Cudalavori

Programma esclusivamente in CUDA, si tratta di un sorting per ricerca statistica. Il programma deve generare uno o piu file random di stringhe numeriche e poi ricercare mie stringhe particolari. Esiste già un vecchio programma scritto in Basic ma e molto lento- Il Pc sul quale deve deve lavorare e un HP pavilion con I7

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

...an ASP.NET Core 9 API up and running on my Windows development machine. The next step is to serve responses from the Ollama Phi3 language model through that API, leveraging an NVIDIA GPU so inference remains fast even under load, and then publish the service so it can be reached publicly from anywhere in the world. Here is what I need: • Configure the Ollama Phi3 model to run on an NVIDIA card (CUDA already installed). • Wire the model into my existing controllers so endpoints stay unchanged for consumers but now call the GPU-accelerated Phi3 instance behind the scenes. • Package everything for production—Docker is fine if it simplifies deployment, but a native Windows service is equally acceptable. • Provide brief, step-by-step documentation so ...

This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling.

...modern GPUs and expose a clean, future-proof API for downstream applications. My end goal is to abstract away vendor-specific quirks so a data-scientist, graphics engineer, or researcher can tap into raw parallel power without worrying about whether the machine is running Windows, Linux, or macOS, or whether it ships with NVIDIA, AMD, or Intel silicon. You’re free to recommend the optimal blend of CUDA, ROCm, OpenCL, Vulkan, or even a custom compute layer—what matters is performance, portability, and clean code that’s easy to extend. I’m open to focusing on a single workload first (machine-learning kernels, real-time graphics effects, or heavy scientific simulations) if that helps us validate the core, then scaling outward. Deliverables I’m exp...

Job Title: CUDA Developer Needed – GPU-Accelerated Bitcoin WIF Key Recovery Tool (Verification Required) Project Description: I am looking for an experienced CUDA / GPU developer to build and optimize a high-performance Bitcoin WIF private key recovery program. This project requires real GPU computation, correct Bitcoin cryptography handling, and verifiable results. This is not a demo or theoretical project. The program must be fully functional and tested. Only apply if you have proven experience with CUDA, cryptography, or Bitcoin key handling. Technical Requirements: - Written in C++ with CUDA - Runs on NVIDIA GPUs - Command-line interface (CLI) - Supports Bitcoin WIF (Base58Check) - Supports compressed and uncompressed private keys - Correct che...

Lead AI / Fullstack Engineer — ...communication. ​Traffic Localization: Optimize routing protocols to maximize performance within the TAS-IX network. ​Candidate Requirements ​AI / ML Engineering: ​Proven experience with End-to-end (E2E) speech models (Moshi, AudioLM, or similar). ​Deep proficiency in PyTorch and Transformer architectures. ​Hands-on experience in Fine-tuning LLMs/S2S models for new language groups. ​Expertise in CUDA 12.x and NVIDIA optimization libraries. ​Fullstack Development: ​Expert-level knowledge of WebRTC / WebSockets for real-time media streaming. ​Demonstrated experience in developing Telegram Mini Apps (TMA). ​Professional mastery of FastAPI and React / Next.js. ​Strong understanding of the constraints and requirements of Low-latency systems.

manual intervention. 3. Re-assemble processed frames back into a single clip using FFmpeg (or similar), ensuring temporal consistency—no flicker or dropped frames. 4. Expose a simple CLI command such as: python --input --output --strength 0.7 --seed 42 5. Provide a short README covering environment setup (Python, diffusers / transformers versions, CUDA requirements), example usage, and expected runtimes Acceptance criteria • The script completes a sample without errors and produces visibly live-action styling throughout. • Code is clean, commented, and includes a or environment.yml. Delivery: source code, README, and one converted sample clip produced by your wrapper.

My current web-scraping pipeline is functional but slow, and the data-processing stage in particular is exhausting the GPU. I want to cut total runtime, shrink GPU memory consumption, and have every run automatically log its own performance so I can measure gains over time. You’ll start by profiling the existing Python code (pandas, NumPy, CUDA-accelerated routines), pinpointing the true hotspots. From there, I need refactored or parallelised logic, smarter batching, and any lightweight caching that prevents redundant computation. I’m open to revisiting earlier extraction steps if a quick tweak there will compound the speed-up, but the main brief is processing-level optimisation. Deliverables: • Optimised processing script(s) with clear inline comments • ...

