://img.shields.io/badge/PyTorch-v1.9.0-blue?logo=pytorch)](https://pytorch.org)
- **🔗 TensorFlow**: [![TensorFlow](https://img.shields.io/badge/TensorFlow-v2.5.0-green?logo=tensorflow)](https://www.tensorflow.org)
- **🔗 scikit-learn**: [![scikit-learn](https://img.shields.io/badge/scikit--learn-v0.24.2-yellowgreen?logo=scikit-learn)](https://scikit-learn.org)
- **☁️ AWS S3**: [![AWS](https://img.shields.io/badge/AWS-S3-orange?logo=amazonaws)](https://aws.amazon.com/s3/)
- **📈 Plotly**: [![Plotly](https://img.shields.io/badge/Plotly-v4.14.3-82D4FF?logo=plotly)](https://plotly.com)
- **📦 PyPMLTracker**: [![PyPMLTracker](https://img.shields.io/badge/PyPMLTracker-v1.0-0078D4?logo=python)](https://github.com/pypmltracker)

---

## Installation ⚙️

### Install PyPMLTracker

To install **PyPMLTracker**, use the following pip command:

```bash
pip install pypmltracker
```

## Optional Features
You can install specific features based on your needs:

- **PyTorch integration**:
```bash
pip install pypmltracker[pytorch]

```
- **TensorFlow integration**:
```bash
pip install pypmltracker[tensorflow]

```
- **scikit-learn integration**:
```bash
pip install pypmltracker[sklearn]
```
- **Cloud storage (AWS S3)**:

```bash


pip install pypmltracker[cloud]

```
- **All features (PyTorch, TensorFlow, scikit-learn, and Cloud)**:

```bash


pip install pypmltracker[all]

```

# Quick Start 🚀
**1. Initialize an Experiment**

```bash
import pypmltracker

experiment = pypmltracker.Experiment(
    project_name="my_project",
    run_name="first_run",
    config={"learning_rate": 0.01, "batch_size": 32}
)
experiment.log({"accuracy": 0.85, "loss": 0.35})
experiment.log_artifact("model", "model.pkl")
experiment.finish()
dashboard = pypmltracker.Dashboard()
dashboard.start(open_browser=True)
```


# Framework Integrations 🤖
PyPMLTracker integrates with several popular machine learning frameworks. Below are examples of how to use PyPMLTracker with PyTorch, TensorFlow, and scikit-learn.

```bash
import torch
import torch.nn as nn
import pypmltracker

experiment = pypmltracker.Experiment(project_name="pytorch_example")
tracker = pypmltracker.PyTorchTracker(experiment, log_gradients=True)
model = nn.Sequential(nn.Linear(10, 5), nn.ReLU(), nn.Linear(5, 1))
tracker.watch(model)

for epoch in range(10):
    loss = train_step(model, data)
    tracker.track_metrics({"loss": loss})
    val_accuracy = validate(model, val_data)
    tracker.on_epoch_end(epoch, model, {"val_accuracy": val_accuracy})
tracker.save_model(model, "final_model")

```




# System Monitoring 🖥️
Track system resources such as CPU, memory, and disk usage during your experiments.

```bash
import pypmltracker
import time

experiment = pypmltracker.Experiment(project_name="system_monitoring")
monitor = pypmltracker.SystemMonitor(experiment)
monitor.start()

for i in range(10):
    time.sleep(1)
    experiment.log({"step": i, "value": i * 2})
monitor.stop()
experiment.finish()

```

# Dashboard 📊
Start the PyPMLTracker web dashboard to visualize and compare your experiments.

```bash
import pypmltracker

dashboard = pypmltracker.Dashboard(
    storage_dir="./pypmltracker_data",
    host="127.0.0.1",
    port=8000
)
dashboard.start(open_browser=True)

```

# Team Collaboration 🤝
PyPMLTracker supports client-server architecture to facilitate team collaboration.

- **Server**
Server Start the server to expose PyPMLTracker functionality via a REST API:

```bash
import pypmltracker

server = pypmltracker.PyPMLTrackerServer(
    storage_dir="./pypmltracker_data",
    host="0.0.0.0",
    port=5000,
    api_key="your-secret-api-key"
)
server.start()

```


- **Client 📡**
Connect to the remote server and interact with it:

```bash
import pypmltracker

client = pypmltracker.PyPMLTrackerClient(
    base_url="http://server-address:5000",
    api_key="your-secret-api-key"
)
projects = client.list_projects()
print("Projects:", projects)

```



# License 📜
PyPMLTracker is licensed under the MIT License.

# Contributing 🤗
Contributions are welcome! If you’d like to contribute to the development of PyPMLTracker, please feel free to submit a pull request.