Search Results for "there is no way to" - Page 4
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Showing 543 open source projects for "there is no way to"
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HubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
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Track leads, jobs, and tasks from one easy to use software. You can access your information wherever you are, get everyone on the same page, and grow your business.
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1
Bottles
Run Windows software and games on Linux
Bottles lets you run Windows software on Linux, such as applications and games. It introduces a workflow that helps you organize by categorizing each software to your liking. Bottles provide several tools and integrations to help you manage and optimize your applications. Bottles introduces a new way to handle Windows prefixes using environments, a combination of ready-to-use settings, libraries and dependencies. -
2
IPyPlot
Fast and efficient plotting of images inside Python Notebooks
IPyPlot is a small python package offering fast and efficient plotting of images inside Python Notebooks. It's using IPython with HTML for faster, richer and more interactive way of displaying big numbers of images. -
3
Finetuner
Task-oriented finetuning for better embeddings on neural search
Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive. Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. Create high... -
4
Textinator
Internationalized highly customizable annotation and evaluation tool
Textinator is an open-source internationalized highly-customizable annotation and evaluation tool for Natural Language Processing (NLP) tasks. The tool offers a web interface with a user-friendly UI and supports a number of NLP tasks out of the box. The recommended way of deploying Textinator is through building a production version of the Docker container, as described in the Deployment guidelines. Note that the production version is most definitely more secure and reliable than... -
Paligo is an end-to-end Component Content Management System (CCMS) solution for technical documentation, policies and procedures, knowledge management, and more.
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5
Nafas
Breathing Gymnastics Application
Breathing gymnastics is a system of breathing exercises that focuses on the treatment of various diseases and general health promotion. Nafas is a collection of breathing gymnastics designed to reduce the exhaustion of long working hours. With multiple breathing patterns, Nafas helps you find your way to a detoxified energetic workday and also improves your concentration by increasing the oxygen level. No need to walk away to take a break, just sit comfortably, run Nafas and let the journey... -
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MechanicalSoup
A Python library for automating interaction with websites
A Python library for automating interaction with websites. MechanicalSoup automatically stores and sends cookies, follows redirects, and can follow links and submit forms. It doesn't do JavaScript. MechanicalSoup was created by M Hickford, who was a fond user of the Mechanize library. Unfortunately, Mechanize was incompatible with Python 3 until 2019 and its development stalled for several years. MechanicalSoup provides a similar API, built on Python giants Requests (for HTTP sessions) and... -
7
Devicon
Set of icons representing programming languages, design & development
Devicon is a set of icons representing programming languages, designing, and development tools. You can use it as a font or directly copy/paste the SVG code into your project. Devicon aims to gather all logos representing development languages and tools. Each icon comes in several versions: font/SVG, original/plain/line, colored/not colored, wordmark/no wordmark. Devicon has 150+ icons. And it's growing! Open icomoon.io and import icomoon.json. Choose yes when being asked if you would like... -
8
Spirit
Spirit is a modern Python based forum built on top of Django framework
... comment you want with a "like." Spirit is mobile-first, it's meant to work in the same way in any given device. The code has been written on python employing django's framework and it can be easily integrated to pre-existing projects. Spirit is part of the open source initiative, and it's a project released under the terms of the MIT license. -
9
Django Filer
File and Image Management Application for django
... interface for django-filer images. This is very useful when later resizing and cropping images with easy_thumbnails. The image can then be cropped automatically in a way, that the important part of the image is always visible. You can configure your project to generate canonical URLs for your public files. -
eloomi combines Learning Management (LMS), Onboarding, Authoring, and continuous Performance Management tools in a cloud-based solution. It allows companies to optimize skill training, onboarding and employee development with strong user experience to enhance productivity and employee satisfaction. As a white label solution, the platform can be customized to mirror a company's branding and logo.
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10
Sanic
Async Python 3.6+ web server/framework
Build fast, run fast with Sanic! Sanic is a Python 3.6+ web server and web framework designed to go fast. It provides a way to get a highly performant HTTP server up and running fast, while also making it easy to build, expand, and eventually scale. Sanic aspires to be as simple as possible while delivering the performance that you require. It allows the usage of the async/await syntax added in Python 3.5, so your code is guaranteed to be non-blocking and speedy. It's also ASGI compliant, so... -
11
Python Fire
Automatically generate CLIs from absolutely any Python object
Python Fire is a library that automatically generates command line interfaces (CLIs) from absolutely any Python object. It’s a really simple and easy way to create CLI in Python, and can also enable you to explore existing code or turn other people’s code into a CLI. Python Fire lets you call Fire on any Python object: be it functions, classes, objects, modules, lists-- you name it! It will help you develop as well as debug Python code, and make transitioning between Bash and Python a whole... -
12
Bytewax
Python Stream Processing
... processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output. -
13
garak
Developers and anyone seeking an LLM solution to scan for vulnerabilit
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... -
14
Otter-Grader
A Python and R autograding solution
Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is designed to work with classes at any scale by abstracting away the autograding internals in a way that is compatible with any instructor's assignment distribution and collection pipeline. Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment... -
15
PySR
High-Performance Symbolic Regression in Python and Julia
... are described in the PySR paper. Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks, as explained in 2006.11287, where we apply it to N-body problems. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks. -
16
Modin
Scale your Pandas workflows by changing a single line of code
Scale your pandas workflow by changing a single line of code. Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. It is not necessary to know in advance the available hardware resources in order to use Modin. Additionally, it is not necessary to specify... -
17
gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is... -
18
whylogs
The open standard for data logging
whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple... -
19
UnionML
Build and deploy machine learning microservices
... learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. This helps you maintain consistent code across your ML stack, from training to prediction logic. -
20
BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics
... the paper or see a brief overview. After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis. By default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence. -
21
NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way... -
22
Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
23
langchain-prefect
Tools for using Langchain with Prefect
Large Language Models (LLMs) are interesting and useful - building apps that use them responsibly feels like a no-brainer. Tools like Langchain make it easier to build apps using LLMs. We need to know details about how our apps work, even when we want to use tools with convenient abstractions that may obfuscate those details. Prefect is built to help data people build, run, and observe event-driven workflows wherever they want. It provides a framework for creating deployments on a whole... -
24
MMGeneration
MMGeneration is a powerful toolkit for generative models
... interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs! For the highly dynamic training in generative models, we adopt a new way to train dynamic models with MMDDP. A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combinations among different modules. Conditional GANs have been supported in our toolkit. More methods and pre-trained weights will come soon. -
25
Segmentation Models
Segmentation models with pretrained backbones. PyTorch
Segmentation models with pre trained backbones. High-level API (just two lines to create a neural network) 9 models architectures for binary and multi class segmentation (including legendary Unet) 124 available encoders (and 500+ encoders from timm) All encoders have pre-trained weights for faster and better convergence. Popular metrics and losses for training routines. All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give you better...