gnn
dstack
| gnn | dstack | |
|---|---|---|
| 3 | 17 | |
| 1,280 | 1,102 | |
| 1.9% | 4.4% | |
| 9.3 | 9.8 | |
| 12 days ago | 6 days 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.
gnn
- FLaNK Stack Weekly 19 Feb 2024
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Google Researchers Open-Source the TensorFlow GNN (TF-GNN): A Scalable Python Library for Graph Neural Networks in TensorFlow
Continue reading | Checkout the paper and github link
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TensorFlow Introduces TensorFlow Graph Neural Networks (TF-GNNs)
Github: https://github.com/tensorflow/gnn
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: Iām 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 ā 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.
What are some alternatives?
transformers - š¤ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
msdocs-python-django-azure-container-apps - Python web app using Django that can be deployed to Azure Container Apps.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
dstack-examples - A collection of examples demonstrating how to use dstack
PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
zenml - ZenML š: Build portable, production-ready MLOps pipelines. https://zenml.io.
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
diffnet - Graph Neural Network based Social Recommendation Model. SIGIR2019.
lambdapi - Serverless runtime environment tailored for code produced by LLMs. Automatic API generation from your code, support for multiple programming languages, and integrated file and database storage solutions.
ncem - Learning cell communication from spatial graphs of cells
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!