Horovod is a distributed deep learning training framework designed for TensorFlow, PyTorch, and Keras. Below are key advanced topics and configurations:
🧠 Distributed Training Optimization
- Multi-GPU Support: Use
HorovodRunner
for multi-machine multi-GPU trainingDistributed_Training - Data Parallelism: Leverage
allreduce
operations for efficient gradient synchronizationData_Parallelism - Custom Backend: Implement custom communication backends for specialized hardwareCustom_Backend
🛠️ Custom Configuration Options
- Environment Variables:
OMPI_MCA_btl
: Control bandwidth allocation for inter-node communicationHOROVOD_GPU_ALLREDUCE
: Enable/disable GPU-based allreduceEnvironment_Variables
- TensorFlow Keras Integration: Use
tf.keras
with Horovod for distributed model trainingTensorFlow_Keras
📊 Performance Monitoring
- TensorBoard Integration: Monitor training metrics across distributed nodesTensorBoard
- System Profiling: Use
horovodrun
with--log
flag for detailed execution logsSystem_Profiling
For deeper insights into training strategies, visit our Advanced Training Strategies guide. 🚀