Chaim RandinTowards Data ScienceTraining AI Models on CPURevisiting CPU for ML in an Era of GPU ScarcitySep 13Sep 13
Chaim RandinTowards Data ScienceUnleashing the Power of Triton: Mastering GPU Kernel Optimization in PythonAccelerating AI/ML Model Training with Custom Operators — Part 2Aug 132Aug 132
Chaim RandinTowards Data ScienceAccelerating AI/ML Model Training with Custom OperatorsOn the potential benefits of creating model-specific GPU kernels and their application to optimizing the use of dynamically shaped tensorsAug 111Aug 111
Chaim RandinTowards Data ScienceMulti-Framework AI/ML Development with Keras 3All hail the return of KerasJun 16Jun 16
Chaim RandinTowards Data ScienceAI Model Training with JAXHit the road to super-fast AI/ML developmentMay 29May 29
Chaim RandinTowards Data SciencePyTorch Native FP8Accelerating PyTorch Training Workloads with FP8 — Part 2May 21May 21
Chaim RandinTowards Data ScienceA Priority Based Scheduler for Amazon SageMaker Training JobsOptimizing the use of limited AI training accelerators — Part 2Mar 8Mar 8
Chaim RandRetaining Amazon SageMaker Instance Capacity with SageMaker Managed Warm PoolsAn Alternative Solution to Cloud Instance ReservationFeb 27Feb 27
Chaim RandinTowards Data ScienceMaximizing the Utility of Scarce AI Resources: A Kubernetes ApproachOptimizing the use of limited AI training acceleratorsFeb 131Feb 131
Chaim RandinTowards Data ScienceHow to Implement a Custom Training Solution Based on Amazon EC2A Simple Solution for Managing Cloud-Based ML-Training — Part 2Jan 301Jan 301
