Search Results for "scheduler linux"
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Showing 6 open source projects for "scheduler linux"
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BigMac
An open-source toolkit for BigMac-style pipeline-parallel training
BigMac is an open-source toolkit for pipeline-parallel training of multimodal large language models. It preserves optimized language-model pipeline schedules while placing encoder and generator work around them. This design reduces activation memory without bringing back cross-module pipeline bubbles. Its scheduler creates global operator plans, while its executor runs those plans through a shared schedule abstraction. A Megatron-Core reference backend and Qwen3 and Qwen3-VL tutorials help... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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FEDML Open Source
The unified and scalable ML library for large-scale training
A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale. TensorOpera AI is the next-gen cloud service for LLMs & Generative AI. It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely. Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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ML workspace
All-in-one web-based IDE specialized for machine learning
...This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated. Usable as remote kernel (Jupyter) or remote machine (VS Code) via SSH. Easy to deploy on Mac, Linux, and Windows via Docker. Jupyter, JupyterLab, and Visual Studio Code web-based IDEs.By default, the workspace container has no resource constraints and can use as much of a given resource as the host’s kernel scheduler allows. -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero...
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