{"id":12197,"date":"2026-09-14T06:04:14","date_gmt":"2026-09-14T06:04:14","guid":{"rendered":"https:\/\/www.hirist.tech\/blog\/?p=12197"},"modified":"2026-09-14T06:04:19","modified_gmt":"2026-09-14T06:04:19","slug":"top-20-pytorch-interview-questions-and-answers","status":"publish","type":"post","link":"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/","title":{"rendered":"Top 20 PyTorch Interview Questions and Answers"},"content":{"rendered":"\n<p>PyTorch interview questions are commonly included in the technical and coding rounds for artificial intelligence, deep learning and machine learning engineer roles. This guide covers 20 frequently asked PyTorch interview questions with clear answers and practical coding examples. It will help you revise important topics such as tensors, autograd, and neural networks so you can prepare confidently for your next technical interview.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-1024x1024.webp\" alt=\"PyTorch Interview Questions by role\" class=\"wp-image-12205\" srcset=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-1024x1024.webp 1024w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-300x300.webp 300w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-150x150.webp 150w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-768x768.webp 768w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-1170x1170.webp 1170w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30-585x585.webp 585w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-1-30.webp 1254w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_65 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title \" >Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#What_is_PyTorch\" title=\"What is PyTorch?\">What is PyTorch?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#PyTorch_Interview_Questions_and_Answers_Basic_and_Intermediate\" title=\"PyTorch Interview Questions and Answers (Basic and Intermediate)\">PyTorch Interview Questions and Answers (Basic and Intermediate)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#1_Why_is_PyTorch_widely_used_for_machine_learning_and_deep_learning\" title=\"1. Why is PyTorch widely used for machine learning and deep learning?\">1. Why is PyTorch widely used for machine learning and deep learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#2_What_is_a_tensor_in_PyTorch_and_how_is_it_different_from_a_NumPy_array\" title=\"2. What is a tensor in PyTorch, and how is it different from a NumPy array?\">2. What is a tensor in PyTorch, and how is it different from a NumPy array?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#3_How_do_PyTorchs_dynamic_computation_graphs_compare_with_TensorFlows_eager_and_graph_execution_modes\" title=\"3. How do PyTorch\u2019s dynamic computation graphs compare with TensorFlow\u2019s eager and graph execution modes?\">3. How do PyTorch\u2019s dynamic computation graphs compare with TensorFlow\u2019s eager and graph execution modes?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#4_What_is_the_difference_between_view_and_reshape_when_changing_a_tensors_shape\" title=\"4. What is the difference between view() and reshape() when changing a tensor\u2019s shape?\">4. What is the difference between view() and reshape() when changing a tensor\u2019s shape?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#5_Which_methods_must_be_implemented_when_creating_a_custom_PyTorch_Dataset\" title=\"5. Which methods must be implemented when creating a custom PyTorch Dataset?\">5. Which methods must be implemented when creating a custom PyTorch Dataset?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#6_What_is_the_difference_between_Dataset_and_DataLoader_in_PyTorch\" title=\"6. What is the difference between Dataset and DataLoader in PyTorch?\">6. What is the difference between Dataset and DataLoader in PyTorch?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#7_How_does_the_Autograd_engine_track_operations_and_what_does_detach_do\" title=\"7. How does the Autograd engine track operations, and what does detach() do?\">7. How does the Autograd engine track operations, and what does detach() do?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#8_What_do_requires_grad_backward_and_the_grad_attribute_do\" title=\"8. What do requires_grad, backward(), and the .grad attribute do?\">8. What do requires_grad, backward(), and the .grad attribute do?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#9_Why_is_optimizerzero_grad_called_during_model_training\" title=\"9. Why is optimizer.zero_grad() called during model training?\">9. Why is optimizer.zero_grad() called during model training?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#10_What_is_the_difference_between_nnModule_and_nnSequential\" title=\"10. What is the difference between nn.Module and nn.Sequential?\">10. What is the difference between nn.Module and nn.Sequential?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#11_What_is_the_difference_between_modeltrain_modeleval_and_torchno_grad\" title=\"11. What is the difference between model.train(), model.eval(), and torch.no_grad()?\">11. What is the difference between model.train(), model.eval(), and torch.no_grad()?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#12_What_is_the_difference_between_the_SGD_and_Adam_optimizers\" title=\"12. What is the difference between the SGD and Adam optimizers?\">12. What is the difference between the SGD and Adam optimizers?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#PyTorch_Coding_Interview_Questions_Practical_and_Scenario-Based\" title=\"PyTorch Coding Interview Questions (Practical and Scenario-Based)\">PyTorch Coding Interview Questions (Practical and Scenario-Based)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#13_Build_a_simple_neural_network_by_extending_nnModule_and_defining_its_forward_method\" title=\"13. Build a simple neural network by extending nn.Module and defining its forward() method.