TensorFlow provides strong support for distributing deep learning across multiple GPUs. TensorFlow is an open source platform that you can use to develop and train machine learning and deep learning models. TensorFlow operations can leverage both CPUs and GPUs. If you're operating from Google Cloud Platform (GCP), you can also use TensorFlow ...In my case, my GPU is listed (yay!), so I know I can install TensorFlow with GPU support. Per the TensorFlow site, I see that there's a dependency library I need to install called libcupti ...
I ran those benchmarks on my AMD R9 Fury (2015), using : Ubuntu 18.04. kernel 4.15. tensorflow-rocm 1.12.0. rocm 2.1. Maybe those numbers will be useful for someone (like me) who has an older GPU, wants to try deep learning and doesn't know if they need a new GPU. Performance seems ok, but the 4GB of HBM is a limiting factor.Figure 8: GPU utilization of Best Effort vs. Equal Share Takeaways. Sharing a GPU among VMs using NVDIA GRID can help increase the consolidation of VMs with vGPU and reduce the hardware, operation, and management costs. The performance impact of sharing a GPU is small in typical use cases when the GPU used is infrequently by users.
RDMA-TensorFlow improves DL training performance by a maximum of 29%, 80%, and 144% compared to default TensorFlow. For example, in 12 Nodes RDMA-TensorFlow improves performance of DL training by 80% (93 vs 51 images) for batch size 16/GPU (total 176) on 12 nodes.
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