PMLE Scaling Prototypes into ML Models Practice Question
An ML engineer is using Vertex AI for distributed training of a PyTorch model across multiple nodes. The training job must use TPUs for high throughput. The engineer sets up the job configuration. Which THREE components are required for the training to work correctly? (Select 3)
⚠ Common exam trap
Google Cloud often tests the distinction between TensorFlow and PyTorch distributed training configurations, and the trap here is assuming that `TF_CONFIG` or `MultiWorkerMirroredStrategy` are universal for all frameworks, when in fact PyTorch uses its own environment variables and the `torch-xla` library for TPU training.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A startup script to configure the TPU pod (e.g., `xla_lib.sh`)
A is correct because TPU pods require a startup script (e.g., `xla_lib.sh`) to initialize the XLA runtime, configure the TPU mesh, and set environment variables like `XRT_TPU_CONFIG`. Without this script, the TPU devices will not be discoverable by the PyTorch/XLA process, causing the training to fail with device-not-found errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A startup script to configure the TPU pod (e.g., `xla_lib.sh`)
Why this is correct
Startup scripts are often needed to initialize TPU devices.
- ✗
A MultiWorkerMirroredStrategy configuration
Why it's wrong here
This is for TensorFlow data parallelism, not PyTorch TPU.
- ✓
A Docker image that includes PyTorch and the TPU library (torch-xla)
Why this is correct
The container must have the necessary dependencies for TPU training.
- ✗
A TF_CONFIG environment variable set for each worker
Why it's wrong here
TF_CONFIG is for TensorFlow distributed training, not PyTorch.
- ✓
A CustomJob with a TPU accelerator type (e.g., v3-32)
Why this is correct
TPU training requires specifying the TPU type in the job.
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