Docs Content Part 1: Homepage and getting started

docs/source/conf.py

@@ -140,8 +140,8 @@ def get_version_path():
     "navbar_center": "navbar-nav",
     "canonical_url": "https://meta-pytorch.org/forge/",
     "header_links_before_dropdown": 7,
-    "show_nav_level": 2,
     "show_toc_level": 2,
+    "navigation_depth": 3,
 }
 
 theme_variables = pytorch_sphinx_theme2.get_theme_variables()
@@ -173,6 +173,7 @@ def get_version_path():
     "colon_fence",
     "deflist",
     "html_image",
+    "substitution",
 ]
 
 # Configure MyST parser to treat mermaid code blocks as mermaid directives

docs/source/getting_started.md

@@ -1,9 +1,281 @@
-# Get Started
+# Getting Started
 
-Welcome to TorchForge! This guide will help you get up and running with TorchForge, a PyTorch-native platform specifically designed for post-training generative AI models.
+This guide will walk you through installing TorchForge, understanding its dependencies, verifying your setup, and running your first training job.
 
-TorchForge specializes in post-training techniques for large language models, including:
+## System Requirements
 
-- **Supervised Fine-Tuning (SFT)**: Adapt pre-trained models to specific tasks using labeled data
-- **Group Relative Policy Optimization (GRPO)**: Advanced reinforcement learning for model alignment
-- **Multi-GPU Distributed Training**: Efficient scaling across multiple GPUs and nodes
+Before installing TorchForge, ensure your system meets the following requirements.
+
+| Component | Requirement | Notes |
+|-----------|-------------|-------|
+| **Operating System** | Linux (Fedora/Ubuntu/Debian) | MacOS and Windows not currently supported |
+| **Python** | 3.10 or higher | Python 3.11 recommended |
+| **GPU** | NVIDIA with CUDA support | AMD GPUs not currently supported |
+| **Minimum GPUs** | 2+ for SFT, 3+ for GRPO | More GPUs enable larger models |
+| **CUDA** | 12.8 | Required for GPU training |
+| **RAM** | 32GB+ recommended | Depends on model size |
+| **Disk Space** | 50GB+ free | For models, datasets, and checkpoints |
+| **PyTorch** | Nightly build | Latest distributed features (DTensor, FSDP) |
+| **Monarch** | Pre-packaged wheel | Distributed orchestration and actor system |
+| **vLLM** | v0.10.0+ | Fast inference with PagedAttention |
+| **TorchTitan** | Latest | Production training infrastructure |
+
+
+## Prerequisites
+
+- **Conda or Miniconda**: For environment management
+  - Download from [conda.io](https://docs.conda.io/en/latest/miniconda.html)
+
+- **GitHub CLI (gh)**: Required for downloading pre-packaged dependencies
+  - Install instructions: [github.com/cli/cli#installation](https://github.com/cli/cli#installation)
+  - After installing, authenticate with: `gh auth login`
+  - You can use either HTTPS or SSH as the authentication protocol
+
+- **Git**: For cloning the repository
+  - Usually pre-installed on Linux systems
+  - Verify with: `git --version`
+
+
+**Installation note:** The installation script provides pre-built wheels with PyTorch nightly already included.
+
+## Installation
+
+TorchForge uses pre-packaged wheels for all dependencies, making installation faster and more reliable.
+
+1. **Clone the Repository**
+
+   ```bash
+   git clone https://github.com/meta-pytorch/forge.git
+   cd forge
+   ```
+
+2. **Create Conda Environment**
+
+   ```bash
+   conda create -n forge python=3.10
+   conda activate forge
+   ```
+
+3. **Run Installation Script**
+
+   ```bash
+   ./scripts/install.sh
+   ```
+
+   The installation script will:
+   - Install system dependencies using DNF (or your package manager)
+   - Download pre-built wheels for PyTorch nightly, Monarch, vLLM, and TorchTitan
+   - Install TorchForge and all Python dependencies
+   - Configure the environment for GPU training
+
+   ```{tip}
+   **Using sudo instead of conda**: If you prefer installing system packages directly rather than through conda, use:
+   `./scripts/install.sh --use-sudo`
+   ```
+
+   ```{warning}
+   When adding packages to `pyproject.toml`, use `uv sync --inexact` to avoid removing Monarch and vLLM.
+   ```
+
+## Verifying Your Setup
+
+After installation, verify that all components are working correctly:
+
+1. **Check GPU Availability**
+
+   ```bash
+   python -c "import torch; print(f'GPUs available: {torch.cuda.device_count()}')"
+   ```
+
+   Expected output: `GPUs available: 2` (or more)
+
+2. **Check CUDA Version**
+
+   ```bash
+   python -c "import torch; print(f'CUDA version: {torch.version.cuda}')"
+   ```
+
+   Expected output: `CUDA version: 12.8`
+3. **Check All Dependencies**
+
+   ```bash
+   # Check core components
+   python -c "import torch, forge, monarch, vllm; print('All imports successful')"
+
+   # Check specific versions
+   python -c "
+   import torch
+   import forge
+   import vllm
+
+   print(f'PyTorch: {torch.__version__}')
+   print(f'TorchForge: {forge.__version__}')
+   print(f'vLLM: {vllm.__version__}')
