Selenium WebDriverを利用したサンプルアプリケーションのテスト
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This document discusses using Selenium WebDriver for end-to-end testing of INTER-Mediator. It recommends using Selenium WebDriver with Ruby for testing web applications built with INTER-Mediator and running those tests on GitHub, Travis CI, CircleCI, and GitLab CI. Examples are provided for setting up Selenium WebDriver with INTER-Mediator on a development VM and running sample tests that launch Google Chrome and Firefox browsers.
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Vaibhav Gupta
Cofounder @ Boundary
in/vaigup
boundaryml/baml
Imagine if every API call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Fault tolerant systems are hard
but now everything must be
fault tolerant
boundaryml/baml
We need to change how we
think about these systems
Aaron Villalpando
Cofounder @ Boundary
Boundary
Combinator
boundaryml/baml
We used to write websites like this:
boundaryml/baml
But now we do this:
boundaryml/baml
Problems web dev had:
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
Low engineering rigor
boundaryml/baml
React added engineering rigor
boundaryml/baml
The syntax we use changes how we
think about problems
boundaryml/baml
We used to write agents like this:
boundaryml/baml
Problems agents have:
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
Low engineering rigor
boundaryml/baml
Agents need
the expressiveness of English,
but the structure of code
F*** You, Show Me The Prompt.
boundaryml/baml
<show don’t tell>
Less prompting +
More engineering
=
Reliability +
Maintainability
BAML
Sam
Greg Antonio
Chris
turned down
openai to join
ex-founder, one
of the earliest
BAML users
MIT PhD
20+ years in
compilers
made his own
database, 400k+
youtube views
Vaibhav Gupta
in/vaigup
[email protected]
boundaryml/baml
Thank you!
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Our Solutions:
Intelligent Media Search
Semantic & contextual search for faster, smarter content discovery.
In-Scene Ad Placement
AI-powered ad insertion to maximize monetization and user experience.
Video Anonymization
Automatically masks sensitive content to ensure privacy compliance.
Vision Analytics
Real-time object detection and engagement tracking.
Why Gyrus AI?
We help media companies streamline operations, enhance media discovery, and stay competitive in the rapidly evolving broadcasting & streaming landscape.
🚀 Ready to Transform Your Media Workflow?
🔗 Visit Us: https://gyrus.ai/
📅 Book a Demo: https://gyrus.ai/contact
📝 Read More: https://gyrus.ai/blog/
🔗 Follow Us:
LinkedIn - https://www.linkedin.com/company/gyrusai/
Twitter/X - https://twitter.com/GyrusAI
YouTube - https://www.youtube.com/channel/UCk2GzLj6xp0A6Wqix1GWSkw
Facebook - https://www.facebook.com/GyrusAI
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Découvrez toutes nos sessions passées et à venir de la communauté UiPath à l’adresse suivante : https://community.uipath.com/geneva/.
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Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...Raffi Khatchadourian Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges---and resultant bugs---involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation---the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators.
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...
Challenges in Migrating Imperative Deep Learning Programs to Graph Execution:...Raffi Khatchadourian
Selenium WebDriverを利用したサンプルアプリケーションのテスト
- 4. • • ‣
- 6. • CI • Travis CI • CircleCI • GitLab CI INTER-Mediator CI
- 7. Web push pull request pull CI Thanks to GitHub, Travis CI, CircleCI, GitLab.com and Slack.
- 8. • CircleCI GitLab CI Docker E2E • Selenium WebDriver + Ruby CI E2E
- 9. • Web Web • • Web • Selenium WebDriver
- 10. • INTER-Mediator-ServerVM • Ruby • gem install rspec selenium-webdriver • Web • Google Chrome & ChromeDriver • Firefox & geckodriver
- 11. • git clone https://github.com/INTER- Mediator/INTER-Mediator • cd INTER-Mediator • ADDR=192.168.56.101 BROWSER=chrome rspec --default- path=dist-docs/docker/spec -f doc -c dist- docs/docker/spec/samples/samples_spec.rb
- 12. Demo
- 13. • • • CI