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 /usr/local/cuda-10.0/samples/bin/x86_64/linux/release/deviceQuery
 
>>>>>>>
/usr/local/cuda-10.0/samples/bin/x86_64/linux/release/deviceQuery Starting...
 CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 6 CUDA Capable device(s)
Device 0: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 20 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 1: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 21 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 2: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 57 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 3: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 58 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 4: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 136 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
Device 5: "Tesla V100-PCIE-32GB"
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.0
  Total amount of global memory:                 32480 MBytes (34058272768 bytes)
  (80) Multiprocessors, ( 64) CUDA Cores/MP:     5120 CUDA Cores
  GPU Max Clock rate:                            1380 MHz (1.38 GHz)
  Memory Clock rate:                             877 Mhz
  Memory Bus Width:                              4096-bit
  L2 Cache Size:                                 6291456 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
  Maximum Layered 1D Texture Size, (num) layers  1D=(32768), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(32768, 32768), 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 7 copy engine(s)
  Run time limit on kernels:                     No
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Enabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Compute Preemption:            Yes
  Supports Cooperative Kernel Launch:            Yes
  Supports MultiDevice Co-op Kernel Launch:      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 137 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
> Peer access from Tesla V100-PCIE-32GB (GPU0) -> Tesla V100-PCIE-32GB (GPU1) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU0) -> Tesla V100-PCIE-32GB (GPU2) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU0) -> Tesla V100-PCIE-32GB (GPU3) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU0) -> Tesla V100-PCIE-32GB (GPU4) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU0) -> Tesla V100-PCIE-32GB (GPU5) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU1) -> Tesla V100-PCIE-32GB (GPU0) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU1) -> Tesla V100-PCIE-32GB (GPU2) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU1) -> Tesla V100-PCIE-32GB (GPU3) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU1) -> Tesla V100-PCIE-32GB (GPU4) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU1) -> Tesla V100-PCIE-32GB (GPU5) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU2) -> Tesla V100-PCIE-32GB (GPU0) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU2) -> Tesla V100-PCIE-32GB (GPU1) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU2) -> Tesla V100-PCIE-32GB (GPU3) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU2) -> Tesla V100-PCIE-32GB (GPU4) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU2) -> Tesla V100-PCIE-32GB (GPU5) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU3) -> Tesla V100-PCIE-32GB (GPU0) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU3) -> Tesla V100-PCIE-32GB (GPU1) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU3) -> Tesla V100-PCIE-32GB (GPU2) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU3) -> Tesla V100-PCIE-32GB (GPU4) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU3) -> Tesla V100-PCIE-32GB (GPU5) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU4) -> Tesla V100-PCIE-32GB (GPU0) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU4) -> Tesla V100-PCIE-32GB (GPU1) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU4) -> Tesla V100-PCIE-32GB (GPU2) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU4) -> Tesla V100-PCIE-32GB (GPU3) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU4) -> Tesla V100-PCIE-32GB (GPU5) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU5) -> Tesla V100-PCIE-32GB (GPU0) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU5) -> Tesla V100-PCIE-32GB (GPU1) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU5) -> Tesla V100-PCIE-32GB (GPU2) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU5) -> Tesla V100-PCIE-32GB (GPU3) : Yes
> Peer access from Tesla V100-PCIE-32GB (GPU5) -> Tesla V100-PCIE-32GB (GPU4) : Yes
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 10.0, NumDevs = 6
Result = PASS
 
>>>>>>>>

gpu issues - Sample torch.device.py

 

No Format
more torch.log_device_placement.py
import torch
torch.cuda.is_available()
# setting device on GPU if available, else CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print()
#Additional Info when using cuda
if device.type == 'cuda':
    print(torch.cuda.get_device_name(0))
    print('Memory Usage:')
    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
    print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')####https://www.tensorflow.org/guide/using_gpu
import tensorflow as tf
# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

Source: https://stackoverflowwww.tensorflow.comorg/questions/48152674/how-to-check-if-pytorch-is-using-the-guide/using_gpu

gpu issues - Sample Tensorflow script

...

  1. https://www.pbsworks.com/pdfs/PBSAdminGuide18.2.pdf
  2. https://conf-ers.griffith.edu.au/download/attachments/21332198/xl270d_gen10.pdf?api=v2
  3. https://www.microway.com/hpc-tech-tips/nvidia-smi_control-your-gpus/
  4. https://weeraman.com/put-that-gpu-to-good-use-with-python-e5a437168c01
  5. https://stackoverflow.com/questions/48152674/how-to-check-if-pytorch-is-using-the-gpu
  6. https://discuss.pytorch.org/t/solved-make-sure-that-pytorch-using-gpu-to-compute/4870/14
  7. https://www.tensorflow.org/guide/using_gpu