flower VS rye

We use Pyenv successfully for developing the Flower open-source project. We use a few simple Bash scripts to manage virtual environments with different Python versions via pyenv and the pyenv-virtualenv plugin.

The main scripts are `venv-create.sh`, `venv-delete.sh` and `bootstrap.sh`. `venv-reset.sh` pulls these three scripts together to make reinstalling your venv a single command.

Here's the link if anyone is interested: https://github.com/adap/flower/tree/main/dev

Flower , an open-source framework for training AI on distributed data. We move the model to the data instead of moving the data to the model. (https://flower.dev/)

22-Mar-2023 Adobe unveils creative generative AI model, Firefly, to aid content creation Google has begun rolling out early access to its Bard chatbot in the US and UK Data Breach At ChatGPT? Users Report Seeing Unknown Conversations On Their Screens GPT-4 is available in preview in Azure OpenAI Service AI-powered coding assistance REPL that pairs GPT-4 (https://github.com/jiggy-ai/pair) Open source alternative to ChatGPT (https://github.com/nichtdax/awesome-totally-open-chatgpt) Run 100B+ language models at home, BitTorrent‑style (https://petals.ml/) Find the most relevant piece of code context. Hover and highlight blocks of code, the tool will point you to the most relevant pieces of information on git, messaging, and ticketing systems. Finally, it provide a summary with the power of GPT.(https://www.watermelontools.com/) Why AI Won't Replace Software Engineers (https://softwarecomplexity.com/why-ai-wont-replace-software-engineers) 23-Mar-2023 'The iPhone Moment of AI' Nvidia to Rent Out Supercomputers Behind ChatGPT to Businesses for $37,000 a Month Bill Gates calls AI revolutionary, says it can reduce some of the world’s worst inequities AI pics of Donald Trump's arrest by 'cop' Joe Biden go viral. Will we no longer be able to tell what’s real vs what’s fake?” - Eluna AI New research shows we can only accurately identify AI writers about 50% of the time. (https://hai.stanford.edu/news/was-written-human-or-ai-tsu) FauxPilot - an open-source GitHub Copilot server(https://github.com/fauxpilot/fauxpilot) Flower , an open-source framework for training AI on distributed data. We move the model to the data instead of moving the data to the model. (https://flower.dev/) OpenAI-Integrated Microsoft Bing Outperforms Google in Page Visits (https://www.gadgets360.com/internet/news/openai-integrated-microsoft-bing-outperforms-google-page-visits-growth-3885069) GitHub Copilot X: GitHub Copilot is evolving to bring chat and voice interfaces, support pull requests, answer questions on docs, and adopt OpenAI’s GPT-4 for a more personalized developer experience. (https://github.blog/2023-03-22-github-copilot-x-the-ai-powered-developer-experience/) Moonshine – open-source, pretrained ML models for satellite (https://github.com/moonshinelabs-ai/moonshine) Mozilla.ai: A startup — and a community — that will build a trustworthy and independent open-source AI ecosystem. Mozilla.ai’s initial focus? Tools that make generative AI safer and more transparent. And, people-centric recommendation systems that don’t misinform or undermine our well-being. (https://blog.mozilla.org/en/mozilla/introducing-mozilla-ai-investing-in-trustworthy-ai/) OpenAI’s policies hinder reproducible research on language models (https://aisnakeoil.substack.com/p/openais-policies-hinder-reproducible) 24-Mar-2023 Adobe has added AI features to Photoshop and Illustrator, while Nvidia has unveiled ‘Picasso’ AI image generation service. ChatGPT-owner OpenAI fixes 'significant issue' exposing user chat titles.A bug in an open-source library caused ChatGPT to leak user conversation titles. Graphic design platform Canva introduces new generative AI tools Gmail for Android, Google Messages to Soon Get Features for AI-Generated Texts Apple: Transformer architecture optimized for Apple Silicon (https://github.com/apple/ml-ane-transformers) ChatGPT plugins, join waitlist (https://openai.com/blog/chatgpt-plugins) Microsoft's paper on OpenAI's GPT-4 had hidden information (https://twitter.com/DV2559106965076/status/1638769434763608064) how to use LoRA to fine-tune LLaMA using Alpaca training data (https://replicate.com/blog/fine-tune-alpaca-with-lora) Helicone: one-line integration logs the prompts, completions, latencies, and costs of your OpenAI requests (https://github.com/Helicone/helicone) RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). (https://github.com/BlinkDL/RWKV-LM) open-source retrieval plugin The open-source retrieval plugin enables ChatGPT to access personal or organizational information sources (with permission). It allows users to obtain the most relevant document snippets from their data sources, such as files, notes, emails or public documentation, by asking questions or expressing needs in natural language. Security considerations The retrieval plugin allows ChatGPT to search a vector database of content, and add the best results into the ChatGPT session. This means it doesn’t have any external effects, and the main risk is data authorization and privacy. Developers should only add content into their retrieval plugin that they are authorized to use and can share in users’ ChatGPT sessions. https://github.com/openai/chatgpt-retrieval-plugin 27-Mar-2023 Autodoc: Toolkit for auto-generating codebase documentation using LLMs (https://github.com/context-labs/autodoc) March 20 ChatGPT outage: Here’s what happened (https://openai.com/blog/march-20-chatgpt-outage) Facebook is going after LLaMA repos with DMCA's (https://twitter.com/theshawwn/status/1638925249709240322) ChatGPT + Wolfram is INSANE! (https://old.reddit.com/r/ChatGPT/comments/1205omc/chatgpt\_wolfram\_is\_insane/) Reproducing the Stanford Alpaca results using low-rank adaptation (LoRA) (https://github.com/chris-alexiuk/alpaca-lora) GOAT, a decentralized way to publish and download AI models.Powered by BitTorrent and Bitcoin.