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AI FAQ

Q: What types of AI workloads are suitable for running on TKE?​

A: TKE supports various AI workloads including:

  • Training workloads: Deep learning model training, large-scale distributed training
  • Inference workloads: Real-time inference, batch inference
  • Preprocessing workloads: Data preprocessing, feature engineering
  • Model management: Model versioning, model deployment

Q: How to configure GPU resources for AI workloads?​

A: GPU resource configuration methods:

  1. Static allocation: Specify GPU resources in Pod spec
  2. Dynamic allocation: Use GPU sharing mechanism
  3. Multi-GPU: Support for multi-GPU training scenarios

Common GPU resource configuration example:

resources:
  limits:
    nvidia.com/gpu: 2
  requests:
    nvidia.com/gpu: 2

A: Recommended storage solutions:

  • Training data: Use high-performance file storage (e.g., CFS)
  • Model storage: Object storage (e.g., COS)
  • Checkpoint storage: Persistent volumes (PVC)

Q: How to optimize network performance for distributed training?​

A: Network optimization strategies:

  • Use high-performance network plugins (e.g., Cilium)
  • Configure appropriate network policies
  • Optimize inter-node communication
  • Use RDMA technology (if supported)