Quadro rtx 8000

Manufactured by NVIDIA

Sourced in United States

The Quadro RTX 8000 is a professional-grade graphics processing unit (GPU) designed for high-performance computing and visualization tasks. It features 4,608 CUDA cores, 576 Tensor cores, and 72 RT cores, enabling it to deliver exceptional performance for a wide range of applications, including scientific research, engineering, and media production.

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Lab products found in correlation

Xeon gold 6248, by NVIDIA (1 mentions) Geforce gtx 1060, by NVIDIA (1 mentions) Titan rtx, by NVIDIA (1 mentions) Spss statistics 20, by IBM (1 mentions)

Close spelling variants of this product

Quadro RTX 8000 GPU (10 citations) RTX 8000 (3 citations) Quadro 8000 (2 citations)

26 protocols using quadro rtx 8000

Protein Structure Prediction with AlphaFold2

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Models for the TPSs were predicted in a Nvidia Quadro RTX 8000 using Alphafold2-multimer (version 3.2.1) (Jumper et al. 2021 (link), Evans et al. 2022 (link)). We utilized global search for the multiple sequence alignment, five recycling rounds, and the amber relaxation was skipped. The best model was ranked based on the iptm+ptm score and was the one selected for the analysis.

Sørensen A.N., Kalmár D., Lutz V.T., Klein-Sousa V., Taylor N.M., Sørensen M.C, & Brøndsted L. (2023). Agtrevirus phage AV101 recognizes four different O-antigens infecting diverse E. coli. microLife, 5, uqad047.

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AI-Aided Macular Degeneration Diagnosis

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In this study, the following two sets of hyperparameters were utilized for training neural networks. A mini-batch size of 16 and neural network input image size 800*800*3, and a mini-batch size of 32 and neural network input image size 600*600*3. As the aim of this study was to use a classification neural network to automatically diagnose macular degeneration, the sparse categorical cross-entropy algorithm was applied to calculate training loss. The Adam optimization algorithm was utilized to conduct a loss function gradient descent at a learning rate of 0.001. A global average pooling layer was added to the EfficientNetB0 neural network followed by a 0.3 dropout rate. Finally, a fully connected SoftMax dense layer was added corresponding to the task classes. A NVIDIA QUADRO RTX 8000 with 48 GB of high-speed GDDR6 memory was utilized for training. EfficientNetB0 deep learning neural network architecture was used for optimizing the hardware efficiency. Gaussian blur was used for image feature enhancement, as explained above. Class weights were applied to cope with class imbalance.

Xie L., Vaghefi E., Yang S., Han D., Marshall J, & Squirrell D. (2023). Automation of Macular Degeneration Classification in the AREDS Dataset, Using a Novel Neural Network Design. Clinical Ophthalmology (Auckland, N.Z.), 17, 455-469.

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Automated Object Detection in X-Ray Imaging

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An object detection package [33 ] for TensorFlow was used for object detection. Inception Resnet v2 (Atrous version), a state-of-the-art object detector, was used as the neural network model. The model was trained using a PC with a Quadro RTX 8000 graphics processing unit (NVIDIA, USA), 48 GB memory and 4608 CUDA cores. The backend algorithms were executed using TensorFlow (version 1.13.1) running on the Ubuntu 18.04 operating system.
A set of 1370 annotated X-ray images were used to train the Faster R-CNN for object recognition. There were 60,000 iterations and an initial learning rate of 0.0003, which was reduced to 0.00006 after 30,000 iterations.
To rapidly determine model performance, the average precision [35 (link)] (AP; i.e., the area under the curve) of the implant and marginal bone loss lesion areas, as well as the mean average precision (mAP) of an intersection over unit (IoU) of > 0.5, were calculated using the following equation:

I O U = \frac{A r e a_{pred} \cap A r e a_{gt}}{A r e a_{pred} \cup A r e a_{gt}}

where Area_pred and Area_gt represent the predicted area of the bounding box and the ground truth bounding box, respectively. The IoU threshold was set at 0.5 because this value is commonly used in studies of object detection [36 ]. The mAP was calculated by determining the mean AP across all classes. Higher values indicated better learning system performance.

Liu M., Wang S., Chen H, & Liu Y. (2022). A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health, 22, 11.

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Parrot: Scalable Language Model Inference

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All the codes were implemented in the python, the rdkit [43 ] cheminformatics toolkit was used for data processing, and the model was constructed based on the pytorch [50 ] library. The web-based GUI was implemented using flask [51 ] library. The Parrot model is trained on Dell Precision 7920 Tower (Intel Xeon Bronze 3204, NVIDIA Quadro RTX8000 GPU, 512 GB RAM), and it can be inferred on a consumer computer Dell OptiPlex 7090 (Intel Core i7-11700, 8 GB RAM) without discrete GPU.

Wang X., Hsieh C.Y., Yin X., Wang J., Li Y., Deng Y., Jiang D., Wu Z., Du H., Chen H., Li Y., Liu H., Wang Y., Luo P., Hou T, & Yao X. (2023). Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center. Research, 6, 0231.

