zenml
huggingface_hub
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zenml | huggingface_hub | |
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33 | 104 | |
3,657 | 1,675 | |
3.1% | 8.7% | |
9.8 | 9.6 | |
1 day ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
zenml
- FLaNK AI - 01 April 2024
- What are some open-source ML pipeline managers that are easy to use?
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[P] I reviewed 50+ open-source MLOps tools. Here’s the result
Currently, you can see the integrations we support here and it includes a lot of tools in your list. I also feel I agree with your categorization (it is exactly the categorization we use in our docs pretty much). Perhaps one thing missing might be feature stores but that is a minor thing in the bigger picture.
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[P] ZenML: Build vendor-agnostic, production-ready MLOps pipelines
GitHub: https://github.com/zenml-io/zenml
- Show HN: ZenML – Portable, production-ready MLOps pipelines
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting:
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How we made our integration tests delightful by optimizing our GitHub Actions workflow
As of early March 2022 this is the new CI pipeline that we use here at ZenML and the feedback from my colleagues -- fellow engineers -- has been very positive overall. I am sure there will be tweaks, changes and refactorings in the future, but for now, this feels Zen.
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Ask HN: Who is hiring? (March 2022)
ZenML is hiring for a Design Engineer.
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.
We’re looking for a Design Engineer with a multi-disciplinary skill-set who can take over the look and feel of the ZenML experience. ZenML is a tool designed for developers and we want to delight them from the moment they land on our web page, to after they start using it on their machines. We would like a consistent design experience across our many touchpoints (including the [landing page](https://zenml.io), the [docs](https://docs.zenml.io), the [blog](https://blog.zenml.io), the [podcast](https://podcast.zenml.io), our social media, the product itself which is a [python package](https://github.com/zenml-io/zenml) etc).
A lot of this job is about communicating complex ideas in a beautiful way. You could be a developer or a non-coding designer, full time or part-time, employee or freelance. We are not so picky about the exact nature of this role. If you feel like you are a visually creative designer, and are willing to get stuck in the details of technical topics like MLOps, we can’t wait to work with you!
Apply here: https://zenml.notion.site/Design-Engineer-m-f-1d1a219f18a341...
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How to improve your experimentation workflows with MLflow Tracking and ZenML
The best place to see MLflow Tracking and ZenML being used together in a simple use case is our example that showcases the integration. It builds on the quickstart example, but shows how you can add in MLflow to handle the tracking. In order to enable MLflow to track artifacts inside a particular step, all you need is to decorate the step with @enable_mlflow and then to specify what you want logged within the step. Here you can see how this is employed in a model training step that uses the autolog feature I mentioned above:
- ZenML helps data scientists work across the full stack
huggingface_hub
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OpenAI's employees were given two explanations for why Sam Altman was fired
Something to think about:
https://github.com/huggingface/huggingface_hub
- Thoughts on a "Text Generation CivitAI"
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Civitai alternatives.
Yes! We have a well documented Python library (https://github.com/huggingface/huggingface_hub) and public endpoints (https://huggingface.co/docs/hub/api#endpoints-table) you can use to retrieve information about the models and potentially build UIs with specific use cases in mind
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Fox Fairy @ Diffusion Forest: Unreal Engine + Stable Diffusion
i think if you search for pixel art here there are some models worth checking out: https://huggingface.co/
- ASK HN: AI is really exciting but where do I start?
- j'ai entraîné une IA à générer Éric Duhaime en clown !
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[Guide] DreamBooth Training with ShivamShrirao's Repo on Windows Locally
I received another error saying OSError: We couldn't connect to 'https://huggingface.co' to load this model, couldn't find it in the cached files and it looks like ./vae is not the path to a directory containing a file named diffusion_pytorch_model.bin
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Training a Deep Learning Language Model for Latin text Generation
I plan to release it on https://huggingface.co/, where all this cool AI stuff is available for free for everyone that wishes to try it.
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Image Upscaling Models Compared (General, Photo and Faces)
For this I used mainly the chainner application with models from here but I also used the google colab automatic1111 stable diffusion webui (for example for Lanczos) and also spaces fromhuggingface like this one or then from the replicate.com website super resolution collection.
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2D Illustration Styles are scarce on Stable Diffusion so i created a dreambooth model inspired by Hollie Mengert's work
you will now need to create a huggingface account ( https://huggingface.co/) if you haven't already. When you have, go here and accept the terms, https://huggingface.co/runwayml/stable-diffusion-v1-5. When you have done both, click on your profile icon and go to settings. Click access tokens and then create token, name it whatever you want, select "write". When you are finished with all this, then you can run the next cell which is the hugging face cell. It will ask for a token, you copy and paste what you just created.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
civitai - A repository of models, textual inversions, and more
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
spaCy - đź’« Industrial-strength Natural Language Processing (NLP) in Python
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
mammography_metarepository - Meta-repository of screening mammography classifiers
Poetry - Python packaging and dependency management made easy
KoboldAI-Client
pulsechain-testnet
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration