Jetson Nano deployment in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Jetson Nano's GPU role in deployment

What is the main advantage of using the Jetson Nano's GPU when deploying a machine learning model for computer vision?

A It allows the model to run faster during inference by parallel processing.

B It speeds up the model's training process on the device.

C It increases the device's storage capacity for large datasets.

D It automatically improves the model's accuracy without retraining.

💡 Hint

Think about what GPUs are best at during model use on devices.

The Jetson Nano's GPU helps run the model faster during inference by handling many calculations at once, which is called parallel processing. It does not speed up training on the device or increase storage.

❓ Predict Output

intermediate

Output of model loading code on Jetson Nano

What will be the output of the following Python code snippet when running on Jetson Nano?

Computer Vision

import torch
model = torch.jit.load('model_scripted.pt')
print(type(model))

A FileNotFoundError

B <class 'torch.jit.ScriptModule'>

C <class 'str'>

D <class 'torch.nn.modules.module.Module'>

💡 Hint

Consider what torch.jit.load returns when loading a scripted model.

torch.jit.load loads a scripted model and returns a ScriptModule object, which is a subclass of nn.Module but specifically scripted for optimized inference.

❓ Hyperparameter

advanced

Choosing batch size for Jetson Nano deployment

When deploying a computer vision model on Jetson Nano, which batch size is most suitable to balance speed and memory constraints?

A Batch size of 1 to minimize memory use and latency.

B Batch size of 64 to maximize throughput.

C Batch size of 128 to fully utilize GPU cores.

D Batch size of 32 to balance speed and memory.

💡 Hint

Think about Jetson Nano's limited memory and real-time inference needs.

Jetson Nano has limited memory and is often used for real-time tasks, so batch size 1 reduces latency and memory use, making it the best choice.

❓ Metrics

advanced

Evaluating model performance on Jetson Nano

You deployed a model on Jetson Nano and measured inference time per image as 120 ms and accuracy as 85%. Which metric should you prioritize improving for a real-time application?

A Keep both metrics as they are for balanced performance.

B Increase accuracy to above 90% even if inference time increases.

C Reduce inference time below 50 ms even if accuracy drops slightly.

D Focus on reducing model size only, ignoring accuracy and speed.

💡 Hint

Real-time applications need quick responses.

For real-time use, faster inference is critical even if accuracy drops a bit. Reducing inference time below 50 ms improves responsiveness.

🔧 Debug

expert

Debugging model deployment error on Jetson Nano

When running a TensorRT optimized model on Jetson Nano, you get the error: 'RuntimeError: CUDA out of memory'. Which is the most likely cause?

A The model file is corrupted and cannot load.

B The Jetson Nano does not support TensorRT optimization.

C The CPU is overloaded causing memory errors.

D The model input size is too large for the available GPU memory.

💡 Hint

Consider what causes CUDA out of memory errors on GPU devices.

CUDA out of memory means the GPU memory is insufficient, often due to large input sizes or batch sizes exceeding capacity.