# Quick Start

## Quick Installation

Install Megatron Core with pip:

1. Install Megatron Core with required dependencies:

    ```bash
    pip install --no-build-isolation megatron-core[mlm,dev]
    ```

2. Clone repository for examples:

    ```bash
    git clone https://github.com/NVIDIA/Megatron-LM.git
    cd Megatron-LM
    pip install --no-build-isolation .[mlm,dev]
    ```

That's it! You're ready to start training.

## Your First Training Run

### Simple Training Example

```bash
# Distributed training example (2 GPUs, mock data)
torchrun --nproc_per_node=2 examples/run_simple_mcore_train_loop.py
```

### LLaMA-3 Training Example

```bash
# 8 GPUs, FP8 precision, mock data
./examples/llama/train_llama3_8b_fp8.sh
```

## Data Preparation

### JSONL Data Format

```json
{"text": "Your training text here..."}
{"text": "Another training sample..."}
```

### Basic Preprocessing

```bash
python tools/preprocess_data.py \
    --input data.jsonl \
    --output-prefix processed_data \
    --tokenizer-type HuggingFaceTokenizer \
    --tokenizer-model /path/to/tokenizer.model \
    --workers 8 \
    --append-eod
```

### Key Arguments

- `--input`: Path to input JSON/JSONL file
- `--output-prefix`: Prefix for output binary files (.bin and .idx)
- `--tokenizer-type`: Tokenizer type (`HuggingFaceTokenizer`, `GPT2BPETokenizer`, etc.)
- `--tokenizer-model`: Path to tokenizer model file
- `--workers`: Number of parallel workers for processing
- `--append-eod`: Add end-of-document token

## Next Steps

- Explore [Parallelism Strategies](../user-guide/parallelism-guide.md) to scale your training
- Learn about [Data Preparation](../user-guide/data-preparation.md) best practices
- Check out [Advanced Features](../user-guide/features/index.md) for advanced capabilities