\">13. Build a simple neural network by extending nn.Module and defining its forward() method.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#14_How_do_you_write_a_standard_training_loop_in_PyTorch\" title=\"14. How do you write a standard training loop in PyTorch?\">14. How do you write a standard training loop in PyTorch?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#15_A_PyTorch_model_fails_because_two_tensors_have_incompatible_shapes_How_would_you_identify_and_fix_the_error\" title=\"15. A PyTorch model fails because two tensors have incompatible shapes. How would you identify and fix the error?\">15. A PyTorch model fails because two tensors have incompatible shapes. How would you identify and fix the error?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#16_How_do_you_move_a_model_and_its_input_data_to_a_GPU\" title=\"16. How do you move a model and its input data to a GPU?\">16. How do you move a model and its input data to a GPU?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#17_How_do_you_save_and_load_a_model_checkpoint_to_resume_training\" title=\"17. How do you save and load a model checkpoint to resume training?\">17. How do you save and load a model checkpoint to resume training?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#18_Your_models_GPU_memory_usage_keeps_increasing_during_training_How_would_you_debug_it\" title=\"18. Your model\u2019s GPU memory usage keeps increasing during training. How would you debug it?\">18. Your model\u2019s GPU memory usage keeps increasing during training. How would you debug it?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#19_How_do_you_use_mixed-precision_training_to_reduce_memory_usage_and_speed_up_training\" title=\"19. How do you use mixed-precision training to reduce memory usage and speed up training?\">19. How do you use mixed-precision training to reduce memory usage and speed up training?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#20_How_do_you_implement_Distributed_Data_Parallel_for_multi-GPU_training\" title=\"20. How do you implement Distributed Data Parallel for multi-GPU training?\">20. How do you implement Distributed Data Parallel for multi-GPU training?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#PyTorch_MCQs_for_Practice\" title=\"PyTorch MCQs for Practice\">PyTorch MCQs for Practice<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#1_What_is_the_shape_of_output_in_the_following_code\" title=\"1. What is the shape of output in the following code?\">1. What is the shape of output in the following code?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#2_What_happens_if_optimizerzero_grad_is_not_called_between_normal_training_batches\" title=\"2. What happens if optimizer.zero_grad() is not called between normal training batches?\">2. What happens if optimizer.zero_grad() is not called between normal training batches?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#3_What_happens_after_calling_only_modeleval\" title=\"3. What happens after calling only model.eval()?\">3. What happens after calling only model.eval()?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#4_Which_methods_are_required_for_indexed_access_in_a_map-style_custom_Dataset\" title=\"4. Which methods are required for indexed access in a map-style custom Dataset?\">4. Which methods are required for indexed access in a map-style custom Dataset?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#5_What_happens_when_a_tensor_is_created_with_torchfrom_numpy\" title=\"5. What happens when a tensor is created with torch.from_numpy()?\">5. What happens when a tensor is created with torch.from_numpy()?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#6_A_model_is_on_a_CUDA_GPU_but_the_input_tensor_is_on_the_CPU_What_will_happen\" title=\"6. A model is on a CUDA GPU, but the input tensor is on the CPU. What will happen?\">6. A model is on a CUDA GPU, but the input tensor is on the CPU. What will happen?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#7_Which_input_and_target_shapes_are_correct_for_nnCrossEntropyLoss_in_a_standard_classification_task_with_32_samples_and_10_classes\" title=\"7. Which input and target shapes are correct for nn.CrossEntropyLoss in a standard classification task with 32 samples and 10 classes?\">7. Which input and target shapes are correct for nn.CrossEntropyLoss in a standard classification task with 32 samples and 10 classes?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#How_to_Prepare_for_a_PyTorch_Interview\" title=\"How to Prepare for a PyTorch Interview\">How to Prepare for a PyTorch Interview<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#Wrapping_Up\" title=\"Wrapping Up\">Wrapping Up<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.hirist.tech\/blog\/top-20-pytorch-interview-questions-and-answers\/#FAQs\" title=\"FAQs\">FAQs<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_PyTorch\"><\/span>What is PyTorch?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-1024x1024.webp\" alt=\"What is PyTorch\" class=\"wp-image-12207\" srcset=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-1024x1024.webp 1024w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-300x300.webp 300w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-150x150.webp 150w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-768x768.webp 768w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-1170x1170.webp 1170w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27-585x585.webp 585w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-2-27.webp 1254w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>PyTorch is an open-source machine learning and deep learning framework used to build, train, and deploy neural networks. It was created at Facebook AI Research in 2016, with Soumith Chintala playing a leading role in its development. The project now operates under the PyTorch Foundation within the Linux Foundation. PyTorch has become a leading framework for AI research and large-scale commercial applications.