+   print(f'CUDA: {torch.version.cuda}')
+   print(f'GPUs: {torch.cuda.device_count()}')
+   "
+   ```
+
+4. **Verify Monarch**
+
+   ```bash
+   python -c "
+   from monarch.actor import Actor, this_host
+
+   # Test basic Monarch functionality
+   procs = this_host().spawn_procs({'gpus': 1})
+   print('Monarch: Process spawning works')
+   "
+   ```
+
+## Quick Start Examples
+
+Now that TorchForge is installed, let's run some training examples.
+
+Here's what training looks like with TorchForge:
+
+```bash
+# Install dependencies
+conda create -n forge python=3.10
+conda activate forge
+git clone https://github.com/meta-pytorch/forge
+cd forge
+./scripts/install.sh
+
+# Download a model
+hf download meta-llama/Meta-Llama-3.1-8B-Instruct --local-dir /tmp/Meta-Llama-3.1-8B-Instruct --exclude "original/consolidated.00.pth"
+
+# Run SFT training (requires 2+ GPUs)
+uv run forge run --nproc_per_node 2 \
+  apps/sft/main.py --config apps/sft/llama3_8b.yaml
+
+# Run GRPO training (requires 3+ GPUs)
+python -m apps.grpo.main --config apps/grpo/qwen3_1_7b.yaml
+```
+
+### Example 1: Supervised Fine-Tuning (SFT)
+
+Fine-tune Llama 3 8B on your data. **Requires: 2+ GPUs**
+
+1. **Access the Model**
+
+   ```{note}
+   Model downloads are no longer required, but Hugging Face authentication is required to access the models.
+
+    Run `huggingface-cli login` first if you haven't already.
+   ```
+
+2. **Run Training**
+
+   ```bash
+    python -m apps.sft.main --config apps/sft/llama3_8b.yaml
+   ```
+
+   **What's Happening:**
+   - `--nproc_per_node 2`: Use 2 GPUs for training
+   - `apps/sft/main.py`: SFT training script
+   - `--config apps/sft/llama3_8b.yaml`: Configuration file with hyperparameters
+   - **TorchTitan** handles model sharding across the 2 GPUs
+   - **Monarch** coordinates the distributed training
+
+### Example 2: GRPO Training
+
+Train a model using reinforcement learning with GRPO. **Requires: 3+ GPUs**
+
+```bash
+python -m apps.grpo.main --config apps/grpo/qwen3_1_7b.yaml
+```
+
+**What's Happening:**
+- GPU 0: Trainer model (being trained, powered by TorchTitan)
+- GPU 1: Reference model (frozen baseline, powered by TorchTitan)
+- GPU 2: Policy model (scoring outputs, powered by vLLM)
+- **Monarch** orchestrates all three components
+- **TorchStore** handles weight synchronization from training to inference
+
+## Understanding Configuration Files
+
+TorchForge uses YAML configuration files to manage training parameters. Let's examine a typical config:
+
+```yaml
+# Example: apps/sft/llama3_8b.yaml
+model:
+  name: meta-llama/Meta-Llama-3.1-8B-Instruct
+  path: /tmp/Meta-Llama-3.1-8B-Instruct
+
+training:
+  batch_size: 4
+  learning_rate: 1e-5
+  num_epochs: 10
+  gradient_accumulation_steps: 4
+
+distributed:
+  strategy: fsdp  # Managed by TorchTitan
+  precision: bf16
+
+checkpointing:
+  save_interval: 1000
+  output_dir: /tmp/checkpoints
+```
+
+**Key Sections:**
+- **model**: Model path and settings
+- **training**: Hyperparameters like batch size and learning rate
+- **distributed**: Multi-GPU strategy (FSDP, tensor parallel, etc.) handled by TorchTitan
+- **checkpointing**: Where and when to save model checkpoints
+
+## Next Steps
+
+Now that you have TorchForge installed and verified:
+
+1. **Explore Examples**: Check the `apps/` directory for more training examples
+2. **Read Tutorials**: Follow {doc}`tutorials` for step-by-step guides
+3. **API Documentation**: Explore {doc}`api` for detailed API reference
+
+## Getting Help
+
+If you encounter issues:
+
+1. **Search Issues**: Look through [GitHub Issues](https://github.com/meta-pytorch/forge/issues)
+2. **File a Bug Report**: Create a new issue with:
+   - Your system configuration (output of diagnostic command below)
+   - Full error message
+   - Steps to reproduce
+   - Expected vs actual behavior
+
+**Diagnostic command:**
+```bash
+python -c "
+import torch
+import forge
+
+try:
+    import monarch
+    monarch_status = 'OK'
+except Exception as e:
+    monarch_status = str(e)
+
+try:
+    import vllm
+    vllm_version = vllm.__version__
+except Exception as e:
+    vllm_version = str(e)
+
+print(f'PyTorch: {torch.__version__}')
+print(f'TorchForge: {forge.__version__}')
+print(f'Monarch: {monarch_status}')
+print(f'vLLM: {vllm_version}')
+print(f'CUDA: {torch.version.cuda}')
+print(f'GPUs: {torch.cuda.device_count()}')
+"
+```
+
+Include this output in your bug reports!
+
+## Additional Resources
+
+- **Contributing Guide**: [CONTRIBUTING.md](https://github.com/meta-pytorch/forge/blob/main/CONTRIBUTING.md)
+- **Code of Conduct**: [CODE_OF_CONDUCT.md](https://github.com/meta-pytorch/forge/blob/main/CODE_OF_CONDUCT.md)
+- **Monarch Documentation**: [meta-pytorch.org/monarch](https://meta-pytorch.org/monarch)
+- **vLLM Documentation**: [docs.vllm.ai](https://docs.vllm.ai)
+- **TorchTitan**: [github.com/pytorch/torchtitan](https://github.com/pytorch/torchtitan)