Search Results for "hardware configuration"
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Showing 9 open source projects for "hardware configuration"
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Humanoid-Gym
Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real
Humanoid-Gym is a reinforcement learning framework designed to train locomotion and control policies for humanoid robots using high-performance simulation environments. The system is built on top of NVIDIA Isaac Gym, which allows large-scale parallel simulation of robotic environments directly on GPU hardware. Its primary goal is to enable efficient training of humanoid robots in simulation while enabling policies to transfer effectively to real-world hardware without additional training.... -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment.... -
3
shimmy
Python-free Rust inference server
The shimmy project is a lightweight local inference server designed to run large language models with minimal overhead. Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
...Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
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HPCC Systems
End-to-end big data in a massively scalable supercomputing platform.
HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions is a proven, open source solution for Big Data insights that can be implemented by businesses of all sizes. With HPCC Systems, developers can design applications with Big Data at their core, enabling businesses to better analyze and understand data at scale, improving business time to results and decisions. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete... -
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Lightning-Hydra-Template
PyTorch Lightning + Hydra. A very user-friendly template
Convenient all-in-one technology stack for deep learning prototyping - allows you to rapidly iterate over new models, datasets and tasks on different hardware accelerators like CPUs, multi-GPUs or TPUs. A collection of best practices for efficient workflow and reproducibility. Thoroughly commented - you can use this repo as a reference and educational resource. Not fitted for data engineering - the template configuration setup is not designed for building data processing pipelines that depend on each other. ... -
7
YOLOv4-large
Scaled-YOLOv4: Scaling Cross Stage Partial Network
...Unlike earlier object detection systems that only scale depth or width, this architecture scales multiple aspects of the neural network including structure, resolution, and channel configuration. This scaling strategy enables the model to adapt to different hardware environments while maintaining a strong balance between speed and detection accuracy. The repository includes multiple model variants such as YOLOv4-tiny, YOLOv4-CSP, and large-scale configurations designed for high-performance detection tasks. -
8
exchange-core
Ultra-fast matching engine written in Java based on LMAX Disruptor
...Goldman Sachs GS Collections), Real Logic Agrona, OpenHFT Chronicle-Wire, LZ4 Java, and Adaptive Radix Trees. Designed for high scalability and pauseless 24/7 operation under high-load conditions and providing low-latency responses. Single order book configuration is capable to process 5M operations per second on 10-years old hardware (Intel® Xeon® X5690) with moderate latency degradation. HFT optimized. Priority is a limit-order-move operation mean latency (currently ~0.5µs). Cancel operation takes ~0.7µs, placing new order ~1.0µs. Disk journaling and journal replay support, state snapshots (serialization) and restore operations, LZ4 compression. ... -
9
Swift AI
The Swift machine learning library
...Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. Each resides in their own repository and can be built with little or no configuration. Each module now contains its own documentation. We recommend that you read the docs carefully for detailed instructions on using the various components of Swift AI. ... -
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