Exploration on caching in `refresh-pipfile-locks` GitHub Action

[jiridanek]

• https://github.com/jiridanek/notebooks/actions/runs/16887364975/job/47838360842

13m56s of locking and 55s of uploading cache

• https://github.com/jiridanek/notebooks/actions/runs/16887364975/job/47839906849

2m4s of loading the cache and 13m5s of locking

---

the cache for python 3.12 images is over 9GB, the cache for python 3.11 images is too big to compress on the runner and it runs out of disk space and fails to upload cache

[jiridanek]

@coderabbitai please ponder and come up with ideas

[coderabbitai]

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🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# Find GitHub Actions workflow files related to pipfile locks
fd -e yml -e yaml . .github/workflows | xargs grep -l "pipfile\|Pipfile\|pip-compile\|lock" -i | head -10

# Also search for any refresh or update related workflows
fd -e yml -e yaml . .github/workflows | xargs grep -l "refresh\|update" -i | head -10

Length of output: 755

---

🏁 Script executed:

#!/bin/bash
# Read the piplock-renewal workflow to understand the caching strategy
cat .github/workflows/piplock-renewal.yaml

Length of output: 2671

---

🏁 Script executed:

#!/bin/bash
# Check the Makefile for the refresh-pipfilelock-files target
grep -A 20 "^refresh-pipfilelock-files:" Makefile || grep -A 20 "refresh-pipfilelock-files:" makefile || grep -A 20 "refresh-pipfilelock-files:" GNUmakefile

# Find all Pipfile locations to understand the scope
fd Pipfile --type f | head -20

# Count total Pipfiles to understand scale
echo "Total Pipfiles in repository:"
fd Pipfile --type f | wc -l

Length of output: 1694

---

@jiridanek After analyzing your GitHub Actions workflow and the caching issue, I've identified several optimization opportunities. The main problem is that your piplock-renewal.yaml workflow has no caching implemented at all, which explains why the locking process takes ~13-14 minutes consistently.

Key Issues Identified

1. No dependency caching: The workflow processes 62 Pipfiles (31 directories) without any caching
2. Sequential processing: Each pipenv lock runs sequentially within a directory
3. Full resolution every time: Each run resolves dependencies from scratch

Optimization Ideas

1. Implement Multi-Layer Caching Strategy

Add caching for pip, pipenv, and dependency resolution:

- name: Cache pip packages
  uses: actions/cache@v4
  with:
    path: ~/.cache/pip
    key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/Pipfile') }}
    restore-keys: |
      ${{ runner.os }}-pip-${{ matrix.python-version }}-
      ${{ runner.os }}-pip-

- name: Cache pipenv virtualenvs
  uses: actions/cache@v4
  with:
    path: ~/.local/share/virtualenvs
    key: ${{ runner.os }}-pipenv-${{ matrix.python-version }}-${{ hashFiles('**/Pipfile') }}
    restore-keys: |
      ${{ runner.os }}-pipenv-${{ matrix.python-version }}-

- name: Cache Pipfile.lock files
  uses: actions/cache@v4
  with:
    path: '**/Pipfile.lock'
    key: ${{ runner.os }}-piplock-${{ matrix.python-version }}-${{ hashFiles('**/Pipfile') }}
    restore-keys: |
      ${{ runner.os }}-piplock-${{ matrix.python-version }}-

2. Parallelize Lock Generation

Modify the Makefile to use GNU parallel or background jobs:

# In Makefile
refresh-pipfilelock-files-parallel:
	@echo "Updating Pipfile.lock files in parallel for Python $(PYTHON_VERSION)"
	@find $(BASE_DIRS) -name Pipfile -type f | \
		parallel -j 4 'cd {//} && pipenv lock --python $(PYTHON_VERSION)'

3. Implement Smart Differential Updates

Only update Pipfile.lock when Pipfile has changed:

- name: Check for Pipfile changes
  id: changes
  run: |
    git diff --name-only origin/${{ env.BRANCH }} -- '**/Pipfile' > changed_pipfiles.txt
    echo "has_changes=$([ -s changed_pipfiles.txt ] && echo 'true' || echo 'false')" >> $GITHUB_OUTPUT

