MindsDB
hasktorch
MindsDB | hasktorch | |
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78 | 15 | |
21,312 | 1,020 | |
1.5% | 1.4% | |
10.0 | 7.2 | |
6 days ago | 4 days ago | |
Python | Haskell | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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.
hasktorch
- BLAS GPU bindings
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Trying out Hasktorch but ghc supported versions conflicts on MacOS M1/2
I assume you are getting https://github.com/hasktorch/hasktorch/issues/631? I suspect you need to upgrade to GHC 9.2 to work reliably on M1.
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Is Haskell okay for prototyping machine learning models for research (discovery and exploration)
You might find the Deep Learning From The First Principles tutorials by Bogdan Penkovsky an interesting survey of native Haskell implementations of deep neural networks, and a bit more. It demonstrates some native charting capabilities, and Day 9 uses Hasktorch.
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Need help Integrating Hasktorch into my Haskell Jupyter environment using Nix
I'm new to Nix and I'm trying to set up a Jupyter notebook environment for Haskell that includes the Hasktorch package. I'm using the jupyenv project from Tweag as the foundation, and I've been able to get it working with some basic Haskell packages. However, I'm running into issues when I try to add Hasktorch to the mix.
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[D] Have their been any attempts to create a programming language specifically for machine learning?
That said, there are some things that try to do this. Haskell has a port of torch called HaskTorch that includes this kind of typed tensor shapes, and calls the Z3 theorem prover on the backend to solve type inference. It can get away with this because of the LiquidHaskell compiler extension, which has refinement types capable of solving this kind of typing problem, and is already pretty mature. Dex is a research language from Google that's based on Haskell and built to explore this kind of typechecking. Really you'd want to do this in Rust, because that's where the tradeoff of speed and safety for convenience makes the most sense, but rust is just barely on the edge of having a type system capable of this. You have to get really clever with the type system to make it work at all, and there's been no sustained push from any company to develop this into a mature solution. Hopefully something better comes along soon
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Haskell deep learning tutorials [Blog]
As rightfully pointed u/gelisam, both Hasktorch and Pytorch are essentially the same things (bindings to existing Torch library). Therefore, it should be generally possible to use existing pretrained models. Here is an example.
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base case
I think it's likely that http://hasktorch.org/ is the library you will want to use for AI models, once you feel comfortable with Haskell.
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looking for simple regression (or classification) library
IF (big if) it turns out you do need deep learning then doing it in Hasktorch http://hasktorch.org/ could be a fun learning project. The team making it is super nice and responsive, too
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Haskell for Artificial Intelligence?
This said, if you want do do deep learning Python is the obvious choice atm, if only for copy-pasting code from examples (however do you know HaskTorch? https://github.com/hasktorch/hasktorch/ )
- GPU-based deep learning in Haskell
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
grenade - Deep Learning in Haskell
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.
dex-lang - Research language for array processing in the Haskell/ML family
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.
finito - A constraint solver for finite domains, written in Haskell.
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.
scikit-learn - scikit-learn: machine learning in Python
Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
lightwood - Lightwood is Legos for Machine Learning.
futhark - :boom::computer::boom: A data-parallel functional programming language