(https://ipfs.io/ipfs/QmYyucgBQVfs9JXZ2MtmkGPAhgUjNgyGE6rcJT1KybQHhp/index.html) Dolly from databricks (https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html) AI powered Developer Tools 2.0. https://www.sequoiacap.com/article/ai-powered-developer-tools/ Turn your designs into production-ready front-end code for mobile apps and the web (https://www.locofy.ai/) Using ChatGPT Plugins with LLaMA (https://blog.lastmileai.dev/using-openais-retrieval-plugin-with-llama-d2e0b6732f14) 28-Mar-2023 Bing AI now allows 20 prompts per session and can make images for you ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks (https://arxiv.org/abs/2303.15056) ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark (https://arxiv.org/abs/2303.13648) AI-controlled Linux Containers (https://github.com/fafrd/aquarium) Microsoft reportedly orders AI chatbot rivals to stop using Bing’s search data (https://www.theverge.com/2023/3/25/23656336/microsoft-chatbot-rivals-stop-using-bing-search-index) 29-Mar-2023 Text2Video-Zero Code and Weights Released by Picsart AI Research (12G VRAM).(https://github.com/Picsart-AI-Research/Text2Video-Zero) Pause Giant AI Experiments: An Open Letter. Huggingface's SF Open-Source AI Meetup officially has 2000 people registered. Cerebras open sources seven GPT-3 models from 111 million to 13 billion parameters. Trained using the Chinchilla formula, these models set new benchmarks for accuracy and compute efficiency.(https://www.cerebras.net/blog/cerebras-gpt-a-family-of-open-compute-efficient-large-language-models/) Independent implementation of LLaMA that is fully open source under the Apache 2.0 license (https://github.com/Lightning-AI/lit-llama) Bootstrap knowledge of LLMs (https://gist.github.com/rain-1/eebd5e5eb2784feecf450324e3341c8d) OPENFLAMINGO: AN OPEN-SOURCE FRAMEWORK FOR TRAINING VISION-LANGUAGE MODELS WITH IN-CONTEXT LEARNING (https://laion.ai/blog/open-flamingo/) gpt4all: a chatbot trained on a massive collection of clean assistant data including code, stories and dialogue (https://github.com/nomic-ai/gpt4all) 30-Mar-2022 Microsoft Security Copilot is a new GPT-4 AI assistant for cybersecurity (https://www.theverge.com/2023/3/28/23659711/microsoft-security-copilot-gpt-4-ai-tool-features) UK details ‘pro-innovation’ approach to AI regulation (https://www.artificialintelligence-news.com/2023/03/29/uk-details-pro-innovation-approach-ai-regulation/) Employees Are Feeding Sensitive Biz Data to ChatGPT, Raising Security Fears (https://www.darkreading.com/risk/employees-feeding-sensitive-business-data-chatgpt-raising-security-fears) In the Age of AI, Don't Let Your Skills Atrophy (https://www.cyberdemon.org/2023/03/29/age-of-ai-skill-atrophy.html) Now ChatGPT is being (mis)used to do #PeerReview (https://mstdn.science/@ukrio/110100752908161183) Bing Chat now has Ads! (https://twitter.com/debarghya\_das/status/1640892791923572737) Cerebras-GPT vs LLaMA AI Model Comparison (https://www.lunasec.io/docs/blog/cerebras-gpt-vs-llama-ai-model-comparison/) Arthur C. Clarke about the future of AI. — 21 September 1964 (https://twitter.com/Rainmaker1973/status/1640016339011076097) ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline (https://medium.com/@yangyou\_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) Create and Embed Custom AI Assistants with Libraria (https://libraria.dev/) 31-Mar-2023 Deranged New AI Has No Guardrails Whatsoever, Proudly Praises Hitler (https://futurism.com/deranged-ai-no-guardrails) Midjourney Kills Free AI Image Generator Access After Explosion of Deep Fakes (https://decrypt.co/124972/midjourney-free-ai-image-generation-stopped-over-deepfakes) Judge asks ChatGPT to decide bail in murder trial (https://nypost.com/2023/03/29/judge-asks-chatgpt-for-decision-in-murder-trial/) Should you use OpenAI's embeddings? Probably not, and here's why. (https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings) Visual Studio Code and GitHub Copilot (https://code.visualstudio.com/blogs/2023/03/30/vscode-copilot) Llama Hub (https://llamahub.ai/) Finetuning LLMs on a Single GPU Using Gradient Accumulation (https://lightning.ai/pages/blog/gradient-accumulation/) Open source ETL framework for retrieval augmented generation (RAG). Sync data from your SaaS tools to a vector store, where they can be easily queried by GPT apps (https://github.com/ai-sidekick/sidekick) HALTT4LLM - Hallucination Trivia Test for Large Language Models (https://github.com/manyoso/haltt4llm) Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality (https://vicuna.lmsys.org/) Iterate.ai Brings Generative AI Capabilities to Interplay, the Low-Code Platform Accelerating Customers’ Digital Innovation (https://www.indianweb2.com/2023/03/iterateai-brings-generative-ai.html) RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). (https://github.com/RosettaCommons/RFdiffusion) Google denies training Bard on ChatGPT chats from ShareGPT