- name: Update only changed Pipfiles
  if: steps.changes.outputs.has_changes == 'true'
  run: |
    while IFS= read -r pipfile; do
      dir=$(dirname "$pipfile")
      echo "Updating $dir/Pipfile.lock"
      cd "$dir" && pipenv lock --python ${{ matrix.python-version }}
      cd -
    done < changed_pipfiles.txt

4. Use pipenv with --keep-outdated flag

For faster resolution when you don't need to update all dependencies:

pipenv lock --keep-outdated  # Only updates direct dependencies

5. Pre-warm Cache in Separate Job

Create a job that runs less frequently to warm the cache:

jobs:
  warm-cache:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ["3.11", "3.12"]
    steps:
      # Download all dependencies without locking
      - name: Pre-download dependencies
        run: |
          pip install --upgrade pip wheel
          pip download -r <(find . -name Pipfile -exec pipenv requirements {} \;) \
            --dest ~/.cache/pip --no-deps

6. Use Docker Layer Caching

Consider containerizing the process with buildkit cache mounts:

# syntax=docker/dockerfile:1
FROM python:$PYTHON_VERSION
RUN --mount=type=cache,target=/root/.cache/pip \
    pip install pipenv

7. Implement Lock File Verification

Add a quick verification step to skip unchanged locks:

# Generate hash of current dependencies
pipenv lock --requirements | sha256sum > /tmp/deps.hash
# Compare with stored hash
if cmp -s /tmp/deps.hash .deps.hash; then
  echo "Dependencies unchanged, skipping lock"
  exit 0
fi

8. Consider Alternative Tools

• pip-tools: Faster than pipenv for simple dependency resolution
• uv: Rust-based Python package installer (10-100x faster than pip)
• poetry: Better caching mechanisms than pipenv

Recommended Implementation Priority

1. First: Add basic pip/pipenv caching (Upload initial basic files into notebooks repository #1) - Quick win with minimal changes
2. Second: Implement parallel processing (Add appropriate instructions into base image's Dockerfile #2) - Significant time reduction
3. Third: Add differential updates (Add protocol to the link for the quay notebooks repo #3) - Reduce unnecessary work
4. Fourth: Consider switching to faster tools like uv (Add packages and extensions for the jupyterlab minimal notebook #8) - Long-term optimization

With proper caching, you should see the execution time drop from ~14 minutes to 2-5 minutes for subsequent runs with unchanged dependencies.

[jiridanek]

@coderabbitai what I've been experimenting with is

      - name: Cache pip and pipenv packages for pipenv lock
        uses: actions/cache@v4
        with:
          path: |
            ~/.cache/pip
            ~/.cache/pipenv

and that ended up making the job run longer in aggregate, restoring cache and running the locking

[coderabbitai]

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@jiridanek That makes perfect sense - you've hit the classic "cache bigger than benefit" problem! With 9GB+ caches taking 2m4s to restore, you're essentially trading 2 minutes of I/O for maybe 1 minute of savings. Here's why this happens and how to fix it:

Why Your Cache Strategy Backfired

The ~/.cache/pip and ~/.cache/pipenv directories contain everything - full wheel files, HTTP cache, temporary files - for all 62 Pipfiles. Since pipenv lock needs to resolve dependencies anyway (even with cached packages), you're not saving much computation time.

Better Approaches for Your Containerized Setup

1. Cache Only Pipfile.lock Files (Fastest)

Since you're using containerized locking, cache the actual lock files to skip resolution entirely when Pipfiles haven't changed:

- name: Cache Pipfile.lock files
  uses: actions/cache@v4
  with:
    path: '**/Pipfile.lock'
    key: locks-${{ matrix.python-version }}-${{ hashFiles('**/Pipfile') }}
    restore-keys: |
      locks-${{ matrix.python-version }}-

Then modify your process to skip unchanged files:

# Only lock if Pipfile is newer than Pipfile.lock
find . -name Pipfile -type f | while read pipfile; do
  lockfile="${pipfile}.lock"
  if [ ! -f "$lockfile" ] || [ "$pipfile" -nt "$lockfile" ]; then
    cd "$(dirname "$pipfile")"
    docker run --platform=linux/amd64 ... pipenv lock
  fi
done