<\/p>\n\n\n\n<p>OpenAI adopted PyTorch as its main deep learning framework. Stable Diffusion and Meta\u2019s Llama models also have strong PyTorch-based ecosystems. Since the framework is used in actual AI development, employers often expect candidates to understand its core concepts and know how to use it in practical situations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"PyTorch_Interview_Questions_and_Answers_Basic_and_Intermediate\"><\/span>PyTorch Interview Questions and Answers (Basic and Intermediate)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Screening calls and technical discussions often begin with questions about how PyTorch works. The PyTorch interview questions and answers in this section cover tensors, autograd, neural networks, datasets, optimizers, and other basic to intermediate concepts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_Why_is_PyTorch_widely_used_for_machine_learning_and_deep_learning\"><\/span>1. Why is PyTorch widely used for machine learning and deep learning?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>PyTorch is a Python-based framework for building and training deep learning models. It provides tensors for numerical computation, Autograd for automatic gradient calculation, and tools for designing neural networks.<\/p>\n\n\n\n<p>Developers prefer PyTorch because its syntax is easy for Python users to understand. Its dynamic computation graph also makes models easier to inspect, change, and debug. PyTorch supports CPU and GPU execution, distributed training, and deployment. It is widely used in computer vision, NLP, generative AI, and large-scale machine learning projects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_What_is_a_tensor_in_PyTorch_and_how_is_it_different_from_a_NumPy_array\"><\/span>2. What is a tensor in PyTorch, and how is it different from a NumPy array?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A tensor is a multidimensional data structure used to store model inputs, outputs, weights, and gradients. It is similar to a NumPy array but includes features designed for deep learning.<\/p>\n\n\n\n<p>PyTorch tensors can run on CPUs and GPUs. They can also track operations for automatic differentiation when requires_grad=True. NumPy arrays are mainly used for general numerical computation and do not provide built-in gradient tracking. A NumPy array can be converted into a tensor using torch.from_numpy(), which may share memory with the original array.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_How_do_PyTorchs_dynamic_computation_graphs_compare_with_TensorFlows_eager_and_graph_execution_modes\"><\/span>3. How do PyTorch\u2019s dynamic computation graphs compare with TensorFlow\u2019s eager and graph execution modes?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>PyTorch builds its computation graph while operations are running. This approach is called define-by-run. It allows developers to use normal Python loops, conditions, and debugging tools inside a model.<\/p>\n\n\n\n<p>The graph is created again during every forward pass, which makes PyTorch useful for models whose structure changes according to the input.<\/p>\n\n\n\n<p>TensorFlow 2 also uses eager execution by default. However, it can convert Python code into a reusable graph with tf.function to improve performance and support deployment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_What_is_the_difference_between_view_and_reshape_when_changing_a_tensors_shape\"><\/span>4. What is the difference between view() and reshape() when changing a tensor\u2019s shape?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Both view() and reshape() change the shape of a tensor without changing its values.<\/p>\n\n\n\n<p>view() works only when the tensor\u2019s memory layout is compatible with the requested shape. It may fail on a non-contiguous tensor unless contiguous() is called first.<\/p>\n\n\n\n<p>reshape() is more flexible. It returns a view when possible but creates a copy when the existing memory layout does not allow a view. Therefore, a tensor returned by reshape() does not always share memory with the original tensor.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_Which_methods_must_be_implemented_when_creating_a_custom_PyTorch_Dataset\"><\/span>5. Which methods must be implemented when creating a custom PyTorch Dataset?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A map-style custom Dataset usually includes three methods:<\/p>\n\n\n\n<p>\u25cf <strong>init<\/strong>() stores data paths, labels, and transformations.<\/p>\n\n\n\n<p>\u25cf <strong>len<\/strong>() returns the total number of samples.<\/p>\n\n\n\n<p>\u25cf <strong>getitem<\/strong>() loads and returns one sample for a given index.<\/p>\n\n\n\n<p>For streaming data that is read in sequence, you can extend IterableDataset and implement <strong>iter<\/strong>() instead.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"577\" src=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-1024x577.webp\" alt=\"custom PyTorch Dataset\" class=\"wp-image-12209\" srcset=\"https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-1024x577.webp 1024w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-300x169.webp 300w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-768x433.webp 768w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-1170x659.webp 1170w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15-585x329.webp 585w, https:\/\/www.hirist.tech\/blog\/wp-content\/uploads\/2026\/09\/extracted-image-3-15.webp 1431w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_What_is_the_difference_between_Dataset_and_DataLoader_in_PyTorch\"><\/span>6. What is the difference between Dataset and DataLoader in PyTorch?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A Dataset defines where the data comes from and how each sample is accessed. A DataLoader controls how those samples are supplied to the model.