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Lunar Lunar is a neural network aim assist that uses real-time object detection accelerated with CUDA on Nvidia GPUs. About Lunar can be modified to work with a variety of FPS games; however,
Optimizing Protein Structure Prediction Model Training and Inference on GPU Clusters
A Python function for Slurm, where I couldn't use nvidia-smi to monitor the GPU information
An early in-development version of training CLIP models with hivemind
Depict GPU memory footprint during DNN training of PyTorch
GPU implementation of $k$-Nearest Neighbors and Shared-Nearest Neighbors
Train large COMET (T5-3B/GPT2-XL) with small memory (on 11GB memory GPUs like 1080/2080) using DeepSpeed
Little demo of how to instantiate a cluster of GPUs with JAX
A simple library to speed up CLIP inference up to 3x (K80 GPU)
Simplication of GPU allocation for Tensorflow2
Get notified as soon as your next CPU, GPU, or game console is in stock
An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library
GPU Accelerated Non-rigid ICP for surface registration
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs
An example showing how to use jax to train resnet50 on multi-node multi-GPU
An inofficial PyTorch implementation of PREDATOR based on KPConv
High performance ptychography reconstruction python package running on GPU
GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks
Loopy lets you easily generate the tedious, complicated code that is necessary to get good performance out of GPUs and multi-core CPUs.
A Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.
An implementation of model parallel GPT-3-like models on GPUs, based on the DeepSpeed library
Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters
Keras Audio Preprocessors – compute STFT, ISTFT, Melspectrogram, and others on GPU real-time.
Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs, it is able to rely on MPI for distributing the workload in a cluster, and it can be accelerated by CUDA.
PyCUDA lets you access Nvidia's CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist-so what's so special about PyCUDA?
A polyphase resampler changes the sample rate of an incoming signal while using polyphase filter banks to preserve the overall shape of the original signal.
GPUtil is a Python module for getting the GPU status from NVIDA GPUs using nvidia-smi. GPUtil locates all GPUs on the computer, determines their availablity and returns a ordered list of available GPUs.