2. Use Sparse HTTP Cache Only

Cache just pipenv's HTTP metadata (much smaller):

- name: Cache pipenv HTTP cache
  uses: actions/cache@v4
  with:
    path: ~/.cache/pipenv/http
    key: pipenv-http-${{ matrix.python-version }}-${{ hashFiles('**/Pipfile') }}
    max-size: 500M  # Limit cache size

3. Parallelize Within Container (No Cache Needed)

Instead of caching, run multiple lock operations in parallel inside one container:

docker run --platform=linux/amd64 -v $PWD:/workspace ubi9 bash -c "
  pip install pipenv
  find /workspace -name Pipfile | xargs -P 4 -I {} bash -c '
    cd \$(dirname {}) && pipenv lock --python 3.12
  '
"

4. Switch to pip-compile (10x Faster)

Consider pip-tools which is much faster and has smaller cache requirements:

# Convert Pipfile to requirements.in, then:
pip-compile --generate-hashes requirements.in  # Much faster than pipenv lock

5. Use GitHub's Artifact Cache Strategically

Split caches by directory to stay under size limits:

- name: Cache per directory
  uses: actions/cache@v4
  with:
    path: ${{ matrix.directory }}/Pipfile.lock
    key: lock-${{ matrix.directory }}-${{ hashFiles(format('{0}/Pipfile', matrix.directory)) }}

My Recommendation

Given your containerized setup and the scale (62 Pipfiles), I'd suggest:

1. First: Try approach Upload initial basic files into notebooks repository #1 (cache lock files only) - this should give you instant skips for unchanged Pipfiles
2. Second: Add parallelization (Add protocol to the link for the quay notebooks repo #3) to handle the remaining files faster
3. Long-term: Consider migrating to uv or pip-tools which are designed for speed

The key insight: don't cache the packages, cache the computation results (the lock files themselves).

---

🧠 Learnings used

Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-07-01T10:41:56.419Z
Learning: jiridanek's team uses containerized dependency locking for cross-platform compatibility in opendatahub-io/notebooks. They run `pipenv lock` inside UBI9 containers with specific platform arguments (`--platform=linux/amd64 --python-version 3.12`) to avoid host OS dependency conflicts when generating Pipfile.lock files.

[jiridanek]

Cache just pipenv's HTTP metadata (much smaller):

@coderabbitai sounds interesting, but won't pipenv end up downloading all the rest anyways, so what's the use of caching the metadata?

[coderabbitai]

Tip

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@jiridanek You're absolutely right! That's a great catch. Caching just the HTTP metadata wouldn't help much because pipenv lock still needs to download the actual packages to compute the SHA256 hashes that go into Pipfile.lock. The HTTP cache only saves PyPI API calls (version listings, etc.), which is a tiny fraction of the time.

The Real Bottleneck

When pipenv lock runs, it must:

1. Query PyPI for available versions (small, cacheable)
2. Download each package to compute hashes (large, this is the slow part)
3. Resolve dependencies
4. Write the lock file

So you're correct - caching metadata alone is nearly useless for your use case.

Better Solutions for Your Containerized Setup

1. Skip Locking Entirely for Unchanged Pipfiles

The most effective approach - cache the lock files themselves and only regenerate when needed:

#!/bin/bash
# Hash each Pipfile and compare with stored hash
for pipfile in $(find . -name Pipfile); do
  dir=$(dirname "$pipfile")
  hash=$(sha256sum "$pipfile" | cut -d' ' -f1)
  hash_file="$dir/.pipfile.hash"
  
  if [ -f "$hash_file" ] && [ "$(cat $hash_file)" = "$hash" ] && [ -f "$dir/Pipfile.lock" ]; then
    echo "Skipping $dir - Pipfile unchanged"
  else
    echo "Locking $dir"
    cd "$dir"
    docker run --platform=linux/amd64 ... pipenv lock
    echo "$hash" > "$hash_file"
  fi
done

2. Use a Persistent Docker Volume for the Container Cache

Mount a persistent volume that survives between container runs:

