Hardware and framework version tracking in MLOps - Step-by-Step Execution

Process Flow - Hardware and framework version tracking

Start Training Job

↓

Detect Hardware Specs

↓

Log Hardware Info

↓

Detect Framework Version

↓

Log Framework Version

↓

Run Training

↓

Save Logs & Metadata

↓

End Training Job

The process starts by detecting hardware and framework versions, logging them, then running the training job while saving all metadata for reproducibility.

Execution Sample

MLOps

import torch
import platform

def log_versions():
    hw = platform.platform()
    fw = torch.__version__
    print(f"Hardware: {hw}")
    print(f"Framework: PyTorch {fw}")

This code detects and prints the hardware platform and PyTorch framework version.

Process Table

Step	Action	Detected Hardware	Detected Framework Version	Output
1	Call platform.platform()	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29		Hardware info string returned
2	Access torch.__version__		2.0.1+cu117	Framework version string returned
3	Print hardware info	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29		Hardware: Linux-5.15.0-1051-azure-x86_64-with-glibc2.29
4	Print framework version		2.0.1+cu117	Framework: PyTorch 2.0.1+cu117
5	End of function	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29	2.0.1+cu117	Versions logged successfully

💡 All hardware and framework versions detected and logged, function completes.

Status Tracker

Variable	Start	After Step 1	After Step 2	Final
hw	None	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29	Linux-5.15.0-1051-azure-x86_64-with-glibc2.29
fw	None	None	2.0.1+cu117	2.0.1+cu117

Key Moments - 2 Insights

Why do we detect hardware info before framework version?

Detecting hardware first ensures we know the environment before checking software versions, as shown in execution_table steps 1 and 2.

What if the framework version is not detected correctly?

If framework version is missing, logging will be incomplete, which can cause reproducibility issues; see step 2 where fw is assigned.

Visual Quiz - 3 Questions

Look at the execution table at step 3, what is printed for hardware info?

A Hardware: Linux-5.15.0-1051-azure-x86_64-with-glibc2.29

B Framework: PyTorch 2.0.1+cu117

C Hardware info string returned

D Versions logged successfully

💡 Hint

Check the Output column at step 3 in the execution_table.

At which step is the framework version detected?

A Step 1

B Step 4

C Step 2

D Step 5

💡 Hint

Look at the Action and Detected Framework Version columns in execution_table.

If hardware detection failed at step 1, what would happen to variable 'hw'?

A 'hw' would have an error string

B 'hw' would remain None

C 'hw' would be set to framework version

D 'hw' would be empty string

💡 Hint

Refer to variable_tracker for 'hw' initial and after step 1 values.

Concept Snapshot

Hardware and framework version tracking:
- Detect hardware info (e.g., platform.platform())
- Detect framework version (e.g., torch.__version__)
- Log both before running training
- Essential for reproducibility and debugging
- Save metadata with training outputs

Full Transcript

This visual execution shows how hardware and framework versions are detected and logged in an MLOps context. First, the system queries the hardware platform using platform.platform(), storing the result in variable 'hw'. Next, it accesses the framework version from torch.__version__, storing it in 'fw'. Both values are printed to the console to confirm detection. The execution table traces each step, showing the values assigned and printed. The variable tracker highlights how 'hw' and 'fw' change from None to their detected strings. Key moments clarify why hardware is detected before framework and the importance of successful detection for reproducibility. The quiz tests understanding of the printed outputs, detection steps, and variable states. This process ensures that training jobs record their environment details, helping teams reproduce results and debug issues effectively.

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