|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Tensorflow||177,730||327||77||2 hours ago||46||October 23, 2019||2,070||apache-2.0||C++|
|An Open Source Machine Learning Framework for Everyone|
|Transformers||112,161||64||1,869||2 hours ago||114||July 18, 2023||812||apache-2.0||Python|
|🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.|
|Pytorch||70,938||3,341||6,728||2 hours ago||37||May 08, 2023||12,734||other||Python|
|Tensors and Dynamic neural networks in Python with strong GPU acceleration|
|Cs Video Courses||59,993||9 days ago||5|
|List of Computer Science courses with video lectures.|
|Keras||59,367||578||2 days ago||80||June 27, 2023||276||apache-2.0||Python|
|Deep Learning for humans|
|D2l Zh||48,273||1||1||4 days ago||47||December 15, 2022||48||apache-2.0||Python|
|Faceswap||47,035||7 days ago||23||gpl-3.0||Python|
|Deepfakes Software For All|
|Tensorflow Examples||42,312||a year ago||218||other||Jupyter Notebook|
|TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)|
|Deepfacelab||42,238||21 days ago||536||gpl-3.0||Python|
|DeepFaceLab is the leading software for creating deepfakes.|
|Yolov5||41,717||19 hours ago||8||September 21, 2021||237||agpl-3.0||Python|
|YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite|
The next-generation platform to monitor and optimize your AI costs in one place
Nebulyis the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
If you like the idea, give us a star to show your support for the project
Apply for enterprise version early access here.
The monitoring platform allows you to monitor 100% of your AI costs. We support 3 main buckets of costs:
The easiest way to install the SDK is via
pip install nebuly
The list of the supported integrations will be available soon.
Once you have full visibility over your AI costs, you are ready to optimize them. We have developed multiple open-source tools to optimize the cost and improve the performances of your AI systems:
Speedster: reduce inference costs by leveraging SOTA optimization techniques that best couple your AI models with the underlying hardware (GPUs and CPUs)
Nos: reduce infrastructure costs by leveraging real-time dynamic partitioning and elastic quotas to maximize the utilization of your Kubernetes GPU cluster
ChatLLaMA: reduce hardware and data costs by leveraging fine-tuning optimization techniques and RLHF alignment
As an open source project in a rapidly evolving field, we welcome contributions of all kinds, including new features, improved infrastructure, and better documentation. If you're interested in contributing, please see the linked page for more information on how to get involved.