- name: Create cache volume
  run: docker volume create pipenv-cache

- name: Run pipenv lock with persistent cache
  run: |
    docker run --platform=linux/amd64 \
      -v pipenv-cache:/root/.cache \
      -v $PWD:/workspace \
      your-image pipenv lock

3. Pre-build a Docker Image with Common Dependencies

Create a base image with frequently used packages pre-installed:

FROM ubi9
RUN pip install numpy pandas scikit-learn torch tensorflow  # your common deps
# These will be available in the container's site-packages

4. Most Practical: Matrix Strategy with Partial Runs

Split the 62 Pipfiles across multiple parallel jobs:

strategy:
  matrix:
    include:
      - directories: "jupyter runtime habana"  # ~20 Pipfiles
      - directories: "codeserver pytorch"       # ~20 Pipfiles  
      - directories: "tensorflow rocm"          # ~22 Pipfiles

Each job handles ~20 Pipfiles = ~5 minutes instead of 14 minutes for all 62.

The Real Solution

For your use case with containerized locking, the most effective approach is #1 - detect which Pipfiles actually changed and only lock those. The 9GB cache problem disappears because you're not caching packages at all, just skipping unnecessary work.

---

🧠 Learnings used

Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-07-01T10:41:56.419Z
Learning: jiridanek's team uses containerized dependency locking for cross-platform compatibility in opendatahub-io/notebooks. They run `pipenv lock` inside UBI9 containers with specific platform arguments (`--platform=linux/amd64 --python-version 3.12`) to avoid host OS dependency conflicts when generating Pipfile.lock files.

---

Learnt from: jiridanek
PR: opendatahub-io/notebooks#968
File: runtimes/rocm-tensorflow/ubi9-python-3.12/Dockerfile.rocm:17-18
Timestamp: 2025-08-06T15:22:17.997Z
Learning: jiridanek requested GitHub issue creation for micropipenv removal from Python 3.12 images during PR #968 review. Analysis revealed 15 affected Dockerfiles with only 1 legitimate usage (jupyter/tensorflow/ubi9-python-3.12/Dockerfile.cuda using micropipenv install --dev) while 14 images install micropipenv unnecessarily. Issue #1685 was created with comprehensive problem description covering ~238MB total waste across unused installations, detailed scope analysis, multiple solution options, clear acceptance criteria, and proper context linking, continuing the established pattern of systematic code quality improvements through detailed issue tracking.

[jiridanek]

@coderabbitai gemini suggested paralelism feature of xargs

       - name: Run make refresh-pipfilelock-files
         run: |
           make refresh-pipfilelock-files PYTHON_VERSION=${{ matrix.python-version }} INCLUDE_OPT_DIRS=${{ env.INCLUDE_OPT_DIRS }}
           # This script runs the 'pipenv lock' commands in parallel to speed up the process.
           # The bottleneck is the CPU-intensive dependency resolution, not network downloads,
           # so parallel execution is the most effective optimization after caching.
 
           # Step 1: Determine the list of directories to process.
           # This logic is based on the Makefile's behavior. If the Makefile changes,
           # this list may need to be updated here as well.
           BASE_DIRS="jupyter/minimal jupyter/datascience jupyter/pytorch jupyter/tensorflow jupyter/trustyai codeserver runtimes/minimal runtimes/datascience runtimes/pytorch runtimes/tensorflow"
           if [ "${{ env.INCLUDE_OPT_DIRS }}" = "true" ]; then
             BASE_DIRS="$BASE_DIRS jupyter/rocm/pytorch runtimes/rocm-pytorch"
           fi
 
           PY_VERSION=${{ matrix.python-version }}
           DIRS=""
           for d in $BASE_DIRS; do
             if [ -d "$d/ubi9-python-$PY_VERSION" ]; then
               DIRS="$DIRS $d/ubi9-python-$PY_VERSION"
             fi
           done
 
           echo "The following directories will be processed in parallel:"
           echo "$DIRS" | tr ' ' '\n'
 
           # Step 2: Define a function to run 'pipenv lock' and export it for xargs.
           # If any lock command fails, it will exit with an error, stopping the entire process.
           run_pipenv_lock() {
             (cd "$1" && echo "--- [START] Locking: $1 ---" && pipenv lock) || exit 255
           }
           export -f run_pipenv_lock
 
           # Step 3: Run the locking process in parallel using xargs.
           # -P 0: Use as many parallel processes as available CPU cores.
           echo "$DIRS" | tr -s ' ' '\n' | xargs -n 1 -P 0 -I {} bash -c 'run_pipenv_lock "{}"'
 
       - name: Commit changes (if any)
         run: |

[jiridanek]

@coderabbitai is it possible to tell uv not to recalculate hashes for packages it already has in its uv.lock? That would speed things up very nicely.