Check out https://flower.dev/ as an example

Large language models like GPT-3 have gained immense popularity recently, and, using Flower, it's easy to transform an existing Hugging Face workflow to train models on decentralized data. This example blog post will show how to fine-tune a pre-trained distilBERT model on the IMDB dataset for sequence classification (determining if a movie review is positive or not). You can also check out the associated Colab notebook and the code example from the Flower repo.

There are some similarities, but also some differences. Flower's take is that it wants to support the entire FL workflow from experimental research to large-scale production deployments and operation. Some other FL frameworks fall either in the "research" or "production deployment" bucket, but few have good support for both.

Flower does a lot under the hood to support these different usage scenarios: it has both a networked engine (gRPC, experimental support for REST, and the possibility to "bring your own communication stack") and a simulation engine to support both real deployment on edge devices/server and simulation of large-scale federations on single machines or compute clusters.

This is - to the best of our knowledge - one of the drivers of our large and active community. The community is very collaborative and there are many downstream projects in the ecosystem that build on top of Flower (GitHub lists 748 dependent projects: https://github.com/adap/flower/network/dependents).

I agree that proprietary data will become more valuable. It is, even today, mostly not accessible for AI training and holds so much value. We are working on Flower (https://flower.dev), which enables training AI on private data without the data owner having to share it.

Flower framework: https://flower.dev/

The traction of federated learning is increasing as well as for our open-source federated learning framework Flower (https://flower.dev/). In federated learning, we do not collect data to train AI models but we train AI models in data silos, only collect the AI models and aggregate them to create a global AI model. The global AI model has the knowledge of all data silos but has never seen their data. Therefore, federated learning connects data silos in a privacy-preserving manner.

Code for https://arxiv.org/abs/2007.14390 found: https://github.com/adap/flower

I tried out rye + uv on a recent greenfield project. They are awesome tools and I'm really excited about their potential.

For me, rye (+ uv underneath) has perhaps the perfect workflow for an open source Python project. So I'm definitely using rye for that from now in -- instead of, say, poetry -- or hatchling directly, following the PyPA boilerplate[1].

