LambdaTest
BrowserStack
Sauce Labs
Selenium
Testsigma
TestRail
TestComplete
Micro Focus ALM
PyTorch
TensorFlow
Keras
Scikit-learn
NumPy
OpenCV
Pandas
CUDA Toolkit
LambdaTest is a cloud-based cross browser testing platform that helps enterprises run web automation tests at scale (through parallel testing).
Selenium Automation Grid and Cypress CLI on LambdaTest You can attain better browser coverage by running tests across 2,000+ different browsers, devices, and operating systems online. LambdaTest provides a secure, scalable, and reliable cloud-based Selenium Grid that helps run Selenium tests at a faster pace. The Cypress CLI on LambdaTest, helps you expand Cypress test coverage to 40+ browser versions across Windows and macOS platforms. Along with automation testing, you can also perform manual tests, visual UI tests, and real-time tests.
**LT Browser - Responsive Web Testing* Additionally, LambdaTest also offers complimentary access of LT browser - a path-breaking developer-oriented tool that helps assess the responsiveness of your website. LT Browser eases the task of mobile testing as responsive tests can run against 50+ different device resolutions. You can also create custom device (or viewports) and test localhost URL without any extensions (or tunnels).
LambdaTest PyTorchInitially we were skeptical whether a cloud infrastructure would be able to give us the flexibility, speed and browser coverage we need for our agile sprints. But LambdaTest has been a complete value for money to us.
They usually get the latest browsers on-board under 2 weeks time and are never compromising the experience over legacy browsers at the same time. The machines are quick to load and we rely heavily over the Monday integration, it helps us share screenshots instantly among ourselves without having to be stuck in long email chains.
We have been using LambdaTest for around 8 months and it has been so far so good.
LambdaTest has made our testing process less tedious with automated parallel testing. Builds that took days to complete with in-house infrastructure were executed in a couple hours. Parallel testing has helped us with faster feedback loops to scale up our go to market efforts.
Having a global user base we have active traffic from varied locations and testing on multiple platforms and browsers is a continuous process for the team. The feature that stands out for us is geolocation testing, all you need to do is run the capabilities and test the website for the desired location. We use Azure Pipelines for CI/CD and LambdaTest extension for Azure has helped us get a seamless testing experience for our privately hosted projects. Thanks to that we are now able to easily ensure browser compatibility for all the changes before we move them to Prod. Kudos to the team!
Have been using Lambdatest for around 6 months now, and could say that it’s a useful testing tool for our team. Offering great combinations of browsers and operating systems for you to test on and most importantly there are many types of additional logs that come with each test which helps in debugging.. Glad to see the integration with Travis CI due to which we could optimally use this tool with our CI CD pipelines directly. We were able to effectively run TestNG and Selenium tests using their documentation and as an added advantage their support team is quick and helpful
Based on our record, PyTorch seems to be a lot more popular than LambdaTest. While we know about 133 links to PyTorch, we've tracked only 10 mentions of LambdaTest. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
AI-Powered Testing: You’ve heard enough about this on LinkedIn & elsewhere on the internet. We’ve seen enough proof that it is unlikely to be a passing cloud. I don’t have specific advice on where you can use these ML, NLP, and LLM technologies, but I already see a lot of testers beginning to use it for test case development, coding & writing emails. Tools like LambdaTest are already leveraging AI to enhance test... - Source: dev.to / 3 months ago
Tools like LambdaTest offer automated traceability features designed to keep your workflows smooth and your team aligned with what matters most. As your projects evolve, LambdaTest scales with you, providing a simple yet effective way to connect the dots, and helping your team stay focused on delivering great results without the extra hassle. - Source: dev.to / 4 months ago
Import asyncio Import pytest From pyppeteer.errors import PageError From urllib.parse import quote Import os Import sys From os import environ From pyppeteer import connect, launch Exec_platform = os.getenv('EXEC_PLATFORM') Test_url = 'https://lambdatest.com/' # Selectors of the page # Pytest fixture for browser setup @pytest.fixture(scope='function') Async def browser(): if exec_platform == 'local': ... - Source: dev.to / over 1 year ago
If you want to perform cross-domain Ajax requests faster, adding the (Access-Control-Allow-Origin: *) rule to your response header will allow you to do so. For example, you can bypass CORS on lambdatest.com when you turn it on while accessing the resources. - Source: dev.to / about 2 years ago
Const { webkit, chromium } = require('playwright'); (async () => { const browser = await chromium.launch(); const page = await browser.newPage(); // Listen for all console logs page.on('console', msg => console.log(msg.text())) // Listen for all console events and handle errors page.on('console', msg => { if (msg.type() === 'error') ... - Source: dev.to / about 2 years ago
To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / about 1 month ago
With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / about 2 months ago
Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / 2 months ago
8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 4 months ago
Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 4 months ago
BrowserStack - BrowserStack is a software testing platform for developers to comprehensively test websites and mobile applications for quality.
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Sauce Labs - Test mobile or web apps instantly across 700+ browser/OS/device platform combinations - without infrastructure setup.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Selenium - Selenium automates browsers. That's it! What you do with that power is entirely up to you. Primarily, it is for automating web applications for testing purposes, but is certainly not limited to just that.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.