GPU vs CPU tensor placement in TensorFlow - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Tensor device placement output

What device will the tensor be placed on and what will be the output of the following code snippet?

TensorFlow

import tensorflow as tf

with tf.device('/CPU:0'):
    a = tf.constant([1.0, 2.0, 3.0])

with tf.device('/GPU:0'):
    b = tf.constant([4.0, 5.0, 6.0])

print(a.device)
print(b.device)

A a.device shows GPU device string, b.device shows CPU device string

B Both a.device and b.device show CPU device string

C a.device shows CPU device string, b.device shows GPU device string

D Both a.device and b.device show GPU device string

💡 Hint

Tensors are placed on the device specified by the tf.device context manager.

The tensor 'a' is created inside the CPU device context, so it is placed on CPU. The tensor 'b' is created inside the GPU device context, so it is placed on GPU. The device strings printed reflect this placement.

🧠 Conceptual

intermediate

Tensor operations device fallback

If you create a tensor on GPU but perform an operation with a tensor on CPU, where will TensorFlow perform the operation?

A On CPU, because one tensor is on CPU

B On GPU, because one tensor is on GPU

C TensorFlow raises an error due to device mismatch

D TensorFlow automatically copies tensors to the same device and performs operation there

💡 Hint

TensorFlow tries to perform operations on the same device by copying tensors if needed.

TensorFlow automatically copies tensors to the same device before performing operations. So if tensors are on different devices, it copies one to the other's device and then performs the operation.

❓ Metrics

advanced

Comparing training speed on CPU vs GPU

You train the same neural network model on CPU and GPU. Which metric difference best indicates GPU acceleration?

A Lower training time per epoch on GPU than CPU

B Higher memory usage on CPU than GPU

C Lower validation accuracy on GPU than CPU

D Higher training loss on GPU than CPU

💡 Hint

GPU is designed to speed up computations, so training time is key.

GPU acceleration typically reduces training time per epoch compared to CPU. Loss or accuracy differences are not direct indicators of device speed.

🔧 Debug

advanced

TensorFlow device placement error

What error will this code raise and why? import tensorflow as tf with tf.device('/GPU:1'): x = tf.constant([1, 2, 3])

A No error, tensor placed on GPU:1

B RuntimeError: GPU device '/GPU:1' not found

C SyntaxError: invalid syntax in device string

D ValueError: Tensor shape mismatch

💡 Hint

Check if the specified GPU device exists on your machine.

If the machine has only one GPU (usually '/GPU:0'), specifying '/GPU:1' causes a runtime error because that device does not exist.

❓ Model Choice

expert

Choosing device placement for large model training

You have a large deep learning model that does not fit into GPU memory. Which strategy is best to train it efficiently?

A Use model parallelism to split model across multiple GPUs

B Place entire model on CPU to avoid GPU memory limits

C Reduce batch size and place model on single GPU

D Convert model to float16 and place on CPU

💡 Hint

Splitting model across GPUs helps handle large models exceeding single GPU memory.

Model parallelism splits the model across multiple GPUs, allowing training of large models that don't fit on one GPU. Placing on CPU is slower, reducing batch size may not be enough, and float16 on CPU doesn't solve memory or speed issues.

Practice

1. What is the main reason to use tf.device() in TensorFlow when working with GPUs and CPUs?

easy

A. To change the data type of a tensor

B. To save the model to disk

C. To initialize variables automatically

D. To specify whether a tensor or operation runs on CPU or GPU

Solution

1. Step 1: Understand the purpose of tf.device()

   This function is used to tell TensorFlow where to place tensors or operations, either on CPU or GPU.

2. Step 2: Compare options with the function's purpose

   Changing data types, initializing variables, or saving models are unrelated to device placement.

3. Final Answer:

   To specify whether a tensor or operation runs on CPU or GPU -> Option D

4. Quick Check:

   tf.device() controls device placement = B [OK]

Hint: tf.device() sets CPU or GPU for tensors [OK]

Common Mistakes:

• Confusing device placement with data type changes
• Thinking tf.device() initializes variables
• Assuming tf.device() saves models

2. Which of the following is the correct syntax to place a tensor on GPU device 0 in TensorFlow?

easy

A. with tf.device('/GPU:0'):
x = tf.constant([1, 2, 3])

B. with tf.device('device:GPU0'):
x = tf.constant([1, 2, 3])

C. with tf.device('GPU0'):
x = tf.constant([1, 2, 3])

D. with tf.device('/CPU:0'):
x = tf.constant([1, 2, 3])

Solution

1. Step 1: Recall TensorFlow device naming conventions

   TensorFlow uses '/GPU:0' to refer to the first GPU device.

2. Step 2: Check each option's device string

   The correct format for GPU device 0 is with tf.device('/GPU:0'): x = tf.constant([1, 2, 3]). Formats like '/CPU:0', 'device:GPU0', and 'GPU0' are incorrect.

3. Final Answer:

   with tf.device('/GPU:0'): x = tf.constant([1, 2, 3]) -> Option A

4. Quick Check:

   Correct GPU device string = D [OK]

Hint: Use '/GPU:0' to specify first GPU device [OK]

Common Mistakes:

• Using 'GPU0' without slash and colon
• Confusing CPU and GPU device strings
• Missing the 'with' context for tf.device

3. What will be the output device placement of the tensor x in the following code if a GPU is available?

with tf.device('/CPU:0'):
    x = tf.constant([1, 2, 3])
print(x.device)

medium

A. It will show a GPU device string like '/job:localhost/replica:0/task:0/device:GPU:0'

B. It will show a CPU device string like '/job:localhost/replica:0/task:0/device:CPU:0'

C. It will raise an error because GPU is available

D. It will show an empty string

Solution

1. Step 1: Analyze the device context used

   The code uses with tf.device('/CPU:0'), so the tensor x is forced to be on CPU.

2. Step 2: Understand device string output

   Printing x.device will show the full device string indicating CPU, regardless of GPU availability.

3. Final Answer:

   It will show a CPU device string like '/job:localhost/replica:0/task:0/device:CPU:0' -> Option B

4. Quick Check:

   Device context forces CPU = C [OK]

Hint: Device context overrides default device placement [OK]

Common Mistakes:

• Assuming GPU is used automatically if available
• Expecting error when CPU is forced
• Thinking device string can be empty

4. Identify the error in this TensorFlow code snippet that tries to place a tensor on GPU:

with tf.device('/GPU:1'):
    x = tf.constant([4, 5, 6])
print(x.device)

Assuming the system has only one GPU device.

medium

A. Syntax error in tf.device string

B. No error, code runs fine on GPU 1

C. Error because GPU device '/GPU:1' does not exist

D. TensorFlow automatically switches to CPU without error

Solution

1. Step 1: Check available GPU devices

   The system has only one GPU, which is '/GPU:0'. Trying to use '/GPU:1' refers to a non-existent second GPU.

2. Step 2: Understand TensorFlow behavior on invalid device

   TensorFlow raises an error if the specified device does not exist.

3. Final Answer:

   Error because GPU device '/GPU:1' does not exist -> Option C

4. Quick Check:

   Invalid GPU index causes error = A [OK]

Hint: Check GPU count before using device index [OK]

Common Mistakes:

• Assuming GPU indices start at 1
• Expecting automatic fallback to CPU
• Ignoring device existence errors

5. You want to speed up a large matrix multiplication in TensorFlow using GPU if available, but fall back to CPU if no GPU exists. Which code snippet correctly implements this logic?

hard

A. if tf.config.list_physical_devices('GPU'):
with tf.device('/GPU:0'):
result = tf.matmul(a, b)
else:
with tf.device('/CPU:0'):
result = tf.matmul(a, b)

B. with tf.device('/GPU:0'):
result = tf.matmul(a, b)

C. result = tf.matmul(a, b) # TensorFlow auto-chooses device

D. with tf.device('/CPU:0'):
result = tf.matmul(a, b)

Solution

1. Step 1: Check for GPU availability

   Use tf.config.list_physical_devices('GPU') to detect if GPU exists.

2. Step 2: Use conditional device placement

   If GPU exists, place operation on '/GPU:0', else place on '/CPU:0' to ensure fallback.

3. Step 3: Verify other options

   Forcing GPU without checking availability risks errors if no GPU. Auto-placement lacks explicit conditional control. Forcing CPU ignores available GPU.

4. Final Answer:

   if tf.config.list_physical_devices('GPU'): with tf.device('/GPU:0'): result = tf.matmul(a, b) else: with tf.device('/CPU:0'): result = tf.matmul(a, b) -> Option A

5. Quick Check:

   Conditional device placement with fallback = A [OK]

Hint: Check GPU presence before device placement [OK]

Common Mistakes:

• Not handling fallback when GPU missing
• Assuming TensorFlow always picks GPU
• Forcing CPU even if GPU is available