Production-Grade Container Scheduling and Management

Kubernetes, also known as K8s, is an open source system for managing containerized applications across multiple hosts. It provides basic mechanisms for deployment, maintenance, and scaling of applications. Kubernetes builds upon a decade and a half of experience at Google running production workloads at scale using a system called Borg, combined with best-of-breed ideas and practices from the community. Kubernetes is hosted by the Cloud Native Computing Foundation (CNCF). If your company wants to help shape the evolution of technologies that are container-packaged, dynamically scheduled, and microservices-oriented, consider joining the CNCF. For details about who's involved and how Kubernetes plays a role, read the CNCF announcement. 

The Python micro framework for building web applications.

FastAPI framework, high performance, easy to learn, fast to code, ready for production

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available. Fast to code: Increase the speed to develop features by about 200% to 300%. * Fewer bugs: Reduce about 40% of human (developer) induced errors. * Intuitive: Great editor support. Completion everywhere. Less time debugging. Easy: Designed to be easy to use and learn. Less time reading docs. Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Robust: Get production-ready code. With automatic interactive documentation. Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. 

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based slicing, fancy indexing, and subsetting of large data sets Intuitive merging and joining data sets Flexible reshaping and pivoting of data sets Hierarchical labeling of axes (possible to have multiple labels per tick) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging 

Define and run multi-container applications with Docker

Docker Compose V2 is a major version bump release of Docker Compose. It has been completely rewritten from scratch in Golang (V1 was in Python). The installation instructions for Compose V2 differ from V1. V2 is not a standalone binary anymore, and installation scripts will have to be adjusted. Some commands are different. For a smooth transition from legacy docker-compose 1.xx, please consider installing compose-switch to translate docker-compose ... commands into Compose V2's docker compose .... . Also check V2's --compatibility flag. 

💫 Industrial-strength Natural Language Processing (NLP) in Python

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license. Support for 60+ languages Trained pipelines for different languages and tasks Multi-task learning with pretrained transformers like BERT Support for pretrained word vectors and embeddings State-of-the-art speed Production-ready training system Linguistically-motivated tokenization Components for named entity recognition, part-of-speech-tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more Easily extensible with custom components and attributes Support for custom models in PyTorch, TensorFlow and other frameworks Built in visualizers for syntax and NER Easy model packaging, deployment and workflow management Robust, rigorously evaluated accuracy 

The fundamental package for scientific computing with Python.

A library for efficient similarity search and clustering of dense vectors.

Faiss contains several methods for similarity search. It assumes that the instances are represented as vectors and are identified by an integer, and that the vectors can be compared with L2 (Euclidean) distances or dot products. Vectors that are similar to a query vector are those that have the lowest L2 distance or the highest dot product with the query vector. It also supports cosine similarity, since this is a dot product on normalized vectors. Most of the methods, like those based on binary vectors and compact quantization codes, solely use a compressed representation of the vectors and do not require to keep the original vectors. This generally comes at the cost of a less precise search but these methods can scale to billions of vectors in main memory on a single server. The GPU implementation can accept input from either CPU or GPU memory. On a server with GPUs, the GPU indexes can be used a drop-in replacement for the CPU indexes (e.g., replace IndexFlatL2 with GpuIndexFlatL2) and copies to/from GPU memory are handled automatically. Results will be faster however if both input and output remain resident on the GPU. Both single and multi-GPU usage is supported. 

NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. NLTK requires Python version 3.7, 3.8, 3.9 or 3.10. For documentation, please visit nltk.org. 

Multilingual Sentence & Image Embeddings with BERT

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases. Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task. For the full documentation, see www.SBERT.net. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (EMNLP 2019) Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation (EMNLP 2020) Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks (NAACL 2021) The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes (arXiv 2020) TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning (arXiv 2021) BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models (arXiv 2021) 

Waitress - A WSGI server for Python 2 and 3

Fine-tune BERT to generate sentence embedding for cosine similarity

Build your faq virtual agent in 5 minutes