Volatile GPU demand from AI/ML workloads makes resource consumption difficult to predict, leading to interruptions in training when resources for parallel training are unavailable, as well as increased spending on costly GPU server expansions.
Federator.ai GPU Booster leverages metadata and operational metrics to gain insights into each individual AI/ML workload pattern and accurately forecast the dynamic GPU resource requirements for each training session, thereby reducing the total execution time by up to 50%.
Visibility of Workload Overview and Detail
Provide visibility with line charts of different AI/ML workloads across clusters over time, and track each workload’s status (running, pending, failed, succeeded) along with its resource requirements down to the pod level.
Predictions of Each Workload for Resource Optimization
Tap into machine learning-based algorithms to offer resource allocation recommendations, allowing trainers to adjust between each epoch, so the new resource configuration aligns closely with workload trends.
Optimal Resource Allocation for MultiTenant AI Training Jobs
Considering the fluctuation of each workload from an accumulated resource requirements perspective is crucial to ensuring sufficient resources for uninterrupted MultiTenant AI/ML/LLM training jobs.