Difference between revisions of "HTCondor에서 GPU 사용하기"

Latest revision as of 06:37, 21 September 2023

HTCondor에서 GPU 사용하기

경북대학교 중점연구소 GPU 자원이 CMS Tier3 HTCondor Farm에 통합되었습니다.

GPU 사용대상

• CMS 사용자중 신청자

GPU 사용신청

• 기존 CMS 사용자: 사용계획서(기존사용자용).hwp를 간단하게 작성하여 lcg_knu@knu.ac.kr로 제출하시면 됩니다.
• 신규 CMS 사용자: 사용계획서(신규사용자용).hwp를 작성하여 lcg_knu@knu.ac.kr로 제출하시면 됩니다.

• 별도 신청서를 받는 이유는 구체적인 수요 현황을 파악하여 향후 자원 증설시 반영하고자 합니다. 양해를 부탁드립니다.

GPU 사용

• GPU 상태 확인

$ condor_status -compact -constraint 'TotalGpus > 0'
Machine        Platform    Slots Cpus Gpus  TotalGb FreCpu  FreeGb  CpuLoad ST Jobs/Min MaxSlotGb

dm01.knu.ac.kr x64/CentOS7     0   40    3   188.96     40   188.96    0.00 Ui     0.00 *
dm02.knu.ac.kr x64/CentOS7     0   48    4   377.93     48   377.93    0.00 Ui     0.00 *

               Machines Owner Claimed Unclaimed Matched Preempting  Drain

   x64/CentOS7        2     0       0         2       0          0      0

         Total        2     0       0         2       0          0      0

$ condor_status -compact -constraint 'TotalGpus > 0' -af Machine TotalGpus CUDADeviceName CUDACapability
dm01.knu.ac.kr 3 TITAN V 1024.64
dm02.knu.ac.kr 4 TITAN Xp 1024.64

• Job submit시 다음 옵션을 추가하면 GPU를 사용할 수 있습니다.

request_gpus = 1

Ex 1) Matrix

matrix.py

import numpy as np
from timeit import default_timer as timer
from numba import vectorize

@vectorize(['float32(float32, float32)'], target='cuda')

def pow(a, b):
    return a ** b

vec_size = 100000000
a = b = np.array(np.random.sample(vec_size), dtype=np.float32)
c = np.zeros(vec_size, dtype=np.float32)
start = timer()
c = pow(a,b)
duration = timer() - start
print(duration)

matrix.sh

#!/bin/bash
python3.6 -m virtualenv myvenv
source myvenv/bin/activate
pip3 install numba
python3.6 matrix.py

matrix.sub

executable              = matrix.sh
arguments               = $(ClusterId)$(ProcId)
output                  = matrix.$(ClusterId).$(ProcId).out
error                   = matrix.$(ClusterId).$(ProcId).err
log                     = matrix.$(ClusterId).log
should_transfer_files   = YES
transfer_input_files    = matrix.py 
when_to_transfer_output = ON_EXIT
request_GPUs = 1
request_CPUs = 1
queue

Ex 2) TensorFlow

tf_ex.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(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='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, verbose=2)

tf_ex.sh

#!/bin/bash
python3.6 -m virtualenv myvenv
source myvenv/bin/activate
pip3 install tensorflow-gpu==2.0.0-rc1
python3.6 tf_ex.py

tf_ex.sub

executable              = tf_ex.sh
arguments               = $(ClusterId)$(ProcId)
output                  = tf_ex.$(ClusterId).$(ProcId).out
error                   = tf_ex.$(ClusterId).$(ProcId).err
log                     = tf_ex.$(ClusterId).log
transfer_input_files    = tf_ex.py
when_to_transfer_output = ON_EXIT
request_GPUs = 1
request_CPUs = 1
queue

Ex 3) Singularity & TensorFlow - cvmfs image

sing.sh

#!/bin/bash
python3.6 tf_ex.py

sing.sub

arguments               = $(ClusterId)$(ProcId)
output                  = sing.$(ClusterId).$(ProcId).out
error                   = sing.$(ClusterId).$(ProcId).err
log                     = sing.$(ClusterId).log
should_transfer_files   = YES
when_to_transfer_output = ON_EXIT
transfer_input_files    = tf_ex.py
request_GPUs = 1
request_CPUs = 1
+SingularityImage = "/cvmfs/singularity.opensciencegrid.org/opensciencegrid/tensorflow-gpu:latest"
queue

Ex 4) Singularity & TensorFlow - local image

sing-local.sub

executable              = sing.sh
arguments               = $(ClusterId)$(ProcId)
output                  = sing.$(ClusterId).$(ProcId).out
error                   = sing.$(ClusterId).$(ProcId).err
log                     = sing.$(ClusterId).log
should_transfer_files   = YES
when_to_transfer_output = ON_EXIT
transfer_input_files    = tf_ex.py
request_GPUs = 1
request_CPUs = 1
+SingularityImage = "/u/user/hanbi/tensorflow-gpu.sif"
queue

Ex 5) Singularity & TensorFlow - docker image

sing-docker.sub

executable              = sing.sh
arguments               = $(ClusterId)$(ProcId)
output                  = sing.$(ClusterId).$(ProcId).out
error                   = sing.$(ClusterId).$(ProcId).err
log                     = sing.$(ClusterId).log
should_transfer_files   = YES
when_to_transfer_output = ON_EXIT
transfer_input_files    = tf_ex.py
request_GPUs = 1
request_CPUs = 1
+SingularityImage = "docker://tensorflow/tensorflow:latest-gpu"
queue

참고자료

위의 예제들은 아래 문서들에서 참고했습니다. 좋은 내용이 많으니 이용에 참고하시기 바랍니다.

• https://batchdocs.web.cern.ch/tutorial/exercise10.html
• https://www.tensorflow.org/tutorials/quickstart/beginner
• https://sylabs.io/guides/3.6/user-guide/singularity_and_docker.html
• https://sylabs.io/guides/3.6/user-guide/gpu.html