[coderabbitai]

Tip

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@jiridanek That's a solid parallelization approach from Gemini! Using xargs -P is indeed one of the most effective ways to speed up the locking process. However, there's one key consideration for your setup:

The Container Compatibility Issue

This suggestion runs pipenv lock directly on the GitHub Actions runner, but based on your team's containerized locking approach (UBI9 containers with --platform=linux/amd64), you'll need to adapt it to maintain cross-platform compatibility.

Adapted Version for Containerized Execution

Here's how to combine parallelization with your Docker requirements:

# Step 1: Same directory discovery logic
BASE_DIRS="jupyter/minimal jupyter/datascience jupyter/pytorch jupyter/tensorflow jupyter/trustyai codeserver runtimes/minimal runtimes/datascience runtimes/pytorch runtimes/tensorflow"
if [ "${{ env.INCLUDE_OPT_DIRS }}" = "true" ]; then
  BASE_DIRS="$BASE_DIRS jupyter/rocm/pytorch runtimes/rocm-pytorch"
fi

PY_VERSION=${{ matrix.python-version }}
DIRS=""
for d in $BASE_DIRS; do
  if [ -d "$d/ubi9-python-$PY_VERSION" ]; then
    DIRS="$DIRS $d/ubi9-python-$PY_VERSION"
  fi
done

# Step 2: Containerized parallel execution
run_containerized_lock() {
  dir="$1"
  echo "--- [START] Locking: $dir ---"
  docker run --rm \
    --platform=linux/amd64 \
    -v "$PWD/$dir:/workspace" \
    -w /workspace \
    registry.access.redhat.com/ubi9/python-312 \
    bash -c "pip install pipenv && pipenv lock --python $PY_VERSION" || exit 255
}
export -f run_containerized_lock

# Step 3: Run with controlled parallelism
# Use -P 2 instead of -P 0 to avoid overwhelming the runner
echo "$DIRS" | tr -s ' ' '\n' | xargs -n 1 -P 2 -I {} bash -c 'run_containerized_lock "{}"'

Optimization Considerations

Parallelization Level

• -P 2 is likely optimal for GitHub Actions (2-core runners)
• -P 4 if using larger runners
• -P 0 (unlimited) might cause memory issues with multiple Docker containers

Performance Impact

With ~31 directories and -P 2:

• Sequential: 31 × 27s = ~14 minutes
• Parallel (2 workers): ~7 minutes
• Parallel (4 workers): ~3.5 minutes (if you have 4+ cores)

Potential Issues to Watch

1. Memory pressure: Each container + pipenv process uses ~500MB-1GB RAM
2. Docker daemon stress: Multiple concurrent containers might slow down
3. PyPI rate limits: Parallel requests might trigger rate limiting
4. Interleaved output: Use unique prefixes for debugging:

   echo "[$dir] Starting lock..." >&2

Hybrid Approach (Best of Both Worlds)

Combine parallelization with change detection:

# Only process directories with changed Pipfiles
CHANGED_DIRS=$(git diff --name-only origin/main -- '*/Pipfile' | xargs -r dirname | sort -u)
if [ -z "$CHANGED_DIRS" ]; then
  echo "No Pipfile changes detected"
  exit 0
fi

echo "$CHANGED_DIRS" | xargs -n 1 -P 2 -I {} bash -c 'run_containerized_lock "{}"'

This way you get both parallelization AND skip unnecessary work, potentially reducing runtime to under 2 minutes for typical PRs!

---

🧠 Learnings used

Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-07-01T10:41:56.419Z
Learning: jiridanek's team uses containerized dependency locking for cross-platform compatibility in opendatahub-io/notebooks. They run `pipenv lock` inside UBI9 containers with specific platform arguments (`--platform=linux/amd64 --python-version 3.12`) to avoid host OS dependency conflicts when generating Pipfile.lock files.

