Search Results for "cpu-x"
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Showing 343 open source projects for "cpu-x"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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ComfyUI
The most powerful and modular diffusion model GUI, api and backend
The most powerful and modular diffusion model is GUI and backend. This UI will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart-based interface. We are a team dedicated to iterating and improving ComfyUI, supporting the ComfyUI ecosystem with tools like node manager, node registry, cli, automated testing, and public documentation. Open source AI models will win in the long run against closed models and we are only at the beginning. Our core mission... -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in... -
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TileLang
Domain-specific language designed to streamline the development
TileLang is a Pythonic domain-specific language for writing high-performance GPU, CPU, and accelerator kernels without dropping fully into low-level vendor code. Its compiler infrastructure is built on TVM and targets workloads such as GEMM, dequantization, FlashAttention, and linear attention. Developers can work at different abstraction levels while still controlling memory layout, scheduling, pipelines, and hardware-specific behavior. -
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AWS X-Ray SDK for Python
AWS X-Ray SDK for the Python programming language
...Oftentimes, it may be useful to be able to disable X-Ray for specific use cases, whether to stop X-Ray from sending traces at any moment or to test code functionality that originally depended on X-Ray instrumented packages to begin segments prior to the code call. For example, if your application relied on an XRayMiddleware to instrument incoming web requests, and you have a method that begins subsegments based on the segment generated. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Django Lifecycle Hooks
Declarative model lifecycle hooks, an alternative to Signals
...However, my team often finds that Signals introduce unnecessary indirection and are at odds with Django's "fat models" approach. Django Lifecycle Hooks supports Python 3.7, 3.8 and 3.9, Django 2.0.x, 2.1.x, 2.2.x, 3.0.x, 3.1.x, and 3.2.x. For simple cases, you might always want something to happen at a certain point, such as after saving or before deleting a model instance. When a user is first created, you could process a thumbnail image in the background and send the user an email. -
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MSA: Memory Sparse Attention
Trainable latent-memory framework for 100M-token contexts
...Document-wise rotary position encoding and top-k routing keep training and inference close to linear complexity. A tiered KV-cache design stores routing keys on GPU while larger content states can remain on CPU. Its Memory Parallel engine distributes scoring and transfers only selected memory back to the accelerator. Memory Interleave alternates retrieval, context expansion, and generation to improve multi-hop reasoning across distant segments. The project reports experiments extending from 16K to 100M tokens, including inference on two A800 GPUs. -
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LocalGPT
Chat with your documents on your local device using GPT models
...An independent verification pass is intended to improve answer reliability before results are returned. The platform works with Ollama models, offers a browser interface and API, and can run through local or Docker-based setups. It supports CPU and several hardware acceleration environments while retaining conversation history within a session. -
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Tactical RMM
A remote monitoring & management tool, built with Django, Vue and Go
...Remote command and script execution (batch, powershell and python scripts). Event log viewer. Services management. Windows patch management. Automated checks with email/SMS alerting (cpu, disk, memory, services, scripts, event logs). Automated task runner (run scripts on a schedule). Remote software installation via chocolatey. Software and hardware inventory. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
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. ... -
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
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TensorFlow Model Garden
Models and examples built with TensorFlow
...To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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Robusta KRR
Prometheus-based Kubernetes Resource Recommendations
Robusta KRR (Kubernetes Resource Recommender) is a CLI tool for optimizing resource allocation in Kubernetes clusters. It gathers pod usage data from Prometheus and recommends requests and limits for CPU and memory. This reduces costs and improves performance. -
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Multimodal
TorchMultimodal is a PyTorch library
...The repository also includes example scripts and datasets for common multimodal tasks (e.g. retrieval, visual question answering, grounding) so you can test and compare models end to end. Installation supports both CPU and CUDA, and the codebase is versioned, tested, and maintained. -
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AIMET
AIMET is a library that provides advanced quantization and compression
...Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. -
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DocTR
