Search Results for "cario-cpu" - Page 2
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Showing 46 open source projects for "cario-cpu"
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PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow) -
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pipeless
A computer vision framework to create and deploy apps in minutes
...You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud. -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels,... -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively... -
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MAX StressTester
MAX StressTester: Python stress testing tool for max CPU, RAM and Disk
Disclaimer:im still working on this project please provide any feedback of bugs if you want :D helps me improve the experience for others. Also, windows might flag the exe file for a virus as it a python to exe file (might fix it later) this explains why it could be flagged as a virus on some virus detectors: https://s.id/1Cuk1 MAX StressTester is an open-source stress testing tool developed by ssit, available on the popular software development platform, SourceForge. The tool is... -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
...Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,... -
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TRFL
TensorFlow Reinforcement Learning
...The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train models using standard optimization routines. TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets. -
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DockStream
A Docking Wrapper to Enhance De Novo Molecular Design
...The flexilibity to specifiy a large variety of docking configurations allows tailored protocols for diverse end applications. DockStream can also parallelize docking across CPU cores, increasing throughput. DockStream is integrated with the de novo design platform, REINVENT, allowing one to incorporate docking into the generative process, thus providing the agent with 3D structural information. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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speedtest-cli
Command line interface for testing internet bandwidth using speedtest
Command line interface for testing internet bandwidth using speedtest.net. It is not a goal of this application to be a reliable latency reporting tool. Latency reported by this tool should not be relied on as a value indicative of ICMP style latency. It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet... -
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
...Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations, and more. Effortless device placement for using multiple CPU/GPU. The high-level API currently supports the most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, etc. -
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Graph Nets library
Build Graph Nets in Tensorflow
...This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. Graph Nets supports both TensorFlow 1 and TensorFlow 2, working with CPU and GPU environments, and includes educational Jupyter demos for shortest path finding, sorting, and physical prediction tasks. The codebase emphasizes modularity, allowing users to easily define their own edge, node, and global update functions. -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
...The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested under GPU and python3. But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. ... -
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vEMan - VMware ESX/ESXi Manager
An unofficial VMware® ESX/ESXi Manager for Linux
vEMan - [v]Mware [E]SX [Man]ager provides a GUI for managing ESX servers natively from within your Linux desktop without the need to use Windows vSphere® anymore! Support vEMan development for v2.0 by gofundme! http://www.gofundme.com/vEMan Why I started vEMan? Because I hate it to start my Windows VM to manage ESX(i) servers ;o) vEMan is (maybe) not needed if you use vCenter 5.x: http://kb.vmware.com/kb/2005377 --> vSphere >= v5 has a "web client" BUT keep in mind that there are... -
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The sandbox libraries (libsandbox & pysandbox) are an open-source suite of software components for C/C++ and Python developers to create automated profiling tools and watchdog programs. The API's are designed for executing and instrumenting simple (single process) tasks, featuring policy-based behavioral auditing, resource quota, and statistics collecting. The sandbox libraries were originally designed and utilized as the core security module of a full-fledged online judge system for...
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Syrupy is a Python script that regularly takes snapshots of the memory and CPU load of one or more running processes, so as to dynamically build up a profile of their usage of system resources.
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Can emulate old computer on text the code pogram ideal to create kernel for operating system The begin the project the can emulate CPU(8080)and z80.
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Monty compiler
Low level programming language and compiler
Low level programming language and compiler close to python. Intended for experiments with in processor design. Consists of language definition, compiler and simple cpu simulation written in python. The compiler can be adapted to different processors easily.
