PyTorch’s open-source framework rigorously tests tens of thousands of combinations of platforms and compilers through Continuous Integration, thoroughly validating every modification. What’s happening in our continuous integration system?
- Customised infrastructure enables dynamic sharding, seamlessly distributing diverse job workloads across various machines.
- Developer-centric dashboards enable seamless tracking of the verdant trajectory of every modification.
- Metrics enable us to track the health of our continuous integration (CI) regarding reliability and time-to-signal.
What drives the pursuit of knowledge? Our necessities.
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The CI statistics and dashboards provide invaluable insights to hundreds of contributors from top-tier companies like Google, Microsoft, and NVIDIA, empowering them with priceless information about PyTorch’s intricate test suite. Henceforth, we desired a knowledge repository with the following characteristics:
Prior to leveraging Rockset, our go-to solution for scalable data warehousing and analytics was Amazon Redshift.
Inner storage from Meta (Scuba)
- Professionals who deliver results quickly, with a scalable approach to every challenge.
- Con: not publicly accessible! Although we didn’t possess sensitive information, we still weren’t able to share our instruments and dashboards with customers.
Given that many people work at Meta, leveraging a well-established, feature-rich information backbone was the logical solution, especially considering the limited availability of PyTorch maintainers and the absence of dedicated Dev Infrastructure staff. With assistance from Meta’s Open Source team, we establish data pipelines for all relevant test scenarios and GitHub webhooks. Enabled us to retail their data, regardless of whether we were happy or not, since our scale is largely insignificant compared to Facebook’s. This allowed for interactive slicing and cubing of information in real-time, eliminating the need to learn SQL, and requiring minimal maintenance from us, as another internal team was dealing with its own challenges.
It’s not until you recall that PyTorch is an open-source library that the illusion of its being a dream begins to dissipate. Despite having compiled comprehensive information, we were unable to disseminate it publicly due to its internal hosting. Our advanced, fine-grained dashboards have thus far been utilized exclusively within our organization, and the custom-built metrics and analytics tools we developed upon this data cannot be shared externally.
Within recent days, our team has been working on identifying “smoke checks” – specific scenarios that are more likely to fail exclusively on Windows and not on other platforms. To this end, we crafted an internal query to represent the set. To streamline the process during pull request improvements, it’s proposed to implement a subset of checks specifically for Windows jobs, given the high cost of Windows GPUs and the need to avoid running unnecessary tests that may not yield significant results. Because the query was nested within other statements, the outcomes were intended for external use rather than internal processing. To address this, Jane devised a makeshift solution: she would periodically execute the interior query by hand and manually update the results externally. Given the complexity of the system, it was inherently susceptible to human error and inconsistencies due to its ease of modification from the outside, which often led to forgotten updates within the interior framework – a task that relied heavily on the expertise of a single engineer.
Data stored in Amazon S3 as compressed JSON files is retrieved efficiently.
When querying large datasets, it’s often desirable to store the data in a compressed format to reduce storage costs and improve query performance. One efficient way to do this is by storing the data as compressed JSON files in an Amazon S3 bucket.
- Professionals seeking a scalable and publicly accessible platform for collaboration often opt for cloud-based solutions like Google Workspace, Microsoft 365, or Slack.
- Terrible isn’t a valid argument against questioning – and it’s certainly not a reliable one. Not really scalable? That’s a vague concern at best. How about something more substantial?
By the end of 2020, we had made the decision to publicly disclose our research endeavors, driven by the need to track our project history, document test regression timelines, and facilitate seamless sharding processes. We chose S3 due to its lightweight nature, making it easy to write and quickly scan. More crucially, the fact that it is publicly accessible proved a significant advantage.
As a result of proactive measures, we effectively mitigated the scalability issue from the outset. Given the existing complexity of submitting data to S3, we elected not to utilize the standard 10,000-document submission process, which would have been an extremely time-consuming endeavor. Instead, we aggregated statistics and transformed them into a JSON format, subsequently compressing the file before uploading it to S3. To gain a better understanding of statistics, we would typically start with the final step in the process and work backwards, ensuring that all relevant data was aggregated from multiple sources.
As it became apparent that sharding was an unforeseen requirement, our team quickly recognized the necessity to track file names retrospectively; however, only a few months into data collection, we realized the importance of monitoring test filename information from the outset.
