Add megatron_ray_fault_tolerant example with comprehensive fault tolerance implementation

README.md

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+# Examples
+
+This repository contains examples for deploying and running distributed applications.
+
+## Job Examples
+
+### 1. Hello World Job
+**Directory:** `01_job_hello_world/`
+
+A simple "Hello World" example demonstrating how to submit and run basic jobs.
+
+### 2. Image Processing
+**Directory:** `image_processing/`
+
+Process large-scale image datasets using Ray Data. This example demonstrates processing the ReLAION-2B dataset with over 2 billion rows.
+
+### 3. Megatron + Ray Fault Tolerant Training
+**Directory:** `megatron_ray_fault_tolerant/`
+
+Implements PPO-style distributed training with Megatron and Ray, featuring comprehensive fault tolerance capabilities:
+- Automatic actor recovery from failures
+- Backup actor groups for seamless replacement
+- Distributed checkpoint saving/loading
+- Process group re-initialization after failures
+- Support for tensor, pipeline, data, and context parallelism
+
+## Service Examples
+
+### 1. Hello World Service
+**Directory:** `02_service_hello_world/`
+
+A simple service deployment example demonstrating the basics of Ray Serve.
+
+### 2. Deploy Llama 3.1 8B
+**Directory:** `03_deploy_llama_3_8b/`
+
+Deploy Llama 3.1 8B model using Ray Serve and vLLM with autoscaling capabilities.
+
+### 3. Deploy Llama 3.1 70B
+**Directory:** `deploy_llama_3_1_70b/`
+
+Deploy the larger Llama 3.1 70B model with optimized serving configuration.
+
+### 4. Tensor Parallel Serving
+**Directory:** `serve_tensor_parallel/`
+
+Demonstrates tensor parallelism for serving large language models across multiple GPUs.
+
+### 5. FastVideo Generation
+**Directory:** `video_generation_with_fastvideo/`
+
+Deploy a video generation service using the FastVideo framework.
+
+## Reinforcement Learning Examples
+
+### SkyRL
+**Directory:** `skyrl/`
+
+Reinforcement learning training example using Ray and distributed computing.
+
+## Getting Started
+
+Most examples include their own README with specific instructions. Generally, you'll need:
+
+1. Install the Anyscale CLI:
+```bash
+pip install -U anyscale
+anyscale login
+```
+
+2. Navigate to the example directory:
+```bash
+cd <example_directory>
+```
+
+3. Deploy the service or submit the job:
+```bash
+# For services
+anyscale service deploy -f service.yaml
+
+# For jobs
+anyscale job submit -f job.yaml
+```
+
+## Requirements
+
+- Anyscale account and CLI access
+- Appropriate cloud credentials configured
+- GPU resources for ML/LLM examples
+
+## Contributing
+
+When adding new examples:
+1. Create a descriptive directory name
+2. Include a README.md with setup and usage instructions
+3. Add appropriate YAML configuration files
+4. Update this main README with your example
+
+## License
+
+See individual example directories for specific licensing information.
+

megatron_ray_fault_tolerant/.gitignore

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+__pycache__/

megatron_ray_fault_tolerant/.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.11.9
+    hooks:
+      - id: ruff
+        args: [ --fix, --exit-non-zero-on-fix ]
+        exclude: (^(skyagent)/.*)$
+
+  # Black needs to be ran after ruff with --fix
+  - repo: https://github.com/psf/black
+    rev: 24.10.0
+    hooks:
+      - id: black
+        exclude: (^(skyagent)/.*)$
+
+  # Detect secrets and sensitive information
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.24.2
+    hooks:
+      - id: gitleaks

