Faced with the challenge of achieving efficient distributed training for ultra-large-scale models, MindSpore is handled as?

Question

Options

• AAutomatic parallel
• BSerial
• CManual parallel

• A
  73% (38)
• B
  10% (5)
• C
  17% (9)

Explanation

MindSpore addresses the challenge of distributed training for ultra-large-scale models through Automatic Parallel (A), where the framework automatically analyzes the model and data, then distributes computation across devices without requiring developers to manually configure parallelism strategies. This is a core design philosophy of MindSpore - abstracting away the complexity of parallelism so engineers can focus on model architecture rather than infrastructure.

Why the distractors are wrong:

• B (Serial): Serial execution processes one batch at a time on a single device, making it completely impractical for ultra-large models that exceed single-device memory and compute capacity.
• C (Manual parallel): While manual parallelism is possible in many frameworks, it places the burden on the developer to explicitly partition tensors and coordinate communication - MindSpore's value proposition is automating exactly this.

Memory tip: Think "MindSpore → Smart parallel" - the framework is smart enough to handle parallelism automatically, which is the key differentiator that makes it suitable for ultra-large-scale models.

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