def test_download_group_simple(self):
"""
A simple test that ensures we have download task group
executed between epoch_group and exit_group.
"""
model = model_helper.ModelHelper(name="test_model")
download_net = core.Net("download_net")
for name in ["input1", "input2", "output", "download_result"]:
model.param_init_net.ConstantFill([], [name],
shape=[
8,
],
value=1.0,
run_once=0)
model.net.Add(["input1", "input2"], ["output"])
download_net.Copy(["output"], ["download_result"])
# All blob values are initialized as 1.0, after download_net executed
# we expect to see download result is the same as training result.
with Job() as job:
with Node("trainer:0"):
with job.init_group:
Task(step=model.param_init_net)
with job.epoch_group:
with Task():
with ops.loop(1):
ops.net(model.net)
with job.download_group:
Task(step=download_net)
epoch_limiter(job, 1)
ws = workspace.C.Workspace()
session = LocalSession(ws)
job_runner = JobRunner(job)
job_runner.train(session)
expected_result = np.full(8, 2.0).astype(np.float32)
self.assertTrue(
np.array_equal(expected_result, ws.fetch_blob("output")))
self.assertTrue(
np.array_equal(expected_result, ws.fetch_blob("download_result")))
def test_download_group_simple(self):
"""
A simple test that ensures we have download task group
executed between epoch_group and exit_group.
"""
model = model_helper.ModelHelper(name="test_model")
download_net = core.Net("download_net")
for name in ["input1", "input2", "output", "download_result"]:
model.param_init_net.ConstantFill([],
[name],
shape=[8, ],
value=1.0,
run_once=0)
model.net.Add(["input1", "input2"], ["output"])
download_net.Copy(["output"], ["download_result"])
# All blob values are initialized as 1.0, after download_net executed
# we expect to see download result is the same as training result.
with Job() as job:
with Node("trainer:0"):
with job.init_group:
Task(step=model.param_init_net)
with job.epoch_group:
with Task():
with ops.loop(1):
ops.net(model.net)
with job.download_group:
Task(step=download_net)
epoch_limiter(job, 1)
ws = workspace.C.Workspace()
session = LocalSession(ws)
job_runner = JobRunner(job)
job_runner.train(session)
expected_result = np.full(8, 2.0).astype(np.float32)
self.assertTrue(np.array_equal(expected_result,
ws.fetch_blob("output")))
self.assertTrue(np.array_equal(expected_result,
ws.fetch_blob("download_result")))
def test_upload_checkpoint(self):
try:
tmpdir = tempfile.mkdtemp()
upload_dir = os.path.join(tmpdir, "upload")
os.mkdir(upload_dir)
num_nodes = 3
# The uploaded files do not exist yet.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertFalse(os.path.exists(upload_path))
# Create and run the job runner.
for node_id in range(3):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
local_upload_builder = UploadToLocalFile(upload_dir)
job_runner = JobRunner(
job,
checkpoint,
upload_task_group_builder=local_upload_builder)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# The uploaded files should exist now.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertTrue(os.path.exists(upload_path))
finally:
shutil.rmtree(tmpdir)
def test_ckpt_save_failure(self):
num_nodes = 3
# The goal of this test is to ensure that the job runs
# successfully even if saving a checkpoint fails.
# Hence tmpdir is a non existent directory to emulate a failure
# while saving checkpoints
tmpdir = "/tmp/path_does_not_exist/"
# Check the saving checkpoint failure does not cause job failure
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
# make sure all epochs are executed even though saving the checkpoint failed
# Saving checkpoint failure should not cause job failure
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
def test_ckpt_save_failure(self):
num_nodes = 3
# The goal of this test is to ensure that the job runs
# successfully even if saving a checkpoint fails.
# Hence tmpdir is a non existent directory to emulate a failure
# while saving checkpoints
tmpdir = "/tmp/path_does_not_exist/"
# Check the saving checkpoint failure does not cause job failure
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
# make sure all epochs are executed even though saving the checkpoint failed
# Saving checkpoint failure should not cause job failure
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
def test_upload_checkpoint(self):
try:
tmpdir = tempfile.mkdtemp()
upload_dir = os.path.join(tmpdir, "upload")
os.mkdir(upload_dir)
num_nodes = 3
# The uploaded files do not exist yet.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertFalse(os.path.exists(upload_path))
# Create and run the job runner.
for node_id in range(3):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
local_upload_builder = UploadToLocalFile(upload_dir)
job_runner = JobRunner(
job, checkpoint,
upload_task_group_builder=local_upload_builder)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# The uploaded files should exist now.