My current web-scraping pipeline is functional but slow, and the data-processing stage in particular is exhausting the GPU. I want to cut total runtime, shrink GPU memory consumption, and have every run automatically log its own performance so I can measure gains over time. You’ll start by profiling the existing Python code (pandas, NumPy, CUDA-accelerated routines), pinpointing the true hotspots. From there, I need refactored or parallelised logic, smarter batching, and any lightweight caching that prevents redundant computation. I’m open to revisiting earlier extraction steps if a quick tweak there will compound the speed-up, but the main brief is processing-level optimisation. Deliverables: • Optimised processing script(s) with clear inline comments • ...

...short written walkthrough covering hardware requirements, model parameters, and tips for further tuning. Acceptance criteria 1. Frame-by-frame identity preservation ≥ 95 % (verified with face-recognition scores). 2. No temporal flicker visible on 30-fps playback. 3. End-to-end generation time under 2× video length on a single high-end GPU. Tech stack keywords: PyTorch, TensorFlow, FFmpeg, CUDA, Google Colab, facial-landmark detection, GAN inversion. Roadmap beyond this delivery Once the core system is proven, I plan to expand into other AI-driven video features—scene synthesis, automated dubbing, even real-time object tracking—so clean, well-documented code is essential for future extension. Ready to start as soon as we agree on the approach, and ...

...- Auto-sync narration and visuals - Options: - voice selection (male/female) - narration speed - background music (optional) - subtitles (optional) Tech Requirements: - Must support OFFLINE mode (local machine) using open-source models (preferred) - Should also support ONLINE mode (server/cloud deployment) - Efficient pipeline (render without crashing) - Works on CPU + GPU if available (CUDA GPU preferred) Preferred Implementation (engineer decides exact tools): - Python backend (FastAPI preferred) - Local model inference pipeline - Video assembly using FFmpeg / MoviePy - Open-source TTS narration (example: XTTS, Piper, Coqui TTS, etc.) - Open-source image generation or whiteboard assets pipeline - LLM for storyboard/script breakdown (open-source model OR cheapest API) ...

...corre sin interrupciones perceptibles. El proyecto consiste en el desarrollo de un motor de inferencia de alta performance para la detección, clasificación y seguimiento de múltiples clases de objetos en entornos dinámicos complejos, utilizando hardware dedicado. Implementación de arquitecturas de detección (YOLO/RT-DETR) y algoritmos de tracking.• Optimización de modelos para hardware NVIDIA (CUDA/TensorRT).• Fusión de datos provenientes de sensores externos (Telemetría/GPS) con flujos de video 4K.• Desarrollo de lógica de persistencia en bases de datos geoespaciales. • Seniority comprobable en Python y OpenCV. Experiencia en el ciclo completo de vida de modelos de visión: desde ...

...advise on the best hardware stack to achieve the 15-50 person real-time requirement: Cameras: Recommend specific sensors or cameras (e.g., Wide FOV, Global Shutter, or IR-capable for low-light/glare robustness). Processing Units: Advice on edge deployment. Can this run on a Raspberry Pi 5 with an AI Kit, or is an NVIDIA Jetson (Orin/Nano) required? If commodity GPUs are needed, specify minimum VRAM/Cuda core requirements. Kits: Recommend specific "plug-and-play" kits or enclosures suitable for the indoor/outdoor environment described. Final Deliverables Hardware Recommendation Report: Detailed list of suggested cameras, lenses, and processing kits (Raspberry Pi, Jetson, etc.) tailored to this specific use case. Source Code: Ready to plug into a Python environment ...

I’m running ...trade-offs you introduce so I can reproduce and benchmark I already run basic line-profiler and torch-autograd checks, so I’m looking for deeper insights—vectorised ops, smarter batching, async data movement, or architectural tweaks I may have missed. Feel free to use tools like PyTorch Profiler, nvprof, or your preferred optimisers as long as the final instructions remain reproducible in a standard CUDA environment. If that sounds straightforward, let me know your availability and how you’d approach the first pass; I’m ready to share the repo right away.

