> ## Documentation Index > Fetch the complete documentation index at: https://docs.coreweave.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to third-party frameworks > Integrate your CoreWeave Kubernetes Service (CKS) clusters with third-party tools and services Any framework that runs on Kubernetes or in Linux containers works on [CoreWeave Kubernetes Service (CKS)](/products/cks) and [SUNK](/products/sunk). The following sections highlight popular AI and ML frameworks, grouped by category, with links to CoreWeave guides where available. ## Interactive development The following table lists notebook environments and interactive UIs. | Framework | Description | CKS guide | SUNK guide | | ----------------------------------------- | ---------------------------------------------------------------------------------- | ------------------------------------------------------------------- | --------------------------------------------------------------- | | [marimo](https://docs.marimo.io/) | Reactive notebook environment for interactive Python development with GPU support. | [marimo notebooks on CKS](/products/cks/tutorials/marimo-notebooks) | [Notebooks on SUNK](/products/sunk/tutorials/notebooks-on-sunk) | | [Jupyter](https://jupyter.org/) | Interactive computing platform for notebooks, code, and data visualization. | Compatible on CKS | [Notebooks on SUNK](/products/sunk/tutorials/notebooks-on-sunk) | | [Open WebUI](https://docs.openwebui.com/) | Web-based chat interface for interacting with locally deployed language models. | [Deploy a model on CKS](/products/cks/deploy-model) | Not applicable | ## Training frameworks The following table lists frameworks used for training models and distributed training. | Framework | Description | CKS guide | SUNK guide | | ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | | [PyTorch](https://pytorch.org/) | Open-source deep-learning framework with GPU acceleration and distributed training through `torchrun`. | Compatible on CKS | [Submit a training job](/products/sunk/tutorials/train-on-sunk/3-submit-a-training-job) | | [TensorFlow](https://www.tensorflow.org/) | Open-source platform for building and deploying machine-learning models across a range of tasks. | Compatible on CKS | [Submit a training job](/products/sunk/tutorials/train-on-sunk/3-submit-a-training-job) | | [Keras](https://keras.io/) | High-level neural network API that runs on top of TensorFlow, JAX, or PyTorch. | Compatible on CKS | Compatible on SUNK | | [JAX](https://jax.readthedocs.io/) | High-performance numerical computing library with automatic differentiation and XLA compilation for GPUs and TPUs. | [JAX on marimo notebooks](/products/cks/tutorials/marimo-notebooks/jax) | Compatible on SUNK | | [Hugging Face Transformers](https://huggingface.co/docs/transformers/) | Library for pretrained models for natural language processing, computer vision, and audio tasks. | Compatible on CKS | Compatible on SUNK | | [DeepSpeed](https://www.deepspeed.ai/) | Deep-learning optimization library for distributed training and inference with memory-efficient techniques. | Compatible on CKS | Compatible on SUNK | | [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) | NVIDIA framework for training large transformer models using model and data parallelism. | Compatible on CKS | Compatible on SUNK | | [Horovod](https://horovod.ai/) | Distributed deep-learning training framework supporting TensorFlow, Keras, PyTorch, and Apache MXNet. | Compatible on CKS | Compatible on SUNK | | [PaddlePaddle](https://www.paddlepaddle.org.cn/en) | Deep-learning framework with support for distributed training and model deployment. | Compatible on CKS | Compatible on SUNK | | [PAX](https://github.com/google/paxml) | JAX-based framework for configuring and running ML experiments at scale. | Compatible on CKS | Compatible on SUNK | | [BigDL](https://github.com/intel-analytics/BigDL) | Distributed deep-learning library for Apache Spark and Intel hardware. | Compatible on CKS | Compatible on SUNK | | [MegEngine](https://github.com/MegEngine/MegEngine) | Deep-learning framework with dynamic and static graph support. | Compatible on CKS | Compatible on SUNK | | [MindSpore](https://www.mindspore.cn/en) | AI computing framework for training and inference across cloud, edge, and device. | Compatible on CKS | Compatible on SUNK | | [torchforge](https://github.com/meta-pytorch/torchforge) | Training framework for reinforcement learning with group relative policy optimization (GRPO). | Compatible on CKS | [torchforge on SUNK](/products/sunk/tutorials/torchforge-on-sunk) | | [veRL](https://github.com/volcengine/verl) | Framework for reinforcement learning training of large language models with Slurm and Ray integration. | Compatible on CKS | [veRL on SUNK](/products/sunk/tutorials/verl-on-sunk) | ## Inference frameworks The following table lists frameworks used for model serving and inference. | Framework | Description | CKS guide | SUNK guide | | ---------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | ------------------ | | [vLLM](https://docs.vllm.ai/) | High-throughput and memory-efficient LLM inference and serving engine. | [Deploy vLLM inference](/products/cks/tutorials/deploy-vllm-inference) | Compatible on SUNK | | [SGLang](https://docs.sglang.io/) | Serving framework for large language models and vision language models. | [SGLang on CKS](/products/cks/clusters/frameworks/sglang) | Compatible on SUNK | | [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) | GPU inference optimizer that delivers low-latency and high-throughput model serving. | Compatible on CKS | Compatible on SUNK | | [NVIDIA TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM/) | Library for optimizing and deploying large language model inference on GPUs. | [TensorRT-LLM on marimo notebooks](/products/cks/tutorials/marimo-notebooks/tensorrt) | Compatible on SUNK | | [NVIDIA NIM](https://docs.nvidia.com/nim/) | NVIDIA inference microservices for deploying optimized AI models as containerized services. | [Deploy NIMs on CKS](/products/cks/tutorials/hello-world-nims-on-cks) | Compatible on SUNK | | [NVIDIA Dynamo](https://github.com/ai-dynamo/dynamo) | Cluster-wide inference orchestration with the Kai scheduler and Grove. | [Dynamo inference on CKS](/products/cks/tutorials/dynamo-inference) | Compatible on SUNK | | [Red Hat AI Inference Stack](https://github.com/opendatahub-io/rhaii-on-xks) | GPU-based LLM inference using llm-d, KServe, Istio, and the Gateway API. | [Red Hat AI on CKS](/products/cks/tutorials/redhat-inference) | Compatible on SUNK | | [OpenVINO](https://docs.openvino.ai/) | Intel toolkit for optimizing and deploying inference across CPU, GPU, and accelerator hardware. | Compatible on CKS | Compatible on SUNK | | [TensorFlow Lite](https://www.tensorflow.org/lite) | Lightweight runtime for running TensorFlow models on edge and mobile devices. | Compatible on CKS | Compatible on SUNK | ## Orchestration and management The following table lists frameworks for scheduling workloads and managing ML operations on clusters. | Framework | Description | CKS guide | SUNK guide | | -------------------------------------- | ------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | ------------------------------------------------------------- | | [Ray](https://docs.ray.io/) | Distributed computing framework for scaling AI and Python applications across clusters. | [Ray with Kueue](/products/cks/clusters/frameworks/ray-with-kueue) | [Ray on SUNK](/products/sunk/tutorials/ray-on-sunk) | | [Anyscale](https://docs.anyscale.com/) | Managed platform for deploying and scaling Ray-based distributed AI and ML workloads. | [Anyscale on CKS](/products/cks/clusters/frameworks/anyscale) | Compatible on SUNK | | [Kubeflow](https://www.kubeflow.org/) | Open-source machine-learning platform for portable, scalable ML workflows on Kubernetes. | [Kubeflow on CKS](/products/cks/clusters/frameworks/kubeflow) | Compatible on SUNK | | [SkyPilot](https://docs.skypilot.co/) | Framework for running AI and ML workloads across cloud infrastructure. | [SkyPilot on CKS](/products/cks/tutorials/skypilot) | [SkyPilot on SUNK](/products/sunk/tutorials/skypilot-on-sunk) | | [Union.ai](https://www.union.ai/) | Managed platform for Flyte, providing workflow orchestration for data