From 3f341d5dd30b9b35db5dd3d7c02e5b53c66ff75c Mon Sep 17 00:00:00 2001 From: Jan Knobloch <116908874+jk4e@users.noreply.github.com> Date: Thu, 10 Oct 2024 15:46:09 +0200 Subject: [PATCH] Sort alphabetical integrations in docs (#16819) --- docs/en/integrations/index.md | 42 ++++++++++++++++----------------- mkdocs.yml | 44 +++++++++++++++++------------------ 2 files changed, 43 insertions(+), 43 deletions(-) diff --git a/docs/en/integrations/index.md b/docs/en/integrations/index.md index bb4de86c81..bdb8b9c907 100644 --- a/docs/en/integrations/index.md +++ b/docs/en/integrations/index.md @@ -27,65 +27,65 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of ## Training Integrations +- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle. + - [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration. - [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments. - [DVC](dvc.md): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively. -- [MLFlow](mlflow.md): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment. - -- [Ultralytics HUB](https://hub.ultralytics.com/): Access and contribute to a community of pre-trained Ultralytics models. +- [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing. -- [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps. +- [IBM Watsonx](ibm-watsonx.md): See how IBM Watsonx simplifies the training and evaluation of Ultralytics models with its cutting-edge AI tools, effortless integration, and advanced model management system. -- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale. +- [JupyterLab](jupyterlab.md): Find out how to use JupyterLab's interactive and customizable environment to train and evaluate Ultralytics models with ease and efficiency. -- [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration. +- [Kaggle](kaggle.md): Explore how you can use Kaggle to train and evaluate Ultralytics models in a cloud-based environment with pre-installed libraries, GPU support, and a vibrant community for collaboration and sharing. -- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects. +- [MLFlow](mlflow.md): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment. -- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle. +- [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps. - [Paperspace Gradient](paperspace.md): Paperspace Gradient simplifies working on YOLO11 projects by providing easy-to-use cloud tools for training, testing, and deploying your models quickly. -- [Google Colab](google-colab.md): Use Google Colab to train and evaluate Ultralytics models in a cloud-based environment that supports collaboration and sharing. +- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale. -- [Kaggle](kaggle.md): Explore how you can use Kaggle to train and evaluate Ultralytics models in a cloud-based environment with pre-installed libraries, GPU support, and a vibrant community for collaboration and sharing. +- [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration. -- [JupyterLab](jupyterlab.md): Find out how to use JupyterLab's interactive and customizable environment to train and evaluate Ultralytics models with ease and efficiency. +- [Ultralytics HUB](https://hub.ultralytics.com/): Access and contribute to a community of pre-trained Ultralytics models. -- [IBM Watsonx](ibm-watsonx.md): See how IBM Watsonx simplifies the training and evaluation of Ultralytics models with its cutting-edge AI tools, effortless integration, and advanced model management system. +- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects. ## Deployment Integrations -- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size. +- [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), is a framework designed for efficiently integrating machine learning models into applications across iOS, macOS, watchOS, and tvOS, using Apple's hardware for effective and secure [model deployment](https://www.ultralytics.com/glossary/model-deployment). - [Gradio](gradio.md) 🚀 NEW: Deploy Ultralytics models with Gradio for real-time, interactive object detection demos. -- [TorchScript](torchscript.md): Developed as part of the [PyTorch](https://pytorch.org/) framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies. +- [NCNN](ncnn.md): Developed by [Tencent](http://www.tencent.com/), NCNN is an efficient [neural network](https://www.ultralytics.com/glossary/neural-network-nn) inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms. + +- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size. - [ONNX](onnx.md): An open-source format created by [Microsoft](https://www.microsoft.com/) for facilitating the transfer of AI models between various frameworks, enhancing the versatility and deployment flexibility of Ultralytics models. - [OpenVINO](openvino.md): Intel's toolkit for optimizing and deploying [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models efficiently across various Intel CPU and GPU platforms. -- [TensorRT](tensorrt.md): Developed by [NVIDIA](https://www.nvidia.com/), this high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference framework and model format optimizes AI models for accelerated speed and efficiency on NVIDIA GPUs, ensuring streamlined deployment. +- [PaddlePaddle](paddlepaddle.md): An open-source deep learning platform by [Baidu](https://www.baidu.com/), PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications. -- [CoreML](coreml.md): CoreML, developed by [Apple](https://www.apple.com/), is