Library for OCR-related tasks powered by Deep Learning
DocTR provides an easy and powerful way to extract valuable information from your documents. Seemlessly process documents for Natural Language Understanding tasks: we provide OCR predictors to parse textual information (localize and identify each word) from your documents. Robust 2-stage (detection + recognition) OCR predictors with pretrained parameters. User-friendly, 3 lines of code to load a document and extract text with a predictor. State-of-the-art performances on public document... -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
...Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU. -
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PyInstaller
Freeze (package) Python programs into stand-alone executables
PyInstaller freezes (packages) Python applications into stand-alone executables, under Windows, GNU/Linux, Mac OS X, FreeBSD, Solaris and AIX. PyInstaller’s main advantages over similar tools are that PyInstaller works with Python 3.5 to 3.9, it builds smaller executables thanks to transparent compression, it is fully multi-platform, and use the OS support to load the dynamic libraries, thus ensuring full compatibility. The main goal of PyInstaller is to be compatible with 3rd-party packages out-of-the-box. ... -
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Free-AppleId-Serve
Shared account, Quantumult X, Circle x, stash, Little Rocket node
Free AppleId Serve is a Chinese-language resource repository centered on shared foreign-region Apple IDs, proxy clients, and network subscription resources. It aggregates information for iOS tools such as Shadowrocket, Quantumult X, Stash, and related applications. The repository also maintains tutorials for proxy clients across iOS, macOS, Windows, and Android. A Python automation script collects publicly shared Apple account credentials from several external providers and updates repository content. Additional sections catalog free proxy subscriptions and services using technologies such as Clash, V2Ray, Shadowsocks, and Trojan. ... -
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PennyLane
A cross-platform Python library for differentiable programming
...Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. ... -
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parallel-ssh
Asynchronous parallel SSH client library.
parallel-ssh is an asynchronous parallel SSH library designed for large-scale automation. It differentiates itself from alternatives, other libraries and higher-level frameworks like Ansible or Chef in several ways. -
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AWS Lambda Python Runtime Interf Client
Seamlessly extend your preferred base images to be Lambda compatible
We have open-sourced a set of software packages, Runtime Interface Clients (RIC), that implement the Lambda Runtime API, allowing you to seamlessly extend your preferred base images to be Lambda compatible. The Lambda Runtime Interface Client is a lightweight interface that allows your runtime to receive requests from and send requests to the Lambda service. The Lambda Python Runtime Interface Client is vended through pip. You can include this package in your preferred base image to make... -
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pwndbg
Exploit Development and Reverse Engineering with GDB Made Easy
...It provides features crucial for efficient debugging in the world of low-level programming. Vanilla GDB is terrible to use for reverse engineering and exploit development. Typing x/g30x $esp is not fun, and does not confer much information. The year is 2024 and GDB still lacks a real hexdump command! GDB's syntax is arcane and difficult to approach. Windbg users are completely lost when they occasionally need to bump into GDB. -
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Jupyter Docker Stacks
Ready-to-run Docker images containing Jupyter applications
Jupyter Docker Stacks provides a curated set of ready-to-run Docker container images that bundle Jupyter applications with popular data science and computing tools, enabling users to quickly start working in a reproducible environment. These stacks support a range of use cases, from lightweight base notebook images to full featured environments that include scientific computing libraries, machine learning tools, and IDE-like notebook interfaces, all within Docker containers that run... -
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loonflow
A workflow engine base on django python
...Scenario services), if there is a certain development capability, it is recommended to use only the back-end engine function, and the front-end customized development according to the scenario can be dispersed in various internal background management systems (such as personnel, operation and maintenance, monitoring, cmdb, etc.). Since version 1.1.x, loonflow comes with a front-end interface for creating and processing work orders, which can be used directly. The official version is shown in the release . It is recommended to use the latest version. -
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bitnet.cpp
Official inference framework for 1-bit LLMs
bitnet.cpp is the official open-source inference framework and ecosystem designed to enable ultra-efficient execution of 1-bit large language models (LLMs), which quantize most model parameters to ternary values (-1, 0, +1) while maintaining competitive performance with full-precision counterparts. At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous...
