Search Results for "tune"
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Showing 10 open source projects for "tune"
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Cloud tools for web scraping and data extractionAutomate web data collection with cloud tools that handle anti-bot measures, browser rendering, and data transformation out of the box. Extract content from any website, push to vector databases for RAG workflows, or pipe directly into your apps via API. Schedule runs, set up webhooks, and connect to your existing stack. Free tier available, then scale as you need to.
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
...This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models. Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. -
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PaoPao
A artistic "twitter like" community built on gin+zinc+vue+ts
...The original purpose of docker-compose.yaml provided by default is to build a local development and debugging environment. If you need to deploy the product for external network access, please tune the configuration parameters yourself or use other methods to deploy. It is recommended that the backend service use the supervisordaemon process, and nginxprovide the API to the frontend service call through the reverse proxy. -
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DefectDojo
DefectDojo is a DevSecOps and vulnerability management tool
...DefectDojo has bi-directional integration with JIRA to manage vulnerabilities in developer's natural backlogs. DefectDojo has smart features that learn over time and can automatically tune results. DefectDojo has everything you need to track projects, personnel, metrics, and tasks. DefectDojo allows you to focus on what you actually enjoy doing, rather than reports and metrics. -
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BenchmarkTools.jl
A benchmarking framework for the Julia language
BenchmarkTools makes performance tracking of Julia code easy by supplying a framework for writing and running groups of benchmarks as well as comparing benchmark results. This package is used to write and run the benchmarks found in BaseBenchmarks.jl. The CI infrastructure for automated performance testing of the Julia language is not in this package but can be found in Nanosoldier.jl. Our story begins with two packages, "Benchmarks" and "BenchmarkTrackers". The Benchmarks package... -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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5
Ray
A unified framework for scalable computing
...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. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. ... -
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Zinc Search Engine
ZincSearch. A lightweight alternative to elasticsearch
...It uses bluge as the underlying indexing library. It is very simple and easy to operate as opposed to Elasticsearch which requires a couple dozen knobs to understand and tune which you can get up and running in 2 minutes. You can index large amounts of text Zinc and search instantly. Zinc is a batteries-included setup. Embedded UI provides an easy way to get started and interact with your data. No need to set up a separate GUI like Kibana. Ingestion and search APIs are elasticsearch compatible, so you could easily migrate applications. ... -
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SObjectizer
It's all about in-process message dispatching!
...SObjectizer allows the creation of a concurrent app as a set of agent-objects which interact with each other through asynchronous messages. It handles message dispatching and provides a working context for message processing. And allows to tune those things by supplying various ready-to-use dispatchers. -
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Rocketry
Modern scheduling library for Python
...It does not make assumptions of your project structure. In addition, Rocketry is very easy to use. It does not require complex setup but it can be used for bigger applications. It has a lot of options to fine-tune and a lot of features to support various needs. Rocketry is designed to be modified and it suits well as the engine for autonomous applications. It is the automation back-end that sets your applications alive. -
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Gin Config
Gin provides a lightweight configuration framework for Python
...Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
Say goodbye to broken revenue funnels and poor customer experiencesLeanData is a Demand Management solution that supports all go-to-market strategies such as account-based sales development, geo-based territories, and more. LeanData features a visual, intuitive workflow native to Salesforce that enables users to view their entire lead flow in one interface. LeanData allows users to access the drag-and-drop feature to route their leads. LeanData also features an algorithms match that uses multiple fields in Salesforce.
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10
Apache PredictionIO
Machine learning server for developers and ML engineers
...Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management.
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