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Dataset<\/th><th>DataLoader<\/th><\/tr><\/thead><tbody><tr><td>Defines how individual samples are stored and accessed<\/td><td>Loads samples from a Dataset and sends them to the model<\/td><\/tr><tr><td>Uses methods such as <strong>len<\/strong>() and <strong>getitem<\/strong>()<\/td><td>Handles batching, shuffling, sampling, and parallel loading<\/td><\/tr><tr><td>Usually returns one sample and its label<\/td><td>Returns batches during training or evaluation<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>In simple terms, a Dataset describes the data, while a DataLoader manages how the data is loaded.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_How_does_the_Autograd_engine_track_operations_and_what_does_detach_do\"><\/span>7. How does the Autograd engine track operations, and what does detach() do?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Autograd records operations performed on tensors when gradient tracking is enabled and at least one tensor has requires_grad=True. These operations form a dynamic computation graph.<\/p>\n\n\n\n<p>When backward() is called, PyTorch moves through the graph in reverse and applies the chain rule to calculate gradients.<\/p>\n\n\n\n<p>detach() returns a tensor that is disconnected from the current graph. It shares data with the original tensor but does not carry its gradient history. It is useful when logging values or reusing outputs without tracking more operations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_What_do_requires_grad_backward_and_the_grad_attribute_do\"><\/span>8. What do requires_grad, backward(), and the .grad attribute do?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>requires_grad=True tells Autograd to track operations involving a tensor. Calling backward() on the final loss runs reverse-mode automatic differentiation and calculates the loss gradient with respect to the tracked parameters.<\/p>\n\n\n\n<p>The calculated gradients are stored in each eligible tensor\u2019s .grad attribute. By default, .grad is populated for leaf tensors such as model parameters. Intermediate non-leaf tensors do not retain their gradients unless retain_grad() is called. Another important point is that .grad values accumulate across backward passes instead of being replaced automatically.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"9_Why_is_optimizerzero_grad_called_during_model_training\"><\/span>9. Why is optimizer.zero_grad() called during model training?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>PyTorch accumulates gradients instead of replacing them after every backward pass. Without clearing them, gradients from the current batch would be added to those from previous batches. This could produce incorrect parameter updates.<\/p>\n\n\n\n<p>optimizer.zero_grad() clears the stored gradients before the next backward pass. It is normally called once during each training iteration, usually before loss.backward().<\/p>\n\n\n\n<p>It should not be called after every batch when gradients are intentionally being accumulated across multiple batches.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"10_What_is_the_difference_between_nnModule_and_nnSequential\"><\/span>10. What is the difference between nn.Module and nn.Sequential?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>nn.Module is the base class used to create PyTorch models and layers. nn.Sequential is a type of nn.Module that sends data through layers in the order they are listed.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>nn.Module<\/th><th>nn.Sequential<\/th><\/tr><\/thead><tbody><tr><td>Used to create custom models and layers<\/td><td>Used to create a simple sequence of layers<\/td><\/tr><tr><td>Requires a custom forward() method<\/td><td>Passes output automatically from one layer to the next<\/td><\/tr><tr><td>Supports branches, skip connections, multiple inputs, and conditions<\/td><td>Best suited for models with a straight layer-by-layer flow<\/td><\/tr><tr><td>Provides greater control over the architecture<\/td><td>Offers a shorter way to build basic networks<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Use nn.Sequential for a simple stack of layers. Use a custom nn.Module when the model requires more control or specialised data flow.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"11_What_is_the_difference_between_modeltrain_modeleval_and_torchno_grad\"><\/span>11. What is the difference between model.train(), model.eval(), and torch.no_grad()?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>These methods control different parts of model execution.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Method<\/th><th>What it does<\/th><th>Gradient tracking<\/th><th>Common use<\/th><\/tr><\/thead><tbody><tr><td>model.train()<\/td><td>Enables training behaviour in layers such as Dropout and Batch Normalization.<\/td><td>Enabled<\/td><td>Training<\/td><\/tr><tr><td>model.eval()<\/td><td>Enables evaluation behaviour. Dropout is disabled, and Batch Normalization uses stored statistics.<\/td><td>Enabled<\/td><td>Validation and inference<\/td><\/tr><tr><td>torch.no_grad()<\/td><td>Stops PyTorch from recording operations for gradient calculation.<\/td><td>Disabled<\/td><td>Validation and inference<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>model.eval() changes how certain layers behave, while torch.no_grad() disables gradient tracking. They are commonly used together during inference:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>model.eval()\nwith torch.no_grad():\n predictions = model(inputs)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"12_What_is_the_difference_between_the_SGD_and_Adam_optimizers\"><\/span>12. What is the difference between the SGD and Adam optimizers?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>SGD updates model parameters using the current gradient. Adam uses moving averages of the gradient and squared gradient to calculate an adaptive update for each parameter.