Hardware and framework version tracking in MLOps - Mini Project: Build & Apply

Hardware and Framework Version Tracking

📖 Scenario: You work in a machine learning team. You want to keep track of the hardware and software versions used for training models. This helps your team reproduce results and debug issues.

🎯 Goal: Create a Python dictionary to store hardware and framework versions. Add a configuration variable for the minimum required framework version. Then filter the dictionary to keep only versions that meet or exceed this minimum. Finally, print the filtered versions.

📋 What You'll Learn

Create a dictionary called versions with exact keys and values

Add a variable called min_framework_version with the exact value 2.0

Use a dictionary comprehension to create valid_versions with framework versions >= min_framework_version

Print the valid_versions dictionary

💡 Why This Matters

🌍 Real World

Tracking hardware and software versions helps teams reproduce machine learning experiments and debug issues effectively.

💼 Career

Version tracking is a key skill for MLOps engineers to ensure consistent environments and smooth collaboration.

1

Create the versions dictionary

Create a dictionary called versions with these exact entries: 'GPU': 'NVIDIA RTX 3090', 'CPU': 'Intel i9-11900K', 'Framework': '2.3', 'CUDA': '11.2'

MLOps

# Create the versions dictionary with exact keys and values
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate keys and values with colons. Use quotes for strings.

2

Add minimum framework version

Add a variable called min_framework_version and set it to the string '2.0'

MLOps

versions = {'GPU': 'NVIDIA RTX 3090', 'CPU': 'Intel i9-11900K', 'Framework': '2.3', 'CUDA': '11.2'}
# Add min_framework_version variable below
# Your code here

Hint

Assign the string '2.0' to the variable min_framework_version.

3

Filter versions by minimum framework version

Use a dictionary comprehension to create a new dictionary called valid_versions that includes all items from versions where the key is not 'Framework' or the value of 'Framework' is greater than or equal to min_framework_version. Convert the version strings to floats for comparison.

MLOps

versions = {'GPU': 'NVIDIA RTX 3090', 'CPU': 'Intel i9-11900K', 'Framework': '2.3', 'CUDA': '11.2'}
min_framework_version = '2.0'
# Create valid_versions dictionary using comprehension
# Your code here

Hint

Use {k: v for k, v in versions.items() if ...} and convert version strings to floats for comparison.

4

Print the filtered versions

Print the valid_versions dictionary using print(valid_versions)

MLOps

versions = {'GPU': 'NVIDIA RTX 3090', 'CPU': 'Intel i9-11900K', 'Framework': '2.3', 'CUDA': '11.2'}
min_framework_version = '2.0'
valid_versions = {k: v for k, v in versions.items() if k != 'Framework' or float(v) >= float(min_framework_version)}
# Print the valid_versions dictionary
# Your code here

Hint

Use the print function to display the dictionary.

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