Google Colab Integrations

Windsurf is a cutting-edge IDE designed for developers to maintain focus and productivity through AI-driven assistance. At the heart of the platform is Cascade, an intelligent agent that not only fixes bugs and errors but also anticipates potential issues before they arise. With built-in features for real-time code previews, automatic linting, and seamless integrations with popular tools like GitHub and Slack, Windsurf streamlines the development process. Developers can also benefit from memory tracking, which helps Cascade recall past work, and smart suggestions that enhance code optimization. Windsurf’s unique capabilities ensure that developers can work faster and smarter, reducing onboarding time and accelerating project delivery.

Access and access management today have become more complex and frustrating. strongDM redesigns access around the people who need it, making it incredibly simple and usable while ensuring total security and compliance. We call it People-First Access. End users enjoy fast, intuitive, and auditable access to the resources they need. Administrators gain precise controls, eliminating unauthorized and excessive access permissions. IT, Security, DevOps, and Compliance teams can easily answer who did what, where, and when with comprehensive audit logs. It seamlessly and securely integrates with every environment and protocol your team needs, with responsive 24/7 support.

Access, share, and store your files effortlessly across any device. Enjoy the initial 15 GB of storage at no cost. With Drive Enterprise, companies are billed solely for the actual storage their employees consume. It includes Google Docs, Sheets, and Slides, ensuring compatibility with Microsoft Office for a smooth experience. Preserve your photos, stories, designs, recordings, videos, and much more. The first 15 GB of storage is complimentary with a Google Account. Files stored in Drive can be accessed from any smartphone, tablet, or computer, allowing your documents to accompany you wherever you venture. You can easily invite others to view, download, and collaborate on your files—all without the hassle of email attachments. Begin your journey with Drive for free today and experience the convenience of cloud storage.

GitHub stands as the leading platform for developers globally, renowned for its security, scalability, and community appreciation. By joining the ranks of millions of developers and businesses, you can contribute to the software that drives the world forward. Collaborate within the most inventive communities, all while utilizing our top-tier tools, support, and services. If you're overseeing various contributors, take advantage of our free GitHub Team for Open Source option. Additionally, GitHub Sponsors is available to assist in financing your projects. We're thrilled to announce the return of The Pack, where we’ve teamed up to provide students and educators with complimentary access to premier developer tools throughout the academic year and beyond. Furthermore, if you work for a recognized nonprofit, association, or a 501(c)(3), we offer a discounted Organization account to support your mission. With these offerings, GitHub continues to empower diverse users in their software development journeys.

Google Workspace, the enhanced version of G Suite, brings together all the essential tools you need to boost productivity in a single, user-friendly platform. This integrated workspace simplifies your workflow, allowing you to focus more on your tasks rather than on managing them. With advanced AI and search capabilities, Google Workspace enables you to prioritize what truly matters while it takes care of the rest. You can work seamlessly from any location and on any device, even without an internet connection, thanks to customizable tools designed to cater to your team's specific requirements. Just like G Suite, Google Workspace offers bespoke email solutions for businesses along with a suite of collaborative tools, including Gmail, Calendar, Meet, Chat, Drive, Docs, Sheets, Slides, and Forms. Explore our assortment of plans tailored to address your distinct business needs, and find the one that aligns best with your goals on our pricing page. Experience how Google Workspace can transform the way your team collaborates and achieves success.

The Jupyter Notebook is a web-based open-source tool that enables users to create and distribute documents featuring live code, visualizations, equations, and written explanations. Its applications are diverse and encompass tasks such as data cleaning and transformation, statistical modeling, numerical simulations, data visualization, machine learning, among others, showcasing its versatility in various fields. Additionally, it serves as an excellent platform for collaboration and sharing insights within the data science community.