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>SGD<\/th><th>Adam<\/th><\/tr><\/thead><tbody><tr><td>Uses the current gradient for parameter updates<\/td><td>Uses moving averages of gradients and squared gradients<\/td><\/tr><tr><td>Usually applies one learning rate to all parameters<\/td><td>Adapts the update size for each parameter<\/td><\/tr><tr><td>Often requires more learning-rate tuning<\/td><td>Usually works well with less initial tuning<\/td><\/tr><tr><td>Uses less optimizer-state memory<\/td><td>Requires more memory for additional statistics<\/td><\/tr><tr><td>Can provide strong generalization on some tasks<\/td><td>Often converges faster during early training<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>SGD is simple and memory-efficient. Adam is easier to tune and often learns faster at the beginning. The better choice depends on the model, dataset, and training goal.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"PyTorch_Coding_Interview_Questions_Practical_and_Scenario-Based\"><\/span>PyTorch Coding Interview Questions (Practical and Scenario-Based)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>These PyTorch coding questions focus on the practical tasks that may appear in coding tests and live technical interviews. They cover model building, training loops, GPU usage, debugging, performance improvement, and machine learning scenarios.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"13_Build_a_simple_neural_network_by_extending_nnModule_and_defining_its_forward_method\"><\/span>13. Build a simple neural network by extending nn.Module and defining its forward() method.<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A custom PyTorch model should inherit from nn.Module. Define its layers inside <strong>init<\/strong>() and describe how the input moves through those layers inside forward().<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\nfrom torch import nn\n\nclass SimpleClassifier(nn.Module):\n    def __init__(\n        self,\n        input_size: int,\n        hidden_size: int,\n        num_classes: int\n    ) -&gt; None:\n        super().__init__()\n        self.fc1 = nn.Linear(input_size, hidden_size)\n        self.relu = nn.ReLU()\n        self.fc2 = nn.Linear(hidden_size, num_classes)\n\n    def forward(self, x: torch.Tensor) -&gt; torch.Tensor:\n        x = self.fc1(x)\n        x = self.relu(x)\n        return self.fc2(x)\n\nmodel = SimpleClassifier(\n    input_size=20,\n    hidden_size=64,\n    num_classes=3\n)\nsample = torch.randn(8, 20)\noutput = model(sample)\nprint(output.shape) # torch.Size(&#91;8, 3])<\/code><\/pre>\n\n\n\n<p>nn.Module registers the layers and trainable parameters automatically. Calling model(sample) runs forward() along with PyTorch\u2019s internal module logic.<\/p>\n\n\n\n<p>Interview tip: Use model(x) instead of calling model.forward(x) directly.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"14_How_do_you_write_a_standard_training_loop_in_PyTorch\"><\/span>14. How do you write a standard training loop in PyTorch?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A standard training loop processes the dataset over several epochs. For each batch, it clears old gradients, performs a forward pass, calculates the loss, runs backpropagation, and updates the model parameters.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>for epoch in range(num_epochs):\n    model.train()\n    total_loss = 0.0\n\n    for inputs, targets in data_loader:\n        inputs = inputs.to(device)\n        targets = targets.to(device)\n\n        # Clear gradients from the previous batch\n        optimizer.zero_grad(set_to_none=True)\n\n        # Forward pass\n        predictions = model(inputs)\n\n        # Calculate loss\n        loss = loss_fn(predictions, targets)\n\n        # Backward pass and parameter update\n        loss.backward()\n        optimizer.step()\n\n        total_loss += loss.item()\n\n    average_loss = total_loss \/ len(data_loader)\n    print(f\"Epoch {epoch + 1}: {average_loss:.4f}\")<\/code><\/pre>\n\n\n\n<p>loss.backward() calculates the gradients and stores them in each parameter\u2019s .grad attribute. optimizer.step() then uses those gradients to update the weights. PyTorch accumulates gradients by default, which is why they must normally be cleared for every batch.<\/p>\n\n\n\n<p>Interview tip: Use loss.item() when recording the loss. Storing the original loss tensor can retain its computation graph.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"15_A_PyTorch_model_fails_because_two_tensors_have_incompatible_shapes_How_would_you_identify_and_fix_the_error\"><\/span>15. A PyTorch model fails because two tensors have incompatible shapes. How would you identify and fix the error?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Start by reading the error message and printing tensor shapes before the failing operation. Check the batch size, feature dimensions, channel order, and the input expected by the next layer.<\/p>\n\n\n\n<p>For example, an nn.Linear layer expects the final input dimension to match its in_features value.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\nfrom torch import nn\n\nclass SimpleCNN(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.features = nn.Sequential(\n            nn.Conv2d(3, 16, kernel_size=3, padding=1),\n            nn.ReLU(),\n            nn.AdaptiveAvgPool2d((7, 7))\n        )\n        self.classifier = nn.Linear(16 * 7 * 7, 10)\n\n    def forward(self, x):\n        x = self.features(x)\n        print(\"Before flattening:\", x.shape)\n        x = torch.flatten(x, start_dim=1)\n        print(\"Before Linear layer:\", x.shape)\n        return self.classifier(x)\n\nmodel = SimpleCNN()\ninputs = torch.randn(32, 3, 28, 28)\noutputs = model(inputs)\nprint(outputs.shape) # torch.Size(&#91;32, 10])<\/code><\/pre>\n\n\n\n<p>The convolutional output has the shape [32, 16, 7, 7]. After flattening, it becomes [32, 784], so the linear layer must use in_features=784.