pytorch GitHub Actions Analytics
GitHub Actions Activity Summary
The Pytorch GitHub organization demonstrated significant activity during June 2025, marked by a robust commit behavior and an active pull request (PR) dynamic. During this period, contributors engaged in a total of 9,761 GitHub Actions runs, achieving a commendable success rate of 72.32%. This reflects a healthy environment for developer contribution trends, highlighting the organization's commitment to continuous improvement and code quality.
Analyzing the PR dynamics, the success rate of workflows varied significantly, with the "Upload test stats" workflow achieving an impressive 99.61% success rate. In contrast, the "pull" workflow faced challenges, with only 19.83% of its runs successful. This disparity in performance illustrates the varied complexities of different workflows and points to areas for potential engineering investment analysis.
Contributor patterns reveal a diverse set of contributors, with the pytorchmergebot leading in total runs and maintaining an 80.38% success rate. This indicates a strong reliance on automated processes for managing contributions, which is essential for scaling contributions effectively. Other contributors, such as facebook-github-bot and tianyu-l, also showcased high success rates, reinforcing the collaborative nature of the Pytorch community.
In summary, the Pytorch GitHub organization displayed dynamic repository activity in June 2025, characterized by effective commit behavior, varied PR dynamics, and a strong contributor base. These insights into GitHub analytics and GitHub insights are vital for understanding the organization's ongoing growth and areas for future development.
GitHub Actions Key Performance Indicators
This panel presents six key performance indicators (KPIs) related to GitHub Actions usage and performance. Each indicator provides a metric with comparison to a reference period, helping you understand trends and changes in your CI/CD pipeline efficiency.
Indicators Explained:
- Average Execution Time: The average time in seconds that workflows take to complete, compared to a previous period.
- Success Rate: The percentage of workflow runs that completed successfully, compared to a previous period.
- Total Runs: The total number of workflow runs, compared to a previous period.
- Successful Runs: The total number of successful workflow completions, compared to a previous period.
- Failed Runs: The total number of failed workflow runs, compared to a previous period.
- Total Run Time: The cumulative execution time of all workflows in hours, compared to a previous period.
Green indicators with an upward arrow generally indicate positive trends, while red indicators with a downward arrow might highlight areas for improvement. However, interpretation depends on the specific metric - for example, a decrease in failed runs is positive despite showing as a negative percentage.
Average Execution Time (s)
2,284.57
300
7.06%
Success Rate
72.44
80
-0.08%
Total Runs
9,786.04
214255
-0.95%
Successful Runs
7,181.04
161171
-0.95%
Failed Runs
1,931.04
35752
-0.94%
Total Run Time (hrs)
6,525.57
772049.04
-0.99%
GitHub Actions Runs Over Time
This chart displays the history of GitHub Actions runs over time, showing both successful and failed runs. The stacked bars represent the total volume of GitHub Actions activity in your repositories.
Each bar is divided into two segments: green bars represent successful runs, while red bars indicate failed runs. This visualization allows you to track the overall workflow activity and identify potential patterns or issues in your CI/CD pipeline.
Benefits and Interpretations:
- Activity Monitoring: Track the volume of GitHub Actions runs over time to understand your team's CI/CD usage patterns.
- Failure Detection: Quickly identify periods with higher failure rates that might require investigation.
- Release Correlation: Correlate spikes in action runs with major releases or development milestones.
Execution Duration Over Time
This chart presents the average execution duration of GitHub Actions workflows over time. The bar height represents the average time in seconds that actions took to complete for each period.
Monitoring execution times helps identify performance trends, potential bottlenecks, or optimization opportunities in your CI/CD pipeline. Longer execution times might indicate increased workflow complexity, resource constraints, or potential issues with specific actions.
Benefits and Interpretations:
- Performance Tracking: Monitor the efficiency of your GitHub Actions workflows over time.
- Resource Optimization: Identify opportunities to optimize workflows with consistently high execution times.
- Cost Management: Since GitHub Actions billing is often based on execution time, this chart can help track and manage CI/CD costs.