a framework designed for efficiently integrating machine learning models into applications across iOS, macOS, watchOS, and tvOS, using Apple's hardware for effective and secure [model deployment](https://www.ultralytics.com/glossary/model-deployment). +- [TF GraphDef](tf-graphdef.md): Developed by [Google](https://www.google.com/), GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware. - [TF SavedModel](tf-savedmodel.md): Developed by [Google](https://www.google.com/), TF SavedModel is a universal serialization format for [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) models, enabling easy sharing and deployment across a wide range of platforms, from servers to edge devices. -- [TF GraphDef](tf-graphdef.md): Developed by [Google](https://www.google.com/), GraphDef is TensorFlow's format for representing computation graphs, enabling optimized execution of machine learning models across diverse hardware. +- [TF.js](tfjs.md): Developed by [Google](https://www.google.com/) to facilitate machine learning in browsers and Node.js, TF.js allows JavaScript-based deployment of ML models. - [TFLite](tflite.md): Developed by [Google](https://www.google.com/), TFLite is a lightweight framework for deploying machine learning models on mobile and edge devices, ensuring fast, efficient inference with minimal memory footprint. - [TFLite Edge TPU](edge-tpu.md): Developed by [Google](https://www.google.com/) for optimizing TensorFlow Lite models on Edge TPUs, this model format ensures high-speed, efficient [edge computing](https://www.ultralytics.com/glossary/edge-computing). -- [TF.js](tfjs.md): Developed by [Google](https://www.google.com/) to facilitate machine learning in browsers and Node.js, TF.js allows JavaScript-based deployment of ML models. - -- [PaddlePaddle](paddlepaddle.md): An open-source deep learning platform by [Baidu](https://www.baidu.com/), PaddlePaddle enables the efficient deployment of AI models and focuses on the scalability of industrial applications. +- [TensorRT](tensorrt.md): Developed by [NVIDIA](https://www.nvidia.com/), this high-performance [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) inference framework and model format optimizes AI models for accelerated speed and efficiency on NVIDIA GPUs, ensuring streamlined deployment. -- [NCNN](ncnn.md): Developed by [Tencent](http://www.tencent.com/), NCNN is an efficient [neural network](https://www.ultralytics.com/glossary/neural-network-nn) inference framework tailored for mobile devices. It enables direct deployment of AI models into apps, optimizing performance across various mobile platforms. +- [TorchScript](torchscript.md): Developed as part of the [PyTorch](https://pytorch.org/) framework, TorchScript enables efficient execution and deployment of machine learning models in various production environments without the need for Python dependencies. - [VS Code](vscode.md): An extension for VS Code that provides code snippets for accelerating development workflows with Ultralytics and also for anyone looking for examples to help learn or get started with Ultralytics. diff --git a/mkdocs.yml b/mkdocs.yml index 5720b1679a..8c12e72ce8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -392,35 +392,35 @@ nav: - Clearml Logging: yolov5/tutorials/clearml_logging_integration.md - Integrations: - integrations/index.md - - TorchScript: integrations/torchscript.md + - Amazon SageMaker: integrations/amazon-sagemaker.md + - ClearML: integrations/clearml.md + - Comet ML: integrations/comet.md + - CoreML: integrations/coreml.md + - DVC: integrations/dvc.md + - Google Colab: integrations/google-colab.md + - Gradio: integrations/gradio.md + - IBM Watsonx: integrations/ibm-watsonx.md + - JupyterLab: integrations/jupyterlab.md + - Kaggle: integrations/kaggle.md + - MLflow: integrations/mlflow.md + - NCNN: integrations/ncnn.md + - Neural Magic: integrations/neural-magic.md - ONNX: integrations/onnx.md - OpenVINO: integrations/openvino.md - - TensorRT: integrations/tensorrt.md - - CoreML: integrations/coreml.md - - TF SavedModel: integrations/tf-savedmodel.md - - TF GraphDef: integrations/tf-graphdef.md - - TFLite: integrations/tflite.md - - TFLite Edge TPU: integrations/edge-tpu.md - - TF.js: integrations/tfjs.md - PaddlePaddle: integrations/paddlepaddle.md - - NCNN: integrations/ncnn.md - - Comet ML: integrations/comet.md + - Paperspace Gradient: integrations/paperspace.md - Ray Tune: integrations/ray-tune.md - Roboflow: integrations/roboflow.md - - MLflow: integrations/mlflow.md - - ClearML: integrations/clearml.md - - DVC: integrations/dvc.md - - Weights & Biases: integrations/weights-biases.md - - Neural Magic: integrations/neural-magic.md - - Gradio: integrations/gradio.md + - TF GraphDef: integrations/tf-graphdef.md + - TF SavedModel: integrations/tf-savedmodel.md + - TF.js: integrations/tfjs.md + - TFLite: integrations/tflite.md + - TFLite Edge TPU: integrations/edge-tpu.md - TensorBoard: integrations/tensorboard.md - - Amazon SageMaker: integrations/amazon-sagemaker.md - - Paperspace Gradient: integrations/paperspace.md - - Google Colab: integrations/google-colab.md - - Kaggle: integrations/kaggle.md - - JupyterLab: integrations/jupyterlab.md - - IBM Watsonx: integrations/ibm-watsonx.md + - TensorRT: integrations/tensorrt.md + - TorchScript: integrations/torchscript.md - VS Code: integrations/vscode.md + - Weights & Biases: integrations/weights-biases.md - HUB: - hub/index.md - Web: