Warmup strategies in PyTorch - Model Pipeline Trace

Model Pipeline - Warmup strategies

This pipeline shows how a warmup strategy helps a model learn better by starting with small learning rates and gradually increasing them before normal training.

Data Flow - 5 Stages

1Data Loading

1000 rows x 10 features→Load dataset with 10 features per sample→1000 rows x 10 features

[[0.5, 1.2, ..., 0.3], [0.1, 0.4, ..., 0.7], ...]

↓

2Preprocessing

1000 rows x 10 features→Normalize features to zero mean and unit variance→1000 rows x 10 features

[[-0.1, 0.3, ..., -0.2], [0.0, -0.5, ..., 0.4], ...]

↓

3Model Initialization

1000 rows x 10 features→Initialize neural network with input size 10 and output size 2→Model ready for training

Neural network with layers: Linear(10->50), ReLU, Linear(50->2)

↓

4Warmup Learning Rate Scheduler

Learning rate = 0.0→Gradually increase learning rate from 0 to 0.01 over 5 epochs→Learning rate = 0.01 after warmup

Epoch 1 LR=0.002, Epoch 5 LR=0.01

↓

5Training with Warmup

1000 rows x 10 features→Train model using warmup learning rate schedule→Trained model with improved convergence

Model weights updated each batch with adjusted learning rate

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | *      
0.7 |  *     
0.5 |   *    
0.4 |    *   
0.35|     *  
0.3 |      * 
0.28|       *
0.25|        *
0.22|         *
    +---------
    Epochs 1-10

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Loss starts high; learning rate is low due to warmup
2	0.9	0.55	Loss decreases as learning rate increases
3	0.7	0.65	Model learns faster with higher learning rate
4	0.5	0.75	Warmup phase nearly complete; accuracy improves
5	0.4	0.80	Warmup ends; learning rate at target value
6	0.35	0.83	Stable training with full learning rate
7	0.30	0.85	Loss continues to decrease; model converging
8	0.28	0.86	Training stabilizes with small improvements
9	0.25	0.88	Model reaches good accuracy
10	0.22	0.90	Training converged with warmup strategy

Prediction Trace - 5 Layers

Layer 1: Input Layer

Layer 2: First Linear Layer (10->50)

Layer 3: ReLU Activation

Layer 4: Second Linear Layer (50->2)

Layer 5: Softmax

Model Quiz - 3 Questions

Why do we start training with a low learning rate in warmup?

A Because the model is already perfect at start

B To make training slower overall

C To prevent large updates that can harm early learning

D To avoid using any learning rate scheduler

Key Insight

Warmup strategies help models start training gently by slowly increasing the learning rate. This prevents sudden large updates that can destabilize learning early on, leading to smoother and more stable convergence.