Available in more than 20 programming languages, including Python, JavaScript and TypeScript, Ruby, TypeScript, Go, Ruby and many others. BLACKBOX AI code search was created so that developers could find the best code fragments to use when building amazing products. Integrations with IDEs include VS Code and Github Codespaces. Jupyter Notebook, Paperspace, and many more. C#, Java, C++, C# and SQL, PHP, Go and TypeScript are just a few of the languages that can be used to search code in Python, Java and C++. It is not necessary to leave your coding environment in order to search for a specific function. Blackbox allows you to select the code from any video and then simply copy it into your text editor. Blackbox supports all programming languages and preserves the correct indentation. The Pro plan allows you to copy text from over 200 languages and all programming languages.

VPVision is a complete energy monitoring solution that monitors all utilities in your company. VPVision software allows you to see the true cost of all utilities and make informed decisions about your investments and costs. You can access your data from anywhere with the latest web technology. You can add channels, customize dashboards and create your own reports to analyze your savings and make them transparent. VPVision is your guide to energy savings. VPVision makes energy saving simple, quick, and rewarding. It makes it possible to view data from anywhere using the latest web technology. VPVision analyzes flow data to reveal your savings potential. VPVision allows you to see all utility costs, including steam, compressed air, technical gas, steam, natural gas and electricity, as well as wastewater, heating fuels and heating fuels. VPVision is flexible and adaptable and can be used by small and large companies as well as those in growth.

Neptune.ai serves as a robust platform for machine learning operations (MLOps), aimed at simplifying the management of experiment tracking, organization, and sharing within the model-building process. It offers a thorough environment for data scientists and machine learning engineers to log data, visualize outcomes, and compare various model training sessions, datasets, hyperparameters, and performance metrics in real-time. Seamlessly integrating with widely-used machine learning libraries, Neptune.ai allows teams to effectively oversee both their research and production processes. Its features promote collaboration, version control, and reproducibility of experiments, ultimately boosting productivity and ensuring that machine learning initiatives are transparent and thoroughly documented throughout their entire lifecycle. This platform not only enhances team efficiency but also provides a structured approach to managing complex machine learning workflows.

Meta's MusicGen is an open-source deep-learning model designed to create short musical compositions based on textual descriptions. Trained on 20,000 hours of music, encompassing complete tracks and single instrument samples, this model produces 12 seconds of audio in response to user prompts. Additionally, users can submit reference audio to extract a general melody, which the model will incorporate alongside the provided description. All generated samples utilize the melody model, ensuring consistency. Furthermore, users have the option to run the model on their own GPUs or utilize Google Colab by following the guidelines available in the repository. MusicGen features a single-stage transformer architecture combined with efficient token interleaving techniques, which streamline the process by eliminating the need for multiple cascading models. This innovative approach enables MusicGen to generate high-quality audio samples that are responsive to both textual inputs and musical characteristics, allowing users to exert greater control over the final output. The combination of these features positions MusicGen as a versatile tool for music creation and exploration.

DeOldify represents a groundbreaking method for adding color to monochrome images. You can experience its capabilities immediately by accessing the complimentary Google Colab notebook designed for both photographs and videos. These notebooks are publicly available and open-source for everyone. To witness the advancements made in DeOldify, be sure to explore the project's GitHub repository and its archives. For those interested in the latest innovations in image restoration and colorization, we welcome your inquiries. The most advanced version of DeOldify is available exclusively through MyHeritage, which offers a variety of options to enhance the color quality of your images. Share these vibrant transformations with your loved ones to bring them joy, and begin your free trial today to explore all the possibilities. By embracing this cutting-edge technology, you can breathe new life into your cherished memories.

HyperCrawl is an innovative web crawler tailored specifically for LLM and RAG applications, designed to create efficient retrieval engines. Our primary aim was to enhance the retrieval process by minimizing the time spent crawling various domains. We implemented several advanced techniques to forge a fresh ML-focused approach to web crawling. Rather than loading each webpage sequentially (similar to waiting in line at a grocery store), it simultaneously requests multiple web pages (akin to placing several online orders at once). This strategy effectively eliminates idle waiting time, allowing the crawler to engage in other tasks. By maximizing concurrency, the crawler efficiently manages numerous operations at once, significantly accelerating the retrieval process compared to processing only a limited number of tasks. Additionally, HyperLLM optimizes connection time and resources by reusing established connections, much like opting to use a reusable shopping bag rather than acquiring a new one for every purchase. This innovative approach not only streamlines the crawling process but also enhances overall system performance.