..., Twilio or Meta API) OR a custom Mobile App (Flutter/React Native) for security staff. Dashboard: A simple web-based or local interface to view live logs, replay detected incidents, and manage sensitivity settings. Technical Requirements: Programming Language: Python. Frameworks: PyTorch, TensorFlow, OpenCV, YOLO (v8/v10), or MediaPipe. Hardware Compatibility: Must be optimized for NVIDIA CUDA cores / TensorRT. Scalability: The code should support multiple camera streams simultaneously. Deliverables: Full Source Code (well-documented). Setup Guide (How to install on the NVIDIA device and connect cameras). A working prototype/MVP demonstrating the detection of basic theft actions. Ideal Candidate: Proven experience in Computer Vision and Action Recognition. Previous ...

I need a Windows-based GPU workstation dedicated to running local large-language-model workflows. I need someone who can walk me through the full setup—hardware , CUDA drivers, PyTorch/TensorFlow installs, plus the extra tools I rely on for text-to-video generation and similar AI workloads. Your first task is to get the machine fully operational: verify BIOS and power settings, install the latest GPU driver stack, configure CUDA/cuDNN, and deploy the core frameworks. From there we’ll layer in local-LLM utilities (e.g., , Ollama) alongside Stable Diffusion or any other video-generation packages I might explore. Clear, repeatable documentation of every step is essential so I can reproduce the environment later. Once the base system is stable, I’d like on...

...3), OCR (paddleocr 2.10.0 on paddlepaddle 3.0.0 / paddlepaddle-gpu 2.6.2), and post-processing with scikit-learn 1.6.0. Although one GPU-ready wheel is present, all processing still executes on the CPU. The goal is full NVIDIA CUDA utilisation across the entire workflow, from frame decoding to final inference. I need you to: • Profile the current code, pinpoint CPU-bound sections, and migrate or rewrite them for GPU execution (CUDA, CuDNN, cuBLAS, or other relevant CUDA-based APIs). • Update or swap libraries where necessary—feel free to recommend faster CUDA-compatible alternatives if they will not break accuracy (e.g., CuPy, TensorRT, NVIDIA Video Codec SDK). • Modify the code so GUI-less batch processing and real-time video run...

...spots a potential anomaly. All processing must happen in real time without introducing perceptible latency to the surgeon’s view. My current hardware outputs standard HDMI and records to DICOM, so your code should sit either between the camera head and the display (FPGA, GPU box, or high-performance PC is fine) or run as a software module on the workstation already attached to the scope. OpenCV, CUDA, TensorFlow, or similarly robust libraries are welcome—just keep licensing constraints clear. Deliverables • Executable or deployable source that enhances image clarity, performs real-time analysis, and triggers automated anomaly detection. • API or integration hooks so I can feed the processed stream back to my recording software. • A concise user gu...

...Engineer - Real-time Edge AI (OpenCV, ONNX, CUDA & TensorRT) Busco un Senior Computer Vision Engineer con experiencia demostrable en Edge AI para desarrollar un sistema de asistencia táctica en tiempo real basado en captura de vídeo externa. Desafío Técnico Principal: El sistema debe procesar un flujo de vídeo HDMI, realizar OCR de alta precisión y detección de objetos, y consultar una base de datos local con una latencia end-to-end inferior a 100ms. Stack Tecnológico Requerido: • Lenguaje: Python 3.10+ con arquitectura OOP escalable (Interfaces abstractas). • Visión: OpenCV avanzado y procesamiento de imágenes para OCR de stacks y botes. • Motores de Inferencia: Experiencia obligatoria con ...

