### Setup Development Environment

Source: https://github.com/dataloop-ai/dtlpy/blob/master/README.md

Steps to clone the repository and install necessary dependencies for local development and contribution.

```bash
git clone https://github.com/dataloop-ai/dtlpy.git
cd dtlpy
pip install -r requirements.txt
```

--------------------------------

### Install DTLPY via pip

Source: https://github.com/dataloop-ai/dtlpy/blob/master/README.md

Instructions for installing the DTLPY package using standard pip commands. Users can install the stable version from PyPI or the latest development version from GitHub.

```bash
pip install dtlpy
pip install git+https://github.com/dataloop-ai/dtlpy.git
```

--------------------------------

### Authenticate and Access Resources with DTLPY SDK

Source: https://github.com/dataloop-ai/dtlpy/blob/master/README.md

Demonstrates how to initialize the DTLPY SDK by logging in and retrieving project and dataset objects. This is the primary method for interacting with Dataloop resources programmatically.

```python
import dtlpy as dl

# Authenticate
dl.login()

# Access a project
project = dl.projects.get(project_name='your-project-name')

# Access dataset
dataset = project.datasets.get(dataset_name='your-dataset-name')
```

--------------------------------

### Execute Dataloop Operations via CLI

Source: https://github.com/dataloop-ai/dtlpy/blob/master/README.md

Shows common command-line interface operations for authenticating and listing projects or datasets. These commands provide a quick way to interact with the platform without writing Python scripts.

```bash
dlp login
dlp projects ls
dlp datasets ls --project-name your-project-name
```

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