Distributed Training with Ray on Mixed Hardware | Generated by AI
Yes, you can use Python for distributed training of machine learning models across your MacBook Air M2 (which uses Apple Silicon with the MPS backend for GPU acceleration) and your Intel i9 desktop with an NVIDIA 4070Ti (which supports CUDA). However, this setup is heterogeneous (different architectures, OSes, and accelerators), so it’s not as straightforward as a homogeneous cluster (e.g., all NVIDIA GPUs). Native distributed training in frameworks like PyTorch won’t work seamlessly out of the box due to incompatible backends—PyTorch on your Mac uses MPS (Metal Performance Shaders), while on the desktop it uses CUDA, and communication libraries like NCCL (required for efficient GPU-to-GPU sync) are NVIDIA-only and unavailable on Apple Silicon.
That said, you can achieve distributed training using higher-level orchestration libraries like Ray, which abstracts away the hardware differences. Other options like Dask or custom frameworks exist but are more limited for deep learning. I’ll outline the feasibility, recommended approach, and alternatives below.
Recommended Approach: Use Ray for Distributed Training
Ray is a Python-based distributed computing framework that’s hardware-agnostic and supports scaling ML workloads across mixed machines (e.g., macOS on Apple Silicon and Windows/Linux on NVIDIA). It installs on both platforms and can handle heterogeneous accelerators by running tasks on each machine’s available hardware (MPS on Mac, CUDA on desktop).
How It Works
- Setup: Install Ray on both machines via pip (
pip install "ray[default,train]"). Start a Ray cluster: one machine as the head node (e.g., your desktop), and connect the Mac as a worker node over the network. Ray handles communication via its own protocol. - Training Pattern: Use Ray Train for scaling frameworks like PyTorch or TensorFlow. For heterogeneous setups:
- Employ a “parameter server” architecture: A central coordinator (on one machine) manages model weights.
- Define workers that run on specific hardware: Use decorators like
@ray.remote(num_gpus=1)for your NVIDIA desktop (CUDA) and@ray.remote(num_cpus=2)or similar for the Mac (MPS or CPU fallback). - Each worker computes gradients on its local device, sends them to the parameter server for averaging, and receives updated weights.
- Ray automatically distributes data batches and syncs across machines.
- Example Workflow:
- Define your model in PyTorch (set device to
"mps"on Mac,"cuda"on desktop). - Use Ray’s API to wrap your training loop.
- Run the script on the head node; Ray dispatches tasks to workers.
- Define your model in PyTorch (set device to
- Performance: Training will be slower than a pure NVIDIA cluster due to network overhead and no direct GPU-to-GPU communication (e.g., via NCCL). The Mac’s M2 GPU is weaker than the 4070Ti, so balance workloads accordingly (e.g., smaller batches on Mac).
- Limitations:
- Best for data-parallel training or hyperparameter tuning; model-parallel (splitting a large model across devices) is trickier in heterogeneous setups.
- For very large models (e.g., 1B+ parameters), add techniques like mixed precision, gradient checkpointing, or integration with DeepSpeed.
- Network latency between machines can bottleneck; ensure they’re on the same fast LAN.
- Tested examples show it working on Apple M4 (similar to M2) + older NVIDIA GPUs, but optimize for your 4070Ti’s strength.
A practical example and code are available in a framework called “distributed-hetero-ml”, which simplifies this for heterogeneous hardware.
Why Ray Fits Your Setup
- Cross-platform: Works on macOS (Apple Silicon), Windows, and Linux.
- Integrates with PyTorch: Use Ray Train to scale your existing code.
- No need for identical hardware: It detects and uses MPS on Mac and CUDA on desktop.
Alternative: Dask for Distributed Workloads
Dask is another Python library for parallel computing, suitable for distributed data processing and some ML tasks (e.g., via Dask-ML or XGBoost).
- How: Set up a Dask cluster (one scheduler on your desktop, workers on both machines). Use libraries like CuPy/RAPIDS on the NVIDIA side for GPU accel, and fall back to CPU/MPS on Mac.
- Use Cases: Good for ensemble methods, hyperparameter search, or scikit-learn-style models. For deep learning, pair with PyTorch/TensorFlow, but sync is manual and less efficient than Ray.
- Limitations: Not optimized for synchronized deep learning training (e.g., no built-in parameter server); better for embarrassingly parallel tasks. GPU support requires CUDA on NVIDIA, but Mac would use CPU or limited MPS integration.
- Setup:
pip install dask distributed, then start a cluster and submit jobs.
Other Options and Considerations
- Horovod: This distributed training wrapper (for PyTorch/TensorFlow) installs on macOS, but lacks explicit MPS support. It can use Gloo or MPI for communication (cross-platform), but efficiency drops without NCCL on the Mac. Possible but untested for your exact mix—try it if Ray doesn’t fit.
- Custom Frameworks: Tools like HetSeq allow heterogeneous GPU training, but they’re geared toward mixed NVIDIA/AMD, not Apple Silicon + CUDA.
- Challenges Across All:
- OS Differences: Assume your desktop runs Linux/Windows; ensure compatible Python versions (3.10+ recommended).
- Dependencies: Install PyTorch with MPS on Mac (
pip install torch --extra-index-url https://download.pytorch.org/whl/cpu) and CUDA on desktop. - Data Sharing: Use a shared filesystem (e.g., NFS) or cloud storage for datasets.
- Testing: Start small—train a simple CNN on MNIST to verify.
- When to Avoid: If models are small, train solely on the 4070Ti (faster). For production, consider cloud GPUs for homogeneity.
If you provide more details (e.g., framework like PyTorch/TensorFlow, model type, OS on desktop), I can refine this.
Being GPU Poor makes you creative
Ray Documentation - Installation
Ray Train: Scalable Model Training
Dask Documentation - GPUs
Horovod Installation Guide
HetSeq GitHub
Accelerated PyTorch training on Mac