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Replicability of LSTM-Lexicon Language Models

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Replicability was confirmed by repeating the complete training of all models (dorsal, ventral, and dual) ten times; only minor variations were observed between iterations. Simulations were conducted on a Linux workstation with an Intel(R) Xeon(R) Gold 5218 CPU running at 2.30 GHz, with 98-GB of RAM, and using an NVIDIA Quadro RTX 8000 (48-GB) graphics card. Simulations were conducted using Python 3.6, TensorFlow 2.2.0, and Keras 2.4.3. Each model required approximately 48 h (except the fused network, which took 96 h) to train on this workstation. The GitHub repository (https://github.com/enesavc/lstm-lex) provides an up-to-date container with all necessary explanations and jupyter notebooks for running our training code and analyses.

Avcu E., Hwang M., Brown K.S, & Gow D.W. (2023). A tale of two lexica: Investigating computational pressures on word representation with neural networks. Frontiers in Artificial Intelligence, 6, 1062230.

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FCN Model Segmentation with Weighted Loss

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In the binary- and multi-class segmentation tasks, we trained the FCN model on each training dataset using the cross-entropy and Dice loss functions with or without the loss weightings. The FCN model was trained from scratch for 30 epochs with the Adam optimization algorithm [33 ] (

α (learning rate) = {1 e - 3, 1 e - 4, and 1 e - 5}

β_{1} = 0.9

β_{2} = 0.999

, and

epsilon = 1 e - 7

) and a batch size of 5 in each training process. For testing, we used the best trained model in the set

{learning rate, epoch} = {1 e - 3, 10}

{1 e - 3, 20}

{1 e - 3, 30}

{1 e - 4, 10}

{1 e - 4, 20}

{1 e - 4, 30}

{1 e - 5, 10}

{1 e - 5, 20}

, and

{1 e - 5, 30}

because the condition for good training convergence, especially learning rate and number of epochs, was different according to the loss weightings.
The FCN model with the weighted loss functions were implemented by using Keras with Tensorflow backend, and the training and prediction were performed on an Ubuntu 16.04 PC (CPU: Intel Xeon Gold 5222 3.80 GHz, RAM: 384 GB) with NVIDIA Quadro RTX8000 GPU cards for deep learning.

Sugino T., Kawase T., Onogi S., Kin T., Saito N, & Nakajima Y. (2021). Loss Weightings for Improving Imbalanced Brain Structure Segmentation Using Fully Convolutional Networks. Healthcare, 9(8), 938.

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nnU-Net Ensemble for Lymph Node Segmentation

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The first cohort (n = 35) was used for model training and optimization using internal cross validation. A nnU-Net ensemble model consisting of a 3D full resolution U-Net and a 2D U-net (27 (link)) (Figure 1) provided the best results with respect to internal cross validation in the training cohort and was selected for external validation in the independent test set (second cohort). Both the 3D full resolution and the 2D U-net model were trained in five folds with each fold using 28 datasets for training and 7 for internal cross validation. Each fold was trained for a total of 1000 epochs. The augmentation in the nnU-Net pipeline was adapted for the task of lymph node level segmentation with the other parts of the nnU-Net pipeline being kept unchanged. As lymph node level labels change with mirroring of datasets, mirroring during online augmentation was disabled and training datasets were augmented with mirroring and adaption of label values before starting nnU-Net model training. For all experiments nnU-net version 1.6.6 (27 (link)) with Python version 3.7.4, PyTorch version 1.9.0 (33 (link)) with CUDA version 11.1 (34 (link)) and CUDNN version 8.0.5 was used. Model training, inference and all computations were carried out on a GPU workstation using a Nvidia Quadro RTX 8000 with 48 Gb of GPU memory.

Weissmann T., Huang Y., Fischer S., Roesch J., Mansoorian S., Ayala Gaona H., Gostian A.O., Hecht M., Lettmaier S., Deloch L., Frey B., Gaipl U.S., Distel L.V., Maier A., Iro H., Semrau S., Bert C., Fietkau R, & Putz F. (2023). Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy. Frontiers in Oncology, 13, 1115258.

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Efficient Neural Network Training

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We implemented the system using PyTorch. The neural network models were trained using NVIDIA Quadro RTX 8000 with 48 GB memory.

Tezuka T., Kumar D., Singh S., Koyanagi I., Naoi T, & Sakaguchi M. (2021). Real-time, automatic, open-source sleep stage classification system using single EEG for mice. Scientific Reports, 11, 11151.

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High-Performance Compute Benchmarking Protocol

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All benchmarks were performed on a machine with 2 Intel Xeon Gold 6248 with a total of 80 threads, 400 GB RAM and an Nvidia Quadro RTX 8000 with 46 GB memory. The GPU was only used to generate embeddings.

Schütze K., Heinzinger M., Steinegger M, & Rost B. (2022). Nearest neighbor search on embeddings rapidly identifies distant protein relations. Frontiers in Bioinformatics, 2, 1033775.

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DL Hardware Performance Benchmarking

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The DL calculations were run on a computer with CPU Core i7‐9800X (Intel Co., CA, USA), GPU Quadro RTX 8000 (NVIDIA Co., CA, USA), OS Windows 10 Pro (Microsoft Co., WA, USA), Framework TensorFlow 2.1.0. (Google Inc., CA, USA), and Python 3.7.7 (opensource).

Terunuma T., Sakae T., Hu Y., Takei H., Moriya S., Okumura T, & Sakurai H. (2022). Explainability and controllability of patient‐specific deep learning with attention‐based augmentation for markerless image‐guided radiotherapy. Medical Physics, 50(1), 480-494.

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