Mesh TensorFlow vs Horovod
Side-by-side — pricing, strengths, tradeoffs, and fit.
Mesh TensorFlow
Model Parallelism Made Easier with Mesh TensorFlow.
Horovod
Distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet.
At a glance
Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
◌ Declining·From $0
Overview
One-liner
One-liner
Model Parallelism Made Easier with Mesh TensorFlow.
One-liner
Distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet.
Maturity
Maturity
DecliningMaturity
DecliningOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams working on large-scale machine learning models who need efficient distribution across multiple devices and machines.
Projects requiring high-performance computing for training deep neural networks.
Developers looking to optimize memory usage in distributed training scenarios.
✓ Best for
Teams that need to scale up their deep learning training across multiple GPUs or machines without significant code changes.
Developers working with TensorFlow, Keras, PyTorch, and Apache MXNet who want to leverage distributed computing for faster model training.
Not ideal for
✕ Not for
Small projects that do not require the complexity of model parallelism.
Teams with limited computational resources who cannot effectively utilize multiple devices and machines.
✕ Not for
Projects requiring real-time inference as Horovod is focused on training rather than deployment
Small-scale projects where the overhead of setting up a distributed environment outweighs the benefits
Strengths
↑ Strengths
Simplifies model parallelism for large-scale machine learning models.
Supports distributed training across multiple devices and machines.
Optimized for efficient computation and memory usage.
↑ Strengths
Support for multiple deep learning frameworks
Easy integration with existing training code
Scalability across multiple GPUs and machines
Compatibility with popular distributed computing systems like MPI
Weaknesses
↓ Weaknesses
Limited documentation and examples for complex use cases
Performance overhead due to Python GIL limitations
↓ Weaknesses
Limited support for non-GPU environments
Complex setup and configuration