Conda Integrations

The simplest way to test and deploy your projects in the cloud or on-prem. Easily sync your projects with Travis CI and you’ll be testing your code in minutes. Check out our features – now you can sign up for Travis CI using your Assembla, Bitbucket, GitHub or GitLab account to connect your repositories! Testing your open-source projects is always 100% free! Log in with your cloud repository, tell Travis CI to test a project, and then push. Could it be any simpler? Many databases and services are pre-installed and can be enabled in your build configuration. Make sure every Pull Request to your project is tested before it’s merged. Updating staging or production as soon as your tests pass has never been easier! Builds on Travis CI are configured mostly through the build configuration stored in the file .travis.yml in your repository. This allows your configuration to be version controlled and flexible.

Package manager and build system for Fortran. There are already many packages available for use with fpm, providing an easily accessible and rich ecosystem of general-purpose and high-performance code. Fortran Package Manager (fpm) is a package manager and build system for Fortran. Its key goal is to improve the user experience of Fortran programmers. It does so by making it easier to build your Fortran program or library, run the executables, tests, and examples, and distribute it as a dependency to other Fortran projects. Fpm’s user interface is modeled after Rust’s Cargo. Its long-term vision is to nurture and grow the ecosystem of modern Fortran applications and libraries. The Fortran package manager has a plugin system that allows it to easily extend its functionality. The fpm-search project is a plugin to query the package registry. Since it is built with fpm we can easily install it on our system.

Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production. Coiled manages cloud infrastructure for you, deploying on your AWS or Google Cloud account in minutes. Giving you a rock-solid deployment solution with zero effort. Customize cluster node types to fit your analysis needs. Run Dask in Jupyter Notebooks with real-time dashboards and cluster insights. Create software environments easily with customized dependencies for your Dask analysis. Enjoy enterprise-grade security. Reduce costs with SLAs, user-level management, and auto-termination of clusters. Coiled makes it easy to deploy your cluster on AWS or GCP. You can do it in minutes, without a credit card. Launch code from anywhere, including cloud services like AWS SageMaker, open source solutions, like JupyterHub, or even from the comfort of your very own laptop.

garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. garak's a free tool, we love developing it and are always interested in adding functionality to support applications. garak is a command-line tool, it's developed in Linux and OSX. Just grab it from PyPI and you should be good to go. The standard pip version of garak is updated periodically. garak has its own dependencies, you can to install garak in its own Conda environment. garak needs to know what model to scan, and by default, it'll try all the probes it knows on that model, using the vulnerability detectors recommended by each probe. For each probe loaded, garak will print a progress bar as it generates. Once the generation is complete, a row evaluating that probe's results on each detector is given.

CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.

Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task.

Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Manage binaries and build artifacts across your software supply chain. Single source of truth for all of your components, binaries, and build artifacts. Efficiently distribute parts and containers to developers. Deployed at more than 100,000 organizations globally. Store and distribute Maven/Java, npm, NuGet, Helm, Docker, P2, OBR, APT, GO, R, Conan components and more. Manage components from dev through delivery: binaries, containers, assemblies, and finished goods. Advanced support for the Java Virtual Machine (JVM) ecosystem, including Gradle, Ant, Maven, and Ivy. Compatible with popular tools like Eclipse, IntelliJ, Hudson, Jenkins, Puppet, Chef, Docker, and more. Deliver innovation 24x7x365 with high availability. A single source of truth for components used across your entire software development lifecycle including QA, staging, and operations. Easily integrate with existing user and access provisioning systems including LDAP, Atlassian Crowd, and more.

The industry’s first IAST solution with active verification and sensitive-data tracking for web-based applications. Automatically retests identified vulnerabilities and validates whether they are real and can be exploited. Is more accurate than traditional dynamic testing. Provides a real-time view of the top security vulnerabilities. Sensitive-data tracking shows you where your most critical information is stored without sufficient encryption, helping ensure compliance with key industry standards and regulations, including PCI DSS and GDPR. Seeker is easy to deploy and scale in your CI/CD development workflows. Native integrations, web APIs, and plugins provide seamless integration with the tools you use for on-premises, cloud-based, microservices-based, and container-based development. You’ll get accurate results out of the box, without extensive configuration, custom services, or tuning.

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately.