Installation and GPU setup in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to install TensorFlow using pip.

TensorFlow

pip install [1]

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Atensorflow

Bnumpy

Cpandas

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to install unrelated packages like numpy or pandas instead of tensorflow.

To install TensorFlow, you use pip install tensorflow. This command downloads and installs the TensorFlow package.

2fill in blank

medium

Complete the code to check if TensorFlow can access a GPU device.

TensorFlow

import tensorflow as tf
print(tf.config.list_physical_devices('[1]'))

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ADISK

BCPU

CTPU

DGPU

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'CPU' instead of 'GPU' will list only CPUs, not GPUs.

To check for GPU devices, you use tf.config.list_physical_devices('GPU'). This lists all GPUs TensorFlow can use.

3fill in blank

hard

Fix the error in the code to enable memory growth for the first GPU device.

TensorFlow

gpus = tf.config.list_physical_devices('GPU')
if gpus:
    try:
        tf.config.experimental.[1](gpus[0], True)
    except RuntimeError as e:
        print(e)

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Aconfigure_memory_growth

Benable_memory_growth

Cset_memory_growth

Dallow_memory_growth

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like 'enable_memory_growth' which do not exist.

The correct function to enable memory growth on a GPU device is set_memory_growth from tf.config.experimental.

4fill in blank

hard

Fill both blanks to import TensorFlow and print the TensorFlow version.

TensorFlow

import [1] as tf
print(tf.[2].VERSION)

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Atensorflow

Bkeras

Cversion

Dconfig

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'keras' instead of 'tensorflow' for import.

Using incorrect attributes like 'config'.

You import TensorFlow as tf and access the version with tf.version.VERSION.

5fill in blank

hard

Fill all three blanks to list GPU physical devices and print their names.

TensorFlow

devices = tf.config.list_physical_devices([1])
for device in devices:
    print(device.[2])

# Check if any device is a GPU
has_gpu = any(device.device_type == [3] for device in devices)
print('GPU available:', has_gpu)

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A'GPU'

Bname

D'CPU'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'CPU' instead of 'GPU' when listing devices or checking device type.

Trying to print 'device_type' instead of 'name' for device names.

To list GPU devices, use tf.config.list_physical_devices('GPU'). Each device has a name attribute. To check device type, compare with the string 'GPU'.