"Data processing must adapt to the emerging data types needed for AI applications, expanding beyond traditional tabular data to include multimodal datasets (which can encompass images, videos, audio, text, and sensor data). This evolution is crucial for supporting inference tasks, which are a fundamental component of AI-powered applications. Additionally, the hardware used for data storage and compute operations needs to support GPUs alongside standard CPUs."
"Model training involves reinforcement learning ( RL) and post-training tasks, including generating new data by running inference on models. Ray's Actor API can be leveraged for Trainer and Generator components. An "Actor" is essentially a stateful worker that creates a new worker class when instantiated and manages method scheduling on that specific worker instance."
AI workloads are increasing in compute and data complexity, requiring production-ready systems that scale. Kubernetes, PyTorch, vLLM, and Ray compose an AI compute stack that supports distributed machine learning and Python applications. Ray orchestrates infrastructure for distributed workloads and originated from Berkeley research. Data processing must expand beyond tabular formats to multimodal datasets and shift toward GPU-based inference alongside CPUs. Model training encompasses reinforcement learning and post-training generation, using Ray's Actor API for stateful Trainer and Generator components. Native RDMA support enables direct GPU object transport to improve distributed performance and model serving.
Read at InfoQ
Unable to calculate read time
Collection
[
|
...
]