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Orchestrating GPU-based distributed training workloads on AI Hypercomputer
- Google Cloud offers AI Hypercomputer with advanced orchestration tools to simplify distributed large-scale training using GPU accelerators.
- Select the right GPU accelerator family, like the versatile G2 machine family, which provides flexibility for inference and testing workloads.
- Google Cloud provides multiple GPU consumption models like Committed Use Discounts (CUDs), Dynamic Workload Scheduler (DWS), On-demand consumption, and Spot VMs to meet the needs of large-scale training.
- Google Kubernetes Engine (GKE), Cluster Toolkit, and Vertex AI custom training pipeline are powerful orchestration strategies on Google Cloud.
- GKE is a good choice for enterprises with unified workload management, providing a flexible and scalable platform to handle diverse workloads on a single platform.
- Cluster Toolkit simplifies the process of deploying HPC, AI, and ML workloads on Google Cloud and provides support for Slurm, one of the most popular HPC job schedulers.
- Vertex AI's fully managed solution removes most of the orchestration burden and provides end-to-end ML platform operations, allowing you to focus on model development and experimentation.
- Google Compute Engine lets you create and configure virtual machines (VMs) with your desired specifications, including the type and number of GPUs, CPU, memory, and storage, providing granular control to optimize infrastructure for specific training workloads.
- For DIY enthusiasts, Google provides example code snippets demonstrating how to use the gcloud compute instance create and gcloud compute instance bulk create API calls to create and manage your vanilla A3 Mega instances.
- With the right orchestration strategy and Google Cloud's robust and leading AI infrastructure, you can achieve your training goals and transform your business objectives into reality.
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