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Horovod is a distributed deep learning training framework that enables seamless scaling of machine learning models across multiple GPUs and machines. It supports major deep learning frameworks like TensorFlow, PyTorch, Keras, and Apache MXNet, providing efficient data parallelism through ring-allreduce architecture. The framework simplifies the complex task of distributing model training workloads, allowing organizations to dramatically reduce training time and costs. For example, a financial services company training a fraud detection model on billions of historical transactions can use Horovod to distribute the workload across 100 GPUs, reducing training time from weeks to hours while maintaining model accuracy and enabling rapid model updates as new fraud patterns emerge.
Horovod's distributed deep learning framework seamlessly integrates with Shakudo's infrastructure, enabling automatic scaling across multiple GPUs and machines without complex configuration. The native integration handles all networking, resource allocation, and cross-framework compatibility for TensorFlow, PyTorch, and MXNet workloads.
Running Horovod through Shakudo eliminates the traditional complexity of distributed training setup, allowing data scientists to focus purely on model development. The platform automatically handles worker coordination, fault tolerance, and optimal resource utilization across your infrastructure.
Teams can leverage Shakudo's expertise to implement production-grade Horovod deployments in weeks rather than months, with built-in monitoring, logging, and the flexibility to adapt as requirements change.
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Secure infrastructure
Deploy Shakudo easily on your VPC, on-premise, or on our managed infrastructure, and use the best data and AI tools the next day.
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Integrate with everything
Empower your team with seamless integration to the most popular data & AI framework and tools they want to use.
Streamlined Workflow
Automate your DevOps completely with Shakudo, so that you can focus on building and launching solutions.
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