Multi-GPU training basics in TensorFlow

Introduction

Using multiple GPUs helps train machine learning models faster by sharing the work. It lets you handle bigger data and more complex models efficiently.

You want to train a deep learning model faster than on a single GPU.

Your dataset is large and training on one GPU takes too long.

You have access to multiple GPUs and want to use them all to save time.

You want to experiment with bigger models that need more memory than one GPU has.

You want to improve training speed without changing your model code much.

Syntax

TensorFlow

import tensorflow as tf

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    model = tf.keras.Sequential([...])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(train_data, epochs=5)

tf.distribute.MirroredStrategy() automatically copies your model on each GPU and keeps them in sync.

All model creation and compilation must happen inside strategy.scope() to use multiple GPUs.

Examples

Basic example creating a simple model inside the multi-GPU strategy scope.

TensorFlow

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(10, activation='relu'),
        tf.keras.layers.Dense(2, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Specifying which GPUs to use explicitly for training.

TensorFlow

strategy = tf.distribute.MirroredStrategy(devices=['/GPU:0', '/GPU:1'])

with strategy.scope():
    model = tf.keras.Sequential([...])
    model.compile(...)

model.fit(train_data, epochs=3)

Sample Model

This code trains a simple model on random data using all available GPUs. It shows how to set up the strategy, build the model inside the scope, and train it.

TensorFlow

import tensorflow as tf
import numpy as np

# Create dummy data
x_train = np.random.random((1000, 20)).astype('float32')
y_train = np.random.randint(0, 2, 1000)

# Setup multi-GPU strategy
strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_shape=(20,)),
        tf.keras.layers.Dense(2, activation='softmax')
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

# Train the model
history = model.fit(x_train, y_train, epochs=3, batch_size=32)

# Print final accuracy
print(f"Final training accuracy: {history.history['accuracy'][-1]:.4f}")

Important Notes

Make sure your system has multiple GPUs and TensorFlow can detect them with tf.config.list_physical_devices('GPU').

Batch size should be large enough to split across GPUs; otherwise, speedup may be limited.

Multi-GPU training works best when the model and data are large enough to benefit from parallelism.

Summary

Multi-GPU training uses tf.distribute.MirroredStrategy() to run your model on several GPUs at once.

Build and compile your model inside the strategy scope to enable multi-GPU support.

This approach speeds up training and allows bigger models or datasets to be handled efficiently.

Practice

1. What is the main purpose of using tf.distribute.MirroredStrategy() in TensorFlow?

easy

A. To train a model on multiple GPUs simultaneously

B. To reduce the size of the dataset

C. To convert a model to run on CPU only

D. To save the model in a special format

Solution

1. Step 1: Understand the role of MirroredStrategy

   tf.distribute.MirroredStrategy() is designed to distribute training across multiple GPUs to speed up the process.

2. Step 2: Compare options with the purpose

   Options A, B, and C do not relate to multi-GPU training. Only training a model on multiple GPUs simultaneously correctly states the purpose.

3. Final Answer:

   To train a model on multiple GPUs simultaneously -> Option A

4. Quick Check:

   Multi-GPU training = MirroredStrategy usage [OK]

Hint: MirroredStrategy means training on many GPUs at once [OK]

Common Mistakes:

• Thinking it reduces dataset size
• Confusing with CPU-only training
• Assuming it changes model saving format

2. Which of the following is the correct way to start using multi-GPU training in TensorFlow?

easy

A. model = build_model()
strategy = tf.distribute.MirroredStrategy()
model.compile()

B. strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = build_model()

C. strategy = tf.distribute.MirroredStrategy()
model = build_model()
model.fit()

D. model = build_model()
model.fit()
strategy = tf.distribute.MirroredStrategy()

Solution

1. Step 1: Identify correct usage of strategy.scope()

   Model building and compiling must happen inside strategy.scope() to enable multi-GPU support.

