solid_queue
MindsDB
solid_queue | MindsDB | |
---|---|---|
6 | 78 | |
1,529 | 21,424 | |
9.4% | 2.1% | |
9.5 | 10.0 | |
6 days ago | 4 days ago | |
Ruby | Python | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
solid_queue
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solid_queue alternatives - Sidekiq and good_job
3 projects | 21 Apr 2024
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How Rails Powers PopaDex for Simplified Financial Planning
One of the key challenges in any applications is managing long-running tasks without affecting the user experience. PopaDex leverages the solid_queue gem to handle background processing efficiently. This "DB-based queuing backend for Active Job" allows for tasks such as report generation and notifications to be processed in the background, ensuring the application remains responsive. The beauty of solid_queue lies in its simplicity and efficiency, obviating the need for more complex solutions like Redis or Sidekiq for background job management. This choice offers several distinct advantages:
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Tuning Rails application structure
Once we are done with default gems, should we look into something we usually use? That's jwt because we need session tokens for our API. Next comes our one and only sidekiq. For a long period of time it was the best in town solution for background jobs. Now we could also consider solid_queue or good_job. In development and testing groups we need rspec-rails, factory_bot_rails and ffaker. Dealing with money? Start doing it properly from the beginning! Do not forget to install money-rails. Once everything is added to the Gemfile do not forget to trigger bundle install.
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Ruby on Rails load testing habits
Rails isn't super opinionated about database writes, its mostly left up to developers to discover that for relational DBs you do not want to be doing a bunch of small writes all at once.
That said it specifically has tools to address this that started appearing a few years ago https://github.com/rails/rails/pull/35077
The way my team handles it is to stick Kafka in between whats generating the records (for us, a bunch of web scraping workers) and and a consumer that pulls off the Kafka queue and runs an insert when its internal buffer reaches around 50k rows.
Rails is also looking to add some more direct background type work with https://github.com/basecamp/solid_queue but this is still very new - most larger Rails shops are going to be running a second system and a gem called Sidekiq that pulls jobs out of Redis.
- FLaNK Weekly 08 Jan 2024
- Solid Queue: Database-backed Active Job back end
MindsDB
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Whatβs the Difference Between Fine-tuning, Retraining, and RAG?
Check us out on GitHub.
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How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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Fine-tuning a Mistral Language Model with Anyscale
MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.
- Vanna.ai: Chat with your SQL database
- FLaNK Weekly 08 Jan 2024
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MindsDB Docker Extension: Build ML powered apps at a much faster pace
MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.
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How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":
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ππ 23 issues to grow yourself as an exceptional open-source Python expert π§βπ» π₯
Repo : https://github.com/mindsdb/mindsdb
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AI-Powered Selection of Asset Management Companies using MindsDB and LlamaIndex
MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDFβs, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.
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Using Large Language Models inside your database with MindsDB
Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.
What are some alternatives?
hyperfine - A command-line benchmarking tool
tensorflow - An Open Source Machine Learning Framework for Everyone
good_job - Multithreaded, Postgres-based, Active Job backend for Ruby on Rails.
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
durdraw - Versatile ASCII and ANSI Art text editor for drawing in the Linux/Unix/macOS terminal, with animation, 256 and 16 colors, Unicode and CP437, and customizable themes
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
scikit-learn - scikit-learn: machine learning in Python
lightwood - Lightwood is Legos for Machine Learning.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Keras - Deep Learning for humans
MLflow - Open source platform for the machine learning lifecycle