Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit http://cntk.ai
Hi! I'm training my own data with complex objects:
python 3.5
CNTK 2.6
no GPUs
epoch=20
C.CNTK.TRAIN_E2E = True
C.CNTK.E2E_MAX_EPOCHS = 20
C.NUM_ROI_PROPOSALS = 2000
C.DATA.NUM_TRAIN_IMAGES = 39
C.DATA.NUM_TEST_IMAGES = 7
C.DATA.PROPOSAL_LAYER_SCALES = [4, 8, 12]
...
So, I'm getting such outputs, is it normal or does it mea a error?
PROGRESS: 0.00%
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
CUDA driver version is insufficient for CUDA runtime version
...
Thank you for some help.
Thx @delzac, but I don't have GPU only CPU:
Selected CPU as the process wide default device.
Using base model: AlexNet
lr_per_sample: [0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 0.0001, 1e-05]
Training model for 7 epochs.
Build info:
Built time: Sep 14 2018 01:23:48
Last modified date: Fri Sep 14 01:16:52 2018
Build type: Release
Build target: CPU-only
With ASGD: yes
Math lib: mkl
Build Branch: HEAD
Build SHA1: 7c1b0fadb64f83a41739020b24fb2d4950015229
MPI distribution: Microsoft MPI
MPI version: 7.0.12437.6
Perhaps, my problem is that the first time I'm trying CNTK.
Hi all. We've been using CNTK with Cuda Toolkit v10.0 and cuDNN v7.5.1. I just tried CNTK with the latest releases of the CUDA Toolkit and cuDNN library for Cuda 10.2. CNTK works fine with these but I was expecting I might see a performance improvement during training given some of NVidia's claims. However, benchmark scores during training are unchanged. Any thoughts here? Was my expectation unreasonable? Just wondering what I'm not getting. (The testing was done on a GTX 1080. I have a newer (but smaller) Quadro T2000 in my laptop that I haven't upgraded for CUDA 10.2 yet. Any reason to expect I'd see an improvement with a different GPU?)
Many thanks.