[coderabbitai]

Tip

For best results, initiate chat on the files or code changes.

🧩 Analysis chain

🌐 Web query:

Does uv Python package manager reuse hashes from existing uv.lock file to avoid recalculating SHA256 hashes when updating dependencies?

💡 Result:

Yes. When a uv.lock file is present, uv reuses the already-locked artifact hashes for packages that remain at the same version and build, so it doesn’t need to redownload or recompute SHA-256 during sync or lock unless a dependency actually changes.[2]

• According to uv’s locking/syncing docs, uv “will prefer the previously locked versions” when using an existing uv.lock; only dependencies that change due to constraints or explicit upgrade flags are re-resolved and updated in the lockfile.[2] In practice, that means the existing lockfile entries—including their recorded hashes—are reused for unchanged packages, avoiding recomputation.
• This behavior aligns with uv’s goal of reproducible installs from a lockfile, where artifacts are pinned by version and hash for verification rather than being recalculated each time.[2][4]

Notes and limits:

• If you run an upgrade (uv lock --upgrade or --upgrade-package …), uv will resolve new versions and record new hashes for the updated artifacts; only those updated entries require new hash calculations.[2]
• Ongoing work is tracking broader coverage such as explicitly locking build dependencies as well, further expanding which hashes are preserved and reused from the lockfile.[4]

Citations:

• 1: https://docs.astral.sh/uv/reference/cli/
• 2: https://docs.astral.sh/uv/concepts/projects/sync/
• 3: https://www.youtube.com/watch?v=6pttmsBSi8M
• 4: Locking of build dependencies astral-sh/uv#5190
• 5: https://astral.sh/blog/uv-unified-python-packaging
• 6: https://realpython.com/dependency-management-python-poetry/
• 7: https://www.hashstudioz.com/blog/uv-a-lightening-fast-python-package-and-project-manager/
• 8: https://docs.astral.sh/uv/reference/settings/
• 9: https://news.ycombinator.com/item?id=44357411
• 10: uv pip compile updates hashes in lockfile without --upgrade astral-sh/uv#1530

---

@jiridanek Yes, uv does exactly that! It intelligently reuses hashes from the existing uv.lock file for unchanged packages, only recalculating hashes for packages that actually change. This is a game-changer for your use case.

How uv's Hash Reuse Works

According to uv's documentation, when a uv.lock file exists:

• Unchanged packages: Hashes are reused directly from the lockfile
• Changed packages: Only these get re-downloaded and re-hashed
• Result: Incremental updates instead of full recalculation

Performance Impact for Your Setup

With 62 Pipfiles taking ~14 minutes with pipenv:

• Initial run: uv would be ~10-100x faster than pipenv (likely ~1-2 minutes total)
• Subsequent runs: Near-instant for unchanged dependencies (seconds, not minutes)
• No 9GB cache needed: uv's built-in hash reuse eliminates the caching problem

Migration Path for Containerized Locking

Since you're using containerized locking, here's how to adapt:

# Install uv in your container
docker run --platform=linux/amd64 -v $PWD:/workspace ubi9 bash -c "
  curl -LsSf https://astral.sh/uv/install.sh | sh
  source ~/.cargo/env
  
  # Convert Pipfile to pyproject.toml (one-time)
  uv pip compile Pipfile -o requirements.txt
  uv init --python 3.12
  uv add -r requirements.txt
  
  # Future updates - only changed deps are re-hashed!
  uv lock
"

Key Advantages Over pipenv

1. Smart incremental updates: Only changed dependencies trigger downloads
2. Parallel resolution: Built-in parallelization (no xargs needed)
3. Smaller lock files: More efficient format than Pipfile.lock
4. Cross-platform by default: No need for platform-specific flags

For your refresh workflow, this means:

• First run: ~1-2 minutes (vs 14 minutes)
• Incremental updates: ~10-30 seconds (vs 14 minutes)
• No cache management headaches

The hash reuse alone makes uv worth considering, but combined with its Rust-based speed, it could reduce your CI time by 90%+!

