def test_experiments_isolation(self, resource_group, location):
self.client.workspaces.create(resource_group.name, 'first',
location).result()
self.client.workspaces.create(resource_group.name, 'second',
location).result()
# Create a cluster, two experiments and a job in each experiment
for workspace in ['first', 'second']:
cluster = self.client.clusters.create(
resource_group.name,
workspace,
'cluster',
parameters=models.ClusterCreateParameters(
vm_size='STANDARD_D1',
scale_settings=models.ScaleSettings(
manual=models.ManualScaleSettings(
target_node_count=0)),
user_account_settings=models.UserAccountSettings(
admin_user_name=helpers.ADMIN_USER_NAME,
admin_user_password=helpers.ADMIN_USER_PASSWORD),
vm_priority='lowpriority')).result()
for experiment in ['exp1', 'exp2']:
self.client.experiments.create(resource_group.name, workspace,
experiment).result()
self.client.jobs.create(
resource_group.name,
workspace,
experiment,
'job',
parameters=models.JobCreateParameters(
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT',
custom_toolkit_settings=models.CustomToolkitSettings(
command_line='true'))).result()
# Delete exp1 in the first workspace
self.client.experiments.delete(resource_group.name, 'first',
'exp1').result()
# Ensure the experiment was actually deleted
self.assertRaises(
CloudError, lambda: self.client.experiments.get(
resource_group.name, 'first', 'exp1'))
for workspace in ['first', 'second']:
# Ensure the clusters are not affected
self.client.clusters.get(resource_group.name, workspace, 'cluster')
# Ensure the other experiments are not affected
for experiment in ['exp1', 'exp2']:
if workspace == 'first' and experiment == 'exp1':
continue
self.client.experiments.get(resource_group.name, workspace,
experiment)
job = self.client.jobs.get(resource_group.name, workspace,
experiment, 'job')
# And check the job are not terminated
self.assertEqual(job.execution_state,
models.ExecutionState.queued)
def create_custom_job(client,
resource_group,
cluster_id,
job_name,
nodes,
cmd,
job_preparation_cmd=None,
container=None):
"""Creates custom toolkit job
:param BatchAIManagementClient client: client instance.
:param str resource_group: resource group name.
:param str cluster_id: resource Id of the cluster.
:param str job_name: job name.
:param int nodes: number of nodes to execute the job.
:param str cmd: command line to run.
:param str or None job_preparation_cmd: Job preparation command line.
:param models.ContainerSettings or None container: container settings to run the job.
:return models.Job: the created job.
"""
job_preparation = None
if job_preparation_cmd:
job_preparation = models.JobPreparation(
command_line=job_preparation_cmd)
client.experiments.create(resource_group,
Helpers.DEFAULT_WORKSPACE_NAME,
Helpers.DEFAULT_EXPERIMENT_NAME).result()
return client.jobs.create(
resource_group,
Helpers.DEFAULT_WORKSPACE_NAME,
Helpers.DEFAULT_EXPERIMENT_NAME,
job_name,
parameters=models.JobCreateParameters(
cluster=models.ResourceId(id=cluster_id),
node_count=nodes,
std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
Helpers.AZURE_FILES_MOUNTING_PATH),
output_directories=[
models.OutputDirectory(
id=Helpers.JOB_OUTPUT_DIRECTORY_ID,
path_prefix=Helpers.JOB_OUTPUT_DIRECTORY_PATH,
path_suffix="files")
],
input_directories=[
models.InputDirectory(
id='INPUT',
path='$AZ_BATCHAI_MOUNT_ROOT/{0}/input'.format(
Helpers.AZURE_FILES_MOUNTING_PATH))
],
container_settings=container,
job_preparation=job_preparation,
custom_toolkit_settings=models.CustomToolkitSettings(
command_line=cmd))).result()
def test_job_environment_variables_and_secrets(self, resource_group,
location, cluster):
"""Tests if it's possible to mount external file systems for a job."""