for node_id in range(num_nodes):
node_name = 'trainer_%d' % node_id
upload_path = os.path.join(upload_dir, node_name)
self.assertTrue(os.path.exists(upload_path))
finally:
shutil.rmtree(tmpdir)
def test_ckpt_name_and_load_model_from_ckpts(self):
try:
num_nodes = 3
tmpdir = tempfile.mkdtemp()
# First, check if the checkpoint name generation mechanism is
# correct.
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
checkpoint.init(job.nodes_to_checkpoint())
for node_id in range(num_nodes):
epoch = 5
node_name = 'trainer_%d' % node_id
expected_db_name = tmpdir + '/' + node_name + '.5'
self.assertEquals(
checkpoint.get_ckpt_db_name(node_name, epoch),
expected_db_name)
shutil.rmtree(tmpdir)
# Next, check mechanism to load model from checkpoints.
tmpdir = tempfile.mkdtemp()
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# There are 17 global blobs after finishing up the job runner.
# (only blobs on init_group are checkpointed)
self.assertEquals(len(ws.blobs), 17)
ws = workspace.C.Workspace()
session = LocalSession(ws)
self.assertEquals(len(ws.blobs), 0)
model_blob_names = ['trainer_1/task_2/GivenTensorInt64Fill:0',
'trainer_2/task_2/GivenTensorInt64Fill:0']
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=1, session=session)
# Check that we can successfully load from checkpoints of epochs
# 1 to 4, but not epoch 5.
for epoch in range(1, 5):
self.assertTrue(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=epoch,
session=session))
# Check that all the model blobs are loaded.
for blob_name in model_blob_names:
self.assertTrue(ws.has_blob(blob_name))
self.assertEquals(
ws.fetch_blob(blob_name),
np.array([EXPECTED_TOTALS[epoch - 1]]))
self.assertFalse(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=5, session=session))
finally:
shutil.rmtree(tmpdir)
def test_ckpt_name_and_load_model_from_ckpts(self):
try:
num_nodes = 3
tmpdir = tempfile.mkdtemp()
# First, check if the checkpoint name generation mechanism is
# correct.
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
checkpoint.init(job.nodes_to_checkpoint())
for node_id in range(num_nodes):
epoch = 5
node_name = 'trainer_%d' % node_id
expected_db_name = tmpdir + '/' + node_name + '.5'
self.assertEquals(
checkpoint.get_ckpt_db_name(node_name, epoch),
expected_db_name)
shutil.rmtree(tmpdir)
# Next, check mechanism to load model from checkpoints.
tmpdir = tempfile.mkdtemp()
workspace.ResetWorkspace()
for node_id in range(num_nodes):
ws = workspace.C.Workspace()
session = LocalSession(ws)
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
num_epochs = job_runner.train(session)
self.assertEquals(num_epochs, len(EXPECTED_TOTALS))
# There are 17 global blobs after finishing up the job runner.
# (only blobs on init_group are checkpointed)
self.assertEquals(len(ws.blobs), 17)
ws = workspace.C.Workspace()
session = LocalSession(ws)
self.assertEquals(len(ws.blobs), 0)
model_blob_names = ['trainer_1/task_2/GivenTensorInt64Fill:0',
'trainer_2/task_2/GivenTensorInt64Fill:0']
checkpoint = MultiNodeCheckpointManager(tmpdir, 'minidb')
with Cluster():
with Job() as job:
for node_id in range(num_nodes):
build_pipeline(node_id)
job.compile(LocalSession)
job_runner = JobRunner(job, checkpoint)
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=1, session=session)
# Check that we can successfully load from checkpoints of epochs
# 1 to 4, but not epoch 5.
for epoch in range(1, 5):
self.assertTrue(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=epoch,
session=session))
# Check that all the model blobs are loaded.
for blob_name in model_blob_names:
self.assertTrue(ws.has_blob(blob_name))
self.assertEquals(
ws.fetch_blob(blob_name),
np.array([EXPECTED_TOTALS[epoch - 1]]))
self.assertFalse(
job_runner.load_blobs_from_checkpoints(
blob_names=model_blob_names, epoch=5, session=session))
finally:
shutil.rmtree(tmpdir)