Transforming basic prototypes into fully functional web applications is now a swift process. You no longer need to make sacrifices regarding performance, customization, or scalability. Taipy boosts performance through effective caching of graphical events, ensuring that graphical components are rendered only when necessary, based on user interactions. With Taipy's integrated decimator for charts, managing extensive datasets becomes a breeze, as it smartly minimizes data points to conserve time and memory while preserving the fundamental structure of your data. This alleviates the challenges associated with sluggish performance and high memory demands that arise from processing every single data point. When dealing with large datasets, the user experience and data analysis can become overly complex. Taipy Studio simplifies these situations with its robust VS Code extension, offering a user-friendly graphical editor. It allows you to schedule method invocations at specific intervals, providing flexibility in your workflows. Additionally, you can choose from a variety of pre-defined themes or craft your own, making customization both simple and enjoyable.

TensorBoard serves as a robust visualization platform within TensorFlow, specifically crafted to aid in the experimentation process of machine learning. It allows users to monitor and illustrate various metrics, such as loss and accuracy, while also offering insights into the model architecture through visual representations of its operations and layers. Users can observe the evolution of weights, biases, and other tensors via histograms over time, and it also allows for the projection of embeddings into a more manageable lower-dimensional space, along with the capability to display various forms of data, including images, text, and audio. Beyond these visualization features, TensorBoard includes profiling tools that help streamline and enhance the performance of TensorFlow applications. Collectively, these functionalities equip practitioners with essential tools for understanding, troubleshooting, and refining their TensorFlow projects, ultimately improving the efficiency of the machine learning process. In the realm of machine learning, accurate measurement is crucial for enhancement, and TensorBoard fulfills this need by supplying the necessary metrics and visual insights throughout the workflow. This platform not only tracks various experimental metrics but also facilitates the visualization of complex model structures and the dimensionality reduction of embeddings, reinforcing its importance in the machine learning toolkit.

Forge is an innovative AI-powered code review tool aimed at significantly cutting down the time developers spend on code evaluations, all while promoting high standards of code quality and maintainability. At present, Forge is in its beta stage and is exclusively offered to Teams users across GitHub Free, GitHub Pro, GitHub Team, and GitHub Enterprise Cloud. By leveraging AI technology, users can gain a clearer understanding of the code changes they are assessing, along with instant suggestions that streamline the review process and align the author with the reviewer. To make the review process more engaging, users can incorporate GIFs and AI-generated ASCII art into their feedback. Moreover, the AI capability allows for a thorough examination of the code, ensuring that it adheres to organization-specific or team-specific coding standards. Functionally, Forge operates as a Chrome extension that transforms the traditional Code Review process into a more dynamic and AI-enhanced experience. With an array of AI features integrated throughout, Forge not only enriches the review workflow but also retains the essential actions that developers typically perform during code assessments. This seamless blend of innovation and familiarity equips developers with the tools they need to enhance their coding practices effectively.

Unsloth is an innovative open-source platform specifically crafted to enhance and expedite the fine-tuning and training process of Large Language Models (LLMs). This platform empowers users to develop customized models, such as ChatGPT, in just a single day, a remarkable reduction from the usual training time of 30 days, achieving speeds that can be up to 30 times faster than Flash Attention 2 (FA2) while significantly utilizing 90% less memory. It supports advanced fine-tuning methods like LoRA and QLoRA, facilitating effective customization for models including Mistral, Gemma, and Llama across its various versions. The impressive efficiency of Unsloth arises from the meticulous derivation of computationally demanding mathematical processes and the hand-coding of GPU kernels, which leads to substantial performance enhancements without necessitating any hardware upgrades. On a single GPU, Unsloth provides a tenfold increase in processing speed and can achieve up to 32 times improvement on multi-GPU setups compared to FA2, with its functionality extending to a range of NVIDIA GPUs from Tesla T4 to H100, while also being portable to AMD and Intel graphics cards. This versatility ensures that a wide array of users can take full advantage of Unsloth's capabilities, making it a compelling choice for those looking to push the boundaries of model training efficiency.

