Hardware and framework version tracking in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the current GPU hardware version using the NVIDIA System Management Interface.

MLOps

import subprocess
result = subprocess.run(['nvidia-smi', '--query-gpu=hardware_version', '--format=csv,noheader'], capture_output=True, text=True)
print(result.[1])

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

Areturncode

Bstdout

Cstderr

Dargs

Attempts:

3 left

💡 Hint

Common Mistakes

Using stderr instead of stdout to get output.

The stdout attribute contains the output of the command, which includes the hardware version.

2fill in blank

medium

Complete the code to get the installed TensorFlow version.

MLOps

import tensorflow as tf
print(tf.[1])

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

A__version__

Bversion

Cget_version

DVERSION

Attempts:

3 left

💡 Hint

Common Mistakes

Using version or get_version which do not exist.

The TensorFlow version is accessed via the __version__ attribute.

3fill in blank

hard

Fix the error in the code to correctly log hardware and framework versions to a file.

MLOps

with open('version_log.txt', 'w') as f:
    f.write('GPU Version: ' + gpu_version + '\n')
    f.write('TensorFlow Version: ' + [1] + '\n')

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

Atf.__version__

Btf.version

Ctensorflow.version

Dtensorflow.__version__

Attempts:

3 left

💡 Hint

Common Mistakes

Using tf.version which is not a string.

The correct attribute to get TensorFlow version is tf.__version__. The others are incorrect or do not exist.

4fill in blank

hard

Fill both blanks to create a dictionary that maps hardware and framework versions for tracking.

MLOps

version_info = {
    'gpu': [1],
    'framework': [2]
}

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

Agpu_version

Btf.__version__

C'unknown'

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Putting string literals instead of variables.

The dictionary keys should map to the variables holding GPU and TensorFlow versions respectively.

5fill in blank

hard

Fill all three blanks to write a function that returns hardware and framework versions as a dictionary.

MLOps

def get_versions():
    gpu = [1]
    framework = [2]
    return [3]

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

Agpu_version

Btf.__version__

C{'gpu': gpu, 'framework': framework}

Dversion_info

Attempts:

3 left

💡 Hint

Common Mistakes

Returning a variable that is not defined inside the function.

The function assigns the GPU and framework versions to variables and returns a dictionary with those values.

Practice

1. Why is it important to track hardware and framework versions in MLOps?

easy

A. To reduce the size of the model files

B. To make the code run faster on any machine

C. To ensure experiments can be reproduced exactly later

D. To avoid using any cloud services

Solution

1. Step 1: Understand reproducibility in experiments

   Reproducibility means you can get the same results again by using the same setup.

2. Step 2: Connect version tracking to reproducibility

   Tracking hardware and framework versions helps recreate the exact environment for experiments.

3. Final Answer:

   To ensure experiments can be reproduced exactly later -> Option C

4. Quick Check:

   Reproducibility = Track versions [OK]

Hint: Reproducibility needs exact version info [OK]

Common Mistakes:

• Thinking tracking speeds up code
• Confusing version tracking with file size
• Assuming cloud use is related

2. Which of the following is the correct way to store framework version in a Python dictionary for tracking?

easy

A. versions = {"tensorflow": "2.12.0"}

B. versions = (tensorflow: 2.12.0)

C. versions = [tensorflow = "2.12.0"]

D. versions = {tensorflow => "2.12.0"}

Solution

1. Step 1: Recall Python dictionary syntax

   Python dictionaries use curly braces with key: value pairs, keys and values as strings need quotes.

2. Step 2: Check each option's syntax

   versions = {"tensorflow": "2.12.0"} uses correct syntax with quotes and colon. Others use invalid syntax for Python dictionaries.

3. Final Answer:

   versions = {"tensorflow": "2.12.0"} -> Option A

4. Quick Check:

   Python dict = {key: value} [OK]

Hint: Python dict uses {"key": "value"} syntax [OK]

Common Mistakes:

• Using parentheses instead of braces
• Using equal sign inside list
• Using => instead of : in dict

3. Given this Python code snippet for tracking versions:

versions = {"tensorflow": "2.12.0", "cuda": "11.8"}
print(versions.get("cuda"))

What is the output?

medium

A. "11.8"

B. 11.8

C. cuda

D. None

Solution

1. Step 1: Understand the dictionary and get method

   The dictionary stores strings as values. The get method returns the value for the key "cuda".

2. Step 2: Identify the value for key "cuda"

   The value is the string "11.8". Printing it outputs 11.8 with quotes because it's a string.

3. Final Answer:

   "11.8" -> Option A

4. Quick Check:

   versions.get("cuda") = "11.8" [OK]

Hint: dict.get(key) returns string value with quotes in output [OK]

Common Mistakes:

• Confusing printed string with quotes included
• Expecting key name as output
• Thinking get returns None if key exists

4. You wrote this code to update hardware version:

hardware_versions = {"GPU": "NVIDIA RTX 3090"}
hardware_versions["GPU"] = NVIDIA RTX 4090
print(hardware_versions)

What error will occur?

medium

A. No error, prints updated dictionary

B. NameError because NVIDIA RTX 4090 is not quoted

C. SyntaxError due to invalid dictionary

D. KeyError because GPU key is missing

Solution

1. Step 1: Check the assignment line syntax

   The value NVIDIA RTX 4090 is not in quotes, so Python treats it as variable names.

2. Step 2: Understand Python error for undefined names

   Since no variable named NVIDIA exists, Python raises a NameError.

3. Final Answer:

   NameError because NVIDIA RTX 4090 is not quoted -> Option B

4. Quick Check:

   Unquoted strings cause NameError [OK]

Hint: Always quote string values in Python [OK]

Common Mistakes:

• Thinking KeyError occurs for existing keys
• Assuming syntax error instead of NameError
• Believing code runs without error

5. You want to track both hardware and framework versions in one dictionary. Which code correctly updates the framework version without losing hardware info?

versions = {"hardware": {"GPU": "NVIDIA RTX 3090"}, "framework": {"tensorflow": "2.11.0", "torch": "1.13.0"}}
# Update tensorflow to 2.12.0 here

hard

A. versions.update({"tensorflow": "2.12.0"})

B. versions["framework"] = {"tensorflow": "2.12.0"}

C. versions["tensorflow"] = "2.12.0"

D. versions["framework"]["tensorflow"] = "2.12.0"

Solution

1. Step 1: Understand nested dictionary structure

   "framework" key holds a dictionary with tensorflow version inside.

2. Step 2: Update tensorflow version inside nested dictionary

   Use versions["framework"]["tensorflow"] = "2.12.0" to update without overwriting hardware info.

3. Step 3: Check other options for overwriting risk

   versions["framework"] = {"tensorflow": "2.12.0"} replaces entire framework dict, versions["tensorflow"] = "2.12.0" and D add keys at top level, losing structure.

4. Final Answer:

   versions["framework"]["tensorflow"] = "2.12.0" -> Option D

5. Quick Check:

   Update nested dict key correctly [OK]

Hint: Update nested dict keys to keep all info [OK]

Common Mistakes:

• Replacing whole nested dict by mistake
• Adding keys at wrong dictionary level
• Using update() incorrectly on nested keys