Conda Integrations

Reo.Dev is a cutting-edge revenue intelligence solution that helps developer-first companies track and engage high-intent accounts. The platform leverages AI to monitor developer activity, allowing sales, marketing, and business development teams to tailor their outreach with precision. Reo.Dev’s insights empower teams to prioritize leads, refine their go-to-market approach, and drive revenue growth by targeting developer-qualified accounts. With seamless integrations and automated workflows, it streamlines customer acquisition and boosts conversion rates.

This is the easiest way to deploy and test your projects on-prem or in the cloud. You can easily sync your Travis CI projects and you'll be able to test your code in just minutes. Check out our features - you can now sign up for Travis CI with your Bitbucket or GitLab account. This will allow you to connect to your repositories. It's always free to test your open-source projects! Log in to your cloud repository and tell Travis CI that you want to test a project. Then push. It couldn't be simpler. Many services and databases are already pre-installed and can easily be enabled in your build configuration. Before merging Pull Requests to your project, make sure they are tested. It's easy to update production or staging as soon as your tests pass. Travis CI builds are set up mainly through the configuration file.travis.yml found in your repository. This allows you to make your configuration version-controlled and flexible.

The Fortran Package Manager (fpm) serves as both a package manager and a build system specifically designed for Fortran. It boasts a wide array of available packages, contributing to a vibrant ecosystem of both general-purpose and high-performance code, enhancing accessibility for users. Aimed at improving the overall experience for Fortran developers, fpm simplifies the process of building Fortran programs or libraries, executing tests, running examples, and managing dependencies for other Fortran projects. Its design draws inspiration from Rust’s Cargo, creating an intuitive user interface. Additionally, fpm has a long-term vision focused on fostering the growth of modern Fortran applications and libraries. One notable feature of fpm is its plugin system, which facilitates the extension of its capabilities. Among these plugins is the fpm-search project, which enables users to query the package registry effortlessly, and because it is built with fpm, installation on any system is straightforward. This synergy not only streamlines the development process but also encourages collaboration among developers within the Fortran community.

Garak evaluates the potential failures of an LLM in undesirable ways, examining aspects such as hallucination, data leakage, prompt injection, misinformation, toxicity, jailbreaks, and various other vulnerabilities. This free tool is designed with an eagerness for development, continually seeking to enhance its functionalities for better application support. Operating as a command-line utility, Garak is compatible with both Linux and OSX systems; you can easily download it from PyPI and get started right away. The pip version of Garak receives regular updates, ensuring it remains current, while its specific dependencies recommend setting it up within its own Conda environment. To initiate a scan, Garak requires the model to be analyzed and, by default, will conduct all available probes on that model utilizing the suggested vulnerability detectors for each. During the scanning process, users will see a progress bar for every loaded probe, and upon completion, Garak will provide a detailed evaluation of each probe's findings across all detectors. This makes Garak not only a powerful tool for assessment but also a vital resource for researchers and developers aiming to enhance the safety and reliability of LLMs.

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

Discover the transformative capabilities of large language models as they redefine Natural Language Processing (NLP) through Spark NLP, an open-source library that empowers users with scalable LLMs. The complete codebase is accessible under the Apache 2.0 license, featuring pre-trained models and comprehensive pipelines. As the sole NLP library designed specifically for Apache Spark, it stands out as the most widely adopted solution in enterprise settings. Spark ML encompasses a variety of machine learning applications that leverage two primary components: estimators and transformers. Estimators possess a method that ensures data is secured and trained for specific applications, while transformers typically result from the fitting process, enabling modifications to the target dataset. These essential components are intricately integrated within Spark NLP, facilitating seamless functionality. Pipelines serve as a powerful mechanism that unites multiple estimators and transformers into a cohesive workflow, enabling a series of interconnected transformations throughout the machine-learning process. This integration not only enhances the efficiency of NLP tasks but also simplifies the overall development experience.

Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.

