Horovod is a distributed deep learning training framework for PyTorch, TensorFlow, Keras and Apache MXNet, making distributed deep learning fast and easy to use.
0
Views
–
Saves
N/A
API Access
Community
Status
Horovod is a distributed deep learning training framework for PyTorch, TensorFlow, Keras and Apache MXNet, making distributed deep learning fast and easy to use.
Horovod is a distributed deep learning training framework originally developed by Uber and now part of the LF AI Foundation. It supports PyTorch, TensorFlow, Keras, and Apache MXNet, enabling users to scale deep learning model training across multiple GPUs. Horovod aims to reduce training time from days or weeks to hours or minutes. It allows users to scale existing training scripts with minimal code changes, typically a few lines of Python. Horovod is designed to be portable, running on-premise, in the cloud (AWS, Azure, Databricks), and on Apache Spark. This makes it possible to unify data processing and model training pipelines. By supporting multiple frameworks, Horovod offers flexibility as machine learning tech stacks evolve. It targets data scientists and machine learning engineers seeking to accelerate and scale their deep learning workflows.
Horovod is a distributed deep learning training framework for PyTorch, TensorFlow, Keras and Apache MXNet, making distributed deep learning fast and easy to use.
Quick visual proof for Horovod. Helps non-technical users understand the interface faster.
Horovod is a distributed deep learning training framework originally developed by Uber and now part of the LF AI Foundation.
Explore all tools that specialize in distributed training of deep learning models. This domain focus ensures Horovod delivers optimized results for this specific requirement.
Explore all tools that specialize in scaling model training across multiple gpus. This domain focus ensures Horovod delivers optimized results for this specific requirement.
Explore all tools that specialize in reducing model training time. This domain focus ensures Horovod delivers optimized results for this specific requirement.
Explore all tools that specialize in integrating deep learning training with apache spark. This domain focus ensures Horovod delivers optimized results for this specific requirement.
Explore all tools that specialize in supporting multiple deep learning frameworks (tensorflow, pytorch, keras, mxnet). This domain focus ensures Horovod delivers optimized results for this specific requirement.
Explore all tools that specialize in running training jobs on-premise. This domain focus ensures Horovod delivers optimized results for this specific requirement.
Open side-by-side comparison first, then move to deeper alternatives guidance.
Horovod leverages MPI (Message Passing Interface) for efficient inter-GPU communication, enabling fast and scalable distributed training.
Horovod supports TensorFlow, Keras, PyTorch, and Apache MXNet, allowing users to choose the framework that best suits their needs.
Horovod can run on top of Apache Spark, enabling a unified data processing and model training pipeline.
Horovod implements optimized all-reduce operations for gradient averaging, minimizing communication overhead during distributed training.
Horovod can fuse small tensors into larger ones before communication, reducing the overhead associated with sending many small messages.
Training a deep learning model on a massive image dataset (e.g., ImageNet) takes a prohibitively long time on a single GPU.
Step 1: Distribute the dataset across multiple GPUs using Horovod.
Step 2: Train the model in parallel on each GPU.
Step 3: Average the gradients across all GPUs using Horovod's all-reduce operation.
Step 4: Update the model parameters and repeat until convergence.
Training large transformer models (e.g., BERT, GPT-3) requires significant computational resources and time.
Step 1: Implement a transformer model using TensorFlow or PyTorch.
Step 2: Integrate Horovod into the training script.
Step 3: Scale the training job across multiple GPUs or nodes.
Step 4: Monitor the training progress and adjust hyperparameters as needed.
Building and training recommender systems on large user-item interaction datasets is computationally intensive.
Step 1: Prepare the user-item interaction data.
Step 2: Implement a collaborative filtering or deep learning-based recommender model.
Step 3: Use Horovod to distribute the training process across multiple GPUs.
Step 4: Evaluate the performance of the recommender system and iterate.
Training object detection models requires processing large volumes of image and video data.
Step 1: Prepare the image and video data.
Step 2: Implement a object detection model (e.g., YOLO, Faster R-CNN).
Step 3: Use Horovod to distribute the training process across multiple GPUs.
Step 4: Evaluate the performance of the trained model and tune parameters.
Training complex time series forecasting models on extensive historical data can be slow and resource-intensive.
Step 1: Preprocess and prepare the time series data.
Step 2: Implement a suitable forecasting model (e.g., LSTM, Transformer).
Step 3: Integrate Horovod for distributed training across multiple GPUs.
Step 4: Evaluate forecasting accuracy and make necessary adjustments.
Install Horovod using pip or conda.
Modify your training script to initialize Horovod.
Wrap your optimizer with `hvd.DistributedOptimizer`.
Pin each GPU to a single process.
Broadcast the model state from rank 0 to all other processes.
Use `hvd.rank()` to assign different parts of the dataset to each process.
Run your training script using `horovodrun` or `mpirun`.
All Set
Ready to go
Verified feedback from other users.
“Horovod focuses on efficient distributed training for deep learning models. It is known for its ease of use and high scaling efficiency.”
0No reviews yet. Be the first to rate this tool.
Choose the right tool for your workflow
DeepSpeed offers memory optimization techniques that may be beneficial for training very large models that Horovod can't handle as efficiently.
DDP is native to PyTorch, which might be simpler to use if you are only working with PyTorch.
MirroredStrategy is native to TensorFlow and might be simpler to use if you are only working with TensorFlow.
Apache TVM is an open-source machine learning compiler framework that compiles and optimizes machine learning models for deployment on diverse hardware platforms.
An open-source machine learning compiler framework for CPUs, GPUs, and specialized accelerators.
ZenML is the AI Control Plane that unifies orchestration, versioning, and governance for machine learning and GenAI workflows.
Zyte provides the tools and services needed to extract clean, ready-to-use web data at scale, enabling businesses to make data-driven decisions.
Xray is a native quality management solution that integrates with Jira to provide AI-powered test case and model generation for smarter, faster test design.
Waydev transforms engineering data into actionable insights, providing real-time visibility and optimizing development processes.
Vuforia is a comprehensive enterprise AR platform providing AR content creation tools for various industrial applications.
Voyage AI provides state-of-the-art embedding models and rerankers to supercharge search and retrieval for unstructured data.