This content originally appeared on DEV Community and was authored by Jesse Williams
The AI boom has been characterized by huge advancements in research and computing hardware, with open source AI libraries emerging as a key contributor. Several of these libraries are specifically engineered to be robust, scalable, and reliable for use in real-world environments. There is a huge adoption of open source libraries. According to the Octaverse 2023 report:
- Developers made over 301 million total contributions to open source projects across GitHub.
- Open Source Program Offices (OSPO) adoption across global companies increased by over 32% since 2022.
In this article, you’ll learn about open source AI libraries specifically designed and optimized for use in production environments, their importance, some challenges they face, and the solution. Here are the top 5.
Top 5 open source AI libraries
Open source libraries offer publicly accessible tools for building and deploying AI models, allowing anyone to view, modify, and share the code. These libraries are constantly updated by users worldwide, which helps keep them reliable and up to date. They support various fields, including computer vision, deep neural networks, reinforcement learning, and natural language processing, making it easier and more affordable to use advanced AI technology in your projects.
The open-source community has grown significantly, offering a wide variety of powerful libraries to choose from. This article will focus on the following libraries:
- KitOps
- PyTorch
- HuggingFace
- LangChain
- TensorFlow
KitOps
KitOps is an open source machine learning platform that bridges the gap between software engineers, data scientists, DevOps engineers, and machine learning engineers. KitOps bundles all machine learning models and dependencies into a ModelKit, making it easier to version, tag, handle, and track these components. KitOps also makes it easy to eventually unpack individual model components, including models, datasets, and code.
As KitOps is compatible with many tools like SageMaker, HuggingFace and others, it’s easier for DevOps teams to build AI/ML automation pipelines within familiar environments. It also abstracts the maintenance of infrastructure from data scientists, making it easy for them to focus on building models. KitOps has a community on Discord, where you can get support, news and product updates.
PyTorch
PyTorch is a tool for building deep learning models, launched by Meta in 2016. It is often used in image recognition, natural language processing, and reinforcement learning. PyTorch is essential for researchers, data scientists, and machine learning engineers.
PyTorch’s ease of use and flexibility, distributed processing, and cloud support make it a good choice for companies looking for open source production-ready solutions. It also has a large ecosystem of tools, such as ParlAI, EinOps, and Accelerate, and a very welcoming community on Slack and PyTorchDiscuss.
HuggingFace
HuggingFace transformer enables you to build, train, and deploy machine learning models. HuggingFace is a registry for several models, allowing you to interact with these models through an API available in Python, JavaScript, and Rust. This makes it straightforward for AI engineers to collaborate, share code, analyze visual data, models, and datasets.
HuggingFace has a robust collection of over two hundred thousand datasets, where users can easily download and use these open datasets to train their ML models. They also have Spaces where you can see some models developed by the community. Speaking of community, HuggingFace has a large community where users can post issues they currently face and get a response.
LangChain
LangChain is a framework that makes it easy for AI developers to connect their language models to external data sources. It allows you to build AI agents that can be easily integrated with your company’s datasets and APIs. It can also be integrated with workflow orchestration tools like n8n, making it easy to build and scale your AI agents.
LangChain has a “chain” of similar products, such as LangSmith, which allows you to easily get your large language model (LLM) applications from prototype to production, and LangGraph, which allows you to create complex agents. Langchain can also be used with programming languages such as Python and JavaScript. LangChain also has a large community on Slack.
TensorFlow
TensorFlow is an open source AI platform widely used for building, training, and deploying machine learning models to production. It has a wide set of libraries, such as TensorFlow Lite, for deploying ML applications on mobile devices; TensorFlow JS, which is an ML tool for JavaScript; and TensorFlow data, which is used to build input pipelines, among others.
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.
These 5 libraries are great, each with its own merit; however, why do we care so much about open source AI libraries in the first place?
Importance of open source AI libraries
The free nature of open source technologies, coupled with the robust community support, makes open source appealing to most organizations. There are numerous benefits to using open-source AI solutions. Some of these are:
- Transparency
- Security
- Robust community support
- Ease of collaboration
Transparency
One of the numerous benefits of open source AI projects is transparency. All the code changes are public. This way, users can see the source code and understand how the software works end-to-end, how it processes data, and what dependencies it needs.
Security
Open source ML libraries allow for extensive code review, leading to more secure software being distributed. Bugs and vulnerabilities are often quickly identified and fixed by the community. This way, users pay close attention to security, adding vulnerability tests to the CI/CD workflows and automating the detection of threats in the libraries.