You have a way of doing local development against any Python interpreter version. You have a way of tweaking dependencies. It all works atop "standard" PyPA infrastructure like pyproject.toml. You have a single command to build[1] project artifacts, like wheels. And you have a single command to publish new artifact versions to PyPI[2].

I think if you're doing local development on a project that is not meant to be published to PyPI, like a private Django project, then whether to use rye becomes more of a debate. For example, for a Django project I'm working on, I decided to just use uv directly, along with a Makefile. This is because during development of a Django project, I preferred to just use a plain requirements.txt (really, requirements.in) file, avoid the sync/lock workflow that rye imposes, and avoid the need to use something like rye run. And rye's ability to package didn't solve a problem since the Django project wasn't being deployed via a PyPA packaging mechanism.

But this is probably also because the Python interpreter/venv management problem, for me, is already handled by pyenv. I think if you're not already a pyenv user, rye is even more appealing because it handles "all" of the Python issues -- interpreters, requirements/dependencies, and packaging/publishing. (As well as a number of other standard issues besides, like testing, linting, and formatting.) But, in my case, I could hand venv management to uv, and then make dependency management part of a larger Makefile for my Django project, including custom linting, testing, and deployment steps. I wrote a little bit about my high level thoughts on Python packaging and dependency management, though this post was written before rye and uv were out.[4]

I'll also say, I found a little bug in how rye (+ hatch) interacted with my local git setup, and reported it to the rye team, and they helped me get to the bottom of it rather quickly.[5]

[1]: https://packaging.python.org/en/latest/tutorials/packaging-p...

[2]: https://rye-up.com/guide/commands/build/

[3]: https://rye-up.com/guide/commands/publish/

[4]: https://amontalenti.com/2022/10/09/python-packaging-and-zig

[5]: https://github.com/astral-sh/rye/issues/793

I've been using Rye[0] lately, which has been pretty good. It's really just a wrapper around a bunch of underlying tools - it's nice to not have to worry about those and let Rye do it's thing.

All that being said, the creator of Rye is 100% cognizant of that XKCD comic, this [1] is a nice read.

I'm not super well versed in Python tooling at all. I've had to work a lot in Python in the past 6+ months, and I become super confused when I tried making a Python project in my spare time.

I settled on Rye because it just seemed to be the easiest to use.

[0]: https://rye-up.com/

I think Rye actually does handle this mostly correctly (as the sibling comment said). I got through some of it here: https://github.com/mitsuhiko/rye/issues/671. I think actually it's very close to what I actually want (maybe not what Armin wants with multiversion).

Rye[1] is an all in one manager for python projects. Including the python versions and virtualenv, pip etc etc... It seperates tool deps from app deps. Its all configured through a pyproject.toml config file.

Its still new but works well. I'm transiting to it from an unholy mess of pyenv, pip installs and other manual hacks.

If you're starting a new python project that is more than just a straightforward script I'd use Rye from the get go.

[1]https://rye-up.com/

Your first comment irked me because it adds zero value to the discussion. You lazily threw out XKCD 927 which the Rye author explicitly mentioned themselves.

If you click into their link "Should Rye Exist" [1] you'll see that XKCD 927 is literally the first sentence and full width image.

[1] https://github.com/mitsuhiko/rye/discussions/6

Try Rye.

Since this is a discussion on dependency management in Python - does anyone use rye [0] regularly now? I'm interested in using it but want a little more social validation before I try - some issues with package managers only appear after you've invested considerable time.

[0]: https://rye-up.com/

The short term solution is "relieving the packaging pain" link in the article.

The long term solution is described in the "What a solution could look like?" section of https://www.bitecode.dev/p/why-is-the-python-installation-pr...

The community is buzzing with attempts to fix those issues this year, so I’m hopping those posts will become obsolete one day.

Flask’s author is attempting something interesting with rye: https://github.com/mitsuhiko/rye

Trio’s author is drafting a spec for the equivalent of wheels, but for the whole python interpreter: https://github.com/njsmith/posy/blob/main/pybi/README.md

Not advocating to use them right now, but the fact is bootstrapping Python is finally acknowledged as one major cause of packaging issues and a priority to solve.

I'm building a package manager for Python (Rye) in Rust and it is modeled after cargo and rustup. It like rustup manages itself. This means it has commands such as rye self update which downloads the latest version and swaps itself out. Likewise there is rye self uninstall which uninstalls rye itself.