GPU vs CPU tensor placement in TensorFlow - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a tensor on the GPU device.

TensorFlow

import tensorflow as tf
with tf.device('[1]'):
    a = tf.constant([1.0, 2.0, 3.0])

Drag options to blanks, or click blank then click option'

A/CPU:0

B/TPU:0

C/GPU:1

D/GPU:0

Attempts:

3 left

💡 Hint

Common Mistakes

Using '/CPU:0' places the tensor on CPU, not GPU.

Using '/GPU:1' when only one GPU is available causes an error.

The code places the tensor a on the first GPU device using tf.device('/GPU:0').

2fill in blank

medium

Complete the code to check if a tensor is placed on GPU.

TensorFlow

import tensorflow as tf
x = tf.constant([1, 2, 3])
print(x.device.endswith('[1]'))

Drag options to blanks, or click blank then click option'

AGPU:0

BTPU:0

CGPU:1

DCPU:0

Attempts:

3 left

💡 Hint

Common Mistakes

Checking for 'CPU:0' when the tensor is on GPU.

Using 'GPU:1' when the tensor is on 'GPU:0'.

The x.device string ends with the device name where the tensor is placed. Checking if it ends with 'GPU:0' confirms if it's on the first GPU.

3fill in blank

hard

Fix the error in the code to place a tensor on CPU explicitly.

TensorFlow

import tensorflow as tf
with tf.device('[1]'):
    b = tf.constant([4, 5, 6])

Drag options to blanks, or click blank then click option'

A/CPU:0

B/TPU:0

C/GPU:0

D/GPU:1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '/GPU:0' places the tensor on GPU, not CPU.

Using '/TPU:0' causes an error if TPU is not available.

To place a tensor explicitly on the CPU, use tf.device('/CPU:0').

4fill in blank

hard

Fill both blanks to create a tensor on GPU and then move it to CPU.

TensorFlow

import tensorflow as tf
with tf.device('[1]'):
    c = tf.constant([7, 8, 9])
with tf.device('[2]'):
    d = tf.identity(c)

Drag options to blanks, or click blank then click option'

A/GPU:0

B/CPU:0

C/GPU:1

D/TPU:0

Attempts:

3 left

💡 Hint

Common Mistakes

Using '/CPU:0' for the first tensor places it on CPU, not GPU.

Using '/GPU:1' when only one GPU is available causes errors.

The tensor c is created on GPU using /GPU:0. Then d is created as a copy on CPU using /CPU:0.

5fill in blank

hard

Fill all three blanks to create a tensor on CPU, then move it to GPU, and finally check its device.

TensorFlow

import tensorflow as tf
with tf.device('[1]'):
    e = tf.constant([10, 11, 12])
with tf.device('[2]'):
    f = tf.identity(e)
print(f.device.endswith('[3]'))

Drag options to blanks, or click blank then click option'

A/CPU:0

B/GPU:0

CGPU:0

DCPU:0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'CPU:0' instead of '/CPU:0' in device context causes errors.

Checking device with '/GPU:0' instead of 'GPU:0' in string endswith.

The tensor e is created on CPU (/CPU:0), then copied to GPU (/GPU:0) as f. The print statement checks if f is on GPU by checking if its device string ends with 'GPU:0'.

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