#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

#cplusplus #computer_graphics #differentiable_programming #gpu #gpu_programming #sparse_computation #taichi

Taichi Lang is a powerful programming language for high-performance numerical computations. It is easy to use because it looks a lot like Python, so you don't need to learn a new language. Taichi Lang can run your code on both GPUs and CPUs, making it very fast. It also works on many different platforms, so you can write your code once and run it anywhere. This makes it great for things like real-time simulations, artificial intelligence, and visual effects in films and games. To get started, you can simply install it using `pip install taichi` and start coding right away. This helps you create complex simulations and computations quickly and efficiently.

https://github.com/taichi-dev/taichi

#swift #battery #bluetooth #clock #cpu #disk #fans #gpu #macos #menubar #monitor #network #sensors #stats #temperature

Stats is a tool that helps you monitor your macOS system from the menu bar. It shows you important information like CPU and GPU usage, memory and disk utilization, network activity, battery level, and more. You can install it manually or using Homebrew. Stats supports many languages and is efficient, though you can disable some modules to reduce energy impact. This tool is beneficial because it keeps you informed about your system's performance without needing to open multiple apps, helping you manage your computer better.

https://github.com/exelban/stats

#cplusplus #cublas #cuda #cudnn #gpu #mlops #networking #nvml #remote_access

SCUDA is a tool that lets you use GPUs from other computers over the internet. This means you can run programs that need powerful GPUs on your local machine, even if it doesn't have one. Here’s how it helps: You can test and develop applications using remote GPUs, train machine learning models from your laptop, perform complex data processing tasks, and even fine-tune pre-trained models without needing a powerful GPU locally. This makes it easier to work with GPUs without having to physically have one, saving time and resources.

https://github.com/kevmo314/scuda

#python #gpu #llm #pytorch #transformers

The `ipex-llm` library is a powerful tool for accelerating Large Language Models (LLMs) on Intel GPUs, NPUs, and CPUs. It integrates seamlessly with popular frameworks like HuggingFace transformers, LangChain, LlamaIndex, and more. Here are the key benefits `ipex-llm` optimizes LLM performance with advanced quantization techniques (FP8, FP6, FP4, INT4) and self-speculative decoding, leading to significant speedups.
- **Wide Model Support** It works on various Intel hardware such as Arc GPUs, Core Ultra NPUs, and CPUs, making it versatile for different setups.
- **Easy Integration** Detailed quickstart guides, code examples, and tutorials help users get started quickly.

Overall, `ipex-llm` enhances the performance and usability of LLMs on Intel hardware, making it a valuable tool for developers and researchers.

https://github.com/intel/ipex-llm

#cplusplus #3d #gpu #imu #lidar #localization #mapping #rgb_d #ros #ros2 #slam

GLIM is a powerful tool for creating 3D maps using various sensors like LiDAR and cameras. It ensures high accuracy by using advanced mathematical techniques and GPU acceleration, making the mapping process faster and better. GLIM is easy to use, allowing you to correct any mistakes in the map manually. It can work with different types of sensors and is flexible enough to be extended with additional features. This makes GLIM very useful for projects that need precise and customizable 3D mapping.

https://github.com/koide3/glim