pytask vs. pytest: Choosing the Right Python Test Runner

In hooks.py you can implement functions to:

• Automatically add markers to tasks.
• Modify task parametrization.
• Hook into task runtimes to collect artifacts.

Distribution and reuse

Package and publish to an internal registry or PyPI. Keep the plugin small and documented with clear configuration options. Versioning helps avoid CI breakages across teams.

2. Parallelism: speed up task execution

pytask supports parallel execution of independent tasks. Effective parallelism requires understanding task dependencies and side effects.

Designing tasks for parallelism

• Keep tasks deterministic and side-effect isolated (write outputs to per-task files/directories).
• Declare file-based dependencies and products so pytask can detect independence.
• Avoid global state mutations during task execution.

Strategies for parallel execution

• Use pytask’s built-in parallel execution option (e.g., –num-workers) to run tasks across multiple worker processes. Choose a number based on CPU cores and I/O characteristics.
• Prefer process-based parallelism for CPU-bound work and consider async or thread pools for IO-bound tasks.
• For long-running or resource-intensive tasks, create resource tokens (semaphores) via a plugin or an external coordination mechanism to limit concurrency for specific task types.

Example: enabling parallel runs

Run:

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pytask -n auto 

or set an explicit worker count:

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pytask -n 8 

Handling shared resources and races

• Use lock files or file-based atomic operations for shared resources.
• Mark tasks that must not run concurrently by grouping them under a single resource name and enforcing mutual exclusion in a plugin or via a simple lock mechanism.

3. CI Integration: reproducible, fast pipelines

Integrating pytask into CI ensures consistent execution and reliable feedback loops.

CI best practices

• Pin dependencies and use a lockfile to guarantee the same environment.
• Cache task artifacts and virtual environments between runs to cut CI time (e.g., pip cache, poetry cache, .venv).
• Run quick, high-value checks first (lint, unit tests) and expensive tasks later or conditionally (on merges or tags).
• Use matrix builds to test multiple Python versions or dependency combinations.

Example GitHub Actions workflow

A minimal GitHub Actions job for pytask:

yaml
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name: CI 
on: [push, pull_request]

jobs:
test:
    runs-on: ubuntu-latest     strategy:
      matrix:
        python-version: [3.10, 3.11]
    steps:
      - uses: actions/checkout@v4       - name: Set up Python         uses: actions/setup-python@v4         with:
          python-version: \(</span><span class="token" style="color: rgb(57, 58, 52);">{</span><span class="token" style="color: rgb(57, 58, 52);">{</span><span> matrix.python</span><span class="token" style="color: rgb(57, 58, 52);">-</span><span>version </span><span class="token" style="color: rgb(57, 58, 52);">}</span><span class="token" style="color: rgb(57, 58, 52);">}</span><span> </span><span>      </span><span class="token" style="color: rgb(57, 58, 52);">-</span><span> </span><span class="token key" style="color: rgb(0, 0, 255);">name</span><span class="token" style="color: rgb(57, 58, 52);">:</span><span> Cache pip </span><span>        </span><span class="token key" style="color: rgb(0, 0, 255);">uses</span><span class="token" style="color: rgb(57, 58, 52);">:</span><span> actions/cache@v4 </span><span>        </span><span class="token key" style="color: rgb(0, 0, 255);">with</span><span class="token" style="color: rgb(57, 58, 52);">:</span><span> </span><span>          </span><span class="token key" style="color: rgb(0, 0, 255);">path</span><span class="token" style="color: rgb(57, 58, 52);">:</span><span> ~/.cache/pip </span><span>          </span><span class="token key" style="color: rgb(0, 0, 255);">key</span><span class="token" style="color: rgb(57, 58, 52);">:</span><span> pip</span><span class="token" style="color: rgb(57, 58, 52);">-</span><span>\){{ hashFiles(’**/poetry.lock’) }}
      - name: Install dependencies         run: pip install -r requirements.txt       - name: Run fast checks         run: pytask -k “fast”       - name: Run full pipeline in parallel         run: pytask -n 4 

Conditional and incremental runs

• Use CI conditions to skip heavy tasks for docs-only changes.
• Implement artifact-based caching so pytask can skip already-completed tasks when inputs and code haven’t changed.

4. Observability and debugging

• Emit clear task logs and artifacts; configure log levels.
• Store intermediate artifacts in a structured output directory per run or task id for post-mort