Mobile deployment (PyTorch Mobile) - Model Pipeline Trace

Model Pipeline - Mobile deployment (PyTorch Mobile)

This pipeline shows how a PyTorch model is prepared and deployed to a mobile device. It starts with training on a computer, then converts the model to a mobile-friendly format, and finally runs predictions on the mobile device.

Data Flow - 6 Stages

1Raw Data Input

1000 rows x 28 x 28 pixels (grayscale images)→Collect handwritten digit images for training→1000 rows x 28 x 28 pixels

Image of digit '7' as 28x28 pixel grayscale array

↓

2Data Preprocessing

1000 rows x 28 x 28 pixels→Normalize pixel values to range 0-1→1000 rows x 28 x 28 pixels (normalized)

Pixel value 150 becomes 0.59 after normalization

↓

3Model Training

1000 rows x 28 x 28 pixels→Train CNN model on normalized images→Trained model with learned weights

Model learns to recognize digit '7' features

↓

4Model Conversion

Trained PyTorch model→Convert model to TorchScript for mobile→TorchScript model file (.pt)

Model saved as 'model_mobile.pt' for deployment

↓

5Mobile Deployment

TorchScript model file→Load model on mobile device using PyTorch Mobile→Model ready to run on mobile

App loads 'model_mobile.pt' and prepares for inference

↓

6Prediction on Mobile

Single 28 x 28 pixel image→Run model inference to predict digit→Prediction probabilities for digits 0-9

Model predicts digit '7' with 92% confidence

Training Trace - Epoch by Epoch

Epoch 1: ************ (loss=1.2)
Epoch 2: ******** (loss=0.8)
Epoch 3: ***** (loss=0.5)
Epoch 4: *** (loss=0.3)
Epoch 5: ** (loss=0.25)

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.55	Model starts learning basic digit features
2	0.8	0.75	Loss decreases, accuracy improves significantly
3	0.5	0.85	Model captures more complex patterns
4	0.3	0.92	Model converges with high accuracy
5	0.25	0.94	Slight improvement, training stabilizes

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Convolutional Layer

Layer 3: Pooling Layer

Layer 4: Fully Connected Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What is the purpose of converting the PyTorch model to TorchScript?

A To increase the model's training speed on a computer

B To make the model run efficiently on mobile devices

C To add more layers to the model automatically

D To change the model's output format to images

Key Insight

Deploying a PyTorch model on mobile requires converting it to a lightweight format like TorchScript. This allows the model to run efficiently on devices with limited resources while maintaining good prediction accuracy.