Robust community support
Open source AI tools usually have large, active communities. This way, when users encounter problems or have some issues regarding the software, they can easily create an issue on GitHub or ask a question on the community forum.
Ease of collaboration
A collaborative environment in open source AI projects builds rapid innovation by bringing together a diverse community of ML engineers, software engineers, technical writers, and users looking for a way to contribute to the library. The open nature allows anyone to contribute to bug fixing and feature implementation, creating an ecosystem of continuous integration and development.
Though open-source AI libraries offer numerous benefits, they can also present challenges that impact efficiency and the overall user experience.
Challenges with open source AI frameworks
While open source AI libraries offer a range of powerful tools, many fall short when addressing the challenges machine learning teams face when deploying models at scale in production. Some of these challenges are:
- High latency
- Version control
- Security
High latency
Latency refers to the time delay between a user's request and the response received from the server. Techniques such as quantization, pruning, and model distillation can be employed to address this issue. Additionally, production-optimized formats like open neural network exchange (ONNX), a platform-independent format for deep-learning models, can reduce latency and improve performance.
Version control
Version control is essential for collaboration in software development, with systems like Git, SVN, and Mercurial playing a crucial role. These tools facilitate collaboration, help track changes, and are indispensable for any development team.
However, versioning AI systems presents unique challenges due to the complex data structures and formats involved. Machine learning models are typically defined by their weights, hyperparameters, preprocessing steps, and architectures, all of which may need to be versioned separately or as a unified entity, adding layers of complexity.
Given the sensitivity of machine learning systems to the data they are trained on, it's vital to version data alongside models to ensure reproducibility and maintain an understanding of the model's performance over time.
Security
Security is another critical concern in AI workflows. This is due to the vast amounts of data used during training and inference, often including sensitive or personally identifiable information *****(PII).* Safeguarding this data is essential for maintaining the integrity of the workflow. Common security threats in AI production environments include:
- Model theft occurs when a user tries to gain unauthorized access to a model, which can involve gaining unauthorized access to a repository containing its models and configurations.
- Adversarial attacks are when users try to deceive the models into making incorrect or harmful predictions by modifying input data.
Having secure storage for your models, datasets, and code is beneficial in ensuring your artifacts are safe and secure.
You’ve learned about some of the challenges faced by open source libraries, let’s look at a few solutions.
How can KitOps help with this?
You saw KitOps earlier in this post. In an AI/ML development setup, it tackles latency, versioning, and tagging challenges through its ModelKit system. ModelKits have several benefits, including:
- Transparency
- Versioning
- Integration with CI/CD
- JozuHub’s secure storage ## Transparency
Your typical AI framework generally consists of dependencies like models, datasets, notebooks, and configurations. KitOps provides ModelKit, which allows teams to include these dependencies in a single ModelKit package. This approach makes it easy to pass around the right packaged bundle and deploy your ModelKit packages across several environments.
Versioning
KitOps addresses the version control challenges with its robust versioning and tagging system. Each ModelKit is tagged, creating clear links between datasets and models, which is essential for ensuring reproducibility and effectively managing model drift. The tamper-proof design of ModelKits, reinforced by SHA digests, guarantees the integrity of models and data throughout the development and deployment lifecycle.
Integration with CI/CD
KitOps also offers automation features for CI/CD workflows, including GitHub Actions, which streamline development, testing, and deployment processes. This approach boosts collaboration between data teams, software engineers, and DevOps professionals, leading to greater team coordination.
JozuHub’s secure storage
JozuHub is a repository offering secure storage for ModelKits, including all versions and the model dependencies, such as code, datasets, models, and docs. It allows you to see the differences between each ModelKit version and tags, making it easy to track and compare changes between versions.
Conclusion
In this article, you learned about open source AI libraries built for production and the integration of KitOps to solve these problems. As organizations integrate open source AI tools into their operations, it is crucial to consider key factors such as latency, security, and versioning. KitOps ensures your AI solutions are secure, version-controlled, and optimized for performance and compliance.
If you have questions about integrating KitOps with your team, join the conversation on Discord and start using KitOps today!
This content originally appeared on DEV Community and was authored by Jesse Williams
Jesse Williams | Sciencx (2024-10-04T13:10:07+00:00) Top 5 Production-Ready Open Source AI Libraries for Engineering Teams. Retrieved from https://www.scien.cx/2024/10/04/top-5-production-ready-open-source-ai-libraries-for-engineering-teams/
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