magika
dstack
magika | dstack | |
---|---|---|
5 | 17 | |
7,387 | 1,110 | |
1.8% | 5.1% | |
9.8 | 9.8 | |
4 days ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Mozilla Public 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.
magika
- Ask HN: How to handle user file uploads?
- FLaNK Stack Weekly 19 Feb 2024
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Magika: AI powered fast and efficient file type identification
As someone that has worked in a space that has to deal with uploaded files for the last few years, and someone who maintains a WASM libmagic Node package ( https://github.com/moshen/wasmagic ) , I have to say I really love seeing new entries into the file type detection space.
Though I have to say when looking at the Node module, I don't understand why they released it.
Their docs say it's slow:
https://github.com/google/magika/blob/120205323e260dad4e5877...
It loads the model an runtime:
https://github.com/google/magika/blob/120205323e260dad4e5877...
They mark it as Experimental in the documentation, but it seems like it was just made for the web demo.
Also as others have mentioned. The model appears to only detect 116 file types:
https://github.com/google/magika/blob/120205323e260dad4e5877...
Where libmagic detects... a lot. Over 1600 last time I checked:
https://github.com/file/file/tree/4cbd5c8f0851201d203755b76c...
I guess I'm confused by this release. Sure it detected most of my list of sample files, but in a sample set of 4 zip files, it misidentified one.
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Show HN: Magika: AI powered fast and efficient file type identification
We are very excited to announce the release of Magika our AI powered fast and efficient file type identification lib and tool - https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-efficient-file-type-identification.html
Thanks to its optimized Keras model, large scale training dataset, and Onnx Magika massively outperform other file identification tools while be very fast even on CPU.
Magika python code and model is open sourced on Github: https://github.com/google/magika and we also provide an experimental TFJS based npm package https://www.npmjs.com/package/magika
With the team we hope you will find Magika useful for your own projects. Let us know what you think or if you have any question!
dstack
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Pyinfra: Automate Infrastructure Using Python
We build a similar tool except we focus on AI workloads. Also support on-prem clusters now in addition to GPU clouds. https://github.com/dstackai/dstack
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Show HN: Open-source alternative to HashiCorp/IBM Vault
Not exactly this, but something related. At https://github.com/dstackai/dstack, we build an alternative to K8S for AI infra.
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Ask HN: How does deploying a fine-tuned model work
You can use https://github.com/dstackai/dstack to deploy your model to the most affordable GPU clouds. It supports auto-scaling and other features.
Disclaimer: I’m the creator of dstack.
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: I Built an Open Source API with Insanely Fast Whisper and Fly GPUs
Great job on the project! It looks fantastic. Thanks to your post, I discovered Fly's GPUs. We are currently developing a platform called https://github.com/dstackai/dstack that enables users to run any model on any cloud. I am curious if it would be possible to add support for Fly.io as well. If you are interested in collaborating on this, please let me know!
- Show HN: Dstack – an open-source engine for running GPU workloads
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[P] I built a tool to compare cloud GPUs. How should I improve it?
I also noticed that the creator of this app, dstack, is affiliated with Tensordock, the top results for most if not all queries. If that's the case, perhaps a direct link to the cheapest machine could be provided? I haven't used Tensordock, so I don't know if this is mechanically possible.
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Running dev environments and ML tasks cost-effectively in any cloud
Here's the repository with all the important links, including documentation, examples, and more: https://github.com/dstackai/dstack
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Dstack Hub
Hey everyone, I'm happy to release dstack Hub, an open-source tool that helps teams manage their ML workflows more effectively without vendor lock-in.
dstack Hub extend dstack [1] with workflow scheduling capabilities and user management. Here's how it works: run dstack Hub via Docker, use its UI to configure projects and cloud credentials, then pass the URL and personal token to the dstack CLI. Now, you can run workflows through the CLI and Hub will orchestrate them in the cloud on your behalf.
This is a beta release and we plan to continuously improve it. We'd love to hear your feedback and answer any questions!
[1] https://github.com/dstackai/dstack
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Running Stable Diffusion Locally & in Cloud with Diffusers & dstack
To help you overcome this challenge, we have written an article to guide you through the simple steps of using both diffusers and dstack to generate images from prompts, both locally and in the cloud, using a simple example.