Voxel51 is the driving force behind FiftyOne, an open-source toolkit designed to enhance computer vision workflows by elevating dataset quality and providing valuable insights into model performance. With FiftyOne, you can explore, search through, and segment your datasets to quickly locate samples and labels that fit your specific needs. The toolkit offers seamless integration with popular public datasets such as COCO, Open Images, and ActivityNet, while also allowing you to create custom datasets from the ground up. Recognizing that data quality is a crucial factor affecting model performance, FiftyOne empowers users to pinpoint, visualize, and remedy the failure modes of their models. Manual identification of annotation errors can be labor-intensive and inefficient, but FiftyOne streamlines this process by automatically detecting and correcting label inaccuracies, enabling the curation of datasets with superior quality. In addition, traditional performance metrics and manual debugging methods are often insufficient for scaling, which is where the FiftyOne Brain comes into play, facilitating the identification of edge cases, the mining of new training samples, and offering a host of other advanced features to enhance your workflow. Overall, FiftyOne significantly optimizes the way you manage and improve your computer vision projects.

RagaAI stands out as the premier AI testing platform, empowering businesses to minimize risks associated with artificial intelligence while ensuring that their models are both secure and trustworthy. By effectively lowering AI risk exposure in both cloud and edge environments, companies can also manage MLOps expenses more efficiently through smart recommendations. This innovative foundation model is crafted to transform the landscape of AI testing. Users can quickly pinpoint necessary actions to address any dataset or model challenges. Current AI-testing practices often demand significant time investments and hinder productivity during model development, leaving organizations vulnerable to unexpected risks that can lead to subpar performance after deployment, ultimately wasting valuable resources. To combat this, we have developed a comprehensive, end-to-end AI testing platform designed to significantly enhance the AI development process and avert potential inefficiencies and risks after deployment. With over 300 tests available, our platform ensures that every model, data, and operational issue is addressed, thereby speeding up the AI development cycle through thorough testing. This rigorous approach not only saves time but also maximizes the return on investment for businesses navigating the complex AI landscape.

DagsHub serves as a collaborative platform tailored for data scientists and machine learning practitioners to effectively oversee and optimize their projects. By merging code, datasets, experiments, and models within a cohesive workspace, it promotes enhanced project management and teamwork among users. Its standout features comprise dataset oversight, experiment tracking, a model registry, and the lineage of both data and models, all offered through an intuitive user interface. Furthermore, DagsHub allows for smooth integration with widely-used MLOps tools, which enables users to incorporate their established workflows seamlessly. By acting as a centralized repository for all project elements, DagsHub fosters greater transparency, reproducibility, and efficiency throughout the machine learning development lifecycle. This platform is particularly beneficial for AI and ML developers who need to manage and collaborate on various aspects of their projects, including data, models, and experiments, alongside their coding efforts. Notably, DagsHub is specifically designed to handle unstructured data types, such as text, images, audio, medical imaging, and binary files, making it a versatile tool for diverse applications. In summary, DagsHub is an all-encompassing solution that not only simplifies the management of projects but also enhances collaboration among team members working across different domains.

Gemma 3, launched by Google, represents a cutting-edge AI model constructed upon the Gemini 2.0 framework, aimed at delivering superior efficiency and adaptability. This innovative model can operate seamlessly on a single GPU or TPU, which opens up opportunities for a diverse group of developers and researchers. Focusing on enhancing natural language comprehension, generation, and other AI-related functions, Gemma 3 is designed to elevate the capabilities of AI systems. With its scalable and robust features, Gemma 3 aspires to propel the evolution of AI applications in numerous sectors and scenarios, potentially transforming the landscape of technology as we know it.

Maintain your usual routine while working within Jupyter Notebooks or any Python setting. Just invoke modelbi.deploy to launch your model, allowing Modelbit to manage it — along with all associated dependencies — in a production environment. Machine learning models deployed via Modelbit can be accessed directly from your data warehouse with the same simplicity as invoking a SQL function. Additionally, they can be accessed as a REST endpoint directly from your application. Modelbit is integrated with your git repository, whether it's GitHub, GitLab, or a custom solution. It supports code review processes, CI/CD pipelines, pull requests, and merge requests, enabling you to incorporate your entire git workflow into your Python machine learning models. This platform offers seamless integration with tools like Hex, DeepNote, Noteable, and others, allowing you to transition your model directly from your preferred cloud notebook into a production setting. If you find managing VPC configurations and IAM roles cumbersome, you can effortlessly redeploy your SageMaker models to Modelbit. Experience immediate advantages from Modelbit's platform utilizing the models you have already developed, and streamline your machine learning deployment process like never before.

Approximately 80% of corporate data is stored in challenging formats such as HTML, PDF, CSV, PNG, and PPTX, among others. Unstructured simplifies the extraction and transformation of intricate data to be compatible with all leading vector databases and LLM frameworks. This platform enables data scientists to preprocess data efficiently at scale, allowing them to allocate more time to modeling and analysis rather than data collection and cleaning. With our enterprise-grade connectors, we can gather data from various sources and convert it into AI-friendly JSON files, making it easier for organizations to integrate AI into their operations. Rely on Unstructured to provide meticulously curated data that is clean of any artifacts and, crucially, ready for use with LLMs. In doing so, we empower businesses to harness the full potential of their data for innovative applications.

The Gemma family consists of advanced, lightweight models developed using the same innovative research and technology as the Gemini models. These cutting-edge models are equipped with robust security features that promote responsible and trustworthy AI applications, achieved through carefully curated data sets and thorough refinements. Notably, Gemma models excel in their various sizes—2B, 7B, 9B, and 27B—often exceeding the performance of some larger open models. With the introduction of Keras 3.0, users can experience effortless integration with JAX, TensorFlow, and PyTorch, providing flexibility in framework selection based on specific tasks. Designed for peak performance and remarkable efficiency, Gemma 2 is specifically optimized for rapid inference across a range of hardware platforms. Furthermore, the Gemma family includes diverse models that cater to distinct use cases, ensuring they adapt effectively to user requirements. These lightweight language models feature a decoder and have been trained on an extensive array of textual data, programming code, and mathematical concepts, which enhances their versatility and utility in various applications.

Introducing MinusX, a Chrome extension designed to streamline the operation of your analytics applications, providing the quickest method to derive insights from your data. With the ability to interoperate with MinusX, users can easily adjust or enhance their existing notebooks. Simply select a specific area, pose inquiries, or request modifications to your data. MinusX is compatible with popular analytics platforms such as Jupyter Notebooks, Metabase, Tableau, and more, allowing you to generate analyses and share outcomes with your team in real-time. Our advanced privacy settings ensure that any data shared is utilized solely for improving the accuracy of our models, with a strict policy against sharing your data with external parties. The seamless integration of MinusX into your existing tools means you can remain within your current workflow while addressing questions. Actionable tasks are prioritized as first-class entities, enabling MinusX to select the most appropriate action based on context. Presently, we support models like Claude Sonnet 3.5, GPT-4o, and GPT-4o mini, and we are actively developing a feature that will allow you to incorporate your own models for even greater customization. This adaptability ensures that MinusX can cater to a wide range of analytical needs and preferences.

