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Showing 53 open source projects for "compiler python linux"
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference,... -
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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
TensorFlow.js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Retrain pre-existing ML models using your own data. Build and train models directly in JavaScript using flexible and intuitive APIs. Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices... -
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PyMC3
Probabilistic programming in Python
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
Build apps or websites quickly on a fully managed platformRun frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure.
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging... -
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TensorFlow Model Garden
Models and examples built with TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models... -
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smclarify
Fairness aware machine learning. Bias detection and mitigation
Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome. A bias measure is a function... -
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DAE Tools Project
Object-oriented equation-based modelling and optimisation software
DAE Tools is a cross-platform equation-based object-oriented modelling, simulation and optimisation software. It is not a modelling language nor a collection of numerical libraries but rather a higher level structure – an architectural design of interdependent software components providing an API for: - Model development/specification - Activities on developed models, such as simulation, optimisation, sensitivity analysis and parameter estimation - Processing of the results, such as... -
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ECLiPSe CLP
ECLiPSe Constraint Logic Programming System
The ECLiPSe Constraint Logic Programming System is designed for solving combinatorial optimization problems, for the development of new constraint solver technology and their hybrids, and for the teaching of modelling, solving and search techniques. -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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TensorFlow.js models
Pretrained models for TensorFlow.js
... run the unit tests for any of the models by running "yarn test" inside a directory. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Build and train models directly in JavaScript using flexible and intuitive APIs. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. -
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Brain AutoML
Google Brain AutoML
This repository contains a list of AutoML related models and libraries. Google Brain researchers have introduced a new way of programming automated machine learning (AutoML) based on symbolic programming. The researchers also proposed PyGlove, a general symbolic programming library for Python, to implement the symbolic formulation of AutoML. AutoML, designed to fill the machine learning industry’s talent gap, is gaining traction among various organizations. -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
... on-boarding for their team members, freeing them up from complex systems admin and security processes. Administrators control data access and resource provisioning for their users. Notebook Instances are another option. They have the familiar Jupyter and JuypterLab interfaces that work well for single users, or small teams where users are also administrators. Advanced users also use SageMaker solely with the AWS CLI and Python scripts using boto3 and/or the SageMaker Python SDK. -
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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly... -
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Pretty Damn Quick (PDQ) analytically solves queueing network models of computer and manufacturing systems, data networks, etc., written in conventional programming languages. Generic or customized reports of predicted performance measures are output.
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SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python... -
18
setupdocx
Multidocument automation by templates - for sphinx, mkdocs, epydoc ...
The ‘setupdocx‘ provides a control layer for continuous documentation by the simplified creation, packaging, and installation of documentation. The provided commands are distributed as entry points and optional base classes for further customization into 'setup.py' - setuptools / distutils. Manages arbitrary document templates for the supported builder, supports multiple builds with arbitrary document layouts, designs, and patched contents. The current release supports the following... -
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DRAKON Editor
A free cross-platform editor for the DRAKON visual language.
... DRAKON Editor to build algorithms in Go, Java, Processing.org, D, C#, C, C++, Python, Tcl, Javascript, Erlang and Lua. -
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Jmr - Java Code Generator
"black technology" will improve development productivity by 1.5 to 5
...(<%%> JSP-like) and Jmr Tag(set/get/for...) Model: Database Model(like Table/View), Java POJO Model, Xml File Model and includes Transform/Mapping/Convert Tool Action: A Java Class can set Java variables to template. Support any kind of project Maven, Gradle, Web, Java, Android, PHP, Python etc. Support any kind of framework spring/springMVC, hibernate, ibatis/mybatis, struts/struts2, JPA etc. Support any kind of language Java the best. -
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That project aims at providing a clean API, and the corresponding C++ implementation, for the basis of Airline IT Business Object Model (BOM), ie, to be used by several other Open Source projects, such as RMOL, Air-Sched, Travel-CCM, OpenTREP, etc.
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...-lK44Q9uQHFio_mGoK0oY829q5lD7i4/viewform GillesPy is a python interface for developing models and simulating them with StochKit. Please see: https://github.com/GillesPy/gillespy/ StochKit currently supports Linux, Windows, and OS X.
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MSCViewer
A tool for visualization and analysis of logs as sequence diagrams
MSCViewer is a tool intended for debugging of control flows in concurrent, distributed systems. The tool loads logs generated by various entities in the system and visualize a sequence diagram chart for events and interactions. The diagram is fully interactive: entity can be added/removed from the diagram and shuffled; events can be filtered, searched, highlighted and annotated with comments. MSCViewer features integration with a Python interpreter which allows writing Python scripts...
