diff --git a/README.md b/README.md index cbdf4dd9..8306f394 100644 --- a/README.md +++ b/README.md @@ -40,14 +40,14 @@ There are multiple modules we actually provide to boost the performances of your ✅ [OpenAlphaTensor](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/open_alpha_tensor): Increase the computational performances of an AI model with custom-generated matrix multiplication algorithms. +✅ [Nos](https://github.com/nebuly-ai/nos): Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest! + ✅ [Forward-Forward](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/forward_forward): The Forward Forward algorithm is a method for training deep neural networks that replaces the backpropagation forward and backward passes with two forward passes. ## Next modules and roadmap We are actively working on incorporating the following modules, as requested by members of our community, in upcoming releases: -- [ ] [Nos](https://github.com/nebuly-ai/nos): Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest! - [ ] [Promptify](https://github.com/nebuly-ai/nebullvm/blob/main/apps/extract/promptify): Effortlessly personalize large APIs generative models from OpenAI, Cohere, HF to your specific context and requirements. - - [ ] [CloudSurfer](https://github.com/nebuly-ai/nebullvm/blob/main/apps/accelerate/cloud_surfer): Automatically discover the optimal cloud configuration and hardware on AWS, GCP and Azure to run your AI models. - [ ] [OptiMate](https://github.com/nebuly-ai/nebullvm/blob/main/apps/accelerate/optimate): Interactive tool guiding savvy users in achieving the best inference performance out of a given model / hardware setup. - [ ] [TrainingSim](https://github.com/nebuly-ai/nebullvm/blob/main/apps/simulate/training_sim): Easily simulate the training of large AI models on a distributed infrastructure to predict training behaviours without actual implementation.