job_name = 'job'
job = self.client.jobs.create(
resource_group.name,
helpers.DEFAULT_WORKSPACE_NAME,
helpers.DEFAULT_EXPERIMENT_NAME,
job_name,
parameters=models.JobCreateParameters(
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
helpers.AZURE_FILES_MOUNTING_PATH),
environment_variables=[
models.EnvironmentVariable(name='VARIABLE', value='VALUE')
],
secrets=[
models.EnvironmentVariableWithSecretValue(
name='SECRET_VARIABLE', value='SECRET')
],
# Check that the job preparation has access to env variables and secrets.
job_preparation=models.JobPreparation(
command_line='echo $VARIABLE $SECRET_VARIABLE'),
# Check that the job has access to env variables and secrets.
custom_toolkit_settings=models.CustomToolkitSettings(
command_line='echo $VARIABLE $SECRET_VARIABLE'))).result(
) # type: models.Job
self.assertEqual(
helpers.wait_for_job_completion(self.is_live, self.client,
resource_group.name, job.name,
helpers.MINUTE),
models.ExecutionState.succeeded)
# Check that environment variables are reported by the server.
self.assertEqual(len(job.environment_variables), 1)
self.assertEqual(job.environment_variables[0].name, 'VARIABLE')
self.assertEqual(job.environment_variables[0].value, 'VALUE')
# Check that secrets are reported back by server, but value is not reported.
self.assertEqual(len(job.secrets), 1)
self.assertEqual(job.secrets[0].name, 'SECRET_VARIABLE')
self.assertIsNone(job.secrets[0].value)
# Check that job and job prep had access to the env variables and secrets.
helpers.assert_job_files_are(
self, self.client, resource_group.name, job.name,
helpers.STANDARD_OUTPUT_DIRECTORY_ID, {
u'stdout.txt': u'VALUE SECRET\n',
u'stderr.txt': u'',
u'stdout-job_prep.txt': u'VALUE SECRET\n',
u'stderr-job_prep.txt': u''
})
def create_job(config,
cluster_id,
workspace,
experiment,
job_name,
image_name,
command,
number_of_vms=1):
''' Creates job
'''
input_directories = [
models.InputDirectory(id='SCRIPT',
path='$AZ_BATCHAI_MOUNT_ROOT/{0}/{1}'.format(
config.fileshare_mount_point, job_name)),
models.InputDirectory(id='DATASET',
path='$AZ_BATCHAI_MOUNT_ROOT/{0}/{1}'.format(
config.fileshare_mount_point, 'data'))
]
std_output_path_prefix = "$AZ_BATCHAI_MOUNT_ROOT/{0}".format(
config.fileshare_mount_point)
output_directories = [
models.OutputDirectory(id='MODEL',
path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
config.fileshare_mount_point),
path_suffix="models"),
models.OutputDirectory(id='NOTEBOOKS',
path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
config.fileshare_mount_point),
path_suffix="notebooks")
]
parameters = models.JobCreateParameters(
location=config.location,
cluster=models.ResourceId(id=cluster_id),
node_count=number_of_vms,
input_directories=input_directories,
std_out_err_path_prefix=std_output_path_prefix,
output_directories=output_directories,
container_settings=models.ContainerSettings(
image_source_registry=models.ImageSourceRegistry(
image=image_name)),
custom_toolkit_settings=models.CustomToolkitSettings(
command_line=command))
client = client_from(config)
_ = client.jobs.create(config.group_name, workspace, experiment, job_name,
parameters)
parameters = ParameterSweep(param_specs)
# create a template for Batch AI job
jcp = models.JobCreateParameters(
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/logs',
output_directories=[
models.OutputDirectory(id='ALL',
path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/output')
],
custom_toolkit_settings=models.CustomToolkitSettings(
command_line=
'python $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts/FF_multi_step_multivariate.py \
--scriptdir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts \
--datadir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/data \
--outdir $AZ_BATCHAI_OUTPUT_ALL \
-l {0} -n {1} -b {2} -T {3} -r {4} -a {5}'.format(
parameters['LATENT_DIM'], parameters['HIDDEN_LAYERS'],
parameters['BATCH_SIZE'], parameters['T'],
parameters['LEARNING_RATE'], parameters['ALPHA'])),
container_settings=models.ContainerSettings(
image_source_registry=models.ImageSourceRegistry(
image=cfg['docker_image'])),
mount_volumes=models.MountVolumes(azure_file_shares=[
models.AzureFileShareReference(
account_name=cfg['storage_account']['name'],