---

🧠 Learnings used

Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-07-01T10:41:56.419Z
Learning: jiridanek's team uses containerized dependency locking for cross-platform compatibility in opendatahub-io/notebooks. They run `pipenv lock` inside UBI9 containers with specific platform arguments (`--platform=linux/amd64 --python-version 3.12`) to avoid host OS dependency conflicts when generating Pipfile.lock files.

---

Learnt from: jiridanek
PR: opendatahub-io/notebooks#968
File: codeserver/ubi9-python-3.11/Dockerfile.cpu:17-18
Timestamp: 2025-08-01T16:10:21.049Z
Learning: jiridanek requested GitHub issue creation for version pinning improvement of micropipenv and uv packages during PR #968 review. Issue #1595 was created with comprehensive problem description covering build reproducibility risks from unpinned package versions across 18 affected Dockerfiles, two solution options (direct pinning vs build arguments), implementation approach with testing requirements, detailed acceptance criteria, and proper context linking, continuing the established pattern of systematic code quality improvements through detailed issue tracking.

[jiridanek]

@coderabbitai implemented uv

• RHAIENG-304: add uv pyproject.tomls for multiple notebooks and runtimes across Jupyter, RStudio, and VSCode #2145

now the locking is already fast by default thanks to uv features without any extra effort on my part

[coderabbitai]

Tip

For best results, initiate chat on the files or code changes.

@jiridanek Fantastic news! 🎉 That's exactly what we hoped would happen with the uv migration. It's great to see that uv's built-in optimizations - the hash reuse, parallel dependency resolution, and overall Rust-based performance - eliminated the need for complex caching strategies entirely.

Your experience perfectly validates uv's design philosophy: make the tool fast enough that developers don't need to worry about elaborate workarounds. No more 9GB caches, no more 14-minute lock times, no more cache restoration overhead!

This is a perfect example of how choosing the right tool can solve architectural problems at their root rather than adding layers of complexity on top.

🐰✨ (happy rabbit enjoying the speed of fast dependency resolution)

---

✏️ Learnings added

Learnt from: jiridanek
URL: https://github.com/opendatahub-io/notebooks/issues/1882

Timestamp: 2025-08-31T11:07:32.358Z
Learning: jiridanek successfully implemented uv in PR #2145 for the opendatahub-io/notebooks project, replacing pipenv for dependency locking. The migration eliminated the previous 14-minute locking times and 9GB cache problems with pipenv, achieving fast locking by default through uv's built-in features (hash reuse, parallel resolution, Rust-based performance) without requiring additional caching strategies or optimization efforts.

Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure review instructions. You can manage existing learnings in the app.

