From T3_KR_KNU
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1 HTCondor에서 GPU 사용하기
1.1 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
1.2 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
1.3 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
1.4 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
1.5 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
1.6 참고자료
위의 예제들은 아래 문서들에서 참고했습니다. 좋은 내용이 많으니 이용에 참고하시기 바랍니다.