autogluon-conda-upgrade

AutoGluon Conda Feedstock Upgrade Workflow

CRITICAL: DO NOT MERGE PULL REQUESTS. Only create PRs. The user will review and merge them manually.

Step 1: Prerequisites Check

1.1 Check GitHub CLI

gh --version

If not installed, stop and tell the user to install from https://github.com/cli/cli#installation and run gh auth login.

1.2 Check Authentication

gh auth status

If not authenticated, ask user to run gh auth login.

1.3 Gather User Input

Ask for:

• New AutoGluon version number (e.g., 1.5.0)
• Working directory (default: ~/autogluon-feedstock-upgrade)

Step 2: Setup Working Directory and Fork/Clone Repos

mkdir -p {WORKING_DIR}
cd {WORKING_DIR}

gh repo fork conda-forge/autogluon.common-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon.features-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon.core-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon.tabular-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon.multimodal-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon.timeseries-feedstock --clone=true --remote=true
gh repo fork conda-forge/autogluon-feedstock --clone=true --remote=true

Step 3: Compute SHA256 Hash

curl -sL "https://github.com/autogluon/autogluon/archive/refs/tags/v{NEW_VERSION}.tar.gz" -o /tmp/autogluon-{NEW_VERSION}.tar.gz
openssl sha256 /tmp/autogluon-{NEW_VERSION}.tar.gz | awk '{print $2}'
rm /tmp/autogluon-{NEW_VERSION}.tar.gz

If curl fails with 404, ask user to verify the version number.

Step 4: Fetch and Analyze Dependencies

4.1 Get Current (Old) Version

Read from {WORKING_DIR}/autogluon.common-feedstock/recipe/meta.yaml:

{% set version = "X.Y.Z" %}

4.2 Fetch Version Bounds

Fetch _setup_utils.py for both versions:

• New: https://raw.githubusercontent.com/autogluon/autogluon/refs/tags/v{NEW_VERSION}/core/src/autogluon/core/_setup_utils.py
• Old: https://raw.githubusercontent.com/autogluon/autogluon/refs/tags/v{OLD_VERSION}/core/src/autogluon/core/_setup_utils.py

Extract DEPENDENT_PACKAGES dictionary and PYTHON_REQUIRES string.

4.3 Fetch Package-Specific Setup Files

For each subpackage (common, features, core, tabular, multimodal, timeseries, autogluon), fetch: https://raw.githubusercontent.com/autogluon/autogluon/refs/tags/v{NEW_VERSION}/{SUBPACKAGE}/setup.py

The install_requires shows which DEPENDENT_PACKAGES each subpackage needs.

4.4 Create Dependency Change Summary

Compare old vs new. Summarize:

1. Changed version bounds
2. Added dependencies
3. Removed dependencies
4. Python version changes

Present summary to user and ask for confirmation before proceeding.

Step 5: Update Each Feedstock

Process in dependency order:

Order	Feedstock	Dependencies
1	autogluon.common-feedstock	(none)
2	autogluon.features-feedstock	common
3	autogluon.core-feedstock	common
4	autogluon.tabular-feedstock	core, features
5	autogluon.multimodal-feedstock	core
6	autogluon.timeseries-feedstock	core, tabular
7	autogluon-feedstock	all subpackages

For Each Feedstock:

5.1 Sync Fork and Create Branch (DO THIS FIRST)

cd {WORKING_DIR}/{FEEDSTOCK_NAME}
git fetch upstream
git checkout main
git reset --hard upstream/main
git checkout -b {NEW_VERSION}

5.2 Read Current meta.yaml

After creating branch, read recipe/meta.yaml to understand current structure.

5.3 Update meta.yaml

1. Update version: {% set version = "{NEW_VERSION}" %}
2. Update sha256: Use computed hash
3. Reset build number: number: 0
4. Update Python version (if changed): python >={{ python_min }},<{NEW_PYTHON_MAX}
5. Update dependency version bounds: Match DEPENDENT_PACKAGES

Rules:

• Keep autogluon.* dependencies as =={{ version }}
• Only include dependencies from that package's setup.py
• Preserve existing comments
• Use conda naming (see Package Name Mappings below)

5.4 Handle python_min Changes

If minimum Python changed, update .ci_support/linux_64_.yaml:

python_min:
- '{NEW_PYTHON_MIN}'

Step 6: Commit and Push

For each feedstock:

cd {WORKING_DIR}/{FEEDSTOCK_NAME}
git add recipe/meta.yaml
git commit -m "Update to v{NEW_VERSION}"
git push -u origin {NEW_VERSION}

Step 7: Create Pull Requests

For each feedstock:

cd {WORKING_DIR}/{FEEDSTOCK_NAME}
gh pr create \
  --repo conda-forge/{FEEDSTOCK_NAME} \
  --title "Update to v{NEW_VERSION}" \
  --body "$(cat <<'EOF'
## Summary
- Update {PACKAGE_NAME} to version {NEW_VERSION}
- Updated dependency version bounds from upstream

## Dependency Changes
{LIST_RELEVANT_CHANGES}

## Checklist
* [x] Used a personal fork of the feedstock to propose changes
* [x] Reset the build number to `0`
* [ ] Re-rendered (Use `@conda-forge-admin, please rerender` in a comment)
EOF
)"

Step 8: Final Summary

8.1 Provide PR Links

List all 7 created PRs with clickable links.

8.2 Merge Order Reminder

Merge PRs in dependency order:

1. autogluon.common (no dependencies)
2. autogluon.features and autogluon.core (parallel)
3. autogluon.tabular and autogluon.multimodal (parallel)
4. autogluon.timeseries
5. autogluon (meta-package)

8.3 Post-Merge Instructions

After each PR's CI passes:

1. Comment: @conda-forge-admin, please rerender
2. Wait for rerender bot to update
3. Once CI passes again, merge
4. Wait for package to be published before merging dependent PRs

---

Appendix A: Dependency Tree

autogluon.common (base - no AG deps)
    │
    ├── autogluon.features (depends: common)
    │
    ├── autogluon.core (depends: common)
    │       │
    │       ├── autogluon.tabular (depends: core, features)
    │       │
    │       ├── autogluon.multimodal (depends: core)
    │       │
    │       └── autogluon.timeseries (depends: core, tabular)
    │
    └── autogluon [meta-package] (depends: all subpackages)

Appendix B: URL Patterns

Resource	URL
Release tarball	https://github.com/autogluon/autogluon/archive/refs/tags/v{VERSION}.tar.gz
Version bounds file	https://raw.githubusercontent.com/autogluon/autogluon/refs/tags/v{VERSION}/core/src/autogluon/core/_setup_utils.py
Package setup.py	https://raw.githubusercontent.com/autogluon/autogluon/refs/tags/v{VERSION}/{SUBPACKAGE}/setup.py

Appendix C: Sample PRs

• autogluon.common PR #6
• autogluon.features PR #5
• autogluon.core PR #8
• autogluon.tabular PR #15
• autogluon.multimodal PR #16
• autogluon.timeseries PR #7
• autogluon PR #6

Appendix D: Package Name Mappings

PyPI Name	Conda-Forge Name
torch	pytorch
Pillow	pillow
scikit-learn	scikit-learn
PyYAML	pyyaml
opencv-python	opencv
tensorflow	tensorflow