PostgresML Integrations

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. Superset can connect to any SQL based datasource through SQLAlchemy, including modern cloud native databases and engines at petabyte scale. Superset is lightweight and highly scalable, leveraging the power of your existing data infrastructure without requiring yet another ingestion layer.

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

C++ is a simple and clear language in its expressions. It is true that a piece of code written with C++ may be seen by a stranger of programming a bit more cryptic than some other languages due to the intensive use of special characters ({}[]*&!|...), but once one knows the meaning of such characters it can be even more schematic and clear than other languages that rely more on English words. Also, the simplification of the input/output interface of C++ in comparison to C and the incorporation of the standard template library in the language, makes the communication and manipulation of data in a program written in C++ as simple as in other languages, without losing the power it offers. It is a programming model that treats programming from a perspective where each component is considered an object, with its own properties and methods, replacing or complementing structured programming paradigm, where the focus was on procedures and parameters.

One of the major problems found in the first-generation stableswaps’ Closed liquidity pools is liquidity fragmentation, where the liquidity of different pools cannot be shared with one another, resulting in higher slippage. The design of other stableswaps requires multiple tokens of equal value within a pool, often complicating its pool compositions (pairing up LP tokens with new tokens). It significantly hinders the scalability of the protocol and leads to a bad user experience. Platypus invents a whole new AMM on Avalanche, open liquidity single-sided AMM managing risk autonomously based on the coverage ratio, allowing maximal capital efficiency. The key concept underpinning Platypus’ design is asset liability management (ALM). Platypus is the first of its kind to use a single-variant slippage function instead of invariant curves. Our open liquidity pool design enables higher capital efficiency and lowers the slippage rate compared with other stableswaps.

Falcon is a blazing fast, minimalist Python web API framework for building robust app backends and microservices. The framework works great with both asyncio (ASGI) and gevent/meinheld (WSGI). The Falcon web framework encourages the REST architectural style. Resource classes implement HTTP method handlers that resolve requests and perform state transitions. Falcon complements more general Python web frameworks by providing extra reliability, flexibility, and performance wherever you need it. A number of Falcon add-ons, templates, and complementary packages are available for use in your projects. We've listed several of these on the Falcon wiki as a starting point, but you may also wish to search PyPI for additional resources.

Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.