...machine freeze during model training? Welcome to your new digital superpower. I bridge the gap between your ideas and the raw power of Microsoft Azure. I don’t just "rent servers"—I architect secure, high-performance environments so you can focus on building the future. What’s in my secret sauce? GPU Beasts: Access NVIDIA N-Series (V100, A10, T4) for AI/ML. Ready-to-Go Stack: I’ll pre-install CUDA, PyTorch, TensorFlow, or Docker. No more driver headaches! Fort Knox Security: Advanced Firewalls & private VPNs. Your VM stays invisible to the public web. Windows or Linux? I speak both. Whether you need an RDP or an SSH terminal, I’ve got you. The "Date Before You Marry" Trial Not sure if the speed is right? For just $5, ...

...with a brand-new RTX 5090 and need TensorRT installed, tuned and ready to accelerate Stream Diffusion inside TouchDesigner. I haven’t settled on a specific release yet, so I’ll rely on your guidance to pick the most stable, future-proof version (including matching CUDA and cuDNN builds) for this GPU. Here’s what I expect: • Recommend the best TensorRT version for an RTX 5090 Windows environment and explain why it’s the right fit. • Handle the full installation: download packages, configure environment variables, and verify driver / CUDA compatibility. • Prove the install works by running a sample inference, then confirm TouchDesigner can see the TensorRT engine for Stream Diffusion. • Leave me with a concise, step-by-step recap ...

...a brand-new RTX 5090 and need TensorRT installed, tuned and ready to accelerate Stream Diffusion inside TouchDesigner. I haven’t settled on a specific release yet, so I’ll rely on your guidance to pick the most stable, future-proof version (including matching CUDA and cuDNN builds) for this GPU. Here’s what I expect: • Recommend the best TensorRT version for an RTX 5090 Windows environment and explain why it’s the right fit. • Handle the full installation: download packages, configure environment variables, and verify driver / CUDA compatibility. • Prove the install works by running a sample inference, then confirm TouchDesigner can see the TensorRT engine for Stream Diffusion. • Leave me with a concise, step-by-step rec...

Looking for developer who can work on below requirment . Lead design and implementation of GPU computers for deep learning; optimize algorithms and mentor team Must have key skills cuda,c++,Gpu Programming Other key skills Parallel Computing,Opengl,Opencl Job description What you’ll do CUDA is a must JD For Senior / Lead Engineer (HPC GPU):- As a Senior / Team Lead (HPC) you will provide leadership in designing and implementing groundbreaking GPU computers that run demanding deep learning, high-performance computing, and computationally intensive workloads. We seek an expert to identify architectural changes and/or completely new approaches for accelerating our deep learning models. As an expert, you will help us with the strategic challenges we encounter, includi...

...virtually no perceptible delay. The tool must lock on to faces accurately, track expressions, match lighting and color, and render the composite at a stable frame rate suitable for streaming or studio recording. The core pipeline should include high-resolution face detection, landmark tracking, real-time inference with a modern GAN or transformer model, and seamless blending. Feel free to lean on CUDA-accelerated TensorFlow or PyTorch, OpenCV for image I/O, and any efficient post-processing libraries you trust—what matters is rock-solid performance and visual fidelity. I want the interface to be simple: a preview window, a slot to load or capture the target face, quick toggles to enable/disable tracking, and an option to record or pipe the output to a virtual camera devic...

...deliver must install and operate smoothly in that environment without the usual Linux-only work-arounds that most Jetson guides assume. Here is what I need from you: build a lightweight, fully-functional miner that recognizes the Jetson Nano’s CUDA-capable GPU, connects to any standard Bitcoin pool I specify, and begins hashing immediately after a one-time setup wizard. The setup flow should auto-detect the board’s hardware, prompt for the pool URL, wallet address, and worker name, then save those settings for future boots. Key technical expectations • CUDA acceleration out of the box—no manual library hunting. • Clean, single-click installer for Windows 11 on ARM. • Real-time dashboard showing hash rate, accepted/rejected shares, p...

I have a project that should work with ComfyUI / WAN2 set-up and now need to turn it into approximately 15–20 minutes of finished, classroom-ready video. We have text...• Final MP4s play without glitches on standard players • Everything is handed over within the agreed, ASAP timeline • ComfyUI API We have GPU Server ready with following config Server Configuration Intel Dual XEON E5-2697v4 CPU Cores: 18 RAM: 256GB DDR4 GPU: 3 x Nvidia Quadro RTX A5000 (3GPU) STORAGE: 240GB SSD (Boot) + 2TB NVMe + 8TB SATA (10TB) GPU Specifications Microarchitecture: Ampere CUDA Cores: 10,752 Tensor Cores: 336 GPU Memory: 24GB GDDR6 FP32 Performance: 38.71 TFLOPS If you already work with ComfyUI or similar AI video pipelines and can hit these language requirements quickly, l...

...training a convolutional neural network and now I want it running reliably on an AWS EC2 instance. I already have an AWS account and am settled on using EC2 rather than SageMaker or Lambda, so the task is purely about standing up the production environment and proving that the model answers live requests. Here’s what I need: • Spin up and configure an EC2 instance (Ubuntu preferred) with GPU drivers, CUDA / cuDNN, Python, and either TensorFlow or PyTorch—whichever my model requires. • Package the model (saved .h5 or .pt plus any preprocessing code) into a lightweight service—Flask, FastAPI, or another simple REST interface is fine. • Expose a secure HTTPS endpoint behind an AWS load balancer or an Nginx reverse proxy so I can hit /predict wi...

...that must appear in the output: 1. Player positions and movement traces throughout the match 2. Types of shots taken and whether they resulted in winners, forced errors or unforced errors 3. Rally durations paired with their outcomes Technology preferences are Python with OpenCV, YOLO-based detection, pose estimation for finer tracking, and GPU-accelerated processing on AWS or GCP (or a local CUDA setup if you prefer). A clean, well-documented codebase and brief setup script are part of the hand-off. When you reply, please show: • Examples of previous computer-vision or sports-analytics projects you’ve delivered • A concise outline of the approach you’d take for detection, tracking and event logic • Your estimated timeline from kick-off to fir...

...RTX 3090 and centres on machine-learning workloads. The goal is a single, modular codebase able to orchestrate three specific tasks—image recognition, natural-language processing, and predictive analytics—while squeezing every ounce of performance the 3090’s CUDA cores and Tensor cores can provide. Core requirements • Modular architecture so each task (vision, NLP, forecasting) lives in its own plug-in or service yet can share common utilities such as data pipes, logging and GPU memory management. • Native GPU acceleration using CUDA 11.x (cuDNN, NCCL, TensorRT or comparable optimiser) with fall-backs abstracted cleanly for future upgrades. • Real-time inference endpoint that exposes a lightweight REST or gRPC API for incoming data a...

...optimization, scheduling, and GPU efficiency Experience with large-scale data processing and dataset pipelines Familiarity with multi-GPU, distributed, or accelerated training frameworks Ability to debug training instabilities, loss issues, and performance bottlenecks Bonus Skills: Experience with custom architectures Prior contributions to ML open-source projects Ability to profile and optimize CUDA workloads Research background in LLMs or generative models Compensation & Recognition $200 for winning competition $400 upon completion of MageV1 Public credit as the Official Trainer of MAGIC and MageV1 on: GitHub HuggingFace Documentation Opportunity for expanded future paid roles as MAGIC evolves Priority consideration for extended research collaborations ### ...

...on small projects that culminate in a functioning robot pipeline—from simulation to deployment on the Orin Nano board. Key areas I’d like to cover: • Setting up Isaac Sim and Isaac Lab environments (Ubuntu, ROS 2, Omniverse). • Building, training, and testing perception models in simulation. • Transferring those models to real hardware and optimizing them for Jetson. • Best practices for CUDA acceleration, TensorRT, and power management on Orin Nano. • Road-mapping steps to scale the same workflow to AGX and Thor in the future. Deliverable: a structured learning path supplemented by live sessions, example code, and working demos that run on my Jetson Orin Nano. If you’ve successfully shipped robots or AI applications on Nvidi...

...analytics. The system will ingest RTSP/ONVIF camera streams, run real-time AI detection (person, vehicle, intrusion, loitering, unattended objects), generate alerts, store snapshots/clips, and provide dashboards, reports, and forensic search. A detailed FRD is ready. Required Skills: Real-time video processing (RTSP, GStreamer, FFMPEG, ONVIF) AI/Computer Vision (YOLO, TensorRT, DeepStream, OpenVINO, CUDA) GPU-accelerated inference pipelines Multi-tenant SaaS backend (Node.js / Python / Go) Cloud deployment (AWS/GCP) Databases: PostgreSQL/MongoDB, Redis Frontend: React or Vue Experience building similar video analytics systems is mandatory Who Should Apply DO NOT APPLY if you don’t have previous experience in video analytics / AI surveillance systems. ONLY APPLY if you...

Hey! I am a filmmaker commissioning the development of a high-performance video effects plugin for Adobe Premiere Pro. I require this to be developed in C++ using the Adobe (AE) SDK, with a mandatory focus on GPU acceleration (Metal/CUDA) to ensure real-time performance within Premiere Pro's 32-bit float color pipeline. This plugin is specified to combine four distinct and highly controllable cinematic effects: Bloom, Glow (Mist), Halation, and Curated Grain. 1. Core Functionality & Rendering Pipeline The plugin must implement the following advanced effects pipeline: Bloom (Atmosphere): A soft, full-screen diffusion effect applied additively to the scene, designed to lift the overall atmospheric feel. This effect should slightly elevate shadows/midtones to enhance diffus...

...Python version o Torch version (include CUDA tag, e.g. 2.3.0+cu121) o Exact location of ComfyUI folder and venv o List of installed node packs and versions (Impact Pack, etc.) 2. Model info o Which Flux Schnell checkpoint(s) you used o Download links (or at least release names / hashes) o Where to place them on my system (e.g., models/checkpoints/) 3. How to run the workflow o Which JSON to load o Which nodes to edit to change:  phrase text  background mode  embellishments o Where the output PNGs are saved. Acceptance Criteria (what I will use to sign off) You can test with the phrase: Real courage is doing the right thing when no one applauds you. Charlie Kirk I’ll accept the job when: 1. ComfyUI starts cleanly with no CUDA or node errors. 2. The workflow loads

...formats—JPEG/PNG for images and H.264/RTSP for video—without external conversion steps. • Object detection must identify user-defined classes with at least 95 % precision/recall on a small validation set we’ll share. • A secondary pass corrects colour variance to sRGB and highlights any frame-to-frame mis-alignments in millimetres or pixels. Acceptance criteria 1. Demo script that runs on CPU or CUDA GPU and processes a 30-second sample feed in under real-time. 2. Clear README covering dependencies, model weights and how to retrain on new classes. 3. Output examples: overlayed frames and a structured log (CSV or JSON) listing detected objects, colour shifts and alignment offsets. If you’ve tackled similar real-time QA or inspection projects, I&...

...reproduce all experiments from my research paper “Evaluating Cross-Dataset Robustness of Machine Learning Models for Malware Detection”. The setup must support LightGBM, CNN, and GNN experiments exactly as implemented in my project files. The freelancer will be responsible for installing and configuring: 1. Required Environment Python 3.10+ Jupyter Notebook Pip & virtual environment GPU support (CUDA & cuDNN) if my laptop supports it Required Python packages including: pandas, numpy scikit-learn lightgbm seaborn, matplotlib tensorflow / keras (for CNN) torch + torch_geometric (for GNN) scipy pillow tqdm networkx utilities 2. Dataset Setup Please prepare the following datasets on my laptop: • EMBER2018 (CSV features) Used for LightGB...

...reallocating resources on the fly when utilization drops. If you prefer to leverage existing frameworks (Kubernetes, Slurm, Ray, etc.) instead of starting from scratch, that’s fine as long as the final solution plugs cleanly into my current PyTorch pipeline. 2. Profile current runs, identify the exact bottlenecks—data loading stalls, communication overhead, memory fragmentation—and implement the fixes. CUDA graphs, mixed-precision, gradient accumulation, or better dataloader caching: use whatever techniques actually reduce idle time. 3. Prove the improvement with before-and-after benchmarks. I need clear numbers showing GPU utilization and total training cost per epoch for the same CNN model and dataset. Deliverables: • Source code or configuration fi...

...Requirements Face detection Landmark mapping Face alignment and segmentation Masking and seamless blending Color tone and lighting correction Identity preservation Performance Requirements Must handle variations in: Angles Skin tones Lighting environments Image resolutions The system should minimize artifacts even in challenging inputs. Scalability (Preferred) GPU acceleration (CUDA) Batch processing support Modular, API-ready architecture Deliverables Primary Deliverables Fully functional FaceSwap script/function (Python preferred) Production-ready codebase Setup guide with or CLI or simple UI for testing swaps Sample outputs using test images Optional Preferred Deliverables GPU optimization Face enhancement integration

...Language Models, and I need an experienced IT professional on-site for roughly two to three days. The job covers network setup, hardware installation, and software configuration across our computers, rack-mounted GPU server, and new routers/switches. Once everything is racked and cabled, you’ll configure VLANs, Wi-Fi, firewall rules, and remote access, then build a clean Python environment with CUDA drivers and the libraries we use for LLM work (Transformers, LangChain, et al.). A quick end-to-end test that a model runs on the GPUs is part of the acceptance criteria, along with concise documentation of what you’ve done. An NDA must be signed before I share topology diagrams, credentials, or code. After this initial setup I’ll likely need you back for a few fol...

...platformach compute (, RunPod, TensorDock, SkyCompute) oraz do uruchomienia dodatkowych usług GPU (NVENC, rendering, inference). Projekt jest jednorazowy + możliwość stałej współpracy. Zakres prac – czego potrzebuję 1. Konfiguracja infrastruktury GPU (2 serwery po 5× GTX 3090) • instalacja i konfiguracja Ubuntu Server • konfiguracja sterowników NVIDIA (stabilne wersje) • instalacja CUDA 11.x / 12.x • Nvidia Container Toolkit • optymalizacja kernel, GRUB, hugepages • watchdog + automatyczne recovery GPU 2. Pełna konfiguracja środowiska Docker do AI compute • Docker + NVIDIA runtime • kontenery testowe GPU • stress-testy + benchmark (GPU burn + perf) • konfiguracja limitów i izolacji GP...

...monophonic voice to Veena timbre; Python notebooks or scripts preferred. • Deliver inference code that accepts a WAV file and returns the converted audio (real-time or offline processing is fine as long as latency is documented). • Provide short demo renders that clearly showcase the model’s output quality. • Write concise setup instructions so I can reproduce results on my own machine (Ubuntu, CUDA available). Acceptance criteria 1. Converted audio retains original melody and phrasing while sounding recognisably like a Veena. 2. Artifacts such as metallic ringing or pitch drift kept to a minimum. 3. Full training/inference workflow reproducible via the provided notebook or script. Tools & skills that would help: TensorFlow, DDSP library, ba...

...Language Models, and I need an experienced IT professional on-site for roughly two to three days. The job covers network setup, hardware installation, and software configuration across our computers, rack-mounted GPU server, and new routers/switches. Once everything is racked and cabled, you’ll configure VLANs, Wi-Fi, firewall rules, and remote access, then build a clean Python environment with CUDA drivers and the libraries we use for LLM work (Transformers, LangChain, et al.). A quick end-to-end test that a model runs on the GPUs is part of the acceptance criteria, along with concise documentation of what you’ve done. An NDA must be signed before I share topology diagrams, credentials, or code. After this initial setup I’ll likely need you back for a few fol...

...on my Windows machine and then use it as the engine for future agents. I need someone who will walk me through the process live, rather than just handing over a script. Scope of work • Help me choose and download a suitable Windows-friendly build of the models I’ve shortlisted (Meta, Gork, DeepSeek or a comparable alternative if you can justify the swap). • Set up all required dependencies—CUDA or CPU builds, Python environment, GPU drivers, libraries such as llama-cpp-python or Hugging Face Transformers—so the model loads locally without errors. • Validate the installation by running a quick inference test from the command line and a simple Python script. • Guide me, step by step, in creating at least one working agent blueprint (...

I need a series of well-researched technical articles that dive deep into Artificial I...diagrams or screenshots referenced in the text • Proper in-text citations with a short reference list • Final copy that passes a plagiarism check Acceptance criteria • Demonstrated technical depth and correctness • Clear, developer-friendly explanations and code snippets where useful • Alignment with the outline provided and adherence to word-count range A solid background in Python, CUDA, and mainstream deep-learning libraries will make this project straightforward. Please share at least two links to previously published technical pieces on AI or related software topics. First draft of Article 1 is expected within one week of kickoff, with the rest follo...

... • 支持多台工业相机同步或近实时触发。 • 提供简单的设备管理界面，方便增减相机。 2. 缺陷检测核心 • 传统模块：边缘检测、纹理分析与模式识别，用于快速筛查与定位。 • 深度学习模块：可用 PyTorch/TensorFlow 训练并推理，补充复杂或模糊缺陷。 • 需要统一输出坐标、缺陷类型与置信度。 3. 系统整合 • 图像流和模型推理流程须线程安全，可连续 24 × 7 运行。 • 结果通过本地 API 或消息队列返回，以便上位机调用。 4. 交付内容 • 完整源代码与可执行文件 • 模型及训练脚本 • 简洁部署文档和使用手册 • 一份示例数据与测试报告，展示检测准确率和性能指标 只要能在实际产线上稳定运行，可自由选择 OpenCV、Halcon、CUDA 等工具。期待与你讨论数据集、照明条件及其他细节。

...benchmarking report - Resource utilization analysis - Qualitative assessment with failure analysis - Deployment and usage documentation - Recommendations for Phase 2 scaling **Artifacts:** - Trained model checkpoints or access links - Configuration files and hyperparameters - Test datasets and evaluation results ## Technical Requirements ### Development Environment - Python 3.9+ - PyTorch 2.0+ with CUDA support - Transformers library (latest) - PEFT library for efficient fine-tuning - Gradio/Streamlit for interface development - Docker for containerization ### Data Handling - Input data in structured JSON format - Preprocessing and augmentation capabilities - Train/validation/test split management - Data quality validation tools ## Project Timeline 3-4 weeks** **Note**...

...camera Target schedule: complete by mid-November 2025 Customize a YOLO-based fall-detection model Train the model on hospital environment video data to distinguish only “fall” postures. Minimize confusion with poses such as sitting on or attempting to lie down on a bed. Produce a high-accuracy inference model suitable for deployment. Optimize for Jetson Orin Nano Support Jetson optimizations (CUDA, TensorRT, etc.) to achieve real-time performance. IP camera integration Configure video streaming between an RJ45-connected IP camera and the Jetson. Implement a real-time video preview. Fall alert system Provide alerts when a fall is detected via GUI and/or voice/message notifications....

...via UDP, upscales it to 4 K at a rock-solid 60 fps, converts the signal to HDR, and then pushes the result back out over UDP for broadcast distribution. The final stream must be fully compatible with modern Smart TVs, so colour metadata (HDR10 or HLG), timing, and bit-rate stability all have to be spot-on. You are free to combine FFmpeg, AI-assisted upscalers, GPU acceleration (NVIDIA TensorRT / CUDA, AMD HIP, or your own preferred stack), and any other open-source or commercial tools—provided the latency stays low enough for live broadcast. Deliverables • A working end-to-end workflow (input UDP → HDR upscaling → output UDP 4 K 60 fps) • Command-line scripts or service configuration files I can drop onto my Linux server and run immediately &bull...

I’m ready to stand up a fully self-hosted language model that is fast, secure, and production-ready. Your job is to spin up a compact model with vLLM or , wrap it in a clean CUDA/Docker stack, and layer in the essentials—streaming responses, smart caching, guardrails, and real-time metrics. Here’s what success looks like: • Working inference service running on my hardware or cloud instance, exposed through a simple REST or gRPC API. • End-to-end streaming in place, delivering low p50/p95 latency and stable throughput under load. • Guardrails that strip or mask PII, with compliant logs saved in a separate, safe location. • Basic eval harness and prompt templates so I can compare future checkpoints quickly. • Clear latency, memory, a...