and ML pipelines with a hosted control plane. | [Union.ai on CKS](/products/cks/clusters/frameworks/union-ai) | Compatible on SUNK | | [Kueue](https://kueue.sigs.k8s.io/) | Kubernetes-native job queueing system for prioritized workload scheduling and resource management. | [Ray with Kueue](/products/cks/clusters/frameworks/ray-with-kueue) | Compatible on SUNK | ## ML libraries The following table lists general-purpose machine-learning and AutoML libraries. | Framework | Description | CKS guide | SUNK guide | | ------------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------- | ----------------- | ------------------ | | [XGBoost](https://xgboost.readthedocs.io/) | Optimized gradient boosting library for classification, regression, and ranking tasks. | Compatible on CKS | Compatible on SUNK | | [scikit-learn](https://scikit-learn.org/) | General-purpose machine-learning library for classification, regression, clustering, and preprocessing. | Compatible on CKS | Compatible on SUNK | | [MLlib](https://spark.apache.org/mllib/) | Apache Spark's distributed machine-learning library for classification, regression, clustering, and pipelines. | Compatible on CKS | Compatible on SUNK | | [ML.NET](https://dotnet.microsoft.com/en-us/apps/machinelearning-ai/ml-dotnet) | Cross-platform machine-learning framework for .NET applications. | Compatible on CKS | Compatible on SUNK | | [TPOT](http://epistasislab.github.io/tpot/) | Automated machine-learning tool that optimizes ML pipelines using genetic programming. | Compatible on CKS | Compatible on SUNK | | [AutoKeras](https://autokeras.com/) | AutoML library built on Keras for automated neural architecture search. | Compatible on CKS | Compatible on SUNK | | [Auto-sklearn](https://automl.github.io/auto-sklearn/) | AutoML toolkit that automatically selects and tunes scikit-learn models. | Compatible on CKS | Compatible on SUNK | | [PlaidML](https://github.com/plaidml/plaidml) | Portable deep-learning backend that runs on a variety of hardware including CPUs and GPUs. | Compatible on CKS | Compatible on SUNK | ## GPU acceleration libraries The following table lists GPU-accelerated computing libraries. | Framework | Description | CKS guide | SUNK guide | | --------------------------------------------- | ---------------------------------------------------------------------------------------------- | ----------------- | ------------------ | | [cuDNN](https://developer.nvidia.com/cudnn) | NVIDIA GPU-accelerated library of primitives for deep neural networks. | Compatible on CKS | Compatible on SUNK | | [cuBLAS](https://developer.nvidia.com/cublas) | NVIDIA GPU-accelerated implementation of the BLAS (Basic Linear Algebra Subprograms) standard. | Compatible on CKS | Compatible on SUNK | ## Deprecated frameworks The following frameworks are deprecated or no longer actively maintained by their upstream projects. They can still run on CKS and SUNK in custom container images. | Framework | Description | Status | | ------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------- | | [Apache MXNet](https://mxnet.apache.org/) | Distributed deep-learning framework. | Retired by the Apache Software Foundation in 2023. | | [Caffe](https://caffe.berkeleyvision.org/) and [Caffe2](https://caffe2.ai/) | Deep-learning frameworks for image classification and convolutional networks. | Caffe2 merged into PyTorch. Caffe is no longer actively maintained. | | [Chainer](https://chainer.org/) | Deep-learning framework with a define-by-run approach. | Development ended in 2019. Successor: PyTorch. | | [Microsoft Cognitive Toolkit (CNTK)](https://learn.microsoft.com/en-us/cognitive-toolkit/) | Deep-learning framework for distributed training. | Archived by Microsoft in 2019. | | [Theano](https://github.com/Theano/Theano) | Numerical computation library with GPU support for defining and optimizing mathematical expressions. | Development ended in 2017. | | [Torch](http://torch.ch/) | Scientific computing framework with GPU support based on Lua. | Superseded by PyTorch. | This list isn't exhaustive. If a framework isn't listed, you can build custom container images and deploy them on CKS or SUNK.