Search Results for "parallel search"
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Showing 14 open source projects for "parallel search"
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1
Nevergrad
A Python toolbox for performing gradient-free optimization
Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and... -
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YAPF
A formatter for Python files
YAPF is a Python code formatter that automatically rewrites source to match a chosen style, using a clang-format–inspired algorithm to search for the “best” layout under your rules. Instead of relying on a fixed set of heuristics, it explores formatting decisions and chooses the lowest-cost result, aiming to produce code a human would write when following a style guide. You can run it as a command-line tool or call it as a library via FormatCode / FormatFile, making it easy to embed in... -
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Bull
Queue package for handling distributed jobs and messages in NodeJS
...Use the promo code "BULLMQ" when signing up to help us sponsor the development of Bull! Supercharge your queues with a professional front end. Get a complete overview of all your queues. Inspect jobs, search, retry, or promote delayed jobs. Metrics and statistics, and many more features. Queues are robust and can be run in parallel in several threads or processes without any risk of hazards or queue corruption. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. ... -
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5
Evolutionary Computation Framework
C++ framework for application of any type of evolutionary computation.
ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners. -
6
Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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RadonDB
RadonDB is an open source, cloud-native MySQL database
RadonDB is a cloud-native database based on MySQL, and architected in fully distributed cluster that enable unlimited scalability (scale-out), capacity and performance. It supported distributed transaction that ensure high data consistency, and leveraged MySQL as storage engine for trusted data reliability. RadonDB is compatible with MySQL protocol, and sup-porting automatic table sharding as well as batch of automation feature for simplifying the maintenance and operation workflow. RadonDB... -
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NppGTags
GTags plugin for Notepad++
This Notepad++ plugin is simply a front-end to GTags (GNU Global source code tagging system): http://www.gnu.org/software/global/global.html It provides tools for easy code navigation. GTags binaries (Win32 ports) are needed by the plugin. Those are supplied with it for convenience. -
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Catberry
Catberry is an isomorphic framework
...The entire architecture of the framework is built using the Service Locator pattern, which helps to manage module dependencies and create plugins, and Flux, for the data layer. Search crawler receives a full page from the server. The whole state of the application is restored from URL. Server-side progressive rendering based on node.js streams and parallel rendering of components in a browser. The framework is well-tested (code coverage is about 90%) and it is already used in production. -
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10
optpack
FE simulation based automatic global optimisation in modern Fortran
...The initial application is for FE simulation of elasto-plastic buckling of steel rods aimed at tuning the hardening model. Unix shell scripts are used extensively. High concurrency is achieved with parallel multi-start global search and with parallel MPI FE. Powell's BOBYQA or LINCOA are used (not included in this package). Automatic mesh generation of 3D cylinders is provided + automatic boundary conditions, all via Fortran and shell scripting. -
11
Fast Matrix for Java
General purpose matrix utilities for Java in Parallel Computing
Fast Matrix for Java (fm4j) is a general-purpose matrix utility library for computing with dense matrices. fm4j encapsulated different underlying implementations and select the optimal one in run-time depending on the size of the input matrix. Moreover, fm4j employs Java (Tm) Concurrency to take advantage of the computation power of multi-cor processors. -
12
Tuple Spaces
Tuple space with time outs and transactions.
Java implementation of a Tuplespace. Moved to https://github.com/mike-k-houghton/tuplespace A Tuple is an ordered list of items. A Tuple Space is a form of associative memory where entries, tuples, are stored in the space and are retrieved using search criteria that are based on the intrinsic properties of the tuples. The two key operations are put get And that is it! There are refinements on these operations including, for example, timeouts where the tuple will only exist... -
13
gpufsknn
A GPU-based efficient data parallel formulation of the kNN problem
A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc. -
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The LisBON Framework is an adaptable framework for developing new parallel Memetic Algorithms (hybrid search algorithms for efficiently solving optimisation problems).
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