Search Results for "cpu memory usage"
Sort By:
Showing 23 open source projects for "cpu memory usage"
View related business solutions-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
$300 in Free Credit Towards Top Cloud ServicesStart your project in minutes. After credits run out, 20+ products include free monthly usage. Only pay when you're ready to scale.
-
1
leak-check
Personal Information “Leakage ” Detection Interface
leak-check is a utility designed to help developers detect memory leaks and resource mismanagement in applications. It provides tools to monitor allocations, track usage patterns, and identify potential leaks during runtime. The project focuses on improving application stability by highlighting inefficiencies in memory handling. It can be integrated into development workflows to catch issues early in the debugging process. leak-check is particularly useful in performance-critical applications where memory management is essential. ... -
2
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. -
3
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... -
4
DeepEP
DeepEP: an efficient expert-parallel communication library
...Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
-
5
Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine... -
6
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. -
7
claude-code-best-practice
Practice made claude perfect
...By combining conceptual guidance with concrete examples and configuration patterns, the project helps teams move from experimental AI usage toward more production-ready agent orchestration. -
8
SDGym
Benchmarking synthetic data generation methods
...Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
9
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. -
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.
-
10
DGL
Python package built to ease deep learning on graph
Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. -
11
DocArray
The data structure for multimodal data
...Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
12
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. -
13
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,... -
14
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... -
15
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... -
16
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... -
17
PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. 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... -
18
cnn-benchmarks
Benchmarks for popular CNN models
The cnn-benchmarks project is a collection of benchmarking scripts designed to evaluate the performance of convolutional neural networks across different hardware and configurations. It provides standardized implementations of popular CNN architectures, enabling developers to measure training speed, memory usage, and computational efficiency. The project focuses on reproducibility, allowing consistent comparisons between models and environments. It is particularly useful for testing GPUs and optimizing deep learning workloads, as it highlights bottlenecks and performance differences across setups. The repository includes scripts for running benchmarks on various architectures and datasets, making it easy to gather comparative metrics. ... -
19
mysql2sqlite
converts mysql database to sqlite database using php, pdo or exe
...Includes two projects : A) A php script B) A windows executable, based on a python script Converts data, indexes and foreign key constraints. About the php script : -don't need a lot of memory -good speed Was projected to use on VDS/VPS. Usage: wget https://sourceforge.net/projects/mysql2sqlite/files/mysql2sqlite/download chmod +x mysql2sqlite ./mysql2sqlite About the Windows executable and the Python script : Usage : edit the in i file to fit your system, and just run the exe or the python script. -
20
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... -
21
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...
-
22
pykb
Python Killboard Platform for EVE Online
...Aggregates exist along with code to maintain them to enable high speed reports for summarized data. A point calculation system is included as is support for parsing German and Russian kill mail text. Caching exists in two forms, a basic in memory dict based caching system suitable for single process tasks and integration with uwsgi's powerful and fast built in cache. Utility modules are provided for adding in killmails, backfilling killmails and dumping them via the command line. These also serve as reusable code for web apps or at least as usage examples. A sample web app using pykb is included. -
23
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.
- Previous
- You're on page 1
- Next
