🔎 Easily run molecular structure predictions on CPUs/GPUs and seamlessly visualize results in an interactive, reproducible and containerized environment with Seqera's Data Studios! Import nf-chai, a prototype #Nextflow pipeline for running predictions on entities supported by Chai-1, directly into your Seqera Workspace from Seqera Pipelines. Try it now: https://hubs.la/Q02_dWhx0
Seqera’s Post
More Relevant Posts
-
Easily get started with Chai-1 -- a multi-modal foundation model for molecular structure prediction -- in Seqera Platform, visualizing the resulting protein structures in Data Studios for a one-stop prediction + presentation platform 👏
🔎 Easily run molecular structure predictions on CPUs/GPUs and seamlessly visualize results in an interactive, reproducible and containerized environment with Seqera's Data Studios! Import nf-chai, a prototype #Nextflow pipeline for running predictions on entities supported by Chai-1, directly into your Seqera Workspace from Seqera Pipelines. Try it now: https://hubs.la/Q02_dWhx0
-
Ultralytics YOLO11 performance YOLOV10 I just performed testing with YOLO11...
Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 🚀 | Vision Language Models
Ultralytics YOLO11 Performance | YOLOv10 🚀 I just performed testing with YOLO11 medium-size model and It is performing well, additionally, the speed is slightly better as compared to YOLOv10 and the other object detection models. 🎯 Tips 💙 You might not notice a big difference between YOLO11 and YOLOv10 when using small or nano models, but the gap becomes clear with medium or large models. 💙 Recently, I noticed that YOLOv10, v9, and v7 run fast on powerful GPUs but struggle on CPUs, while YOLO11 excels on both. The hardware with software specifications and YOLO11 accuracy comparison pictures are shared in the comments below. 👇 Learn more ➡️ https://lnkd.in/dSi_MkC6
-
Computer Vision, Growth @ ultralytics | visionusecases.com | 120K+ Reads & 250K+ Views on Medium | Open Source Contributor | YOLO11 🚀 | Vision Language Models
Ultralytics YOLO11 Performance | YOLOv10 🚀 I just performed testing with YOLO11 medium-size model and It is performing well, additionally, the speed is slightly better as compared to YOLOv10 and the other object detection models. 🎯 Tips 💙 You might not notice a big difference between YOLO11 and YOLOv10 when using small or nano models, but the gap becomes clear with medium or large models. 💙 Recently, I noticed that YOLOv10, v9, and v7 run fast on powerful GPUs but struggle on CPUs, while YOLO11 excels on both. The hardware with software specifications and YOLO11 accuracy comparison pictures are shared in the comments below. 👇 Learn more ➡️ https://lnkd.in/dSi_MkC6
-
Are you wondering if FREDmpc is worth it? It depends on what you’re using it for! Are you tracing a very large number of rays? Do you frequently have to run calculations overnight or even for a few days? Do some of your models consist of thousands of surfaces? If so, FREDmpc will make your life drastically easier! FREDmpc is the only general-purpose software that can raytrace on the GPU (Graphics Processing Unit) to ensure that your calculations can be completed up to 1000X faster when compared to traditional multi-threaded CPU ray tracing. Its capabilities go beyond FRED and FRED Optimum to supply you with everything you will need so you can work as efficiently as ever! We are constantly searching for new ways to upgrade FREDmpc’s features. With every release, new innovative features are added. Check out the latest version of FRED - https://ed.gr/dp2p6 #Calculations #Optics #GPU
-
-
This #TechnicalThursday, we put Setonix GPU Power 💪 at your fingertips. Unlock the potential of AMD GPUs for your research on Pawsey's Setonix supercomputer with our Setonix GPU Partition Quick Start guide available in our documentation! 📄✨Learn how to: 🎛️ Access Setonix powerful AMD MI250X GPUs 🖥️ Run popular scientific applications 🚄Manage resources and submit jobs for peak performance This page will provide you with in-depth explanations, code examples, and valuable resources to unleash the full potential of Setonix GPUs. 📚Get Started Today: https://bit.ly/4bx1v0o #PawseySupercomputing #GPUs #ScientificComputing #AMD
-
-
Surprise surprise, writing a hand-tuned kernel specific to your application wins out on performance. Libraries are great when the provider has your problem in mind, but what if they don't ? Tune in to our live stream on Thursday, Nov. 7 at 3pm ET to learn about a few ways to calculate vector divergence in 3-D and how we optimized performance for AMD GPUs https://lnkd.in/eFd_raHP
Six ways to implement spectrally accurate vector divergence on CPUs and GPUs
https://www.youtube.com/
-
#electronic #load #inductance #current #step #slewrate Quantifying The Impact Of Series Inductance On E-Load Edge Rates And Current Monitoring Accuracy At low supply voltages, used for modern CPUs and AI processors, current path series inductance can have a considerable impact on the e-load di/dt and its ability to support the projected step size. Details: https://lnkd.in/eE9sTfTJ
-
-
Read this blog to learn about GPU acceleration techniques to optimize the extraction of rectilinear images using both #CPU and #GPU simultaneously. #MedTech #IAmIntel
Utilizing GPU or CPU and GPU for Equirectangular Image Extraction
share.intel.com
-
Read this blog to learn about GPU acceleration techniques to optimize the extraction of rectilinear images using both #CPU and #GPU simultaneously. #MedTech #IAmIntel
Utilizing GPU or CPU and GPU for Equirectangular Image Extraction
share.intel.com