megatron_ray_fault_tolerant/Dockerfile

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+FROM anyscale/ray:2.51.0-slim-py312-cu128
+
+RUN sudo apt-get update -y && sudo apt-get install -y wget kmod libxml2 build-essential libnuma-dev
+
+# the cuda compiler here is needed for deepspeed
+RUN wget https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run \
+    && sudo sh cuda_12.8.0_570.86.10_linux.run --silent --toolkit && rm -rf cuda_12.8.0_570.86.10_linux.run
+
+RUN curl -LsSf https://astral.sh/uv/0.9.4/install.sh | sh
+RUN echo "export RAY_RUNTIME_ENV_HOOK=ray._private.runtime_env.uv_runtime_env_hook.hook" >> /home/ray/.bashrc
+
+
+RUN sudo apt-get update \
+    && sudo apt-get install -y openssh-server iputils-ping net-tools iproute2 traceroute netcat \
+    libopenexr-dev libxi-dev libglfw3-dev libglew-dev libomp-dev libxinerama-dev libxcursor-dev tzdata \
+    && sudo apt-get clean && sudo rm -rf /var/lib/apt/lists/*
+
+RUN sudo apt update && sudo apt install --fix-broken && sudo apt install -y default-jre-headless openjdk-8-jdk \
+    && sudo apt-get clean \
+    && sudo rm -rf /var/lib/apt/lists/*
+
+# ---------- PyTorch + cuDNN + Transformer Engine ----------
+# PyTorch + cuDNN + Transformer Engine
+RUN pip install --no-cache-dir "torch==2.7.1" "nvidia-cudnn-cu12>=9.3" && \
+    CUDNN_PATH="$(python -c 'import inspect, nvidia.cudnn as c, os; print(os.path.dirname(inspect.getfile(c)))')" && \
+    sudo mkdir -p /opt && sudo ln -sfn "$CUDNN_PATH" /opt/cudnn && \
+    echo "/opt/cudnn/lib" | sudo tee /etc/ld.so.conf.d/cudnn.conf >/dev/null && sudo ldconfig
+
+ENV CUDNN_PATH=/opt/cudnn
+ENV CPATH=${CUDNN_PATH}/include:${CPATH}
+ENV LD_LIBRARY_PATH=${CUDNN_PATH}/lib:${LD_LIBRARY_PATH}
+
+RUN pip install --no-cache-dir --no-build-isolation "transformer_engine[pytorch]==2.5.0"
+# --------------------

