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Questions and Answers
Which convention is commonly used to save PyTorch checkpoints?
Which convention is commonly used to save PyTorch checkpoints?
- Querying the dictionary
- Loading them locally
- Appending them to the dictionary
- Using a specific file extension (correct)
What should be done before running inference to ensure consistent results?
What should be done before running inference to ensure consistent results?
- Resuming training
- Setting dropout and batch normalization layers to evaluation mode (correct)
- Initializing the models and optimizers
- Loading the dictionary locally
When is it helpful to warmstart the training process using trained parameters?
When is it helpful to warmstart the training process using trained parameters?
- When transferring learning
- When loading from a partial state_dict
- When loading a state_dict with more keys than the model
- When training a new complex model (correct)
Which function in PyTorch is used to access the learnable parameters of a model?
Which function in PyTorch is used to access the learnable parameters of a model?
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Which type of layers have entries in the model's state_dict?
Which type of layers have entries in the model's state_dict?
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What is the purpose of the optimizer's state_dict in PyTorch?
What is the purpose of the optimizer's state_dict in PyTorch?
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Which method is recommended for saving models in PyTorch?
Which method is recommended for saving models in PyTorch?
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What should be done before running inference with dropout and batch normalization layers?
What should be done before running inference with dropout and batch normalization layers?
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Which format is recommended for scaled inference and deployment in PyTorch?
Which format is recommended for scaled inference and deployment in PyTorch?
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What should be saved when creating a general checkpoint for inference and/or resuming training?
What should be saved when creating a general checkpoint for inference and/or resuming training?
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