GPUX.AI

🚀 April 20th, 2023 -V2 is launched! 1s starts from cold! 🎉BlogDeploy 'AI' Fast Run Serverless InferenceRun GPUs 🏃Talk to a Founder ☎️StableDiffusionXLESRGANWHISPERcurl https://i.gpux.ai/gpux/sdxl?prompt=swordBlogAllReleasesHow-ToCase StudyAI TechnologyJuly 19, 2023Make StableDiffusionXL 50% faster on RTX 40901s Cold StartReadWrite VolumesP2PThe Right Fit Nike only began crafting women's cleats in 2023, addressing the crucial anatomical differences that unisex cleats had previously overlooked. Much like footwear, machine learning workloads need the right fit. Discover it at GPUX.StableDiffusionSDXL0.9AlpacaLLMWhisperRun Inference Sell requests on your private model to other organizations.Meet our TeamAnnie - Marketing Meet her in KrakowLinkedInIvan - Tech Meet him in TorontoLinkedInHenry - Operations Meet him in HefeiWeChatGPUX Inc.18 King Street East • Suite 1400Toronto • Canada Contact UsBLOGAIBlenderTranscodeLive StreamingHomomorphic EncryptionGPUXBlogLINKSApp (V2)LEGALTerms of ServicePrivacy Policyⓒ  2021-2022 GPUx. All rights reserved, Built with ♥EARN ONWINDOWS 10EARN ONLINUX OSGET IT ONGITHUB --- 🚀 April 20th, 2023 -V2 is launched! 1s starts from cold! 🎉BlogMake StableDiffusionXL 50% faster on RTX 4090⚡AI TechnologyThe ProblemPytorch2 has a lot of optimization improvements but the upstream version when running pip3 install pytorch will pull pytorch2.0.1 with cu117.The problem with this is cu117 does not properly support newer GPUs like the RTX4090 or H100, infact cu117 wont even run on a H100.Popular cloud gpu providers that are used for deep learning often carry the cu117 default, hurting performance.The FixTo fix this we should be targetting cu118 until its adapted in upstream pip packages.This means upgrading to an image cuda version of atleast 11.8, this can be done in docker by usingdocker.io/nvidia/cuda:12.2.0-devel-ubuntu22.04 as our base image. Or by upgrading our NVIDIA driver + CUDA version.Also we need to pull pytorch with cu118 by doingpip3 install torch==2.0.1+cu118 \--extra-index-url https://download.pytorch.org/whl/cu118We can grab torchvision and torchaudio as wellpip3 install torch==2.0.1+cu118 \torchvision==0.15.2+cu118 \torchaudio \--extra-index-url https://download.pytorch.org/whl/cu118The RewardEnjoy an overall 50% speedup on all ADA LOVELACE workloads now!-- cu117 -- 68%|███████▏ | 34/50 [00:04<00:01, 4.88it/s]70%|███████▍ | 35/50 [00:04<00:01, 4.88it/s]-- cu118 -- 72%|███████▏ | 36/50 [00:04<00:01, 7.78it/s]74%|███████▍ | 37/50 [00:04<00:01, 7.78it/s]76%|███████▌ | 38/50 [00:04<00:01, 7.78it/s]78%|███████▊ | 39/50 [00:05<00:01, 7.78it/s]80%|████████ | 40/50 [00:05<00:01, 7.78it/s]Building an AI App?Contact us for information to get startedJOIN DISCORDGPUX Inc.18 King Street East • Suite 1400Toronto • Canada Contact UsBLOGAIBlenderTranscodeLive StreamingHomomorphic EncryptionGPUXBlogLINKSApp (V2)LEGALTerms of ServicePrivacy Policyⓒ  2021-2022 GPUx. All rights reserved, Built with ♥EARN ONWINDOWS 10EARN ONLINUX OSGET IT ONGITHUB --- 🚀 April 20th, 2023 -V2 is launched! 1s starts from cold! 🎉BlogAI with Distributed Learning⚡AI TechnologyIntroductionWhen building machine learning models, we often come across tasks that would be more efficient and effective if they could be parallelized. For example, if you want to train a large model within a specific environment, you might consider using distributed training. I'll share how to do this using Docker containers (fully supported on GPUX), which allow you to create and destroy environments as needed.Train a model simultaneously on different GPUsDistributed learning is a method of training a model simultaneously on different GPUs. With distributed learning, you can train one model using all the GPUs available in the network. This allows you to maximize your GPU resources and make better use of compute power. You can create a Dockerfile to run PyTorch over a cluster so that each node in the cluster runs the same code with training data.Training a single model with thousands of nodesThe process of training a model is simple: you just need to specify the data and the model, and then run it through your favorite training algorithm. However, if you have multiple GPUs or many machines at your disposal, distributing the model across them becomes a more complex problem. One way to distribute a large-scale neural network across multiple GPUs is called Distributed Learning. It allows you to train multiple models on different subsets of data processed by different GPUs in parallel. You can then merge these models together for further analysis or prediction.Docker containers are perfect for this kind of taskDocker containers are lightweight, portable and easy to use. They allow us to create and destroy environments as needed. For example, if you want to experiment with