megatron_ray_fault_tolerant/README.md

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+# Megatron + Ray Fault Tolerant Training
+
+This example implements PPO-style distributed training using Megatron and Ray with comprehensive fault tolerance capabilities. The system can automatically recover from actor failures during training by utilizing backup actors and re-initializing process groups.
+
+## Key Features
+
+### Fault Tolerance Mechanisms
+
+1. **Actor Health Monitoring**: Continuously monitors the health of distributed training actors
+2. **Backup Actor Pool**: Pre-allocated backup actors ready to replace failed workers
+3. **Automatic Recovery**: Seamlessly recovers from failures by:
+   - Detecting dead actors
+   - Destroying old process groups
+   - Replacing failed actors with backup actors
+   - Re-initializing process groups with new world size
+   - Reloading model and optimizer state from checkpoints
+
+4. **Distributed Checkpointing**: Implements efficient sharded checkpoint saving/loading using Megatron's distributed checkpointing
+5. **Process Group Management**: Handles NCCL process group initialization, destruction, and re-initialization
+
+### Parallelism Support
+
+- **Data Parallelism (DP)**: Distributes training data across multiple GPUs
+- **Tensor Parallelism (TP)**: Splits model tensors across GPUs
+- **Pipeline Parallelism (PP)**: Distributes model layers across GPUs
+- **Context Parallelism (CP)**: Enables sequence parallelism for long contexts
+
+### Advanced Training Features
+
+- **PPO Training**: Implements Proximal Policy Optimization with micro-batch accumulation
+- **Mixed Precision**: Supports BF16 training for improved performance
+- **Gradient Accumulation**: Handles micro-batches with automatic gradient accumulation
+- **Distributed Optimizer**: Uses Megatron's distributed optimizer for memory efficiency
+
+## Architecture
+
+### Core Components
+
+1. **MegatronActor** (`megatron_actor.py`): 
+   - Individual training actor wrapping Megatron models
+   - Handles model initialization, forward/backward passes, and checkpointing
+   - Supports dynamic process group re-initialization
+
+2. **MegatronActorGroup** (`megatron_actor.py`):
+   - Manages a group of distributed actors
+   - Implements fault recovery logic
+   - Coordinates distributed training operations
+
+3. **Dispatch System** (`dispatch.py`):
+   - **MeshDispatch**: Distributes data across the device mesh (DP, SP, TP, PP)
+   - **PassThroughDispatch**: Broadcasts same data/commands to all actors
+   - Handles data sharding and result collection
+
+4. **Training Batch** (`training_batch.py`):
+   - Defines input/output batch structures for PPO training
+   - Supports chunking and concatenation for distributed operations
+
+5. **Checkpoint I/O** (`file_io.py`):
+   - Cloud-aware file I/O supporting S3, GCS, and local storage
+   - Efficient checkpoint upload/download with parallel transfers
+
+## Getting Started
+
+### Quick Start
+
+```bash
+uv run --isolated main.py
+```
+
+This will:
+1. Create a placement group with workers and backup GPUs
+2. Initialize the actor group and model
+3. Run a training step
+4. Save a checkpoint
+5. Simulate a failure by killing actors
+6. Recover from the failure using backup actors
+7. Resume training after recovery
+
+### Configuration
+
+Edit the `Config` class in `main.py` to customize:
+
+```python
+@dataclass
+class Config:
+    model: str = "Qwen/Qwen3-0.6B"  # HuggingFace model name
+    num_nodes: int = 1
+    num_gpus_per_node: int = 4
+    num_spare_gpus: int = 4  # Backup actors for fault tolerance
+    mini_batch_size: int = 16
+    micro_train_batch_size_per_gpu: int = 2
+
+    # Megatron parallelism settings
+    megatron_config: MegatronConfig = field(default_factory=MegatronConfig)
+```
+
+### Megatron Parallelism Configuration
+
+```python
+@dataclass
+class MegatronConfig:
+    tensor_model_parallel_size: int = 1  # TP degree
+    pipeline_model_parallel_size: int = 1  # PP degree
+    context_parallel_size: int = 1  # CP degree
+    expert_model_parallel_size: int = 1  # For MoE models
+```
+
+## Fault Recovery Workflow
+
+1. **Training Phase**:
+   - Actors perform distributed training using Megatron
+   - Periodic checkpoints saved to cloud storage
+
+2. **Failure Detection**:
+   - System detects actor failures via health checks
+   - Identifies affected data parallel groups
+
+3. **Recovery Process**:
+   - Destroy old process groups on healthy actors
+   - Pop backup actors from the backup pool
+   - Insert backup actors at failed ranks
+   - Update world size and reassign ranks
+   - Re-initialize process groups with new configuration
+   - Reload model/optimizer state from checkpoint
+
+4. **Resume Training**:
+   - Continue training with recovered actor group
+   - No loss of training progress (from last checkpoint)
+
+## Advanced Usage
+
+### Custom Dispatch Types
+
+Register custom dispatch strategies:
+
+```python
+from dispatch import register_dispatch_type, Dispatch
+
+class CustomDispatch(Dispatch):
+    # Implement dispatch, collect, and validate methods
+    pass
+
+register_dispatch_type("custom", CustomDispatch)
+```
+
+### CPU Offloading (Experimental)
+
+For faster recovery, offload model/optimizer state to CPU memory:
+
+```python
+# Before failure
+ray.get(actor_group.async_run_ray_method("pass_through", "offload_to_cpu"))
+
+# After recovery, on healthy actors
+ray.get(actor_group.async_run_ray_method("pass_through", "backload_to_gpu"))
+```
+
+## Dependencies
+
+See `pyproject.toml` for full dependency list. Key dependencies:
+- Ray for distributed orchestration
+- Megatron-Core for model parallelism
+- PyTorch with CUDA support
+- Transformers for model loading
+- vLLM and related libraries
+
+## Running on Anyscale
+
+Submit the job using:
+
+```bash
+anyscale job submit -f job.yaml
+```
+
+The job configuration in `job.yaml` specifies:
+- Container image with dependencies
+- GPU instance types (g6e.12xlarge with 4xL4)
+- Resource limits and scaling
+- Environment variables for NCCL configuration
+
+## Limitations and Future Work
+
+- Virtual pipeline parallelism not yet supported
+- CPU offloading optimization in progress
+- Async checkpoint saving planned for future releases
+
+## References
+
+- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+- [Ray Documentation](https://docs.ray.io/)
+- [Anyscale Platform](https://docs.anyscale.com/)