Cuda jobs

...Demo Dashboard Requirements A simple web dashboard should include: Live Camera View AI Detection Overlay Event Alerts Event History Detection Screenshots Camera Management (Basic) Dashboard Statistics A polished UI is preferred but not mandatory for the demo. Preferred Technology Stack Python FastAPI YOLO OpenCV TensorFlow or PyTorch React.js PostgreSQL Docker Ubuntu Linux NVIDIA GPU Support (CUDA) Equivalent technologies are acceptable if performance and scalability are maintained. Future Scope If the demo is approved, the selected developer/team will continue with the complete platform development, including: Face Analytics Safety Analytics Vehicle Analytics Object Analytics Attendance Analytics Multi-location Dashboard Mobile Application Notifications (Email, WhatsApp,...

...on my side. • Deliver the trained model file, an inference script or API endpoint, and a brief report explaining your methodology, final accuracy, and any recommendations for future improvements. I will supply the images and their class labels as soon as we start, and I’m happy to discuss target accuracy or class-imbalance strategies up front. The code should run on a standard GPU instance (CUDA 11.x). Once the model meets the agreed accuracy on my held-out validation set, the project is finished and paid in full....

...database. * Experience with Docker and production deployment. * Experience integrating or managing GPU-powered backend services. * Strong debugging, architecture, and performance-optimization skills. * Excellent spoken and written English. * Ability to communicate technical decisions clearly. ## Preferred Experience Experience with any of the following will be highly valuable: * NVIDIA RTX GPUs. * CUDA, TensorRT, PyTorch, or GPU inference. * AI-powered text-to-3D or image-to-3D systems. * GPU rendering or mesh-processing services. * GLTF, GLB, OBJ, STL, or similar 3D formats. * Cloud GPU infrastructure. * Redis, BullMQ, RabbitMQ, or similar job-queue systems. * AWS, Google Cloud, Azure, RunPod, or another GPU platform. * SaaS authentication, subscriptions, cloud storage, and u...

We are installing EDGE AI devices — mainly NVIDIA JETSON series and higher power units in jails and prisons to detect inmate activity. We need someone familiar with setting up these devices. NVIDIA Jetson™ modules deliver accelerated AI performance at the edge. With the NVIDIA JetPack™ SDK, you can develop and deploy innovative products across industries. The Jetson family uses unified NVIDIA® CUDA-X™ software and supports cloud-native technologies for streamlined AI development and deployment. These prisons and jails often have different routers, securuty and networking. We need experience here to get the message stream from the NVIDIA devices to the Cloud management system.

Looking for an experienced CUDA/C++ developer for short GPU optimization and debugging tasks. Work will require: Direct work on my development environment via AnyDesk/TeamViewer. Tasks include debugging CUDA kernels, fixing tensor indexing issues, improving performance, and optimizing GPU usage.

CUDA Developer Needed – Long-Term Remote Work (AnyDesk/Remote Access) I am looking for an experienced CUDA/C++ developer to help me complete GPU optimization tasks. This is a long-term collaboration opportunity for someone with strong experience in CUDA kernels, PyTorch, and performance optimization. Work setup: Remote work through AnyDesk or another remote desktop tool. You will work directly on my development environment. Tasks involve debugging, improving, and optimizing CUDA implementations. Each task typically requires around 3 hours of focused work. Payment: Rate: $8–$10 per hour depending on experience. Payment will be released after 2 weeks of completed work. This is intended to become a long-term working relationship with regular ta...

I need a Senior Computer Vision and Deep Learning Engineer to build a complete production-ready AI background removal system for AKPRINTHUB, with qualit...third-party paid API should be used. The developer will be responsible for the complete project, including AI model selection/fine-tuning, Python FastAPI backend, GPU optimization, frontend integration with my existing PHP/JavaScript website, testing, bug fixing, deployment and production launch. Required technologies include Python, PyTorch, OpenCV, image segmentation, alpha matting, ONNX/TensorRT, CUDA, Docker and GPU deployment. Complete source code, trained model weights, training scripts, deployment files and documentation must be handed over. Payment will be milestone-based after quality and speed testing against a privat...

CUDA Developer Needed – Long-Term Remote Work (AnyDesk/Remote Access) I am looking for an experienced CUDA/C++ developer to help me complete GPU optimization tasks. This is a long-term collaboration opportunity for someone with strong experience in CUDA kernels, PyTorch, and performance optimization. Work setup: Remote work through AnyDesk or another remote desktop tool. You will work directly on my development environment. Tasks involve debugging, improving, and optimizing CUDA implementations. Each task typically requires around 3 hours of focused work. Payment: Rate: $8–$10 per hour depending on experience. Payment will be released after 2 weeks of completed work. This is intended to become a long-term working relationship with regular ta...

Job Description: I'm running a self-hosted, real-time conversational voice AI pipeline (STT → LLM → streaming TTS over WebSocket) on a RunPod GPU pod (CSM-1B TTS model, using the davidbrowne17/csm-streaming fork with Sesame's Mimi/moshi audio codec, PyTorch ). I need an experienced PyTorch/CUDA engineer to find and fix a reproducible latency bug that's blocking production readiness. The problem: At sentence/turn boundaries in a multi-turn conversation, generation stalls for ~7-8 seconds of dead air. I've already localized this precisely via wall-clock instrumentation: the stall occurs specifically inside Mimi's () call, when encoding a previous turn's generated audio into context — and specifically only when enc...

...on a specific AI provider. --- # Required Skills Strong experience with: * Python * FastAPI * PostgreSQL * Redis * Celery * Docker * Linux * REST API Design * Background Workers * Authentication & Authorization * Enterprise SaaS Development Experience with AI model integration is highly preferred. --- Experience with: * AudioCraft * Amphion * OpenVoice * RVC * ComfyUI * GPU Infrastructure * CUDA * RunPod or other GPU cloud providers * AI Media Pipelines --- # Project Status The project architecture has already been designed. Documentation already exists for: * Engineering Constitution * Software Architecture * Database Architecture * Master Roadmap * Development Standards * Security Architecture * AI Platform Architecture we have a "Serverless" NVIDIA acco...

We are building our project AI-native Music SaaS Platform. The platform allows users to generate high-quality songs using AI, including lyrics generation, music composition, ...Mention your experience with Python FastAPI and AI model integration. Please provide links to your GitHub or a project where you built a complex AI pipeline. Skills required Developer Skills • Python (5–10+ years preferred) • FastAPI • PostgreSQL • Redis • Docker • REST APIs • JWT/RBAC • Async programming • System Design • AI integrations • PyTorch • Hugging Face • ACE-Step • JWhisper • FFmpeg • CUDA • GPU Optimization • ComfyUI We would like to humbly request that the same person who has previously worked on ...

...and displays the corresponding information. The initial database will contain approximately **150 registered people**, with the possibility of expanding it in the future. --- # Scope of Work The freelancer is expected to complete the following tasks: ## 1. Prepare the Jetson Platform * Install the appropriate operating system for Jetson. * Configure all required drivers. * Properly install CUDA, cuDNN, TensorRT, and other necessary NVIDIA components. * Configure the Python environment. --- ## 2. Install All Required Dependencies Install and configure all libraries required by the project, including but not limited to: * OpenCV * InsightFace * ONNX Runtime (if required) * FAISS * TensorRT * NumPy * SciPy * PySerial * Any other dependencies required by the project. --- ...

We turn padel courts into smart courts. Two cameras watch each match; players open a shared page afterward to see their highlight c...processing real-time video, so comfort optimizing models for constrained hardware and working with RTSP/GStreamer is valued — the ML is the heart of the role, but you'll build it on the device. You should be at home working on a headless Linux box remotely over SSH (no GUI), moving code and files via scp, and running/debugging everything from the command line. Familiarity with the Jetson/JetPack stack (CUDA, TensorRT) is a plus. The rig is already built and remotely accessible; a full technical handoff goes to shortlisted candidates. To apply: share relevant CV/ML work and a short note on how you'd approach the detection → track...

...It's currently failing on newer NVIDIA GPU hardware due to CUDA/driver/dependency compatibility issues. I need someone to: Get the pipeline running cleanly on the target GPU environment (debugging CUDA/driver/dependency conflicts) Run evaluation and confirm standard output metrics are generated correctly Deliver clean, runnable source code with basic documentation and a demo of it working Requirements: Strong PyTorch experience Experience with CARLA simulator or similar simulation environments Comfortable debugging CUDA/driver/dependency issues, ideally with newer GPU hardware Access to a compatible GPU environment (local or cloud) Please tell me: Your experience with CARLA and/or autonomous driving models Your experience with CUDA/GPU compatibility...

I need a production-ready GPU instance on E2E Cloud that runs the Qwen vLLM model for object detection. The service must accept image files through a simple REST endpoint, run inference, and return bounding boxes and labels—stable enough to handle 200 k – 500 k calls every day. Here’s the workflow I have in mind. You’ll provision a suitably powerful E2E Cloud VM, install CUDA, cuDNN, vLLM, pull the latest Qwen checkpoints, and wire everything together with a lightweight Python server—FastAPI is my usual choice, but feel free to suggest an alternative as long as it stays lean and well-documented in OpenAPI/Swagger. Throughput matters: I’m aiming for at least 2–5 sustained requests per second, so you can rely on batching, concurrent workers, ...

... • A fraud-detection layer that flags suspicious listings or bidding patterns before a transaction closes. Everything must sit behind a clean, minimal interface—no clutter—because both food and pharmaceutical professionals will be using it every day under time pressure. I’m open on stack, but Python with TensorFlow/PyTorch for the models and a lightweight React or Vue front end makes sense; CUDA optimisation is a plus for the Inception pitch. Acceptance criteria for the MVP: 1. End-to-end workflow: seller upload → AI price suggestion → live auction → automated & manual bids → secure checkout. 2. Latency for price predictions under two seconds on a single Nvidia GPU. 3. Admin dashboard that surfaces fraud alerts and key marke...

Tired of boring, soul-crushing jobs? So are we. We're not just another AI shop – we're a people-first business on a mission to solve real-world problems, from removing CO₂ from the atmo...to Include: · CV/Resume highlighting Python AI production experience · GitHub/Portfolio with AI deployment projects · Brief proposal on how you'd approach this · References from previous clients/employers Subject Line: "Python AI Engineer - [Your Name]" --- IMPORTANT: · Do NOT apply if you only have Jupyter/R&D experience – this is production. · Do NOT apply if you cannot work with GPUs and CUDA. · Do NOT apply if you cannot commit to the 7–10 day timeline. · Be ready to start immediately. ...

...NVIDIA GPU Operator, NVIDIA Network Operator, CNI, CSI, and similar Kubernetes ecosystem tools. * Experience with job scheduling systems such as Slurm. * Strong Linux system administration skills. * Proficiency in scripting and automation using Python and Bash. * Experience with observability and monitoring platforms such as Prometheus, Grafana, and Loki. * Knowledge of GPU architectures, NVIDIA CUDA, NCCL, and AI/ML infrastructure is a strong advantage. * Strong troubleshooting and root-cause analysis skills with the ability to analyze logs, metrics, and system performance data. * Excellent communication, collaboration, and problem-solving abilities. Preferred Skills * Large-scale Kubernetes cluster operations. * AI/ML infrastructure and GPU cluster management. * Infrastructur...

I need the open-source InsightFace face-swap model installed and fu...on my own Ubuntu server or an inexpensive rented GPU instance. • A test run that proves the pipeline can process sample footage and photos without quality loss, including a short 1080p clip and a few stills. • Guidance on how to queue jobs, control face detection, and tweak blending parameters so each swap costs only a fraction of a cent in compute time. Please make sure any dependencies—CUDA, PyTorch, FFmpeg, and InsightFace weights—are pulled automatically or documented precisely so I can rebuild the environment later. If you prefer another lightweight orchestration tool, I’m open to it as long as the result remains simple to maintain. Once everything runs end-to-end on my m...

...images for Tenstorrent AI ASICs to expand our hardware ecosystem beyond current GPU deployment - Migrate Python code and VLLM implementations to new VLLM images and adapt them for specific GPU cards Required Qualifications: - Proven experience with large language model optimization techniques - Strong understanding of transformer architectures and attention mechanisms - Proficiency with PyTorch, CUDA, and GPU optimization techniques - Experience with vLLM, FlashInfer, or similar inference optimization frameworks - Familiarity with Docker containerization and GPU workload management Preferred Qualifications: - Experience with Claude Code Max (will be provided if needed) - Previous experience with Gonka or similar decentralized AI networks - Background in competitive ML or distri...

... • Helm or ArgoCD handles drift management so that the desired state remains in sync. • At runtime, the service must auto-scale both CPU and GPU requests, proving horizontal and vertical elasticity. • Token or currency budgeting is tracked per request so I can see live cost data and later hook it into FinOps dashboards. Inside the cluster • Nvidia AI Enterprise stack (Triton, TensorRT, CUDA) installed via the operator. • LLM endpoints wrapped with LangChain agents that can call external tools. • A vector database (FAISS, Milvus, or anything OSS) stores embeddings for RAG. • One sample agent demonstrates question-answering over the vector store; another shows a multi-tool plan/act loop. What I need from you 1. Terraform or simil...

I already have a RunPod GPU instance waiting and need a clean, production-ready setup of an open-source large language model that will power a technical-support chatbot. Here’s what has to happen: • Provision the RunPod container (CUDA drivers, Python, Hugging Face Transformers, LangChain, bitsandbytes, etc.) and verify the GPU is fully utilized. • Load and configure an open LLM (I’m leaning toward Llama-2 or similar; suggestions welcome) with the right quantization to fit the available VRAM while maintaining answer quality. • Expose a secure REST or WebSocket endpoint—FastAPI is fine—so my front-end can pass user queries and receive streamed responses. • Add basic retrieval-augmented generation hooks so the bot can reference m...

...current setup, identify the bottlenecks, and implement practical optimizations so the model replies faster and runs leaner without harming output quality. We’ll decide together which metric (latency, memory footprint, throughput) will matter most once you’ve profiled the system, but the immediate goal is measurable, benchmarked improvement. Expect to work with PyTorch, Hugging Face Transformers, and CUDA; if you prefer alternatives such as TensorRT, ONNX, or quantization/pruning frameworks, feel free to propose them. Deliverables • A concise optimisation plan after your initial audit • Updated code, scripts, or checkpoints reflecting the changes • A before-and-after benchmark report showing the gains I’m paying hourly and will fund mileston...

...help you hit the ground running. What I expect from you 1. Build the Python rule services, wired into the message queue layer. 2. Create REST/JSON APIs so external apps can trigger, modify, and monitor rules. 3. Provide Terraform/Ansible (or comparable) scripts that spin up VMs, schedule containers, and deploy updates with zero downtime. 4. Optimise for GPU tasks when a node advertises CUDA. 5. Document the entire flow clearly: architecture diagrams, setup steps, and example calls. 6. Deliver a full test suite that covers rule correctness, scaling behaviour, and fail-over scenarios. Acceptance is straightforward: I’ll spin up the provided scripts on a fresh cloud account, run the tests, and verify that a sample rule set executes end-to-end across at least two ...

...augmentation and dataset pipelines comparable to tf.data. • Model Evaluation – training loops, metric tracking, checkpointing and model save/load logic. Runtime expectations The library will be used in standalone fashion; it only needs to expose a public API that any C# solution can reference. Low-level compute may target CPU initially, but the architecture should stay extensible enough to plug in CUDA, DirectML or other accelerators later. Deliverables 1. Source code with clear namespace organisation and XML documentation. 2. A sample project that trains and evaluates at least one CNN on MNIST using only the new library. 3. Step-by-step build instructions and a short design document outlining graph execution, back-propagation implementation and extension p...

...Python 3.11, FastAPI, SQLAlchemy + Alembic, PostgreSQL, Redis, WebSocket • AI layer – YOLOv8 (people detection / counting), DeepSORT (tracking), RetinaFace + InsightFace-ArcFace (face detection / recognition), FAISS for vector search, OpenCV to pull RTSP streams • Frontend – 15 with TypeScript, Tailwind CSS, Recharts for dashboards • Infrastructure – Docker, docker-compose, NVIDIA CUDA runtime for GPU inference, Nginx reverse proxy Backend reliability and performance sit at the top of the priority list, so I expect an asynchronous FastAPI setup, connection pooling, health checks, graceful shutdown, and clear separation between I/O-bound and GPU-bound tasks. Deliverables should include: 1. Well-documented repo (or mono-repo) layout f...

...powered by an RTX 5090 and want to turn it into a reliable, local playground for the latest stable releases of Stability Matrix, Stable Diffusion, MidJourney, ComfyUI, and Kohya. I need more than a basic installer—I’d like the full environment properly configured, GPU-accelerated, and ready to create images the moment we finish. You’ll connect through a secure remote session, handle every dependency (CUDA, Python, libraries, checkpoints, etc.), and tune paths so each tool cooperates without conflicts. Where optional models or extensions improve usability, feel free to suggest and add them as long as they remain stable. Deliverables • All five applications installed in their most recent stable versions • Verified ability to generate a sample image...

I am looking for an expert to develop a high-performance, 8-channel automated farming system for the 3D tactical shooter Delta Force. My requirements: Technical Skills & Experience: - Proven experience in real-time computer vision (YOLO, TensorRT) with batch inference and low latency on RTX 4070Ti/4060Ti GPUs. - Strong proficiency in C++ (or high-performance Python with CUDA/C++ backend) and PCIe capture card integration. - Familiarity with KMBOX Net HID-level mouse/touch emulation and anti-cheat evasion (Tencent ACE or similar). - Experience designing centralized dashboards and robust fail-safe management for 24/7 operations. Core Deliverables: - Real-time detection (loot, enemies, extraction points) and UI state recognition via 8x 1080p 60Hz streams. - Visual navigation usi...

I need an Ubunt...so I can run machine-learning workloads. The job is purely the installation and configuration of everything required for the guest OS to recognise and fully utilise the GPU (nvidia-smi must work). Key points • VMware host is already installed and stable; you decide whether PCI passthrough, vGPU or another VMware feature makes most sense. • Inside the VM I will need the correct NVIDIA driver, CUDA toolkit and cuDNN ready for TensorFlow or PyTorch later on. • Once complete I should be able to launch a quick test script that confirms the GPU is visible from within Ubuntu. Deliverable A step-by-step session (screenshare or detailed command log) that leaves the VM running and the GPU operational, plus any commands I can rerun if I ever have to r...

...Video Processing * FFmpeg integration required --- ## File & Project Structure Each project should store: * Audio file * Lyrics * Scene data (JSON) * Generated images * Generated clips * Final output --- ## Advanced Features (Preferred) * Beat-synced cuts and transitions * AI-assisted prompt generation * Style consistency across scenes * Character continuity (optional) * GPU acceleration (CUDA support) --- ## Deliverables * Fully working desktop application * Clean, maintainable Python code * Installation/setup instructions * Ability to run locally without cloud dependency (preferred) --- ## Notes for Developer * This is not a simple generator — it is a **production tool** * User control over scenes is critical * Performance and stability are important * M...

...Ubuntu and I need the open-source WAN Video 2.7 stack fully installed and running on it. You will connect over AnyDesk, handle the complete setup, pull all required libraries and system dependencies, and verify the application launches cleanly with GPU acceleration enabled. During the session I will stay online to provide root access and restart the box if needed. Please be comfortable working in a CUDA-centric environment, compiling from source when binaries are not available, and troubleshooting driver or codec conflicts that sometimes appear on DGX hardware. Acceptance criteria • WAN Video Open Source 2.7 starts without errors and streams a test feed. • All supporting packages and services are documented in a short README so I can replicate the build later. &...

...Long-term work possible if successful Scope of Work: - Diagnose deep learning pipeline issues - Fix model execution errors - Debug training / inference workflow - Resolve dependency or environment conflicts - Optimize pipeline stability - Ensure end-to-end execution works correctly - Provide brief documentation of fixes Technical Stack: - Python - PyTorch / TensorFlow - HuggingFace / Transformers - CUDA / GPU acceleration - Docker / Linux environment - API integration & Data preprocessing pipeline Requirements: - Strong experience in Deep Learning production workflows - Experience debugging complex AI pipelines - Comfortable working under urgent timelines and ability to start immediately Timeline: Start: Immediately. Expected turnaround: 24–48 hours. Proposal Requi...

I’m building a camera-based ... log every reading with a timestamp, and trigger a visual or audible alert whenever negative emotions are detected repeatedly within a short window. A lightweight dashboard served with either Streamlit or Flask will let me: • watch the annotated video feed • view rolling emotion statistics and charts • review and download the timestamped log of events and alerts Optimisation for Jetson (CUDA, cuDNN, TensorRT where appropriate) is essential, and the finished app should launch from a single command, open the dashboard in a browser, sustain real-time performance, and shut down cleanly. Please keep the code modular and well commented so I can retrain or swap models later and, if convenient, provide a Dockerfile or setup script ...

...personalized address generator written in C/C++ and CUDA. The tool should have the following features: • Read any number of prefix-suffix patterns from the `` file (example format: `Taaa*1111` or `Tbbb*222`). • Launch a GPU kernel to continuously generate wallet addresses and compare each address with all patterns. If a match is found, write the matching address and its private key to disk. • Fully utilize GPU performance, achieving the same speed as my current test version (approximately 8 billion addresses per second). Please display a "addresses per second" counter in real-time during program execution. • Generate a plain text log file recording key events: startup time, device information, running hash rate snapshots, and each match found. ...

... "Zero-Shot" Virtual Try-On pipeline into an existing Flutter/Python e-commerce stack. Technical Stack Requirements AI/ML: Experience with IDM-VTON, Cat-VTON, or OOTDiffusion. Mastery of Stable Diffusion (ControlNet/IP-Adapter) is mandatory. Computer Vision: Expertise in MediaPipe or OpenPose (pose estimation) and DensePose (surface mapping). Backend: Python (FastAPI/PyTorch), gRPC/REST, and CUDA optimization. Frontend Integration: Flutter (Dart) for image handling and state management. Key Deliverables The "Zero-Retrain" Pipeline: A model that accepts a flat garment image and a user photo to produce a drape-accurate result without per-SKU training. Latency Optimization: Implementation of TensorRT or AITemplate to bring inference time under 3 seconds on ...

...post_content string. No Raw HTML: Mapping must use native Divi 5 module settings (Colors, Padding, Fonts, Flexbox) to ensure the layout is fully editable. Technical Stack Language: Python (FastAPI/Flask for backend, PyQt or Streamlit for local UI). Browser Automation: Playwright or Selenium (Stealth mode). OS: Windows 11. Optimization: Must be able to handle local inference calls via RTX 5090 (CUDA). Budget & Milestones ($1000 Total) Milestone 1 ($200): Functional Site Crawler (URL Listing & Selection). Milestone 2 ($400): Core Conversion Engine (Successfully importing a complex Section into Divi 5 at 100% progress). Milestone 3 ($400): Full UI Implementation, Section Slicing, and Local API Integration. Note to Freelancers: I will provide a Reference JSON file ...

...from a watch-list I will provide. Because the cameras operate 24/7 in very mixed environments—low-light corridors, exposed outdoor zones that face rain or glare, and busy high-traffic entry points—the model must remain accurate under those conditions. Solutions that leverage YOLO, TensorFlow, PyTorch, OpenCV or comparable frameworks are fine as long as they run on my existing Nvidia GPU server (CUDA-enabled). Deliverables 1. Trained model files plus any custom scripts. 2. A lightweight API or service (Python preferred) that ingests RTSP streams, performs detection, and triggers my existing alerting webhook. 3. Setup instructions and a brief validation report showing performance in the three stated conditions (night-time, outdoor weather, high traffic). I ...

... Here is what I need delivered: • High-quality masks for every image, respecting a class list that includes typical road-scene elements (road, sidewalk, vehicles, sky, vegetation, building façades, pedestrians) plus key indoor objects you would expect in a café setting (tables, chairs, walls, floor, counter). • A training pipeline in PyTorch or TensorFlow that I can run on Ubuntu 22.04 with CUDA, along with a clear README covering dataset preparation, training, and inference. • A model that reaches at least 0.75 mIoU on a private test split I will share once the annotations are complete. You are free to use tools such as CVAT, LabelMe, Detectron2, DeepLabV3+, SegFormer—or any comparable framework—as long as the final workflow remain...

...into the transcript with millisecond accuracy. Both real-time feedback (small overlay suggestions) and post-video analytics (downloadable PDF/CSV plus on-screen dashboard) are needed. I’m happy for you to build with tools such as OpenCV, MediaPipe, TensorFlow, PyTorch, spaCy or similar—use what you are fastest with as long as the models run efficiently in a web environment (GPU acceleration via CUDA or WebGL is a plus). Deliverables 1. Source-controlled codebase ready to deploy on a standard cloud stack (Docker image or Heroku-style procfile). 2. Front-end UI (React, Vue or vanilla JS) that lets users toggle between real-time and upload modes. 3. Modular inference services for vision and audio that can be retrained or swapped if I add new metrics later. 4. C...

...data and route only the most promising parameter sets back to the gate model. Latencies must stay sub-millisecond from signal to order, so a coherent design for GPU–FPGA–QPU orchestration is essential. Deliverables • A documented architecture diagram showing data flow between classical AI, middleware, and the chosen quantum SDK (Qiskit, Braket or similar). • Clean, modular Python code with C++/CUDA kernels where latency demands it, fully containerised for reproducibility. • Back-test and forward-test reports on at least one major FX pair and a US equity futures contract, including Sharpe, max drawdown, and execution slippage statistics. • Deployment guide for a colocation environment, covering queue management to the quantum back-end and f...

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...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.

...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...

...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...