In recent years, continuous integration and deployment (CI/CD) has become increasingly popular in both the open-source community and industry. Evaluating CI/CD performance is a critical aspect of software development, as it not only helps minimize execution costs but also ensures faster feedback for developers. Despite its importance, there is limited fine-grained knowledge about the performance of CI/CD processes, while this knowledge is essential for identifying bottlenecks and optimization opportunities. Moreover, the availability of large-scale, publicly accessible datasets of CI/CD logs remains scarce. The few datasets that do exist are often outdated and lack comprehensive coverage. To address this gap, we introduce GHALogs, a new dataset comprising 116k CI/CD workflows executed using GitHub Actions (GHA) across 25k public code projects spanning 20 different programming languages. This dataset includes 513k workflow runs encompassing 2.3 million individual steps. For each workflow run, we provide detailed metadata along with complete run logs. To the best of our knowledge, this is the largest dataset of CI/CD runs that includes full log data. The inclusion of these logs enables more in-depth analysis of CI/CD pipelines, offering insights that cannot be gleaned solely from code repositories. We postulate that this dataset will facilitate future CI/CD pipeline behavior research through log-based analysis. Potential applications include performance evaluation (e.g., measuring task execution times) and root cause analysis (e.g., identifying reasons for pipeline failures).