Search Results for "state-thread" - Page 2
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Showing 97 open source projects for "state-thread"
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Catalyst.jl
Chemical reaction network and systems biology interface
Catalyst.jl is a symbolic modeling package for analysis and high-performance simulation of chemical reaction networks. Catalyst defines symbolic ReactionSystems, which can be created programmatically or easily specified using Catalyst's domain-specific language (DSL). Leveraging ModelingToolkit and Symbolics.jl, Catalyst enables large-scale simulations through auto-vectorization and parallelism. Symbolic ReactionSystems can be used to generate ModelingToolkit-based models, allowing the easy... -
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Cleanlab
The standard data-centric AI package for data quality and ML
...To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. ... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
...Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. ... -
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Recommenders
Best practices on recommendation systems
...Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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Ridgepole
Ridgepole is a tool to manage DB schema. It defines DB schema
Ridgepole is a database schema management tool that treats your schema as code by expressing it in a Ruby DSL and applying diffs to keep databases in sync. You describe the desired state in a “Schemafile” (tables, columns, indexes, constraints), and Ridgepole compares it with the live database to generate only the necessary changes. This diff-and-apply approach makes schema changes repeatable and reviewable, avoiding hand-written migrations for routine structural edits. It supports multiple environments and options to ignore benign differences (such as auto-generated defaults), reducing churn in teams and CI pipelines. ... -
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Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop... -
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Nebula Graph
A distributed, fast open-source graph database
The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and... -
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relax
Molecular dynamics by NMR data analysis
...It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, the Lipari and Szabo model-free analysis, study of domain motions via the N-state model and frame order dynamics theories using anisotropic NMR parameters such as RDCs and PCSs, the investigation of stereochemistry in dynamic ensembles, and the analysis of relaxation dispersion data. -
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protoactor-go
Proto Actor - Ultra fast distributed actors for Go, C# and Java/Kotlin
...The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. This allows Proto.Actor to leverage in-process performance for realtime stream processing. -
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FastAI.jl
Repository of best practices for deep learning in Julia
FastAI.jl is a Julia library for training state-of-the-art deep learning models. From loading datasets and creating data preprocessing pipelines to training, FastAI.jl takes the boilerplate out of deep learning projects. It equips you with reusable components for every part of your project while remaining customizable at every layer. FastAI.jl comes with support for common computer vision and tabular data learning tasks, with more to come. -
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ProxSDP.jl
Semidefinite programming optimization solver
ProxSDP is an open-source semidefinite programming (SDP) solver based on the paper "Exploiting Low-Rank Structure in Semidefinite Programming by Approximate Operator Splitting". The main advantage of ProxSDP over other state-of-the-art solvers is the ability to exploit the low-rank structure inherent to several SDP problems. -
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FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia
...It can be used to generate a fast generic sequential and parallel iteration over complex collections. Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provides a simple distributed executor. -
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CliMA Land
Everything within the Land model
...This project is supposed to be a community effort, leveraging all the work that has been done in Land Surface Modeling from various groups around the world. The ultimate goal is to build a bio-physical model that represents the state of the art and can be coupled to the CliMA Earth System Model (ESM), i.e. Caltech's CliMA initiative. -
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MonteCarlo.jl
Classical and quantum Monte Carlo simulations in Julia
MonteCarlo.jl is a package implementing classical and quantum Monte Carlo simulations primarily for solid state physics. Currently the focus is on finding a overall design for the package and verifying that determinant Quantum Monte Carlo (DQMC) is working correctly. As such the package may still go through significant changes and the documentation may be outdated and incomplete. Note that classical Monte Carlo is also not a focus at this point. -
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ThreadsX.jl
Parallelized Base functions
...Everything inside ThreadsX.Implementations is an implementation detail. The public API functions of ThreadsX expect that the data structure and function(s) passed as argument are "thread-friendly" in the sense that operating on distinct elements in the given container from multiple tasks in parallel is safe. For example, ThreadsX.sum(f, array) assumes that executing f(::eltype(array)) and accessing elements as in array[i] from multiple threads is safe. -
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Remotery
Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer
Remotery is a real-time CPU/GPU profiler implemented as a single C file, providing developers with immediate insights into the performance of their applications. It features a remote web-based viewer that runs in browsers like Chrome, Firefox, and Safari, allowing for cross-platform performance analysis. Remotery supports profiling multiple threads and GPU contexts, offering a comprehensive view of an application's performance characteristics. -
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TSNE-CUDA
GPU Accelerated t-SNE for CUDA with Python bindings
...Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. Time taken compared to other state of the art algorithms on synthetic datasets with 50 dimensions and four clusters for varying numbers of points. Note the log scale on both the points and time axis, and that the scale of the x-axis is in thousands of points (thus, the values on the x-axis range from 1K to 10M points. Dashed lines on SkLearn, BH-TSNE, and MULTICORE-4 represent projected times. ... -
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Perceptron
The birth of modern video feedback art.
