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An enterprise machine learning team is optimizing its infrastructure on Google Cloud to train large deep learning models using specialized accelerators, such as NVIDIA A100 GPUs. The training jobs are batch-oriented, delay-tolerant, and require guaranteed runtime completion once started, without unexpected mid-run interruptions or preemptions.
The team wants to optimize accelerator acquisition and compute costs while ensuring efficient scheduling of high-demand GPU resources on Vertex AI.
Which compute scheduling architecture should the team implement for their Vertex AI custom training jobs?
Dynamic Workload Scheduler (DWS) is an advanced compute orchestration feature in Google Cloud and Vertex AI designed to optimize accelerator allocation for large-scale AI/ML workloads. It queues training jobs until the requested compute accelerators (such as NVIDIA A100 or H100 GPUs) become fully available, running them with predictable performance and discounted economics.
StudyCerts
StudyCertsFLEX_START strategy, jobs consume preemptible quota allocations rather than scarce on-demand quotas, offering cost-optimized pricing tiers while acquiring top-tier accelerator hardware.max_wait_duration (e.g., 1800s), the team defines how long Vertex AI will hold the job in queue waiting for GPU capacity before timing out.aiplatform.CustomJob using gca_custom_job_compat.Scheduling.Strategy.FLEX_START.DWS FLEX_START strikes the exact balance between cost optimization and workload stability for large AI models, providing discounted, non-preemptible accelerator runtime for batch training.
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