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Qs: Regarding the output, there are some print lines in my code that help me to monitor how my program is working. like the error of model and so
on. So is there any way to see this kind of online output on the terminal or log files while the job is being processed by the cluster?
Ans: There are a few ways of doing this.
1. You may run an interactive pbs job with "-I" option.
For example: qsub -I -q dljun@n060 -W group_list=deeplearning -A deeplearning -l select=1:ncpus=1:ngpus=1:mem=12gb,walltime=100:00:00
After this you will be given a shell and then you can run your command:
module load anaconda/5.3.1py3
module load cuda/10.0
source activate tensorflow-gpu
python3 /export/home/s5108500/lscratch/Nick/DeepModels/keypoints/baseline_main.py
2. Alternatively, submit the job. Run the script named watch_jobs.sh
It will ask for the compute node name and the pbs job number and basically will run this command:
tail -f /var/spool/pbs/spool/$JOBNO.n060.*
e.g:
sh watch_jobs.sh
n060:
Req'd Req'd Elap
Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
58.n060 s2594054 dljun IndyTestDL 45304 1 1 12gb 100:0 R 00:11 n060/0
===========================
Please enter Node Number e.g: n060
n060
Please enter Job number e.g 9066
58
===========================
| 5 Tesla V100-PCIE... On | 00000000:89:00.0 Off | 0 |
| N/A 33C P0 26W / 250W | 0MiB / 32480MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
?
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+ |
GPU issues - deviceQuery
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Check if this returns correctly
/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 Tensorflow script
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https://www.tensorflow.org/tutorials
cat tensorflowTutorial.py
###########################
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
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gpu issues - sample pbs script
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Here is sample pbs scripts
Sample PBS script:
==================
cat pbs.tensor.01
#!/bin/bash
#PBS -m abe
#PBS -M Youremail@griffith.edu.au
#PBS -V
#PBS -N testImage
#PBS -q dljun@n060
#PBS -W group_list=deeplearning -A deeplearning
#PBS -l select=1:ncpus=1:ngpus=1:mem=32gb,walltime=300:00:00
#PBS -j oe
module load anaconda/5.3.1py3
#conda info --envs
#source activate deeplearning
source activate tensorflow-gpu
##nvidia-debugdump -l
##nvidia-smi
###python main.py --cfg cfg/config3.yml --gpu 0
python /export/home/s12345/lpbs/cuda/tensorflowTutorial.py
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