<\/p>\n\n\n\n<p>Common fixes include:<\/p>\n\n\n\n<p>\u25cf Using reshape(), flatten(), unsqueeze(), or squeeze() when a dimension is missing or misplaced<\/p>\n\n\n\n<p>\u25cf Using permute() when channels are in the wrong order<\/p>\n\n\n\n<p>\u25cf Correcting the in_features value of an nn.Linear layer<\/p>\n\n\n\n<p>\u25cf Ensuring predictions and labels have the shapes expected by the loss function<\/p>\n\n\n\n<p>\u25cf Checking whether two tensors follow PyTorch\u2019s broadcasting rules before applying element-wise operations<\/p>\n\n\n\n<p>Interview tip: Do not reshape a tensor blindly just to remove the error. First determine what each dimension represents and confirm the shape expected by the next operation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"16_How_do_you_move_a_model_and_its_input_data_to_a_GPU\"><\/span>16. How do you move a model and its input data to a GPU?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Check whether CUDA is available and select the appropriate device. The model, inputs, and targets must all be placed on the same device.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\n\ndevice = torch.device(\n    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\n# Move the model before creating the optimizer\nmodel = model.to(device)\n\noptimizer = torch.optim.Adam(\n    model.parameters(),\n    lr=0.001\n)\n\nfor inputs, targets in data_loader:\n    inputs = inputs.to(device)\n    targets = targets.to(device)\n\n    optimizer.zero_grad(set_to_none=True)\n    predictions = model(inputs)\n    loss = loss_fn(predictions, targets)\n    loss.backward()\n    optimizer.step()<\/code><\/pre>\n\n\n\n<p>Moving the model before constructing the optimizer is the safest order because device conversion may replace its parameter objects.<\/p>\n\n\n\n<p>Interview tip: .to(device) returns the moved tensor. Write inputs = inputs.to(device) rather than calling it without saving the result.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"17_How_do_you_save_and_load_a_model_checkpoint_to_resume_training\"><\/span>17. How do you save and load a model checkpoint to resume training?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A resumable checkpoint should store the model state, optimizer state, current epoch, and any other information needed to continue training.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\n\ncheckpoint = {\n    \"epoch\": epoch,\n    \"model_state\": model.state_dict(),\n    \"optimizer_state\": optimizer.state_dict(),\n    \"loss\": loss.item()\n}\n\ntorch.save(checkpoint, \"checkpoint.pth\")<\/code><\/pre>\n\n\n\n<p>Create the model and optimizer before loading the checkpoint:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>model = SimpleClassifier(\n    input_size=20,\n    hidden_size=64,\n    num_classes=3\n).to(device)\n\noptimizer = torch.optim.Adam(\n    model.parameters(),\n    lr=0.001\n)\n\ncheckpoint = torch.load(\n    \"checkpoint.pth\",\n    map_location=device,\n    weights_only=True\n)\n\nmodel.load_state_dict(checkpoint&#91;\"model_state\"])\noptimizer.load_state_dict(checkpoint&#91;\"optimizer_state\"])\nstart_epoch = checkpoint&#91;\"epoch\"] + 1\nmodel.train()<\/code><\/pre>\n\n\n\n<p>The model\u2019s state_dict stores its parameters and registered buffers. The optimizer state contains values such as momentum or Adam\u2019s moving averages. If training uses a learning-rate scheduler or AMP scaler, save their state dictionaries as well.<\/p>\n\n\n\n<p>Interview tip: Model weights alone are usually enough for inference. Resuming training also requires the optimizer state and current epoch.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"18_Your_models_GPU_memory_usage_keeps_increasing_during_training_How_would_you_debug_it\"><\/span>18. Your model\u2019s GPU memory usage keeps increasing during training. How would you debug it?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>First, check whether your code is keeping tensors attached to the computation graph. Saving those tensors in lists or dictionaries prevents PyTorch from releasing the graph after each iteration.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Incorrect: retains computation graphs\nloss_history.append(loss)\nsaved_outputs.append(predictions)\n\n# Correct\nloss_history.append(loss.item())\nsaved_outputs.append(predictions.detach().cpu())<\/code><\/pre>\n\n\n\n<p>Other common causes include:<\/p>\n\n\n\n<p>\u25cf Using retain_graph=True when it is not required<\/p>\n\n\n\n<p>\u25cf Failing to detach recurrent hidden states<\/p>\n\n\n\n<p>\u25cf Saving every batch output on the GPU<\/p>\n\n\n\n<p>\u25cf Running validation without torch.no_grad()<\/p>\n\n\n\n<p>\u25cf Keeping references to large intermediate tensors<\/p>\n\n\n\n<p>You can inspect CUDA memory with:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>allocated = torch.cuda.memory_allocated() \/ 1024**2\nreserved = torch.cuda.memory_reserved() \/ 1024**2\n\nprint(f\"Allocated: {allocated:.2f} MB\")\nprint(f\"Reserved: {reserved:.2f} MB\")\n\nprint(torch.cuda.memory_summary())<\/code><\/pre>\n\n\n\n<p>memory_allocated() reports memory occupied by live tensors. memory_reserved() also includes memory held by PyTorch\u2019s caching allocator, so high reserved memory does not automatically indicate a leak.<\/p>\n\n\n\n<p>Interview tip: torch.cuda.empty_cache() releases unused cached blocks. It cannot free tensors that are still referenced by your program.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"19_How_do_you_use_mixed-precision_training_to_reduce_memory_usage_and_speed_up_training\"><\/span>19. How do you use mixed-precision training to reduce memory usage and speed up training?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Automatic Mixed Precision uses lower precision for suitable operations while keeping numerically sensitive operations at higher precision. Current PyTorch code combines torch.autocast with torch.amp.GradScaler.