---

🧠 Learnings used

Learnt from: jiridanek
PR: opendatahub-io/notebooks#2145
File: Makefile:451-457
Timestamp: 2025-08-27T16:04:49.874Z
Learning: jiridanek requested GitHub issue creation for uv command optimization during PR #2145 review, specifically to explore using `uv export -o pylock.toml` instead of `uv pip compile pyproject.toml` for lockfile generation. Issue #2163 was created addressing the technical question of which uv command is more appropriate given the current workflow that uses both `uv lock` and pylock.toml generation. The investigation covers technical differences, workflow integration, performance comparison, and alignment with uv best practices to determine the optimal approach for the project's lockfile generation pipeline across 18+ notebook/runtime environments.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#2145
File: Makefile:451-457
Timestamp: 2025-08-27T16:04:49.874Z
Learning: jiridanek requested GitHub issue creation for uv command optimization during PR #2145 review, specifically to explore using `uv export -o pylock.toml` instead of `uv pip compile pyproject.toml` for lockfile generation. Issue addresses the technical question of which uv command is more appropriate given the current workflow that uses both `uv lock` and pylock.toml generation. The investigation will cover technical differences, workflow integration, performance comparison, and alignment with uv best practices to determine the optimal approach for the project's lockfile generation pipeline.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-08-30T07:57:33.466Z
Learning: uv's official documentation recommends against using both uv.lock and requirements.txt (or exported formats like pylock.toml) simultaneously. The recommended approach is to use either uv.lock for uv-native workflows OR exported formats for compatibility, but not both. If both are needed, uv suggests opening an issue to discuss the specific use case.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#0
File: :0-0
Timestamp: 2025-07-01T10:41:56.419Z
Learning: jiridanek's team uses containerized dependency locking for cross-platform compatibility in opendatahub-io/notebooks. They run `pipenv lock` inside UBI9 containers with specific platform arguments (`--platform=linux/amd64 --python-version 3.12`) to avoid host OS dependency conflicts when generating Pipfile.lock files.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#2145
File: jupyter/minimal/ubi9-python-3.12/Dockerfile.cpu:70-70
Timestamp: 2025-08-27T15:00:11.931Z
Learning: jiridanek requested GitHub issue creation for uv pip sync optimization during PR #2145 review. Issue #2150 was created addressing the systematic replacement of `uv pip install --requirements=` with `uv pip sync` across 34+ Dockerfiles migrated from requirements.txt to pylock.toml. The issue includes comprehensive problem description covering drift risk and redundant flags, detailed solution with benefits analysis, clear acceptance criteria for all affected files, and proper context linking to PR #2145 review comment, assigned to jiridanek.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#2217
File: .github/workflows/security.yaml:22-30
Timestamp: 2025-08-29T15:01:07.674Z
Learning: jiridanek created PR #2223 to systematically address uv setup configuration improvements (version pinning, caching, environment activation) during PR #2217 review, continuing the established pattern of implementing systematic improvements through dedicated PRs rather than applying individual point fixes during reviews.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#968
File: codeserver/ubi9-python-3.11/Dockerfile.cpu:17-18
Timestamp: 2025-08-01T16:10:21.049Z
Learning: jiridanek requested GitHub issue creation for version pinning improvement of micropipenv and uv packages during PR #968 review. Issue #1595 was created with comprehensive problem description covering build reproducibility risks from unpinned package versions across 18 affected Dockerfiles, two solution options (direct pinning vs build arguments), implementation approach with testing requirements, detailed acceptance criteria, and proper context linking, continuing the established pattern of systematic code quality improvements through detailed issue tracking.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#968
File: runtimes/tensorflow/ubi9-python-3.12/Dockerfile.cuda:152-158
Timestamp: 2025-08-02T10:26:52.388Z
Learning: CodeRabbit made an incorrect assessment about uv pip install --strict flag support during PR #968 review, suggesting its removal when the flag is actually supported in both uv 0.8.0 and 0.8.4. jiridanek correctly demonstrated the flag's availability through command line verification, showing the importance of thorough verification and accepting corrections when evidence contradicts initial analysis.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#968
File: runtimes/tensorflow/ubi9-python-3.12/Dockerfile.cuda:152-158
Timestamp: 2025-08-02T10:26:52.388Z
Learning: jiridanek corrected CodeRabbit's false assessment about uv pip install --strict flag support during PR #968 review. User demonstrated that uv 0.8.0 does support the --strict flag through command line verification, contradicting CodeRabbit's analysis using uv 0.8.4 that incorrectly suggested the flag was unsupported.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#2145
File: runtimes/tensorflow/ubi9-python-3.12/Dockerfile.cuda:161-167
Timestamp: 2025-08-27T15:35:10.919Z
Learning: jiridanek requested GitHub issue creation for --no-deps flag assessment with pylock.toml lockfiles during PR #2145 review. Issue #2159 was created addressing the systematic usage of --no-deps flag across 22 Dockerfiles migrated to pylock.toml format. The issue includes comprehensive risk assessment framework covering uv behavior analysis, roadmap research, multiple solution options with pros/cons, clear acceptance criteria for informed decision making, detailed scope analysis covering all affected files, and proper context linking to PR #2145 review comment, assigned to jiridanek.

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Learnt from: jiridanek
PR: opendatahub-io/notebooks#2185
File: jupyter/pytorch/ubi9-python-3.12/Dockerfile.cuda:109-117
Timestamp: 2025-08-29T08:48:55.985Z
Learning: jiridanek prefers to implement systematic cleanup improvements through dedicated GitHub issues (like #2076) rather than applying individual point fixes during PR reviews, maintaining consistency with their established pattern of comprehensive code quality improvements.