2. Step 2: Check each option for correct order

   Only strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = build_model() builds the model inside the strategy scope. Others build or compile outside, which disables multi-GPU benefits.

3. Final Answer:

   strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = build_model() -> Option B

4. Quick Check:

   Build model inside strategy.scope() = strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = build_model() [OK]

Hint: Always build and compile inside strategy.scope() [OK]

Common Mistakes:

• Building model before strategy.scope()
• Compiling model outside strategy.scope()
• Calling fit before defining strategy

3. Given the code below, what will be the output shape of the predictions if the input batch size is 64 and there are 2 GPUs?

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')

import numpy as np
inputs = np.random.random((64, 20))
predictions = model.predict(inputs)
print(predictions.shape)

medium

A. (10, 64)

B. (32, 10)

C. (128, 10)

D. (64, 10)

Solution

1. Step 1: Understand batch size and MirroredStrategy behavior

   MirroredStrategy splits the batch across GPUs internally but the output predictions shape matches the input batch size.

2. Step 2: Check model output shape

   The model outputs 10 classes per input. Input batch size is 64, so output shape is (64, 10).

3. Final Answer:

   (64, 10) -> Option D

4. Quick Check:

   Output shape = (batch_size, classes) = (64, 10) [OK]

Hint: Output shape matches input batch size, not multiplied by GPUs [OK]

Common Mistakes:

• Multiplying batch size by number of GPUs
• Confusing output shape dimensions
• Assuming output shape is (10, 64)

4. You wrote this code but get an error: RuntimeError: tf.distribute.MirroredStrategy() must be used with model creation inside its scope. What is the likely mistake?

strategy = tf.distribute.MirroredStrategy()
model = build_model()
with strategy.scope():
    model.compile(optimizer='adam', loss='mse')

medium

A. Model was compiled outside the strategy.scope()

B. MirroredStrategy cannot be used with compile()

C. Model was built outside the strategy.scope()

D. The optimizer 'adam' is not supported

Solution

1. Step 1: Analyze error message and code

   The error says model creation must be inside strategy.scope(). Here, model is built before entering the scope.

2. Step 2: Identify correct fix

   Building the model inside the scope fixes the error. Compiling inside scope is correct.

3. Final Answer:

   Model was built outside the strategy.scope() -> Option C

4. Quick Check:

   Build model inside scope to avoid RuntimeError [OK]

Hint: Build model inside strategy.scope() to fix this error [OK]

Common Mistakes:

• Building model before strategy.scope()
• Thinking compile() causes error
• Blaming optimizer choice

5. You want to train a large model on 4 GPUs using TensorFlow. Which of the following steps correctly applies multi-GPU training and ensures the model can handle larger batch sizes efficiently?

hard

A. Use tf.distribute.MirroredStrategy(), build and compile the model inside its scope, then increase batch size in model.fit()

B. Build the model first, then use tf.distribute.MirroredStrategy() only during model.fit() with default batch size

C. Use tf.distribute.MirroredStrategy() but keep batch size small to avoid memory issues

D. Train the model separately on each GPU without using MirroredStrategy and average weights manually

Solution

1. Step 1: Apply MirroredStrategy correctly

   Use tf.distribute.MirroredStrategy() and build/compile the model inside its scope to enable multi-GPU training.

2. Step 2: Adjust batch size for efficiency

   Increasing batch size in model.fit() leverages multiple GPUs to handle larger data efficiently.

3. Final Answer:

   Use tf.distribute.MirroredStrategy(), build and compile the model inside its scope, then increase batch size in model.fit() -> Option A

4. Quick Check:

   Build inside scope + increase batch size = efficient multi-GPU training [OK]

Hint: Build inside scope and increase batch size for multi-GPU speed [OK]

Common Mistakes:

• Building model outside strategy scope
• Not increasing batch size to use GPUs fully
• Manually averaging weights instead of using MirroredStrategy