To accommodate sharding, we completely rewrote our JSON logic. If you’re interested in seeing just how complex it was before, take a glance at the category definitions within this file.
As we reflect on the past two years, I am pleased to acknowledge the invaluable support this code has provided, consistently backing our existing sharding infrastructure as well as empowering us in the present. Despite its slightly rough appearance, the chuckle was gentle because the resolution proved surprisingly effective in addressing our original concerns: sharding files, grouping incremental tests, and creating a script to track test history. As we sought more, however, it only became an even greater drawback. To rectify the issue, we needed to revisit Windows smoke tests – the same ones used in the preceding part – and improve flaky test monitoring, both of which necessitated more sophisticated queries on test scenarios across various jobs on diverse commits beyond just yesterday’s changes. As we’ve finally encountered the scalability bottleneck. Are subtle transformations being applied to these JSON files, resulting in intricate dance of data manipulation and processing? We would likely need to process countless JSON files instead. As an alternative to pursuing this route further, we selected Amazon RDS as a solution that enables more straightforward querying and data management.
Amazon RDS
- Professionals scale; publicly accessible platforms enable swift inquiry.
- Con: larger upkeep prices
When Amazon RDS first emerged, we lacked knowledge of Rockset and considered it a public cloud-based database solution with no alternative options. To meet our growing needs, we invested several weeks in planning and executing a seamless RDS migration, deploying multiple AWS Lambdas to support the database, quietly absorbing the increasing maintenance costs. With Amazon RDS, we’ve been able to host public dashboards for our metrics (such as test redness and flakiness) on the platform, which has been a significant win.
Life With Rockset
We were probably poised to continue using RDS for another couple of years, accepting the increasing operational costs as a necessary expense, when one of our engineers, Michael, decided to “go rogue” in late 2021 and explore alternative options? The phrase “if it ain’t broke, don’t fix it” hovered in the atmosphere, with many of us failing to discern any urgency or value in this undertaking. Michael maintained that minimising maintenance costs was crucial, especially for a team of limited engineers, and he was entirely justified in his stance. Instead of searching for an additive solution, which can be akin to creating another layer to mask a problem, it’s often more effective to adopt a subtractive approach whenever possible, effectively “removing the pain” rather than trying to add complexity.
With swift results already apparent, Michael was able to deploy Rockset and rebuild key elements of our original dashboard within just two weeks. Rockset exceeded all of our expectations and proved to be significantly more manageable.
While other data backend solutions consistently met the primary three necessities, Rockset stood out in meeting the “no-ops setup and maintenance” requirement with a significant margin. Beyond providing a comprehensive, fully-managed solution that meets our needs, Rockset brought several distinct benefits to our knowledge infrastructure.
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Schemaless ingest
- It’s unnecessary to pre-plan our approach by creating a conceptual framework. With almost all of our data residing in JSON format, it’s highly beneficial to have the capability to rapidly ingest the entire dataset directly into Rockset, allowing us to query and utilize the information immediately without any intermediate processing steps.
- This has significantly elevated the rate of improvement. We can effortlessly incorporate novel options and data, without incurring extra effort to maintain consistency throughout.
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Actual-time information
- With the need for a more reliable information pipeline, we migrated away from Amazon S3 and instead leveraged Rockset’s native connector to seamlessly synchronize our continuous integration statistics from DynamoDB.
With Rockset’s scalable architecture, we’ve found a reliable partner for our needs, leveraging its open and accessible cloud service to quickly process massive datasets. With unprecedented speed, processing 10 million documents per hour has become the new standard, all while maintaining seamless querying functionality. With our consolidated metrics and dashboards now streamlined onto a single platform utilizing a unified backend, we’ve successfully eliminated unnecessary complexity by leveraging AWS Lambda and self-hosted servers to bypass the limitations of RDS.
In our previous discussion about Scuba, we explored its similarities with Rockset, which can be a powerful alternative when hosted on a public cloud infrastructure.
What Subsequent?
We’re thrilled to phase out our outdated infrastructure and streamline our operations by integrating the majority of our tools into a unified digital backbone. We’re keen to explore what innovative instruments we can create with Rockset.