a new programming language or framework, you can create a Docker container with your new environment of choice and then destroy it afterward without leaving any traces on the host machine or in any other containers that also exist on that machine. Docker is not only used for development tasks; it is also suitable for running production web applications because it allows us to build images using base images provided by third-party vendors (like Red Hat) which have already been configured with security updates and other security enhancements. GPUX supports Dockerfiles natively.Machine learning tasks can take days to runOne of the most powerful tools in machine learning is parallelization. Parallelization is when you use multiple machines (or processes), each running its own copy of your program, to do the same task faster.For example, let's say you have one machine with 8 cores and eight processes that each take 10 minutes to run. If you run them sequentially, then it will take 80 minutes for all 8 processes to finish running! But if we run them in parallel on different parts of the problem at once (e.g., 4 cores on 2 processes), then it only takes 40 minutes total for all processes to complete.This makes sense: if you're waiting for a single process (or core) to finish before starting another one, then doing so sequentially means that any time wasted by any given process slows down all other tasks until it finishes its work--which wastes lots of time overall!ConclusionDistributed learning is a great way to massively scale up your machine learning operation. It can be used for many different kinds of tasks, from text classification to computer vision, and it even works with existing models/frameworks that you already know how to use!Get started on GPUX today!Building an AI App?Contact us for information to get startedJOIN DISCORDGPUX Inc.18 King Street East • Suite 1400Toronto • Canada Contact UsBLOGAIBlenderTranscodeLive StreamingHomomorphic EncryptionGPUXBlogLINKSApp (V2)LEGALTerms of ServicePrivacy Policyⓒ  2021-2022 GPUx. All rights reserved, Built with ♥EARN ONWINDOWS 10EARN ONLINUX OSGET IT ONGITHUB --- 🚀 April 20th, 2023 -V2 is launched! 1s starts from cold! 🎉Blog0% lost time🚀 100x BoostIntroductionDo you want to eliminate your fixed costs? Are you looking for a simple, affordable and professional solution to render your Blender scenes? Do you want to have full control over the entire rendering process? If the answer is yes, then our Blender Network Render Farm is what you need!Is time a huge concern in your animation and film production?Are you aware of how much time really matters in your animation and film production? Time is money, after all. And if you're working on a limited budget, every extra minute spent rendering will cost more than the average render farm employee would make in an hour! Solving this problem for you are our render farm services. We're one of the cheapest render farms out there, so even if you have a tight budget but still want to get things done quickly we can help.Our Network is the fastest distributed render farm for Blender out there. You will be surprised!Our render farm is the fastest distributed rendering system out there. You can be sure that your renders will be done in a fraction of the time it would take on a single workstation. We have a network of thousands of computers, all connected together. This makes it possible to use all available GPUs (graphics cards) at once and get results even faster!OctaneRender, Redshift, Cycles, Arnold or any other GPU or CPU renderer are fully supported by our render farm through Blender Cycles - Network Render addonRender farms are a great way to get the most out of your rendering. You can use render farms to render with GPU or CPU, batch renders and even easily scale up your farm by adding workers.Eliminate all fixed costs while paying only for the actual rendering time per minuteYou can eliminate all fixed costs while paying only for the actual rendering time per minute. This means no contracts, no minimum usage requirements and no expensive equipment that you don't need.ConclusionBy using our Blender Render Farm, you will save precious time and resources. It is the perfect solution for your rendering needs. Whether you are an independent artist or professional production studio looking to rent our render farm on demand or as a monthly subscription service, we cater to all your needsGet started on GPUX today!Building an AI App?Contact us for information to get startedJOIN DISCORDGPUX Inc.18 King Street East • Suite 1400Toronto • Canada Contact UsBLOGAIBlenderTranscodeLive StreamingHomomorphic EncryptionGPUXBlogLINKSApp (V2)LEGALTerms of ServicePrivacy Policyⓒ  2021-2022 GPUx. All rights reserved, Built with ♥EARN ONWINDOWS 10EARN ONLINUX OSGET IT ONGITHUB