Apache TVM
Apache TVM is an open-source machine learning compiler framework that compiles and optimizes machine learning models for deployment on diverse hardware platforms.
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 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.
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.
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`.
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