AntBatchInfer: A Scalable and Fault-Tolerant Batch Inference Framework for Kubernetes Clusters

Core Concepts

AntBatchInfer is an elastic batch inference framework that provides multi-level fault tolerance and improves inference efficiency through pipelining, intra-node, and inter-node scaling, particularly for complicated multiple-model batch inference scenarios.

Abstract

AntBatchInfer is a batch inference framework designed for Kubernetes clusters that addresses the challenges of stability and performance in offline batch inference tasks.
The framework consists of four key modules:

Stateful Data Sharding Service (Stateful DDS): Elastically distributes data samples to workers based on their computation capacity, and manages the lifecycle of data samples at the shard level to ensure data fault tolerance.

Data Handler: Responsible for I/O operations and CPU-intensive data preprocessing. It collaborates with the Stateful DDS to fetch data samples and report shard completion status.

Elastic Controller: Manages the lifecycle of worker pods, including pod-level fault tolerance and elastic scaling of computing nodes.

Elastic Predictor Scheduler: Elastically scales out intra-node predictors to improve resource utilization, and manages the lifecycle of these processes for fine-grained application-level fault tolerance.

The multi-level fault tolerance mechanism in AntBatchInfer ensures stability throughout the inference pipeline, handling pod failures, application failures, and data failures.
For efficiency, the framework reduces overall job completion time by elastically allocating data samples to workers based on their real-time throughput. It also optimizes single-model batch inference by decoupling the pipeline into data loading, prediction, and writing stages, and overlapping their execution. For multiple-model batch inference, AntBatchInfer encapsulates each model into a separate predictor process and schedules them in a pipelined manner.
Extensive experiments and real-world usage at Ant Group demonstrate the superiority of AntBatchInfer in terms of stability and efficiency, outperforming baseline systems by at least 2x and 6x in single-model and multiple-model batch inference, respectively.

Stats

The experiment shows that AntBatchInfer achieves a throughput of 1200 samples/sec, which is at least 2 times faster than the baseline in a single-model batch inference job for a graph neural network with half a billion nodes and 6 billion edges.
In a multiple-model batch inference scenario, AntBatchInfer achieves a throughput of 398 samples/sec, which is nearly 6 times faster than the baseline.
The DDS-based data distribution method in AntBatchInfer achieves a 12% to 30% speedup in job completion time compared to the even data partition strategy, especially in the non-dedicated cluster.
AntBatchInfer scales linearly when adding up to 120 CPU nodes, each with 20 cores, demonstrating the negligible synchronization cost between the Stateful DDS and worker nodes.

Quotes

"AntBatchInfer addresses these challenges by providing multi-level fault-tolerant capabilities, enabling the stable execution of versatile and long-running inference tasks."
"It also improves inference efficiency by pipelining, intra-node, and inter-node scaling. It further optimizes the performance in complicated multiple-model batch inference scenarios."

Key Insights Distilled From

AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster

by Siyuan Li,Yo... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09686.pdf

Deeper Inquiries

How can AntBatchInfer's fault tolerance and elasticity mechanisms be extended to support other types of distributed workloads beyond batch inference, such as streaming or interactive applications

AntBatchInfer's fault tolerance and elasticity mechanisms can be extended to support other types of distributed workloads by incorporating adaptive resource allocation and dynamic scaling based on workload characteristics. For streaming applications, the system can implement real-time monitoring of data streams and adjust resource allocation dynamically to handle fluctuating workloads. This can involve prioritizing certain tasks, such as data ingestion and processing, based on their criticality and adjusting resource allocation accordingly. Additionally, for interactive applications, AntBatchInfer can introduce mechanisms for prioritizing user requests and ensuring low latency responses by dynamically scaling resources to meet demand spikes. By integrating feedback loops and adaptive algorithms, the system can optimize resource allocation for different types of workloads beyond batch inference, ensuring efficient and reliable performance.

What are the potential trade-offs or limitations of the pipelining and resource allocation strategies used in AntBatchInfer, and how could they be further optimized for specific workload characteristics or hardware configurations

The pipelining and resource allocation strategies used in AntBatchInfer may have potential trade-offs and limitations that could be addressed for specific workload characteristics or hardware configurations. One limitation could be the overhead introduced by pipelining, which may lead to increased latency for certain tasks. To optimize this, the system could implement task prioritization within the pipeline to ensure critical tasks are processed efficiently. Additionally, for resource allocation, AntBatchInfer could benefit from fine-tuning resource utilization based on workload patterns and hardware configurations. By incorporating machine learning algorithms to predict resource demands and adjust allocation dynamically, the system can optimize performance and resource utilization. Furthermore, considering hardware-specific optimizations, such as GPU utilization for compute-intensive tasks, can further enhance efficiency and performance for specific workload characteristics.

Given the growing importance of responsible AI, how could AntBatchInfer's design be enhanced to better support model monitoring, explainability, and fairness considerations in large-scale batch inference deployments

To enhance AntBatchInfer's design for better support of responsible AI practices in large-scale batch inference deployments, several considerations can be integrated into the system. Firstly, incorporating model monitoring capabilities to track model performance metrics, data drift, and model degradation over time can ensure the reliability and accuracy of inference results. Additionally, introducing explainability features, such as model interpretability and transparency in decision-making processes, can enhance the trustworthiness of the system. By integrating fairness considerations, such as bias detection and mitigation techniques, AntBatchInfer can ensure equitable outcomes for diverse user groups. Moreover, implementing governance mechanisms for model versioning, auditing, and compliance with regulatory standards can strengthen the system's accountability and ethical use of AI technologies in production environments.