TensorFlow/PyTorch Framework Fundamentals Questions
Practical knowledge of a major deep learning framework. Includes understanding tensors, operations, building neural network layers, constructing models, and training loops. Ability to read and modify existing code in these frameworks. Knowledge of how to work with pre-built layers and models.
HardTechnical
44 practiced
You're observing training loss decreasing but validation F1 dropping over time for a classification problem. Using TensorFlow or PyTorch tooling, design a prioritized diagnostics and mitigation plan that includes data validation, augmentation, regularization, learning rate schedule adjustments, early stopping, and how to use callbacks/profilers to gather evidence.
MediumTechnical
38 practiced
Write a PyTorch learning rate scheduler that warms up linearly for warmup_steps and then follows cosine annealing until total_steps. Show how to integrate it with an optimizer and how to save/restore its state for checkpointing.
HardSystem Design
40 practiced
Design how to shard and stream a very large dataset stored in cloud object storage (S3/GCS) for multi-node distributed training. Discuss file formats (TFRecord, LMDB, Parquet), how to ensure each worker sees a distinct shard without overlap, caching strategies, and handling eventual consistency and network variability.
HardSystem Design
71 practiced
Design a distributed training strategy using PyTorch DistributedDataParallel (DDP) to train a model across 8 nodes with 8 GPUs each (64 GPUs total). Describe initialization, choice of backend, data sharding, gradient synchronization, handling of BatchNorm, checkpointing strategy, fault tolerance, and how to scale the learning rate appropriately.
EasyTechnical
36 practiced
Explain and demonstrate how to freeze base model parameters and train only a new classification head in both PyTorch and TensorFlow. Provide code snippets showing how to set requires_grad / trainable flags and how to later unfreeze selected layers for fine-tuning with a reduced learning rate.
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