Check failed: error == cudaSuccess during training SSD - deep-learning

Question

I am training SSD and I have error as
I0116 13:10:31.206343 3447 net.cpp:761] Ignoring source layer drop6
I0116 13:10:31.207219 3447 net.cpp:761] Ignoring source layer drop7
I0116 13:10:31.207229 3447 net.cpp:761] Ignoring source layer fc8
I0116 13:10:31.207233 3447 net.cpp:761] Ignoring source layer prob
F0116 13:10:31.227303 3447 parallel.cpp:130] Check failed: error == cudaSuccess (10 vs. 0) invalid device ordinal
*** Check failure stack trace: ***
# 0x7f158382e5cd google::LogMessage::Fail()
# 0x7f1583830433 google::LogMessage::SendToLog()
# 0x7f158382e15b google::LogMessage::Flush()
# 0x7f1583830e1e google::LogMessageFatal::~LogMessageFatal()
# 0x7f158412f7bd caffe::DevicePair::compute()
# 0x7f15841354e0 caffe::P2PSync<>::Prepare()
# 0x7f1584135fee caffe::P2PSync<>::Run()
# 0x40af10 train()
# 0x407608 main
# 0x7f1581fbd830 __libc_start_main
# 0x407ed9 _start
# (nil) (unknown)
Aborted (core dumped)
My Graphic is Quadro4200.
./deviceQuery gives me
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Quadro K4200"
CUDA Driver Version / Runtime Version 9.0 / 8.0
CUDA Capability Major/Minor version number: 3.0
Total amount of global memory: 4034 MBytes (4230479872 bytes)
( 7) Multiprocessors, (192) CUDA Cores/MP: 1344 CUDA Cores
GPU Max Clock rate: 784 MHz (0.78 GHz)
Memory Clock rate: 2700 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 524288 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 4 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = Quadro K4200
Result = PASS
I can successfully test SSD library, just that I have error in training.
Is that Graphic card not powerful enough to train the library?

Answer 1

I found the error.
If we run this command python examples/ssd/ssd_pascal.py in ssd, the next step of training command is as follow.
gdb --args ./build/tools/caffe train --solver="models/VGGNet/VOC0712/SSD_300x300/solver.prototxt" --weights="models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" --gpu 0,1,2,3 2>&1 | tee jobs/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300.log
this --gpu 0,1,2,3 2>&1 is giving the issue. I changed to --gpu 0 and run from the training command directly as
./build/tools/caffe train --solver="models/VGGNet/VOC0712/SSD_300x300/solver.prototxt" --weights="models/VGGNet/VGG_ILSVRC_16_layers_fc_reduced.caffemodel" --gpu 0 | tee jobs/VGGNet/VOC0712/SSD_300x300/VGG_VOC0712_SSD_300x300.log,
then it solved.