Distributed training basics in MLOps - Step-by-Step Execution

Process Flow - Distributed training basics

Start: Prepare Data & Model

↓

Split Data Across Nodes

↓

Send Model & Data to Each Node

↓

Each Node Trains Locally

↓

Nodes Share Gradients/Weights

↓

Aggregate Updates on Master Node

↓

Update Global Model

↓

Repeat Until Training Complete

↓

End

The flow shows how data and model are split, trained in parallel on nodes, then updates are combined to improve the global model repeatedly.

Execution Sample

MLOps

for epoch in range(2):
  for node in nodes:
    node.train_one_batch()
  aggregate_gradients()
  update_global_model()

This code simulates two training epochs where each node trains locally, then gradients are aggregated and the global model is updated.

Process Table

Step	Epoch	Node	Action	Local Model State	Global Model State
1	0	Node 1	Train batch	Weights updated locally	Initial weights
2	0	Node 2	Train batch	Weights updated locally	Initial weights
3	0	Node 3	Train batch	Weights updated locally	Initial weights
4	0	Master	Aggregate gradients	N/A	Aggregated weights from nodes
5	0	Master	Update global model	N/A	Global model updated
6	1	Node 1	Train batch	Weights updated locally	Global model updated
7	1	Node 2	Train batch	Weights updated locally	Global model updated
8	1	Node 3	Train batch	Weights updated locally	Global model updated
9	1	Master	Aggregate gradients	N/A	Aggregated weights from nodes
10	1	Master	Update global model	N/A	Global model updated
11	2	Exit	Training complete	N/A	Final global model

💡 Training stops after completing 2 epochs with global model updated.

Status Tracker

Variable	Start	After Step 5	After Step 10	Final
Global Model Weights	Initial weights	Aggregated weights from epoch 0	Aggregated weights from epoch 1	Final global model weights
Node 1 Local Weights	Initial weights	Updated after epoch 0 batch	Updated after epoch 1 batch	N/A
Node 2 Local Weights	Initial weights	Updated after epoch 0 batch	Updated after epoch 1 batch	N/A
Node 3 Local Weights	Initial weights	Updated after epoch 0 batch	Updated after epoch 1 batch	N/A

Key Moments - 3 Insights

Why do nodes train locally before aggregation?

Each node updates its local model with its data batch first (see steps 1-3 and 6-8), so the master can aggregate meaningful gradients from all nodes.

What happens if the global model is not updated after aggregation?

Without updating the global model (steps 5 and 10), nodes would keep training on stale weights, preventing learning progress.

Why do we repeat training for multiple epochs?

Repeating epochs (steps 1-10) allows the model to improve gradually by multiple rounds of local training and global updates.

Visual Quiz - 3 Questions

Look at the execution table, what is the global model state after step 5?

A Aggregated weights from nodes

B Initial weights

C Final global model weights

D Weights updated locally

💡 Hint

Check the 'Global Model State' column at step 5 in the execution table.

At which step does the training complete?

A Step 10

B Step 11

C Step 9

D Step 6

💡 Hint

Look for the 'Exit' action in the 'Node' column in the execution table.

If nodes did not share gradients, how would the global model state change?

A It would update faster

B It would update normally

C It would remain at initial weights

D It would become random

💡 Hint

Refer to steps 4 and 5 where aggregation updates the global model.

Concept Snapshot

Distributed training splits data and model across nodes.
Each node trains locally on its data batch.
Nodes share gradients or weights with a master node.
Master aggregates updates and refreshes the global model.
Process repeats for multiple epochs to improve model accuracy.

Full Transcript

Distributed training basics involve splitting data and model across multiple nodes. Each node trains locally on its assigned data batch, updating its local model weights. After local training, nodes share their gradients or updated weights with a master node. The master node aggregates these updates to form a new global model. This updated global model is then sent back to the nodes for the next training round. This cycle repeats for several epochs until the model converges or training completes. This approach speeds up training by parallelizing work and combining knowledge from all nodes.