...Perceptron * recursively transforms images and video streams in realtime and produces a combination of Julia fractals, IFS fractals, and chaotic patterns due to video feedback * evolves geometric patterns into the realm of infinite details and deepens the thought * records animations (movies) * saves and opens presets (state files) * loads user photographs or captures screen and webcam input * has user interface based on multiple mouse cursors, and almost the entire keyboard * has multiple windows and fullscreen mode * takes complex geometric transforms as input * applies numerous coloring techniques * smoothly transforms fractals and creates endless psychedelic journeys * resonates with the human perception Visit the Perceptron home page at http://perceptron.sourceforge.net -
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Gabedit is a Graphical User Interface for FireFly (PC-Gamess), Gamess-US, Gaussian, Molcas, Molpro, MPQC, NWChem, OpenMopac, Orca, PSI4 and Q-Chem computational chemistry packages.
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RayTracer.jl
Differentiable RayTracing in Julia
This package was written in the early days of Flux / Zygote. Both these packages have significantly improved over time. Unfortunately, the current state of this package of has not been updated to reflect those improvements. It also seems that it might be better to gradually transition to defining the adjoints directly using ChainRules. A Ray Tracer written completely in Julia. This allows us to leverage the AD capabilities provided by Zygote to differentiate through the Ray Tracer. -
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DEPRECATED - KVFinder
Cavity Detection PyMOL plugin
...[parKVFinder] A Linux/macOS version is available in this GitHub repository, https://github.com/LBC-LNBio/parKVFinder, while a Windows version is in this GitHub repository, https://github.com/LBC-LNBio/parKVFinder-win. Please read and cite the original paper ParKVFinder: A thread-level parallel approach in biomolecular cavity detection (10.1016/j.softx.2020.100606). [pyKVFinder] pyKVFinder is available in this Python Package Index (PyPI) repository, https://pypi.org/project/pyKVFinder and this GitHub repository, https://github.com/LBC-LNBio/pyKVFinder. Please read and cite the original paper pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science (10.1186/s12859-021-04519-4). -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. -
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ksqlDB
The database purpose-built for stream processing applications
Build applications that respond immediately to events. Craft materialized views over streams. Receive real-time push updates, or pull current state on demand. Seamlessly leverage your existing Apache Kafka® infrastructure to deploy stream-processing workloads and bring powerful new capabilities to your applications. Use a familiar, lightweight syntax to pack a powerful punch. Capture, process, and serve queries using only SQL. No other languages or services are required. ksqlDB enables you to build event streaming applications leveraging your familiarity with relational databases. ... -
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NYCOpenData-Profiling-Analysis
Open Data Profiling, Quality and Analysis on NYC OpenData dataset
...You will profile a large collection of open data sets and derive metadata that can be used for data discovery, querying, and identification of data quality problems. For each column, identify and summarize the semantic types present in the column. These can be generic types (e.g., city, state) or collection-specific types (NYU school names, NYC agency). For each semantic type T identified, enumerate all the values encountered for T in all columns present in the collection.