The Universal Sentence Encoder (USE) transforms text into high-dimensional vectors that are useful for a range of applications, including text classification, semantic similarity, and clustering. It provides two distinct model types: one leveraging the Transformer architecture and another utilizing a Deep Averaging Network (DAN), which helps to balance accuracy and computational efficiency effectively. The Transformer-based variant generates context-sensitive embeddings by analyzing the entire input sequence at once, while the DAN variant creates embeddings by averaging the individual word embeddings, which are then processed through a feedforward neural network. These generated embeddings not only support rapid semantic similarity assessments but also improve the performance of various downstream tasks, even with limited supervised training data. Additionally, the USE can be easily accessed through TensorFlow Hub, making it simple to incorporate into diverse applications. This accessibility enhances its appeal to developers looking to implement advanced natural language processing techniques seamlessly.

Illuminate the black box and install 3LC to achieve the insight necessary for implementing impactful modifications to your models swiftly. Eliminate uncertainty from your model training process and accelerate iteration. Gather metrics for each sample and visualize them conveniently in your web browser. Examine your training process to identify and resolve problems within your dataset. Experience model-guided, interactive debugging and data enhancement. Discover significant or ineffective samples, gaining insight into which elements perform well and where the model encounters difficulties. Enhance your model through various methods by adjusting the weight of your data. Execute precise, non-invasive changes to single samples or in bulk while preserving a comprehensive record of all modifications, allowing you to revert to any prior version effortlessly. Explore beyond conventional experiment tracking with metrics that focus on each sample for every epoch and meticulous data management. Combine metrics by sample characteristics, rather than merely by epoch, to uncover hidden patterns. Ensure that every training session is linked to a specific dataset version, guaranteeing complete reproducibility. With these powerful tools at your disposal, your modeling process can evolve into a finely tuned and insightful journey.

ClaimBuster serves as the overarching platform for all fact-checking initiatives within the IDIR Lab. Originally launched to develop an AI system capable of identifying claims that warrant verification, it has progressively advanced towards achieving the ideal of fully automated fact-checking. While journalists primarily utilize ClaimBuster, it is designed for anyone motivated to confront misinformation. Accessing our models is straightforward through our API, which only requires the registration of a free API key. The success of ClaimBuster relies heavily on the contributions from individuals who label data. We encourage you to create an account and participate in labeling to enhance our model's effectiveness. Additionally, we've made our machine learning model training code available as open-source, inviting skilled AI engineers to contribute their expertise. Our claim-detection model even retweets posts it identifies as potentially needing fact-checking, expanding its utility in real-time. By participating, you can play an essential role in the ongoing battle against misinformation.

Google Cloud's Text-to-Speech API has unveiled Chirp 3, a feature that allows users to develop custom voice models by utilizing their own high-quality audio recordings. This innovation streamlines the process of generating unique voices for audio synthesis via the Cloud Text-to-Speech API, catering to both streaming and long-form text applications. Due to safety protocols, access to this voice cloning feature is limited to select users, and those interested in gaining access must reach out to the sales team for inclusion on the allowed list. The Instant Custom Voice capability supports a variety of languages, such as English (US), Spanish (US), and French (Canada), ensuring a broad reach for users. Moreover, this service is operational across multiple Google Cloud regions and offers a range of supported output formats, including LINEAR16, OGG_OPUS, PCM, ALAW, MULAW, and MP3, depending on the chosen API method. As voice technology continues to evolve, the possibilities for personalized audio experiences are expanding rapidly.

Effortlessly convert your comments into functional code, as demonstrated in the example where we swiftly generate a Plotly bar chart. You can seamlessly construct complete functions without the need to search for specific library methods or parameters; for instance, we developed a function to upload a DataFrame to an AWS bucket in parquet format. Additionally, you can write SQL queries simply by instructing CodeSquire on the data you wish to extract, join, and organize, similar to the example where we identify the top 10 most prevalent names. CodeSquire is also capable of elucidating someone else's code; just request an explanation of the preceding function, and you'll receive a clear, straightforward description. Furthermore, it can assist in crafting intricate functions that incorporate multiple logical steps, allowing you to brainstorm ideas by starting with basic concepts and progressively integrating more advanced features as you refine your project. This collaborative approach makes coding not only easier but also more intuitive.