fix: use math.ceil() instead of int() for n_local_cores to prevent ZeroDivisionError on single-core machines

[Mani212005]

Describe your changes

When using --dask-distributed-local-core-fraction with a value between 0 and 1 on a
single-core machine (e.g. standard GitHub CI), the following line:

n_local_cores = int(args.dask_distributed_local_core_fraction * n_system_cores)

truncates towards zero. For example:

int(0.5 * 1) = 0

This causes a ZeroDivisionError when computing memory_per_worker:

memory_per_worker = total_memory / n_local_cores * args.dask_distributed_local_memory_fraction
#                                  ^^^^^^^^^^^^^ ZeroDivisionError when n_local_cores == 0

The fix replaces int() with math.ceil() to always round up to at least 1 worker:

n_local_cores = math.ceil(args.dask_distributed_local_core_fraction * n_system_cores)

This ensures n_local_cores >= 1 regardless of the fraction or number of system cores.

Changes:

• cli.py — Added import math and replaced int() with math.ceil() for the n_local_cores calculation.
• test_distributed.py — Added "0.5" as a parametrized test case to cover the fractional value scenario that triggered the bug.

Testing — all 4 distributed tests pass:

tests/test_distributed.py::test_run_distributed[args0]  PASSED  (core-fraction=1.0)
tests/test_distributed.py::test_run_distributed[args1]  PASSED  (core-fraction=0.5) ← new
tests/test_distributed.py::test_run_distributed[args2]  PASSED  (core-fraction=0.0)
tests/test_distributed.py::test_run_distributed[args3]  PASSED  (no dask args)

4 passed in 150.24s

Issue Link

Closes #32

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

Checklist before requesting a review

• My branch is up-to-date with the target branch - if not update your fork with the changes from the target branch (use pull with --rebase option if possible).
• I have performed a self-review of my code
• For any new/modified functions/classes I have added docstrings that clearly describe its purpose, expected inputs and returned values
• I have placed in-line comments to clarify the intent of any hard-to-understand passages of my code
• I have updated the documentation to cover introduced code changes
• I have added tests that prove my fix is effective or that my feature works
• I have given the PR a name that clearly describes the change, written in imperative form (context).
• I have requested a reviewer and an assignee (assignee is responsible for merging)

Checklist for reviewers

Each PR comes with its own improvements and flaws. The reviewer should check the following:

• the code is readable
• the code is well tested
• the code is documented (including return types and parameters)
• the code is easy to maintain

Author checklist after completed review

• I have added a line to the CHANGELOG describing this change, in a section reflecting type of change (add section where missing):
  • added — when you have added new functionality
  • changed — when default behaviour of the code has been changed
  • fixes — when your contribution fixes a bug

Checklist for assignee

• PR is up to date with the base branch
• the tests pass
• author has added an entry to the changelog (and designated the change as added, changed or fixed)
• Once the PR is ready to be merged, squash commits and merge the PR.

[joeloskarsson]

Hi! Could you reformat this PR using our existing PR template: https://github.com/mllam/mllam-data-prep/blob/main/.github/pull_request_template.md ? This guarantees that we follow the development workflows that we have in place. If you have questions about some parts of it feel free to ask!

[Mani212005]

@joeloskarsson
Sorry for that! I have corrected the format kindly review the code.
Thank you !

[joeloskarsson]

This all looks good to me. Can you also add a changelog entry? And double-check that the linting passes. Will assign @leifdenby for a final ok and merging.