Cudamunkák

...job is to create the complete vision-detection module—from model training or fine-tuning through to a clean ROS 2 node that subscribes to an image topic and spits out the detected objects with bounding boxes (or masks) and a confidence score. OpenCV, TensorFlow/PyTorch and any of the common ROS 2 image-transport plugins are all fine as long as the final node runs on Humble and stays GPU-agnostic (CUDA acceleration is a bonus, not a requirement). I already have a test rig with a standard USB camera; if you need specific calibration images I can capture them for you. Please deliver: • Source code for the detection model and ROS 2 node • A launch file that brings everything up with default parameters • A brief README explaining setup, parameters and expect...

...environment that emulates Jet Nano hardware for research and development on machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so any teammate can spin up the en...

... • Accepts at least JPEG files for input; adding PNG or BMP later should remain possible. • Generates a short video (MP4 preferred) by feeding the image through Stable Diffusion and WAN2.6. • Interface must feel intuitive for non-technical users while exposing advanced settings in an “expert” panel. • Conversion speed is critical; please optimise GPU utilisation and let me choose device (CUDA / DirectML). • Output parameters—resolution, frame rate, length, prompt text, CFG scale, seed—should all be editable before rendering. Deliverables 1. Executable installer (or portable folder) with all weights and dependencies bundled for offline use. 2. Source code with clear build instructions so I can re-compile if models up...

Backend for a my app using FastAPI, WebSock...and concurrency: Comfortable designing and debugging async workflows. Hands‑on AI integration experience with at least one of: Whisper STT or other speech‑to‑text engines. LLaMA/transformer‑based LLMs or OpenAI‑style APIs. TTS systems such as Coqui, Kokoro, or Piper. Realtime systems: WebSockets, WebRTC, or other low‑latency streaming architectures. Nice to Have GPU & deployment experience: CUDA, GPU environments, and performance tuning (CPU vs GPU). Docker, nginx, PM2, and production deployment pipelines. Background processing: Job queues/workers for heavy audio/video processing. Experience orchestrating long‑running media/AI tasks. Video processing tools: FFmpeg, Wav2Lip, or similar for video generation and post‑processing.

...Secure Rebuild * Implement a clean, well-documented architecture * Ensure no hidden network calls or external communications * Deliver fully auditable source code ### 3. GPU Acceleration * Implement high-performance generation using CUDA or OpenCL * Optimize parallel processing * Benchmark performance improvements ### 4. Database Matching * Efficient in-memory comparison against a large dataset * Maintain existing database structure: ``` address ``` * Optimize lookup speed and memory usage --- ## Required Skills * Strong C++ or Rust experience * CUDA / OpenCL expertise * Cryptographic systems knowledge * Experience with high-performance computing * Secure software architecture practices --- ## Deliverables * Clean source code * Build instructions * Securi...

...Predict response to therapy (Responder / Non-responder) Predict survival category Predict recurrence risk For MVP: Start with diagnosis, then add treatment prediction. STEP 2: Setup Development Environment Install Dependencies Python 3.9+ PyTorch MONAI pydicom numpy scikit-learn FastAPI or Flask Example: pip install monai torch torchvision pydicom fastapi uvicorn scikit-learn Setup GPU Local CUDA GPU OR Cloud (AWS/GCP/Azure) STEP 3: PET Scan Dataset Preparation Collect Dataset Public PET database (e.g., TCIA) Research partnership dataset Must include: PET images Diagnosis labels (Optional) treatment outcome labels Organize Data Structure: data/ train/ val/ test/ Handle DICOM Files Use pydicom to read images Convert to 3D tensors Normalize voxel intensity STEP 4:...

...training worker (Docker, from scratch) - PHP/MySQL licensing backend + Stripe webhook integration - Unified cross-platform installer (detects DAWs, installs everything in one pass) - GitHub Actions CI/CD (Windows + macOS builds) - Full Apple + Windows code signing pipeline - Documentation (User Guide + Developer Guide + BYOK Setup) Key technical requirements: - CPU default with automatic NVIDIA CUDA detection for Live Mode - RMVPE primary pitch extraction + user toggle (Harvest/Crepe/FCPE) - High-quality resampling (44.1k-96k) in C++ wrapper - AI Cleaning (de-reverb/isolation) in front of inference chain - Index Rate + .index file exposed in UI/API - Batch processing via ZMQ socket bridge Terms agreed: - Budget: $2,500 (6 milestones) - Timeline: 6 weeks (Feb 23 - Apr 3, 2026) -...

...similar) - Weasyprint or ReportLab for PDF - Typer CLI with subcommands: - transcribe - diarize - lesson-report - aggregate - YAML config file - Logging, progress bars, caching (skip if output exists), error handling Deliverables: - Full repo structure - All source code (src/ layout, CLI, config, prompts, PDF renderer) - Installation instructions for Windows 11 (Python, ffmpeg, Poetry, CUDA) - Example commands - Test guide with sample audio Please show experience with WhisperX / faster-whisper, Pyannote, Ollama, and Weasyprint on Windows + GPU setups in your proposal. Thank you! Vladimir...

...与准确性，同时保持训练过程的稳定性和可读性。 目前的情况 • 代码环境：Python（PyTorch） • 优化重点：提高模型准确性（训练时间和显存占用可稍后微调） • 评估方式：我已准备好一套一致的指标与验证脚本，可即时对比优化前后的表现 你需要完成的工作 1. 审阅现有实现，定位瓶颈与冗余计算 2. 重新实现或改写损失函数（含向后传播部分），确保梯度计算无误 3. 添加必要的张量操作优化（向量化、批处理、内存共享等） 4. 在我的测试集上运行并提交结果报告，其中至少包括： ‑ 指标提升幅度与对比表 ‑ 主要改动点与实现思路 ‑ 后续可扩展或进一步精简的建议 交付标准 • 指标提升需在我提供的基线之上达到统计显著 • 代码应符合 PEP-8，包含注释与简明 README • 所有修改应能在标准 GPU 环境（CUDA 11+）一次性跑通，无额外依赖冲突 如果你熟悉元学习以及高效的 PyTorch 实践，并对性能调优有系统方法，请直接告诉我你做过的相关项目、预计的优化思路与最快可投入的时间。我期待与你合作，把这段关键代码打磨到科研级水准。

...rapid target motion • Adapt to scale and orientation changes • Maintain lock under partial occlusion • Recover gracefully if tracking confidence drops • Avoid drift over time A re-detection or hybrid tracking strategy is preferred if it improves robustness. Technical Requirements Preferred stack: • Python + OpenCV OR C++ + OpenCV • Modular architecture • Hardware acceleration support (CUDA / TensorRT) is a strong plus • Experience with: • Siamese-based trackers • DeepSORT-like approaches • Hybrid detection + tracking pipelines Clean, well-documented code is mandatory. Deliverables 1. Fully functional Linux application 2. Source code repository 3. Setup instructions + dependency list 4. Short demo video...

...website. I have the hardware available but need an expert who can install the model, configure all dependencies, and expose an endpoint that my front-end widget can call. Here is what I have in mind: • Select and download an open-weight GPT-like model that can reasonably run on local hardware (e.g., Llama-2, Mistral, or another suitable alternative). • Set up the execution environment—Python, CUDA, PyTorch or TensorFlow—plus any supporting libraries (LangChain, FastAPI, uvicorn, etc.). • Create or refine an inference script that keeps response times low enough for smooth chat. • Build a lightweight API (REST or WebSocket) so the website can pass the user’s prompt and receive the model’s reply. • Hand me clear, repeatable...

...expectations • Real-time theft detection logic that raises an event or REST webhook the moment a suspicious removal is spotted • On-screen bounding boxes and confidence scores for detected grocery items and customers • Continuous customer counter with hourly CSV/JSON export • Installers or scripts for Windows 10/11 and Raspberry Pi OS, including all required Python, OpenCV, PyTorch/ONNX, CUDA (where available) dependencies • A simple dashboard that shows live feed thumbnails, current customer count, and the last N theft alerts • Clear instructions on adding new grocery SKUs later Acceptance will be based on: 1. Smooth 25-30 fps inference on 1080p streams under Windows with GPU, and ≥10 fps on Raspberry Pi using CPU or a USB accele...

...the hardware allocated and wallets ready; what I need is an engineer who can take the nodes from zero to profitable operation and then keep them humming. Key tasks • Provision and secure each H100 instance, configure networking, firewalls, SSH keys and wallets • Containerise the stacks with Docker (Kubernetes or Podman are possible later, but Docker is fine for the first iteration) • Tune CUDA-level settings so every GPU cycle counts and rewards are maximised • Build simple Bash or Python scripts that monitor logs, restart on failure and push basic alerts • Produce step-by-step documentation so the setup can be replicated or audited at any time Acceptance criteria • Nodes reach consensus, stay above 99 % uptime and begin generating rewa...

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.

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.

...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...