![]() ![]() We also found that this benchmark does not use two GPUs it only runs on a single GPU. If we take the batch size / Latency, that will equal the Throughput (images/sec) which we plot on our charts. The results are Inference Latency (in sec). We can change the batch size to 16, 32, 64, 128 and precision to INT8, FP16, and FP32. ![]() –fp16: Use FP16 precision (for Volta or Turing GPUs), no specification will equal FP32 –iterations: The number of iterations to run –batch: Batch size to use for inferencing –deploy: Path to the Caffe deploy (.prototxt) file used for training the model Nvidia-docker run -shm-size=1g -ipc=host -ulimit memlock=-1 -ulimit stack=67108864 -rm -v ~/Downloads/models/:/models -w /opt/tensorrt/bin nvcr.io/nvidia/tensorrt:20.11-p圓 trtexec -deploy=/models/ResNet-50-deploy.prototxt -model=/models/ResNet-50-model.caffemodel -output=prob -batch=16 -iterations=500 -fp16 In our benchmarks for Inferencing, a ResNet50 Model trained in Caffe will be run using the command line as follows. Organized by the WordNet hierarchy, hundreds of image examples represent each node (or category of specific nouns). ImageNet is an image classification database launched in 2007 designed for use in visual object recognition research. ResNet-50 Inferencing in TensorRT using Tensor Cores NVIDIA GeForce RTX 3090 NVLink Deep Learning benchmarksīefore we begin, we wanted to note that over time we expect performance to improve for these cards as NVIDIA’s drivers and CUDA infrastructure matures. ![]()
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