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Add CI checks for formatting + clean up CI (#217)
* Format files with latest ruff * Add .claude/ to gitignore 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Fix unused variables and add notebook exclusion to format script - Fix unused variables across multiple Python files - Fix syntax error in zencoder/test_starcoder_bigcode.py - Exclude Jupyter notebooks from linting checks in format.sh 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * remove unused CI workflows * Add code formatting CI workflow - Created new GitHub Actions workflow 'code-formatting.yml' that runs the formatting script - Workflow checks if code meets formatting standards and fails if any changes are needed - Updated pull_request.yml to include the new formatting check - Ensures PRs cannot be merged until code passes formatting checks * Fix workflow_call trigger in code-formatting.yml * Update llm-complete-guide/utils/llm_utils.py Co-authored-by: Copilot <[email protected]> * Update magic-photobooth/frontend.py Co-authored-by: Copilot <[email protected]> * small redundancy fix * Update CI to install latest ruff version and display version in logs * Update README and CONTRIBUTING.md with formatting requirements * Update CI * Formatting --------- Co-authored-by: Claude <[email protected]> Co-authored-by: Copilot <[email protected]>
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‎.github/workflows/code-formatting.yml

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name: Code Formatting
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on:
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workflow_call:
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push:
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branches:
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- main
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jobs:
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formatting-check:
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name: Code Formatting Check
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runs-on: ubuntu-latest
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if: github.event.pull_request.draft == false
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env:
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ZENML_DEBUG: 1
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ZENML_ANALYTICS_OPT_IN: false
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Install latest ruff
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run: pip install --upgrade ruff
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- name: Run formatting script
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run: bash scripts/format.sh
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- name: Check for changes
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id: git-check
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run: |
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git diff --exit-code || echo "changes=true" >> $GITHUB_OUTPUT
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- name: Fail if changes were made
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if: steps.git-check.outputs.changes == 'true'
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run: |
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echo "::error::Formatting check failed. Please run 'scripts/format.sh' locally and commit the changes."
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exit 1

‎.github/workflows/gpt4_summarizer.yml

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‎.github/workflows/production_run_complete_llm.yml

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‎.github/workflows/pull_request.yml

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name: Spell Checking
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name: Pull Request Checks
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on:
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pull_request:
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markdown-link-check:
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uses: ./.github/workflows/markdown-link-check.yml
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if: github.event.pull_request.draft == false
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code-formatting-check:
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uses: ./.github/workflows/code-formatting.yml
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if: github.event.pull_request.draft == false
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readme-projects-check:
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uses: ./.github/workflows/readme-projects-check.yml
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if: github.event.pull_request.draft == false

‎.github/workflows/readme-projects-check.yml

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name: README Projects Check
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on:
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workflow_call:
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jobs:
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readme-projects-check:
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name: Check Projects in README
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runs-on: ubuntu-latest
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if: github.event.pull_request.draft == false
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: '3.9'
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- name: Run README projects check
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run: python3 scripts/check_readme_projects.py

‎.github/workflows/staging_run_complete_llm.yml

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‎.gitignore

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# ollama ignores
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nohup.out
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# Claude
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.claude/

‎CONTRIBUTING.md

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4. Checkout the **main** branch <- `git checkout main`.
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5. Create a branch locally off the **main** branch with a succinct but descriptive name.
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6. Commit changes to the branch.
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7. Push changes to your fork.
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8. Open a PR in our repository to the `main` branch and
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7. Format your code by running `bash scripts/format.sh` before committing.
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8. Push changes to your fork.
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9. Open a PR in our repository to the `main` branch and
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follow the PR template so that we can efficiently review the changes.
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#### Code Formatting
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All code must pass our formatting checks before it can be merged. We use [ruff](https://github.com/astral-sh/ruff) for code formatting and linting.
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To format your code locally:
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```bash
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# Run from the project root
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bash scripts/format.sh
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```
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Our CI pipeline automatically checks if your code is properly formatted. If the check fails, you'll need to run the formatting script locally and commit the changes before your PR can be merged.
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### 🚨 Reporting a Vulnerability
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If you think you have found a vulnerability, and even if you are not sure about it,

‎README.md

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We welcome contributions from anyone to showcase your project built using ZenML.
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See our [contributing guide](./CONTRIBUTING.md) to start.
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## Code Quality
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All code contributions must pass our automated code quality checks:
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- **Code Formatting**: We use [ruff](https://github.com/astral-sh/ruff) for code formatting and linting
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- **Spelling**: We check for typos and spelling errors
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- **Markdown Links**: We verify that all links in documentation work properly
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Our CI pipeline will automatically check your PR for these issues. Remember to run `bash scripts/format.sh` locally before submitting your PR to ensure it passes the formatting checks.
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# 🆘 Getting Help
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By far the easiest and fastest way to get help is to:

‎databricks-production-qa-demo/steps/deployment/deployment_deploy.py

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@step(enable_cache=False)
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def deployment_deploy() -> (
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Annotated[
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Optional[DatabricksDeploymentService],
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ArtifactConfig(
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name="databricks_deployment", is_deployment_artifact=True
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),
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]
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):
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def deployment_deploy() -> Annotated[
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Optional[DatabricksDeploymentService],
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ArtifactConfig(name="databricks_deployment", is_deployment_artifact=True),
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]:
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"""Predictions step.
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This is an example of a predictions step that takes the data in and returns

‎gamesense/steps/log_metadata.py

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# limitations under the License.
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#
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from typing import Any, Dict
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from zenml import get_step_context, log_metadata, step
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"""
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context = get_step_context()
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metadata_dict: Dict[str, Any] = context.pipeline_run.steps[
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step_name
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].outputs[artifact_name]
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# Access the artifact metadata but don't store the unused variable
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_ = context.pipeline_run.steps[step_name].outputs[artifact_name]
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log_metadata(
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artifact_name=artifact_name,

‎huggingface-sagemaker/steps/deploying/huggingface_deployment.py

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save_model_to_deploy.entrypoint()
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logger.info("Model saved locally. Pushing to HuggingFace...")
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assert secret, "No secret found with name 'huggingface_creds'. Please create one with your `token`."
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assert secret, (
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"No secret found with name 'huggingface_creds'. Please create one with your `token`."
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)
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token = secret.secret_values["token"]
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api = HfApi(token=token)

‎huggingface-sagemaker/steps/promotion/promote_get_metrics.py

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@step
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def promote_get_metrics() -> (
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Tuple[
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Annotated[Dict[str, Any], "latest_metrics"],
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Annotated[Dict[str, Any], "current_metrics"],
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]
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):
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def promote_get_metrics() -> Tuple[
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Annotated[Dict[str, Any], "latest_metrics"],
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Annotated[Dict[str, Any], "current_metrics"],
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]:
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"""Get metrics for comparison for promoting a model.
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This is an example of a metric retrieval step. It is used to retrieve

‎llm-complete-guide/steps/eval_retrieval.py

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@step
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def retrieval_evaluation_small() -> (
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Annotated[float, "small_failure_rate_retrieval"]
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):
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def retrieval_evaluation_small() -> Annotated[
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float, "small_failure_rate_retrieval"
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]:
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"""Executes the retrieval evaluation step without reranking.
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Returns:
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@step
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def retrieval_evaluation_small_with_reranking() -> (
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Annotated[float, "small_failure_rate_retrieval_reranking"]
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):
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def retrieval_evaluation_small_with_reranking() -> Annotated[
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float, "small_failure_rate_retrieval_reranking"
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]:
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"""Executes the retrieval evaluation step with reranking.
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Returns:

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