#python #nlp #sparsity #tensorflow #keras #pytorch #deep_learning_algorithms #image_classification #deep_learning_library #pruning #object_detection #automl #computer_vision_algorithms #onnx #deep_learning_models #sparsification #pruning_algorithms #smaller_models #model_sparsification #sparsification_recipes #recipe_driven_approaches
https://github.com/neuralmagic/sparseml
https://github.com/neuralmagic/sparseml
GitHub
GitHub - neuralmagic/sparseml: Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling…
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models - neuralmagic/sparseml
#python #deep_learning #deep_learning_library #image_captioning #multimodal_datasets #multimodal_deep_learning #salesforce #vision_and_language #vision_framework #vision_language_pretraining #vision_language_transformer #visual_question_anwsering
https://github.com/salesforce/LAVIS
https://github.com/salesforce/LAVIS
GitHub
GitHub - salesforce/LAVIS: LAVIS - A One-stop Library for Language-Vision Intelligence
LAVIS - A One-stop Library for Language-Vision Intelligence - salesforce/LAVIS
#cplusplus #cpp #cuda #deep_learning #deep_learning_library #gpu #nvidia
CUTLASS is a powerful tool for high-performance matrix operations on NVIDIA GPUs. It helps developers create efficient code by breaking down complex tasks into reusable parts, making it easier to build custom applications. CUTLASS supports various data types and architectures, including the new Blackwell SM100 architecture, which means users can optimize their programs for different hardware. This flexibility and support for advanced features like Tensor Cores improve performance significantly, benefiting users who need fast computations in fields like AI and scientific computing.
https://github.com/NVIDIA/cutlass
CUTLASS is a powerful tool for high-performance matrix operations on NVIDIA GPUs. It helps developers create efficient code by breaking down complex tasks into reusable parts, making it easier to build custom applications. CUTLASS supports various data types and architectures, including the new Blackwell SM100 architecture, which means users can optimize their programs for different hardware. This flexibility and support for advanced features like Tensor Cores improve performance significantly, benefiting users who need fast computations in fields like AI and scientific computing.
https://github.com/NVIDIA/cutlass
GitHub
GitHub - NVIDIA/cutlass: CUDA Templates and Python DSLs for High-Performance Linear Algebra
CUDA Templates and Python DSLs for High-Performance Linear Algebra - NVIDIA/cutlass
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