synchronization

gpushare-scheduler-extender/samples/docker/Dockerfile

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+FROM cheyang/gpu-player
+
+COPY main.py /app/main.py
+
+COPY run.sh /app/run.sh
+
+RUN chmod u+x /app/run.sh
+
+CMD ["/app/run.sh"]

gpushare-scheduler-extender/samples/docker/main.py

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+#!/usr/bin/env python
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+
+import tensorflow as tf
+
+FLAGS = None
+
+def train(fraction=1.0):
+	config = tf.ConfigProto()
+	config.gpu_options.per_process_gpu_memory_fraction = fraction
+
+	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.
+	config = tf.ConfigProto()
+	config.gpu_options.per_process_gpu_memory_fraction = fraction
+	sess = tf.Session(config=config)
+	# Runs the op.
+	while True:
+		sess.run(c)
+
+
+if __name__ == '__main__':
+	parser = argparse.ArgumentParser()
+	parser.add_argument('--total', type=float, default=1000,
+                      help='Total GPU memory.')
+	parser.add_argument('--allocated', type=float, default=1000,
+                      help='Allocated GPU memory.')
+	FLAGS, unparsed = parser.parse_known_args()
+	# fraction = FLAGS.allocated / FLAGS.total * 0.85
+	fraction = round( FLAGS.allocated * 0.7 / FLAGS.total , 1 )
+
+	print(fraction)
+	train(fraction)

gpushare-scheduler-extender/samples/docker/run.sh

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+#!/usr/bin/env bash
+
+echo ALIYUN_COM_GPU_MEM_DEV=$ALIYUN_COM_GPU_MEM_DEV
+echo ALIYUN_COM_GPU_MEM_CONTAINER=$ALIYUN_COM_GPU_MEM_CONTAINER
+
+python /app/main.py --total=$ALIYUN_COM_GPU_MEM_DEV --allocated=$ALIYUN_COM_GPU_MEM_CONTAINER