CUDA/GPU Compatibility Fix Needed

@MuhammadUzair16

Hi I am a software engineer with over 16 years of experience. I have extensive experience with PyTorch pipelines, CUDA/runtime issues, NVIDIA driver mismatches, and simulation-based ML workflows, including work around autonomous driving perception/evaluation setups similar to CARLA. For this project I would first reproduce the failure in a clean GPU environment, identify whether the break is coming from PyTorch/CUDA toolkit, driver, CARLA/Python package versions, or compiled dependencies, then lock down a working environment so the evaluation runs consistently. After that I will run the evaluation, confirm the expected metrics are generated, and provide the runnable source with basic setup notes and a short demo showing it working. I have debugged CUDA compatibility problems across different GPU generations before, including cases where code worked on older cards but failed on newer NVIDIA hardware due to binary/package conflicts. I can use a local or cloud GPU environment as needed. A few quick questions: what target GPU and driver version are you using, and is the pipeline currently Docker-based or installed directly on the host? Also, which CARLA and PyTorch versions were last known to work? My quote is $240 with an estimated timeline of 4 days, assuming the current codebase is available and the expected evaluation command is documented. Please contact me to discuss details.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@kamran2012

This looks like a great fit, I will get your PyTorch evaluation pipeline running cleanly on the target GPU by resolving the CUDA, driver, and dependency conflicts, then verify the standard output metrics generate correctly. I will test everything on a staging environment first so nothing breaks your existing setup. The first thing I will check is the CUDA toolkit version against your driver and PyTorch build, since that mismatch is where most newer GPU failures originate. Questions: 1) Which GPU model is the target, and what driver version is currently installed? 2) Are you using a specific CARLA version or a containerized setup? I can start this week if the timing works. Looking forward to your response. Best regards, Kamran

@intelliresponse

As an AI developer and Python expert with a vast experience of more than a decade, I am confident that I am the right fit for your "CUDA/GPU Compatibility Fix" project. My knowledge and skills extend beyond web automation and data mining to include a strong background in AI solutions, Full-Stack, Mobile, as well as exploiting the power of CUDA. Not only am I capable of debugging complex drivers/dependency issues, but my familiarity with the CARLA simulator and similar environments like it makes me even more appealing for this task. Throughout my career, I have successfully undertaken numerous projects involving autonomous driving models just like yours. I have also dealt with CUDA/GPU compatibility debugging many times in the past which gives me an advantage when it comes to addressing such conflicts that are unique to newer GPU hardware. With access to a compatible GPU environment, either local or cloud-based, we will be able to address any compatibility conflicts promptly and efficiently. The estimated timeline for this project is dependent on the extent of the runtime errors. However, what remains constant is my commitment and dedication to delivering exceptional solutions on time. In terms of cost, my rates are highly competitive without compromising on quality. #ethystablished November20 yyyy

@devendrathakur12

Hello, CUDA, PyTorch, and CARLA compatibility issues usually come down to matching drivers, toolkit, PyTorch build, and simulator dependencies correctly. Once that is stable, I can verify the evaluation pipeline and confirm the expected metrics. One question: what are the target GPU model, CUDA version, and current PyTorch version on the environment? Looking forward to working together, and let's make your pipeline drive better than its dependency manager. Dev S.

@Muhammadzeesha59

Hello Sir/MAM I am a skilled full stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms, Linux , Windows , Cloud , Azure . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@softsolution2000

I have extensive experience working with CARLA and autonomous driving models, having successfully implemented various projects in this domain. I am proficient in debugging CUDA/driver/dependency issues, especially on newer GPU hardware, ensuring smooth compatibility. My strong background in PyTorch and machine learning enables me to deliver efficient solutions. I can provide a detailed timeline for the project and a competitive quote. Looking forward to the opportunity to work on this CUDA/GPU compatibility fix project. See the above links please. Please go through my profile its 15 years old see the work I did over the years. ---> No Win No Fee means that your satisfaction is my utmost priority. <---- Lets discuss the job details. Moreover, I am willing to start the job and perform tasks without even being hired; it is just to show my commitment to this project. Looking forward to hear from you. Regards Shah

@Hrolgar

CARLA's eval stack is picky about the exact CUDA, cuDNN and PyTorch triplet it was built against, and newer NVIDIA cards usually ship with a driver and compute capability the original pins never accounted for. It's rarely one fix, more a chain of version mismatches. I'd start by pinning CUDA toolkit, driver and PyTorch to the combination CARLA's Python API actually expects, sorting through torch and torchvision wheel compatibility and any custom CUDA extensions along the way. Once the environment holds, I'd run the evaluation end to end and check the metrics land in the expected range before calling it done. I'll hand back the working environment file or Dockerfile with the version matrix documented, so it doesn't break again on the next driver update. This is an indicative estimate from the brief, I'll give you a firm quote once the scope is locked, but for a CUDA/PyTorch pin plus a verified run, 250 USD over 3 days covers it as one milestone: environment fixed, evaluation run, metrics confirmed. If you can tell me the GPU model and driver version you're on, that'll sharpen the scope before I start.

@datacode0023

let me fix it. first part FOR FREE Hello, I have over 9 years of experience working on AI projects and have successfully contributed to multiple projects in this field. I also hold a Master's degree in Artificial Intelligence. I would be happy to discuss how my experience and expertise can support your needs. Please feel free to contact me to discuss further. Have a nice day.

@Specgi

I am confident I can help you resolve your CUDA/GPU compatibility issues by thoroughly diagnosing the current setup and identifying the root cause of the problem. I will begin by reviewing your system configuration, including GPU model, CUDA toolkit version, driver version, and framework dependencies (such as PyTorch or TensorFlow if applicable). This ensures we pinpoint whether the issue is due to version mismatch, missing drivers, unsupported architecture, or environment misconfiguration. Once the problem is identified, I will implement a clean and stable fix by aligning the CUDA toolkit with the correct NVIDIA driver version and ensuring all libraries are properly installed and compatible. If needed, I will rebuild or reconfigure your environment (Conda, virtualenv, or system-level setup) and verify GPU detection and functionality through diagnostic tests. My focus will be on ensuring stable GPU acceleration without conflicts or runtime errors. Finally, I will validate the entire setup by running test workloads to confirm CUDA is fully operational and optimized for performance. I will also provide clear documentation of the changes made, along with recommendations to prevent future compatibility issues during updates. My goal is to deliver a fully functional, reliable GPU environment so your compute tasks run efficiently without interruptions.

@techsty2014

Interesting project, We will debug and resolve the CUDA, driver, and PyTorch dependency conflicts so your CARLA evaluation pipeline runs cleanly on the newer GPU hardware. Our first step will be mapping the exact CUDA toolkit version against your driver and PyTorch build. Most failures here trace to a mismatched CUDA runtime versus driver API. We will pin every dependency, verify the evaluation metrics, and deliver documented setup instructions. A couple of quick things to confirm: 1) Which GPU model and driver version is the target environment running? 2) Are you using a specific CARLA version (0.9.13, 0.9.14, or newer)? The number quoted here is a starting estimate. The exact cost and timeline will be confirmed after we go through the full scope together. Looking forward to discussing further. Best regards, Faizan

@mahads31

**Quality development, clear communication, and on-time delivery are what I bring to every project. After reviewing your requirements, I'm confident I can diagnose and resolve the CUDA, driver, and dependency issues preventing your PyTorch-based CARLA evaluation pipeline from running reliably on newer NVIDIA GPUs.** I have experience troubleshooting PyTorch environments, CUDA compatibility, GPU drivers, and complex Python dependencies. I'll identify the root cause, restore a stable runtime, validate the evaluation pipeline by generating the expected metrics, and deliver a clean, reproducible setup with documented dependencies and a working demonstration. My focus is on providing a solution that's easy to maintain and deploy on future GPU environments. Before we get started, I'd like to clarify a few details: Which NVIDIA GPU model and operating system are you using? What versions of CARLA, PyTorch, CUDA, and Python does the project currently depend on? Also, is the environment Docker-based, Conda-based, or running directly on the host system? Regards, Solves Inn

@carlosd178

Hello, As a result of a detailed review of your project requirements, I fully understand the scope and expectations. I have experience debugging PyTorch ML pipelines and GPU dependency issues, and I'm available to start your project right now. I bring deep expertise in Python, PyTorch, CUDA, Deep Learning, Machine Learning, CARLA, C++, GPU Environment Setup, and AI Model Development. One of the key challenges in projects like this is matching the correct CUDA, NVIDIA driver, PyTorch, CARLA, and Python dependency versions so the evaluation pipeline runs reliably on newer GPU hardware. I can audit the current environment, fix compatibility conflicts, run the CARLA evaluation, verify that standard metrics are generated correctly, and deliver clean setup notes with a working demo. Regarding your questions: I have experience with simulation/ML pipelines, CUDA dependency debugging, and PyTorch model execution on GPU environments. After reviewing the repo and target GPU details, I can provide a precise timeline and fixed quote. I have one quick question. • Which NVIDIA GPU, driver version, CUDA version, and OS are you currently using? Best regards, Carlos.

