Search Results for "waydroid-image" - Page 2
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Showing 146 open source projects for "waydroid-image"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Ralph
Ralph is the CMDB / Asset Management system for data center
Ralph is built on top of Django and Python 3 and is easy to extend and customize without writing boilerplate code. REST API, Workflows code extensions allow for easy customization. We've chosen the best features of DCIM, Asset Mgmt and CMDB systems to create one, easy and well-integrated system. One interface is easier than 3. Keep track of assets purchases and their life cycle. Flexible flow system for assets life cycle. Data center and back office support. DC visualization built-in. Ralph... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
PaddleX is a deep learning full-process development tool based on the core framework, development kit, and tool components of Paddle. It has three characteristics opening up the whole process, integrating industrial practice, and being easy to use and integrate. Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. ... -
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SageMaker Spark Container
Docker image used to run data processing workloads
...It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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DomainBed
DomainBed is a suite to test domain generalization algorithms
...DomainBed also integrates multiple standard datasets—including RotatedMNIST, PACS, VLCS, Office-Home, DomainNet, and subsets from WILDS—allowing consistent experimentation across image classification tasks. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference,... -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. ... -
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loonflow
A workflow engine base on django python
a workflow engine base on django The django-based workflow engine system (called through the http interface, can be used as a unified workflow engine within the enterprise, providing all workflows such as permission application, resource application, release application, leave, reimbursement, it service, etc. Scenario services), if there is a certain development capability, it is recommended to use only the back-end engine function, and the front-end customized development according to the... -
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CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research in multi-view 3D reconstruction, novel view synthesis, and geometry-aware representation learning. Each of the thousands of sequences in CO3Dv2 captures a common object (from categories like cars, chairs, or plants) from multiple real-world viewpoints. ... -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
...These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run consistently across machines. Users can pull a particular stack image and launch a Jupyter server without worrying about installing Python, R, or complex dependencies themselves — everything needed is baked into the container. This makes the stacks especially useful for education, demos, collaborative coding, and CI/CD workflows where consistent environments are crucial, and it integrates smoothly with cloud platforms, JupyterHub deployments, and Binder for interactive sharing. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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AWS SAM CLI
CLI tool to build, test, debug, and deploy Serverless applications
The AWS Serverless Application Model (SAM) CLI is an open-source CLI tool that helps you develop serverless applications containing Lambda functions, Step Functions, API Gateway, EventBridge, SQS, SNS and more. The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and... -
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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
...After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. A SageMaker Model contains references to a model.tar.gz file in S3 containing serialized model data, and a Docker image used to serve predictions with that model. A Batch Transform job runs an offline-inference job using your TensorFlow Serving model. Input data in S3 is converted to HTTP requests, and responses are saved to an output bucket in S3. -
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Uncertainty Baselines
High-quality implementations of standard and SOTA methods
...Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. Techniques include deep ensembles, Monte Carlo dropout, temperature scaling, stochastic variational inference, heteroscedastic heads, and out-of-distribution detection workflows. Each baseline emphasizes reproducibility: fixed seeds, standard splits, and strong metrics such as calibration error, AUROC for OOD, and accuracy under shift. -
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Flask App Builder
Simple and rapid application development framework
...Labels and descriptions for each field. Automatic base validators from the model's definition. Custom validators, extra fields, and custom filters for related dropdown lists. Image and File support for upload and database field association. Field sets for Forms (Django style). -
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Django Lifecycle Hooks
Declarative model lifecycle hooks, an alternative to Signals
...For simple cases, you might always want something to happen at a certain point, such as after saving or before deleting a model instance. When a user is first created, you could process a thumbnail image in the background and send the user an email. -
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MuJoCo Playground
An open source library for GPU-accelerated robot learning
...The project includes classic control benchmarks from dm_control, advanced quadruped and bipedal locomotion systems, and dexterous as well as non-prehensile manipulation setups. It also offers optional vision-based training capabilities through integration with Madrona-MJX, allowing researchers to train policies directly from image input on GPUs. MuJoCo Playground supports both the MJX JAX implementation and the Warp physics engine, enabling flexible use across research pipelines. The environments are designed for fast training, compatibility with reinforcement learning libraries, and real-time trajectory visualization using rscope. -
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GenAI Processors
GenAI Processors is a lightweight Python library
GenAI Processors is a lightweight Python library for building modular, asynchronous, and composable AI pipelines around Gemini. Its central abstraction is the Processor, a unit of work that consumes an asynchronous stream of parts (text, images, audio, JSON) and produces another stream, making it natural to chain operations and keep everything streaming end-to-end. Processors can be composed sequentially (to build multi-step flows) or in parallel (to fan-out work and merge results), which... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
...These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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LINE Messaging API SDK for Python
LINE Messaging API SDK for Python
The LINE Messaging API SDK for Python makes it easy to develop bots using LINE Messaging API, and you can create a sample bot within minutes. You must upload a rich menu image and link the rich menu to a user for the rich menu to be displayed. You can create up to 1000 rich menus for one LINE Official Account with the Messaging API. The LINE Messaging API SDK for Python includes experimental asyncio support. (API may change without notice in a future version). Send push messages to multiple users at any time. ... -
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Wagtail
A Django content management system focused on flexibility & UX
Wagtail is a powerful, open source content management system that’s focused on flexibility and user experience. Built on Django, Wagtail offers precise control and flexibility for designers, developers and editors. Designed by developers for developers, Wagtail plays nicely with everything else in your tech stack so you can do more and focus on perfecting your site. Designers will find Wagtail’s simple templating system ideal for building beautiful websites just the way they want, without... -
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Lightly
A python library for self-supervised learning on images
...We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. ... -
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Jittor
Jittor is a high-performance deep learning framework
...A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. ... -
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Jina
Build cross-modal and multimodal applications on the cloud
...Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS. Intuitive design pattern for high-performance microservices. Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. ... -
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Pixelization
Stable-diffusion-webui-pixelization
This is a specialized extension for the popular Stable Diffusion Web UI (AUTOMATIC1111) that focuses on converting or “pixelizing” images into a pixel-art aesthetic. It's designed as a plugin you install into the Web UI so that in the “Extras” or “Pixelization” tab you can drag in an input image and produce a stylized, block-based version with control over cell size, color depth, and segmentation. The extension uses pre-trained models and optionally can co-operate with the Web UI’s other features (image-to-image, prompt-based generation) so you can combine pixelization with generative workflows. For digital art, game assets, or retro aesthetic workflows, this offers a fast path from photo or high-res asset to stylized tiles or sprites. ... -
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uwsgi-nginx-flask
Docker image with uWSGI and Nginx for Flask applications in Python
Image with uWSGI and Nginx for Flask apps in Python running in a single container. Docker image with uWSGI and Nginx for Flask web applications in Python running in a single container. Optionally using Alpine Linux. This Docker image allows you to create Flask web applications in Python that run with uWSGI and Nginx in a single container. -
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
Image processing tool that encodes pixel data as indices within the first 16.7 million digits of PI (π). Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ...
