Data parallelism vs model parallelism in MLOps - Visual Side-by-Side Comparison

Process Flow - Data parallelism vs model parallelism

Start Training Task

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Data Parallelism

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Split Data into Batches

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Copy Full Model to Each

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Each Device Trains on

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Different Data Batch

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Sync Gradients Across

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Devices After Each Step

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Update Model Weights

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Training Continues

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End

Shows the two ways to split training work: by splitting data across devices or by splitting model parts across devices.

Execution Sample

MLOps

# Pseudocode for data parallelism
for batch in data_batches:
    outputs = model(batch)
    loss = loss_fn(outputs, labels)
    loss.backward()
    sync_gradients()
    optimizer.step()

This code shows data parallelism where each device trains on different data batches with a full model copy.

Process Table

Step	Parallelism Type	Action	Device Work	Communication	Result
1	Data Parallelism	Split data into batches	Each device gets a data batch	None yet	Ready to train on batches
2	Data Parallelism	Copy full model to each device	Full model on each device	Model copied once	Models ready for training
3	Data Parallelism	Each device computes forward pass	Compute outputs on batch	None	Outputs computed per device
4	Data Parallelism	Each device computes backward pass	Compute gradients	None	Gradients computed per device
5	Data Parallelism	Sync gradients across devices	None	Gradients averaged across devices	Synchronized gradients
6	Data Parallelism	Update model weights	Update local model weights	None	Models updated identically
7	Model Parallelism	Split model into parts	Each device assigned model part	None yet	Model parts assigned
8	Model Parallelism	Forward pass across devices	Each device computes its part	Intermediate outputs sent between devices	Partial outputs computed
9	Model Parallelism	Backward pass across devices	Each device computes gradients for its part	Gradients communicated as needed	Gradients computed per part
10	Model Parallelism	Update model parts	Update assigned model part	None	Model parts updated
11	End	Training continues or ends	All devices synchronized	Communication as needed	Training progresses
12	Exit	Training complete or stopped	N/A	N/A	Training finished or paused

💡 Training stops when all batches processed or training criteria met

Status Tracker

Variable	Start	After Step 2	After Step 5	After Step 6	After Step 10	Final
Data Batches	Full dataset	Split into batches	Batches processed	Batches processed	Batches processed	All batches processed
Model Copy (Data Parallelism)	One model	Copied to devices	Weights synced	Weights updated	N/A	Updated model on all devices
Model Parts (Model Parallelism)	One model	N/A	N/A	N/A	Parts updated	Updated model parts across devices
Gradients	None	Computed per device	Synchronized (data parallel)	Used to update weights	Computed per part (model parallel)	Used to update parts
Communication	None	Model copied once	Gradients synced	None	Intermediate outputs and gradients exchanged	None

Key Moments - 3 Insights

Why do we need to sync gradients in data parallelism?

Because each device computes gradients on different data batches, syncing ensures all devices update the model weights consistently, as shown in step 5 of the execution table.

How does model parallelism handle the forward pass?

The model is split into parts, and each device computes its assigned part. Intermediate outputs are sent between devices to continue the forward pass, as seen in step 8.

When is data parallelism preferred over model parallelism?

Data parallelism is preferred when the model fits into each device's memory but the dataset is large. This is implied by the full model copy in step 2 and batch splitting in step 1.

Visual Quiz - 3 Questions

Look at the execution table, at which step do devices synchronize gradients in data parallelism?

A Step 5

B Step 3

C Step 8

D Step 10

💡 Hint

Check the 'Communication' column for data parallelism steps where gradients are averaged.

According to the variable tracker, what happens to the model after step 6 in data parallelism?

A Model parts updated separately

B Model weights updated identically on all devices

C Model copied to devices

D Gradients synchronized

💡 Hint

Look at the 'Model Copy (Data Parallelism)' row after step 6.

If the model is too large to fit on one device, which parallelism type is better according to the concept flow?

A Data parallelism

B Neither

C Model parallelism

D Both equally

💡 Hint

Refer to the concept flow where model is split into parts for large models.

Concept Snapshot

Data parallelism splits the data into batches and copies the full model to each device.
Each device trains on its batch and gradients are synced to update the model.
Model parallelism splits the model into parts assigned to devices.
Devices compute their parts and communicate intermediate results.
Use data parallelism when model fits device memory; use model parallelism for very large models.

Full Transcript

This visual execution compares data parallelism and model parallelism in machine learning training. Data parallelism splits the dataset into batches and copies the full model to each device. Each device trains on its batch independently, then synchronizes gradients to update the model weights consistently. Model parallelism splits the model itself into parts, assigning each part to different devices. Devices compute their assigned parts and communicate intermediate outputs during forward and backward passes. The execution table shows step-by-step actions, device work, communication, and results for both parallelism types. The variable tracker follows data batches, model copies, model parts, gradients, and communication states across steps. Key moments clarify why gradient synchronization is needed in data parallelism, how model parallelism handles forward passes, and when to prefer data parallelism. The quiz tests understanding of synchronization steps, model updates, and suitability of parallelism types. The snapshot summarizes the main differences and usage guidance.