@codeandcommand

CUDA version conflicts with PyTorch in CARLA setups are one of the trickier environment issues to pin down. I would trace your toolkit version, match it against your PyTorch CUDA build, verify cuDNN, and sort out any C++ extension errors in the eval pipeline. Can start today and have this fixed within 24 hours once I see the error logs. These numbers are starting points and may adjust once we go through the full setup. Want to jump on a quick call?

@modesorehari6

Hi there, I can resolve the CUDA, driver, and dependency issues to get your PyTorch CARLA evaluation pipeline running on the target GPU. I'll verify the evaluation, confirm the output metrics, and deliver clean source code with documentation and a demo. Could you share the current GPU model, CUDA version, PyTorch version, and whether the environment is local or cloud? I have solid experience with Python, PyTorch, CUDA debugging, Docker, Linux, and ML environments, including resolving GPU compatibility and dependency conflicts. Timeline: 1 to 3 days. Quote: $150. I can provide reliable results and start immediately after we finalize the plan. Let's discuss the details via chat. Best regards, Malix

@ahmaddar2970

This project requires expert knowledge in CUDA, GPU compatibility, and deep learning with PyTorch, all of which are core to my experience. I have a strong background in debugging CUDA/driver issues and optimizing PyTorch models for newer NVIDIA GPUs. My previous work includes integrating deep learning models with simulation environments similar to CARLA, ensuring accurate evaluation metrics and reliable code delivery. I will set up a robust debugging process to resolve compatibility issues, run thorough testing, and provide comprehensive documentation and a functional demo. Your source code will be clean, well-commented, and ready for deployment on your preferred GPU setup, whether local or cloud. I am confident I can deliver within a week, ensuring high quality and performance.

@AhteshamAK

Hello, I have strong experience with PyTorch, CUDA environments, GPU dependency troubleshooting, and deploying ML applications on NVIDIA hardware. I can identify and resolve the CUDA, driver, and library compatibility issues, get your CARLA evaluation pipeline running reliably, verify the evaluation metrics, and deliver clean, documented, runnable source code. Please share your current environment details (GPU model, CUDA version, PyTorch version, CARLA version, and the error logs), and I'll estimate the required effort accurately. I can begin immediately and provide regular progress updates throughout the debugging process. Best regards, Ahtesham

@cuyodigital

Hi there, I can get that PyTorch pipeline running on your new GPU without the headache. I work daily with Python and CUDA, debugging exactly these kinds of dependency conflicts. PyTorch version mismatches, driver incompatibilities, cuDNN issues—I've untangled all of them. I'm comfortable diving into error logs, checking environment variables, and pinning the right combinations until everything clicks. I also have solid experience with CARLA. I've worked with autonomous driving evaluation pipelines before, so I know what the output metrics should look like and how to confirm they're generating correctly. Let me give you an example: a few months ago, a client had a computer vision pipeline that crashed on an RTX 4090 because of an old PyTorch build. The error messages were cryptic, but I traced it to a specific CUDA runtime mismatch and a conflicting driver version. After rebuilding the environment with the right dependencies and tweaking a few settings, it ran without a single error and produced identical results to their old setup. For your fix, I can get it running and verified in 3 days. That includes debugging, running the evaluation, confirming the metrics, and documenting the working setup so you can replicate it. Happy to jump on a screen share and debug it live if that helps speed things up. Thanks!

@Lazarone1

hi! there. this project is really interesting because autonomous driving pipelines are extremely sensitive to environment stability, and getting them running reliably on newer gpu hardware is critical for trustworthy evaluation results. the biggest challanges will be resolving cuda, pytorch and driver compatibility conflicts across the carla stack and making sure evaluation metrics remain consistent after fixes, but i have strong experiance with gpu debugging, pytorch environments and dependency troubleshooting to get this running cleanly and reproducibly. thanks!!