Monitoring memory usage in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the module used for monitoring memory usage.

NumPy

import [1]

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

Apsutil

Bos

Csys

Dtime

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'os' which does not provide detailed memory info.

Using 'sys' which is for system-specific parameters.

The psutil module provides functions to monitor system resources like memory usage.

2fill in blank

medium

Complete the code to get the current process memory usage in bytes.

NumPy

process = psutil.Process()
memory_info = process.[1]()

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

Amemory_info_ex

Bmemory_full_info

Cmemory_info

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'memory_full_info' which may not be available on all platforms.

The memory_info() method returns memory usage details of the current process.

3fill in blank

hard

Fix the error in the code to print the resident set size (RSS) memory in MB.

NumPy

print(f"Memory usage: {memory_info.[1] / (1024 * 1024):.2f} MB")

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Avms

Brss

Cuss

Dshared

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'vms' which is virtual memory size, not physical.

Using 'uss' which is unique set size and may not be available everywhere.

The rss attribute gives the resident set size, which is the non-swapped physical memory used by the process.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps array names to their memory usage in bytes.

NumPy

memory_usage = {name: arr.[1] * arr.[2] for name, arr in arrays.items()}

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Anbytes

Bitemsize

Csize

Dshape

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'nbytes' which already gives total bytes, so multiplying again causes error.

Using 'shape' which is a tuple, not a number.

The total memory used by a NumPy array is size * itemsize, where size is the number of elements and itemsize is the bytes per element.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters arrays with memory usage greater than 1MB.

NumPy

large_arrays = {name: arr for name, arr in arrays.items() if arr.[1] * arr.[2] [3] 1024 * 1024}

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

Asize

Bitemsize

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which would select smaller arrays.

Using 'nbytes' instead of size and itemsize multiplication.

This comprehension selects arrays where size * itemsize is greater than 1MB (1024 * 1024 bytes).