Search Results for "arch linux custom" - Page 2
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Showing 60 open source projects for "arch linux custom"
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and... -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. It is easy to customize or extend. Users can find their... -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can... -
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seq2seq-couplet
Play couplet with seq2seq model
seq2seq-couplet is a deep learning application that generates Chinese couplet responses using a sequence-to-sequence model built with TensorFlow. Its purpose is not general machine translation, but a specialized text generation task in which the model produces a matching second line for a given first line in the style of traditional couplets. The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score... -
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Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. - CPU-compatible desktop build The standard... -
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GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT app is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the... -
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Featuretools
An open source python library for automated feature engineering
An open source Python framework for automated feature engineering. Featuretools automatically creates features from temporal and relational datasets. Featuretools uses DFS for automated feature engineering. You can combine your raw data with what you know about your data to build meaningful features for machine learning and predictive modeling. Featuretools provides APIs to ensure only valid data is used for calculations, keeping your feature vectors safe from common label leakage problems.... -
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UpTrain
Your open-source LLM evaluation toolkit
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison... -
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Autodistill
Images to inference with no labeling
Autodistill uses big, slower foundation models to train small, faster supervised models. Using autodistill, you can go from unlabeled images to inference on a custom model running at the edge with no human intervention in between. You can use Autodistill on your own hardware, or use the Roboflow hosted version of Autodistill to label images in the cloud. -
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TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the... -
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YoloV3 Implemented in TensorFlow 2.0
YoloV3 Implemented in Tensorflow 2.0
YoloV3 Implemented in TensorFlow 2.0 is built using TensorFlow 2.0. The project provides a modern deep learning implementation of the popular YOLOv3 algorithm, which is widely used for real-time object detection in images and video streams. YOLOv3 works by dividing an image into grid regions and predicting bounding boxes and class probabilities simultaneously, allowing objects to be detected quickly and efficiently. The repository includes training scripts, inference tools, and configuration... -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common... -
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OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
OpenNMT is an open-source ecosystem for neural machine translation and neural sequence learning. OpenNMT-tf is a general-purpose sequence learning toolkit using TensorFlow 2. While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the... -
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Merlion
A Machine Learning Framework for Time Series Intelligence
Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting, anomaly detection, and change point detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to... -
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Texthero
Text preprocessing, representation and visualization from zero to hero
Texthero is a python package to work with text data efficiently. It empowers NLP developers with a tool to quickly understand any text-based dataset and it provides a solid pipeline to clean and represent text data, from zero to hero. -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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U-Net Fusion RFI
U-Net for RFI Detection based on @jakeret's implementation
See original code here: https://github.com/jakeret/tf_unet Currently this project is based on Tensorflow 1.13 code base and there are no plans to transfer to TF version 2. The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers to... -
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YOLOv4-large
Scaled-YOLOv4: Scaling Cross Stage Partial Network
YOLOv4-large is an open-source implementation of the Scaled-YOLOv4 object detection architecture, designed to improve both the accuracy and scalability of real-time computer vision models. The project provides a PyTorch implementation of the Scaled-YOLOv4 framework, which extends the original YOLOv4 architecture using Cross Stage Partial (CSP) networks and new scaling techniques. Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects... -
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FARM
Fast & easy transfer learning for NLP
FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning... -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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GluonNLP
NLP made easy
GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training...