Coiled simplifies the process of using Dask at an enterprise level by managing Dask clusters within your AWS or GCP accounts, offering a secure and efficient method for deploying Dask in a production environment. With Coiled, you can set up cloud infrastructure in mere minutes, allowing for a seamless deployment experience with minimal effort on your part. You have the flexibility to tailor the types of cluster nodes to meet the specific requirements of your analysis. Utilize Dask in Jupyter Notebooks while gaining access to real-time dashboards and insights about your clusters. The platform also facilitates the easy creation of software environments with personalized dependencies tailored to your Dask workflows. Coiled prioritizes enterprise-level security and provides cost-effective solutions through service level agreements, user-level management, and automatic termination of clusters when they’re no longer needed. Deploying your cluster on AWS or GCP is straightforward and can be accomplished in just a few minutes, all without needing a credit card. You can initiate your code from a variety of sources, including cloud-based services like AWS SageMaker, open-source platforms like JupyterHub, or even directly from your personal laptop, ensuring that you have the freedom and flexibility to work from anywhere. This level of accessibility and customization makes Coiled an ideal choice for teams looking to leverage Dask efficiently.

Easily switch between command and editor modes using just one keystroke while navigating through cells with arrow keys. Take advantage of all standard Jupyter shortcuts for a smoother experience. Experience fully interactive outputs positioned directly beneath the cell for enhanced visibility. When working within code cells, benefit from intelligent code suggestions, real-time error detection, quick-fix options, streamlined navigation, and many additional features. You can operate with local Jupyter notebooks or effortlessly connect to remote Jupyter, JupyterHub, or JupyterLab servers directly within the IDE. Execute Python scripts or any expressions interactively in a Python Console, observing outputs and variable states as they happen. Split your Python scripts into code cells using the #%% separator, allowing you to execute them one at a time like in a Jupyter notebook. Additionally, explore DataFrames and visual representations in situ through interactive controls, all while enjoying support for a wide range of popular Python scientific libraries, including Plotly, Bokeh, Altair, ipywidgets, and many others, for a comprehensive data analysis experience. This integration allows for a more efficient workflow and enhances productivity while coding.

Sonatype Nexus Repository is an essential tool for managing open-source dependencies and software artifacts in modern development environments. It supports a wide range of packaging formats and integrates with popular CI/CD tools, enabling seamless development workflows. Nexus Repository offers key features like secure open-source consumption, high availability, and scalability for both cloud and on-premise deployments. The platform helps teams automate processes, track dependencies, and maintain high security standards, ensuring efficient software delivery and compliance across all stages of the SDLC.

Seeker® is an advanced interactive application security testing (IAST) tool that offers exceptional insights into the security status of your web applications. It detects trends in vulnerabilities relative to compliance benchmarks such as OWASP Top 10, PCI DSS, GDPR, CAPEC, and CWE/SANS Top 25. Moreover, Seeker allows security teams to monitor sensitive information, ensuring it is adequately protected and not inadvertently recorded in logs or databases without the necessary encryption. Its smooth integration with DevOps CI/CD workflows facilitates ongoing application security assessments and validations. Unlike many other IAST tools, Seeker not only uncovers security weaknesses but also confirms their potential for exploitation, equipping developers with a prioritized list of verified issues that need attention. Utilizing its patented techniques, Seeker efficiently processes a vast number of HTTP(S) requests, nearly eliminating false positives and fostering increased productivity while reducing business risks. In essence, Seeker stands out as a comprehensive solution that not only identifies but also mitigates security threats effectively.

Amazon SageMaker Studio Lab offers a complimentary environment for machine learning (ML) development, ensuring users have access to compute resources, storage of up to 15GB, and essential security features without any charge, allowing anyone to explore and learn about ML. To begin using this platform, all that is required is an email address; there is no need to set up infrastructure, manage access controls, or create an AWS account. It enhances the process of model development with seamless integration with GitHub and is equipped with widely-used ML tools, frameworks, and libraries for immediate engagement. Additionally, SageMaker Studio Lab automatically saves your progress, meaning you can easily pick up where you left off without needing to restart your sessions. You can simply close your laptop and return whenever you're ready to continue. This free development environment is designed specifically to facilitate learning and experimentation in machine learning. With its user-friendly setup, you can dive into ML projects right away, making it an ideal starting point for both newcomers and seasoned practitioners.