def test_gpu_cluster_spec(self, mock_available, mock_get_gpus,
mock_get_spec):
"""Request GPU when multiple TF nodes land on same executor"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
mock_get_spec.return_value = {
'chief': ['1.1.1.1:2222'],
'worker': ['1.1.1.1:2223', '1.1.1.1:2224', '2.2.2.2:2222']
}
self.cluster_meta['cluster_template'] = {
'chief': [0],
'worker': [1, 2, 3]
}
self.parser.add_argument("--num_gpus",
help="number of gpus to use",
type=int)
tf_args = self.parser.parse_args(["--num_gpus", "1"])
print("tf_args:", tf_args)
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([2]) # worker:1
mock_get_gpus.assert_called_with(1, 2, format=gpu_info.AS_LIST)
def test_run(self):
"""Minimal function w/ args and ctx"""
def fn(args, ctx):
print("{}:{} args: {}".format(ctx.job_name, ctx.task_index, args))
self.assertEqual(ctx.job_name, self.job_name)
self.assertEqual(ctx.task_index, 0)
tf_args = self.parser.parse_args([])
map_fn = TFSparkNode.run(fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
def test_gpu_unavailable(self):
"""Request GPU with no GPUs available, expecting an exception"""
self.parser.add_argument("--num_gpus",
help="number of gpus to use",
type=int)
tf_args = self.parser.parse_args(["--num_gpus", "1"])
with self.assertRaises(Exception):
map_fn = TFSparkNode.run(self.default_fn, tf_args,
self.cluster_meta, self.tensorboard,
self.log_dir, self.queues,
self.background)
map_fn([0])
def test_gpu_default(self, mock_available, mock_get_gpus):
"""Default to one GPU if not explicitly requested"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
tf_args = self.parser.parse_args([])
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '0')
mock_get_gpus.assert_called_with(1, 0, format=gpu_info.AS_LIST)
def test_gpu_available(self, mock_available, mock_get_gpus):
"""Request available GPU"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
self.parser.add_argument("--num_gpus",
help="number of gpus to use",
type=int)
tf_args = self.parser.parse_args(["--num_gpus", "1"])
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '0')
def test_gpu_spark_fallback(self, mock_available, mock_get_gpus,
mock_spark_resources, mock_context):
"""Spark resource API w/ no available GPU with fallback to original resource allocation"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
mock_spark_resources.return_value = True
mock_context_instance = mock_context.return_value
mock_context_instance.resources.return_value = {}
tf_args = self.parser.parse_args([])
print("tf_args:", tf_args)
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '0')
mock_get_gpus.assert_called_with(1, 0, format=gpu_info.AS_LIST)
def test_gpu_spark_unavailable_default(self, mock_available, mock_get_gpus,
mock_spark_resources, mock_context):
"""Spark resource API w/ no available GPU and no fallback (in K8s)"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
mock_spark_resources.return_value = True
mock_context_instance = mock_context.return_value
mock_context_instance.resources.return_value = {}
tf_args = self.parser.parse_args([])
print("tf_args:", tf_args)
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '')
mock_get_gpus.assert_not_called()
def test_gpu_spark_available(self, mock_available, mock_get_gpus,
mock_spark_resources, mock_context):
"""Spark resource API w/ available GPU"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
mock_spark_resources.return_value = True
mock_context_instance = mock_context.return_value
mock_context_instance.resources.return_value = {
'gpu': type("ResourceInformation", (object, ),
{"addresses": ['0']})
}
tf_args = self.parser.parse_args([])
print("tf_args:", tf_args)
map_fn = TFSparkNode.run(self.default_fn, tf_args, self.cluster_meta,
self.tensorboard, self.log_dir, self.queues,
self.background)
map_fn([0])
self.assertEqual(os.environ['CUDA_VISIBLE_DEVICES'], '0')
mock_get_gpus.assert_not_called()
def test_gpu_spark_unavailable_but_requested(self, mock_available,
mock_get_gpus,
mock_spark_resources,
mock_context):
"""Spark resource API w/ no available GPU and no fallback (in K8s) with num_gpus set"""
mock_available.return_value = True
mock_get_gpus.return_value = ['0']
mock_spark_resources.return_value = True
mock_context_instance = mock_context.return_value
mock_context_instance.resources.return_value = {}
self.parser.add_argument("--num_gpus",
help="number of gpus to use",
type=int)
tf_args = self.parser.parse_args(["--num_gpus", "1"])
print("tf_args:", tf_args)
with self.assertRaises(Exception):
map_fn = TFSparkNode.run(self.default_fn, tf_args,
self.cluster_meta, self.tensorboard,
self.log_dir, self.queues,
self.background)
map_fn([0])