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import torch\n\ndevice = torch.device(\"cuda\")\nmodel = model.to(device)\nscaler = torch.amp.GradScaler(\"cuda\")\n\nfor inputs, targets in data_loader:\n    inputs = inputs.to(device)\n    targets = targets.to(device)\n\n    optimizer.zero_grad(set_to_none=True)\n\n    with torch.autocast(\n        device_type=\"cuda\",\n        dtype=torch.float16\n    ):\n        predictions = model(inputs)\n        loss = loss_fn(predictions, targets)\n\n    scaler.scale(loss).backward()\n    scaler.step(optimizer)\n    scaler.update()<\/code><\/pre>\n\n\n\n<p>autocast chooses a suitable precision for each operation. GradScaler scales the loss before backpropagation to reduce the risk of small float16 gradients underflowing to zero.<\/p>\n\n\n\n<p>Interview tip: When using gradient clipping, call scaler.unscale_(optimizer) before clipping the gradients.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"20_How_do_you_implement_Distributed_Data_Parallel_for_multi-GPU_training\"><\/span>20. How do you implement Distributed Data Parallel for multi-GPU training?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Distributed Data Parallel uses one process per GPU. Each process trains a model replica on a different part of the dataset, while DDP synchronizes gradients during the backward pass.<\/p>\n\n\n\n<p>Code example:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import os\nimport torch\nimport torch.distributed as dist\nfrom torch.nn.parallel import DistributedDataParallel as DDP\nfrom torch.utils.data import DataLoader, DistributedSampler\n\ndef main() -&gt; None:\n    local_rank = int(os.environ&#91;\"LOCAL_RANK\"])\n    torch.cuda.set_device(local_rank)\n    dist.init_process_group(backend=\"nccl\")\n    device = torch.device(\"cuda\", local_rank)\n\n    model = SimpleClassifier(\n        input_size=20,\n        hidden_size=64,\n        num_classes=3\n    ).to(device)\n\n    model = DDP(\n        model,\n        device_ids=&#91;local_rank]\n    )\n\n    optimizer = torch.optim.Adam(\n        model.parameters(),\n        lr=0.001\n    )\n\n    sampler = DistributedSampler(\n        dataset,\n        shuffle=True\n    )\n\n    data_loader = DataLoader(\n        dataset,\n        batch_size=16,\n        sampler=sampler,\n        shuffle=False\n    )\n\n    for epoch in range(num_epochs):\n        sampler.set_epoch(epoch)\n        model.train()\n\n        for inputs, targets in data_loader:\n            inputs = inputs.to(device)\n            targets = targets.to(device)\n\n            optimizer.zero_grad(set_to_none=True)\n            predictions = model(inputs)\n            loss = loss_fn(predictions, targets)\n            loss.backward()\n            optimizer.step()\n\n        # Save one shared checkpoint\n        if dist.get_rank() == 0:\n            torch.save(\n                model.module.state_dict(),\n                \"model.pth\"\n            )\n\n    dist.destroy_process_group()\n\nif __name__ == \"__main__\":\n    main()<\/code><\/pre>\n\n\n\n<p>Run the script on four GPUs with:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>torchrun --standalone --nproc-per-node=4 train.py<\/code><\/pre>\n\n\n\n<p>The important steps are:<\/p>\n\n\n\n<ol>\n<li>Initialize the distributed process group.<\/li>\n\n\n\n<li>Assign one process to each GPU.<\/li>\n\n\n\n<li>Wrap the model with DistributedDataParallel.<\/li>\n\n\n\n<li>Use DistributedSampler to divide the data.<\/li>\n\n\n\n<li>Call sampler.set_epoch(epoch) before each epoch.<\/li>\n\n\n\n<li>Save shared checkpoints only from rank 0.<\/li>\n<\/ol>\n\n\n\n<p>DDP synchronizes gradients but does not split the input data automatically. DistributedSampler ensures that each process receives a different part of the dataset.<\/p>\n\n\n\n<p>Interview tip: DDP is used when the model fits on one GPU but training needs to be scaled across multiple GPUs. It is different from model parallelism, which splits a model that cannot fit on one device.<\/p>\n\n\n\n<pre class=\"wp-block-verse\"><strong>Also Read - <a href=\"https:\/\/www.hirist.tech\/blog\/artificial-intelligence-resume-examples-guide\/\" target=\"_blank\" rel=\"noreferrer noopener\">Artificial Intelligence Resume Examples &amp; Guide<\/a><\/strong><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"PyTorch_MCQs_for_Practice\"><\/span>PyTorch MCQs for Practice<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Here are some PyTorch interview questions in MCQ form to help you test your understanding of the main concepts commonly assessed in technical interviews.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_What_is_the_shape_of_output_in_the_following_code\"><\/span>1. What is the shape of output in the following code?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>x = torch.randn(16, 3, 32, 32)\noutput = torch.flatten(x, start_dim=1)<\/code><\/pre>\n\n\n\n<p>A. [16, 3, 1024]<\/p>\n\n\n\n<p>B. [16, 3072]<\/p>\n\n\n\n<p>C. [48, 1024]<\/p>\n\n\n\n<p>D. [16, 32, 32]<\/p>\n\n\n\n<p>Answer: B<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_What_happens_if_optimizerzero_grad_is_not_called_between_normal_training_batches\"><\/span>2. What happens if optimizer.zero_grad() is not called between normal training batches?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. PyTorch automatically replaces the old gradients<\/p>\n\n\n\n<p>B. The model parameters stop updating<\/p>\n\n\n\n<p>C. Gradients from different batches accumulate<\/p>\n\n\n\n<p>D. The loss becomes zero<\/p>\n\n\n\n<p>Answer: C<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_What_happens_after_calling_only_modeleval\"><\/span>3. What happens after calling only model.eval()?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. Gradient tracking is disabled<\/p>\n\n\n\n<p>B. Dropout and Batch Normalization switch to evaluation behaviour<\/p>\n\n\n\n<p>C. Model parameters are frozen permanently<\/p>\n\n\n\n<p>D. The model is moved to the CPU<\/p>\n\n\n\n<p>Answer: B.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_Which_methods_are_required_for_indexed_access_in_a_map-style_custom_Dataset\"><\/span>4. Which methods are required for indexed access in a map-style custom Dataset?