Success vs Failure
This pie chart shows the proportion of successful versus failed GitHub Actions runs. The green segment represents successful runs, while the red segment shows failed runs.
This visualization provides a quick overview of your workflow reliability. A high percentage of successful runs indicates a stable CI/CD pipeline, while a significant proportion of failures might indicate issues that need addressing.
Benefits:
- Reliability Assessment: Quickly gauge the overall health of your GitHub Actions workflows.
- Quality Control: Track your success rate as a key metric for CI/CD quality.
- Trend Analysis: When viewed over different time periods, helps identify improvement or degradation in workflow stability.
Success
Failure
Workflow Time Distribution
This pie chart displays the distribution of workflow execution time across different GitHub Actions workflows.
Each segment represents a different workflow, with the size proportional to the time consumed by that workflow. This visualization helps identify which workflows are consuming the most execution time.
Benefits:
- Resource Analysis: Identify which workflows consume the most execution time.
- Optimization Targets: Quickly determine which workflows would benefit most from optimization efforts.
- Cost Management: Since GitHub Actions billing is based on execution time, this helps manage CI/CD resource usage.
pull
slow
trunk
inductor-rocm
rocm
Others
GitHub Actions by Workflow
This table breaks down GitHub Actions metrics by individual workflow, providing detailed performance data for each type of automation in your repositories.
Columns Explained:
- Workflow: The name of the GitHub Actions workflow.
- Total Runs: The total number of executions for this workflow.
- Successful Runs: The number of successful completions for this workflow.
- Failed Runs: The number of failed executions for this workflow.
- Success Rate: The percentage of runs that completed successfully.
- Average Duration: The average execution time in seconds for this workflow.
This table helps identify which specific workflows might be problematic or inefficient, allowing targeted improvements to your CI/CD pipeline.
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Workflow Duration
This chart displays the average execution duration in seconds for different workflows in your GitHub Actions.
Longer bars indicate workflows that take more time to execute. This visualization helps identify workflows that might need optimization or have performance issues.
Benefits and Interpretations:
- Performance Analysis: Quickly identify which workflows consume the most execution time.
- Resource Optimization: Target optimization efforts on workflows with the longest durations.
- Cost Management: Since GitHub Actions billing is often based on execution time, this can help manage CI/CD costs.
Workflow Success Rates
This chart shows the success rates as percentages for different workflows in your GitHub Actions.
Higher percentages indicate more reliable workflows, while lower percentages might indicate issues that need addressing. A 100% success rate is ideal, showing complete reliability of the workflow.
Benefits and Interpretations:
- Reliability Assessment: Quickly see which workflows are most reliable vs. which are prone to failures.
- Quality Control: Track success rates as a key metric for CI/CD quality across different workflows.
- Issue Prioritization: Focus debugging efforts on workflows with the lowest success rates.
GitHub Actions by Author
This table displays key metrics for GitHub Actions grouped by the author who triggered the workflow runs. It provides insights into each contributor's usage patterns and success rates.
Columns Explained:
- Author: The GitHub user who triggered the workflow runs.
- Total Runs: The total number of workflow runs initiated by this author.
- Successful Runs: The number of successful workflow completions for this author.
- Failed Runs: The number of failed workflow runs for this author.
- Success Rate: The percentage of workflow runs that completed successfully.
- Average Duration: The average execution time in seconds for this author's workflow runs.
This table helps identify patterns in workflow usage and reliability across different team members, potentially highlighting training opportunities or different development approaches.
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GitHub Actions by Repository
This table presents GitHub Actions metrics organized by repository, showing how CI/CD workflows perform across different projects.
Columns Explained:
- Repository: The GitHub repository where the workflows are defined.
- Total Runs: The total number of workflow runs in this repository.
- Successful Runs: The number of successful workflow completions in this repository.
- Failed Runs: The number of failed workflow runs in this repository.
- Success Rate: The percentage of workflow runs that completed successfully.
- Average Duration: The average execution time in seconds for workflows in this repository.
This table helps compare CI/CD performance across different repositories, potentially highlighting which projects have more reliable pipelines or which might need optimization.
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