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.

Practice

1. What is the main difference between data parallelism and model parallelism in machine learning training?

easy

A. Data parallelism splits the data across workers, while model parallelism splits the model across workers.

B. Data parallelism splits the model across workers, while model parallelism splits the data across workers.

C. Data parallelism uses only one worker, model parallelism uses multiple workers.

D. Data parallelism trains different models, model parallelism trains the same model multiple times.

Solution

1. Step 1: Understand data parallelism

   Data parallelism means dividing the input data into parts and sending each part to a different worker. Each worker runs the full model on its data part.

2. Step 2: Understand model parallelism

   Model parallelism means splitting the model itself into parts and assigning each part to a different worker. The data flows through these parts sequentially.

3. Final Answer:

   Data parallelism splits the data across workers, while model parallelism splits the model across workers. -> Option A

4. Quick Check:

   Data vs Model split [OK]

Hint: Data parallelism splits data; model parallelism splits model [OK]

Common Mistakes:

• Confusing which is split: data or model
• Thinking both split data only
• Assuming model parallelism uses one worker

2. Which of the following is the correct way to describe data parallelism in a distributed training setup?

easy

A. The data is duplicated on one worker and processed sequentially.

B. Each worker trains a different part of the model on the full dataset.

C. The model is split into layers, each trained by a different worker on the full data.

D. Each worker trains the full model on a subset of the data.

Solution

1. Step 1: Analyze data parallelism setup

   In data parallelism, the full model is copied to each worker. Each worker trains on a different subset of the data.

2. Step 2: Evaluate options

   Each worker trains the full model on a subset of the data. correctly states that each worker trains the full model on a subset of data. Other options describe model splitting or incorrect data handling.

3. Final Answer:

   Each worker trains the full model on a subset of the data. -> Option D

4. Quick Check:

   Full model + data subset [OK]

Hint: Data parallelism = full model per worker, split data [OK]

Common Mistakes:

• Thinking model is split in data parallelism
• Assuming data is duplicated on one worker
• Confusing model layers with data chunks

3. Consider a model split into 3 parts for model parallelism across 3 workers. If input data batch size is 90, how is the data processed?

medium

A. Each worker processes 30 data samples independently on the full model.

B. All 90 samples flow sequentially through the 3 model parts on different workers.

C. Each worker processes all 90 samples on its model part independently.

D. The data is split into 3 parts, each processed by a different worker on the full model.

Solution

1. Step 1: Understand model parallelism data flow

   In model parallelism, the model is split into parts on different workers. The full data batch flows through these parts sequentially.

2. Step 2: Analyze data processing

   All 90 samples pass through the first model part on worker 1, then output flows to worker 2's model part, and so on.

3. Final Answer:

   All 90 samples flow sequentially through the 3 model parts on different workers. -> Option B

4. Quick Check:

   Model split, data flows through [OK]

Hint: Model parallelism splits model; data flows through all parts [OK]

Common Mistakes:

• Assuming data is split in model parallelism
• Thinking each worker processes full data independently
• Confusing data parallelism with model parallelism

4. You tried to implement model parallelism but noticed workers are idle waiting for data. What is the likely cause?

medium

A. Model parts are not connected properly causing data flow delays.

B. Data is not being split correctly across workers.

C. Each worker is running the full model on the full data.

D. Data parallelism was used instead of model parallelism.

Solution

1. Step 1: Identify symptoms of idle workers in model parallelism

   Idle workers waiting for data usually mean data flow between model parts is blocked or delayed.

2. Step 2: Analyze model part connections

   If model parts are not connected properly, data cannot flow smoothly, causing some workers to wait.

3. Final Answer:

   Model parts are not connected properly causing data flow delays. -> Option A

4. Quick Check:

   Idle workers = broken model part connections [OK]

Hint: Idle workers? Check model part connections in model parallelism [OK]

Common Mistakes:

• Blaming data splitting in model parallelism
• Confusing full model runs with model splitting
• Mixing up data and model parallelism issues

5. You have a very large model that does not fit into one GPU memory. Which approach is best to train it efficiently?

hard

A. Use data parallelism by splitting data across GPUs, each with full model copy.

B. Train the model on CPU only to avoid GPU memory limits.

C. Use model parallelism by splitting the model across GPUs, each handling part of the model.

D. Reduce batch size and train on a single GPU.

Solution

1. Step 1: Understand GPU memory limits

   If the model is too large to fit in one GPU, copying full model to each GPU (data parallelism) is not possible.

2. Step 2: Choose model parallelism

   Splitting the model across GPUs allows each GPU to hold only a part of the model, enabling training of large models.

3. Final Answer:

   Use model parallelism by splitting the model across GPUs, each handling part of the model. -> Option C

4. Quick Check:

   Large model fits by splitting model [OK]

Hint: Large model? Split model across GPUs (model parallelism) [OK]

Common Mistakes:

• Trying data parallelism with too large model
• Ignoring GPU memory limits
• Reducing batch size instead of splitting model