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. forward() and backward()<\/p>\n\n\n\n<p>B. load() and transform()<\/p>\n\n\n\n<p>C. <strong>len<\/strong>() and <strong>getitem<\/strong>()<\/p>\n\n\n\n<p>D. step() and zero_grad()<\/p>\n\n\n\n<p>Answer: C.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_What_happens_when_a_tensor_is_created_with_torchfrom_numpy\"><\/span>5. What happens when a tensor is created with torch.from_numpy()?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. The NumPy array is always copied<\/p>\n\n\n\n<p>B. The tensor and array share memory<\/p>\n\n\n\n<p>C. The tensor is automatically moved to a GPU<\/p>\n\n\n\n<p>D. Gradient tracking is automatically enabled<\/p>\n\n\n\n<p>Answer: B<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_A_model_is_on_a_CUDA_GPU_but_the_input_tensor_is_on_the_CPU_What_will_happen\"><\/span>6. A model is on a CUDA GPU, but the input tensor is on the CPU. What will happen?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. PyTorch automatically moves the input to CUDA<\/p>\n\n\n\n<p>B. The operation becomes slower but still runs<\/p>\n\n\n\n<p>C. A device-mismatch runtime error occurs<\/p>\n\n\n\n<p>D. The model is moved back to the CPU<\/p>\n\n\n\n<p>Answer: C<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_Which_input_and_target_shapes_are_correct_for_nnCrossEntropyLoss_in_a_standard_classification_task_with_32_samples_and_10_classes\"><\/span>7. Which input and target shapes are correct for nn.CrossEntropyLoss in a standard classification task with 32 samples and 10 classes?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A. Input [32], target [32, 10]<\/p>\n\n\n\n<p>B. Input [32, 10], target [32]<\/p>\n\n\n\n<p>C. Input [10, 32], target [10]<\/p>\n\n\n\n<p>D. Input [32, 1], target [32, 10]<\/p>\n\n\n\n<p>Answer: B<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_Prepare_for_a_PyTorch_Interview\"><\/span>How to Prepare for a PyTorch Interview<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Use trusted learning resources and practise small tasks that commonly appear in coding and technical rounds.<\/p>\n\n\n\n<p>\u25cf Start with PyTorch\u2019s Learn the Basics tutorials to revise tensors, datasets, models, Autograd, optimization, and model saving.<\/p>\n\n\n\n<p>\u25cf Rebuild a small neural network and training loop without copying the tutorial code.<\/p>\n\n\n\n<p>\u25cf Use PyTorch Recipes for short examples on specific topics instead of reading long courses.<\/p>\n\n\n\n<p>\u25cf Practise with the official PyTorch Examples repository to see how complete vision, text, and reinforcement-learning projects are organized.<\/p>\n\n\n\n<p>\u25cf Run code with small tensors such as torch.randn(8, 20) before working with a full dataset.<\/p>\n\n\n\n<p>\u25cf Before the interview, write one model, one Dataset, and one training loop from memory within 30 minutes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Wrapping_Up\"><\/span>Wrapping Up<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>So, these are the 20 PyTorch interview questions and answers that can help you revise core concepts, practise coding tasks, and prepare for technical rounds. Keep working with small models, datasets, and debugging exercises to build confidence. Ready to apply your skills? Visit Hirist to find IT jobs, including AI, machine learning, and deep learning <a href=\"https:\/\/www.hirist.tech\/k\/pytorch-jobs?ref=blog\" target=\"_blank\" rel=\"noreferrer noopener\">roles that require PyTorch<\/a> experience.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<!-- Frontend Visible FAQ Section -->\n<div class=\"schema-faq wp-block-yoast-seo-faq-block\">\n  <div class=\"schema-faq-section\" id=\"faq-question-1\">\n    <strong class=\"schema-faq-question\">Do I need to write PyTorch code from scratch in an interview?<\/strong>\n    <p class=\"schema-faq-answer\">Yes. Interviewers often ask you to implement a small model, training loop, custom Dataset, tensor operation, or GPU setup without copying existing code.<\/p>\n  <\/div>\n  <div class=\"schema-faq-section\" id=\"faq-question-2\">\n    <strong class=\"schema-faq-question\">What PyTorch concepts should I memorise?<\/strong>\n    <p class=\"schema-faq-answer\">Memorise the basic <code>nn.Module<\/code> structure, <code>forward()<\/code>, a typical training loop, <code>Dataset<\/code>\/<code>DataLoader<\/code>, device handling, and <code>state_dict<\/code>. You don\u2019t need to remember every function.<\/p>\n  <\/div>\n  <div class=\"schema-faq-section\" id=\"faq-question-3\">\n    <strong class=\"schema-faq-question\">How should I handle a tensor\u2011shape error during the interview?<\/strong>\n    <p class=\"schema-faq-answer\">Print the tensor\u2019s shape before the failing operation, then verify batch size, channel order, flattened features, and the dimensions expected by the next layer.<\/p>\n  <\/div>\n  <div class=\"schema-faq-section\" id=\"faq-question-4\">\n    <strong class=\"schema-faq-question\">Is Distributed Data Parallel (DDP) required for entry\u2011level positions?<\/strong>\n    <p class=\"schema-faq-answer\">Usually not. Entry\u2011level candidates should understand basic GPU device handling; DDP is more relevant for senior or research roles.<\/p>\n  <\/div>\n  <div class=\"schema-faq-section\" id=\"faq-question-5\">\n    <strong class=\"schema-faq-question\">Should I focus only on PyTorch for an ML interview?<\/strong>\n    <p class=\"schema-faq-answer\">No. Complement PyTorch practice with Python, core machine\u2011learning concepts, data handling, model evaluation, mathematics, and any other skills listed in the job description.<\/p>\n  <\/div>\n<\/div>\n\n<!-- Background JSON-LD Schema for Googlebot -->\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Do I need to write PyTorch code from scratch in an interview?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. 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