Multi-GPU training in PyTorch - Model Pipeline Trace

Model Pipeline - Multi-GPU training

This pipeline shows how training a neural network can be sped up by using multiple GPUs at the same time. The data is split and sent to each GPU, the model trains in parallel, and results are combined to improve learning speed.

Data Flow - 4 Stages

1Data Loading

10000 rows x 20 features→Load dataset and batch into groups of 100→100 batches x 100 rows x 20 features

Batch 1: [[0.5, 1.2, ..., 0.3], ..., [0.7, 0.8, ..., 1.1]]

↓

2Data Distribution to GPUs

100 batches x 100 rows x 20 features→Split each batch evenly across 2 GPUs→100 batches x 2 GPUs x 50 rows x 20 features

GPU 0 batch slice: 50 rows, GPU 1 batch slice: 50 rows

↓

3Model Training on GPUs

50 rows x 20 features per GPU→Each GPU trains model on its data slice in parallel→50 rows x 10 output classes per GPU

GPU 0 output: [0.1, 0.7, ..., 0.05], GPU 1 output: [0.2, 0.6, ..., 0.1]

↓

4Gradient Aggregation

Gradients from 2 GPUs→Combine gradients from both GPUs to update model weights→Single updated model weights

Weights updated using average gradients from GPU 0 and GPU 1

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.55|*
0.45| 
    +------------
    Epochs 1 to 5

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Initial training with high loss and low accuracy
2	0.9	0.60	Loss decreased, accuracy improved as model learns
3	0.7	0.72	Continued improvement, model converging
4	0.55	0.80	Loss dropping steadily, accuracy rising
5	0.45	0.85	Training nearing good performance

Prediction Trace - 4 Layers

Layer 1: Input Split

Layer 2: Forward Pass on GPU 0

Layer 3: Forward Pass on GPU 1

Layer 4: Combine Outputs

Model Quiz - 3 Questions

Why do we split data batches across multiple GPUs?

A To reduce the size of the dataset

B To increase the number of model layers

C To train the model faster by parallel processing

D To avoid using GPUs

Key Insight

Using multiple GPUs allows the model to train faster by splitting data and computations. This parallelism helps the model learn more quickly while maintaining accuracy improvements.