#python #5g #6g #communications #deep_learning #gpu_acceleration #link_level_simulation #machine_learning #open_source #reproducible_research

https://github.com/NVlabs/sionna

#rust #gpu #native_libraries #webgpu

https://github.com/gfx-rs/wgpu

#other #control #cpu #curves #fan #fancontrol #gpu #pwm #speed #temperature

https://github.com/Rem0o/FanControl.Releases

#python #cublas #cuda #cudnn #cupy #curand #cusolver #cusparse #cusparselt #cutensor #gpu #nccl #numpy #nvrtc #nvtx #rocm #scipy #tensor

https://github.com/cupy/cupy

#cplusplus #compiler #gpu_programming #high_performance #llvm #parallel_programming #python

https://github.com/exaloop/codon

#python #cloud_computing #cloud_management #data_science #deep_learning #distributed_training #gpu #hyperparameter_tuning #job_queue #job_scheduler #machine_learning #ml_infrastructure #multicloud #serverless #spot_instances #tpu

https://github.com/skypilot-org/skypilot

#java #cpu #deep_learning #docker #gpu #kubernetes #machine_learning #metrics #mlops #optimization #pytorch #serving

https://github.com/pytorch/serve

#python #command_line_tool #console #cuda #curses #gpu #gpu_monitoring #htop #monitoring #monitoring_tool #nvidia #nvidia_smi #nvml #process_monitoring #resource_monitor #top

https://github.com/XuehaiPan/nvitop

#scala #big_data #gpu #rapids #spark

https://github.com/NVIDIA/spark-rapids

#python #billion_parameters #compression #data_parallelism #deep_learning #gpu #inference #machine_learning #mixture_of_experts #model_parallelism #pipeline_parallelism #pytorch #trillion_parameters #zero

DeepSpeed is a powerful tool for training and using large artificial intelligence models quickly and efficiently. It allows you to train models with billions or even trillions of parameters, which is much faster and cheaper than other methods. With DeepSpeed, you can achieve significant speedups, reduce costs, and improve the performance of your models. For example, it can train ChatGPT-like models 15 times faster than current state-of-the-art systems. This makes it easier to work with large language models without needing massive resources, making AI more accessible and efficient for everyone.

https://github.com/microsoft/DeepSpeed

#go #device_plugin #gpu_management #gpu_virtualization #kubernetes_gpu_cluster #vgpu

HAMi is a tool that helps manage different types of devices like GPUs and NPUs in Kubernetes. It allows these devices to be shared among various tasks and makes sure they are used efficiently. This means you can use these powerful devices without changing your applications. HAMi benefits users by providing a unified way to manage these devices, ensuring better performance and resource utilization, and it is widely used in many industries. It also supports multiple types of devices and has a strong community for support and contributions.

https://github.com/Project-HAMi/HAMi

#python #autograd #deep_learning #gpu #machine_learning #neural_network #numpy #python #tensor

PyTorch is a powerful Python package that helps you with tensor computations and deep neural networks. It uses strong GPU acceleration, making your computations much faster. Here are the key benefits PyTorch allows you to use GPUs for tensor computations, similar to NumPy, but much faster.
- **Flexible Neural Networks** You can seamlessly use other Python packages like NumPy, SciPy, and Cython with PyTorch.
- **Fast and Efficient**: PyTorch has minimal framework overhead and is highly optimized for speed and memory efficiency.

Overall, PyTorch makes it easier and faster to work with deep learning projects by providing a flexible and efficient environment.

https://github.com/pytorch/pytorch