credentials=models.AzureStorageCredentialsInfo(
account_key=cfg['storage_account']['key']),
azure_file_url='https://' + cfg['storage_account']['name'] +
'.file.core.windows.net/logs',
relative_mount_path='logs'),
ts_from = sys.argv[1]
ts_to = sys.argv[2]
device_ids = j['device_ids']
tags = j['tags']
job_name_template = j['job_name']
credentials = ServicePrincipalCredentials(client_id=CLIENT,
secret=KEY,
tenant=TENANT_ID)
batchai_client = batchai.BatchAIManagementClient(
credentials=credentials, subscription_id=subscription_id)
cluster = batchai_client.clusters.get(resource_group_name, cluster_name)
# run an async job for each sensor
for device_id in device_ids:
for tag in tags:
job_name = job_name_template.format(device_id, tag)
custom_settings = baimodels.CustomToolkitSettings(
command_line=command_line.format(device_id, tag, ts_from, ts_to,
config_file_path))
print('command line: ' + custom_settings.command_line)
params = baimodels.job_create_parameters.JobCreateParameters(
location=location,
cluster=baimodels.ResourceId(cluster.id),
node_count=node_count,
std_out_err_path_prefix=std_out_err_path_prefix,
custom_toolkit_settings=custom_settings)
batchai_client.jobs.create(resource_group_name, job_name, params)
parameters = ParameterSweep(param_specs)
# create a template for Batch AI job
jcp = models.JobCreateParameters(
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/logs',
output_directories=[
models.OutputDirectory(id='ALL',
path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/output')
],
custom_toolkit_settings=models.CustomToolkitSettings(
command_line=
'python $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts/RNN_teacher_forcing.py \
--scriptdir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts \
--datadir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/data \
--outdir $AZ_BATCHAI_OUTPUT_ALL \
-l {0} -b {1} -T {2} -r {3} -a {4}'.format(
parameters['LATENT_DIM_1'], parameters['BATCH_SIZE'],
parameters['T'], parameters['LEARNING_RATE'],
parameters['ALPHA'])),
container_settings=models.ContainerSettings(
image_source_registry=models.ImageSourceRegistry(
image=cfg['docker_image'])),
mount_volumes=models.MountVolumes(azure_file_shares=[
models.AzureFileShareReference(
account_name=cfg['storage_account']['name'],
credentials=models.AzureStorageCredentialsInfo(
account_key=cfg['storage_account']['key']),
azure_file_url='https://' + cfg['storage_account']['name'] +
'.file.core.windows.net/logs',
relative_mount_path='logs'),
# create a template for Batch AI job
jcp = models.JobCreateParameters(
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/logs',
output_directories=[
models.OutputDirectory(
id='ALL',
path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/output'
)
],
custom_toolkit_settings=models.CustomToolkitSettings(
command_line='python $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts/RNN_multi_step_encoder_decoder_simple.py \
--scriptdir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/scripts \
--datadir $AZ_BATCHAI_JOB_MOUNT_ROOT/resources/data \
--outdir $AZ_BATCHAI_OUTPUT_ALL \
-e1 {0} -e2 {1} -d1 {2} -d2 {3} -b {4} -T {5} -r {6} -a {7}'.format(parameters['ENCODER_DIM_1'], parameters['ENCODER_DIM_2'],
parameters['DECODER_DIM_1'], parameters['DECODER_DIM_2'],
parameters['BATCH_SIZE'], parameters['T'],
parameters['LEARNING_RATE'], parameters['ALPHA'])
),
container_settings=models.ContainerSettings(
image_source_registry=models.ImageSourceRegistry(image=cfg['docker_image'])
),
mount_volumes = models.MountVolumes(
azure_file_shares=[
models.AzureFileShareReference(
account_name=cfg['storage_account']['name'],
credentials=models.AzureStorageCredentialsInfo(account_key=cfg['storage_account']['key']),
azure_file_url='https://'+cfg['storage_account']['name']+'.file.core.windows.net/logs',
relative_mount_path='logs'),
models.AzureFileShareReference(
def test_job_level_mounting(self, resource_group, location, cluster,
storage_account, storage_account_key):
"""Tests if it's possible to mount external file systems for a job."""
job_name = 'job'
# Create file share and container to mount on the job level
if storage_account.name != FAKE_STORAGE.name:
files = FileService(storage_account.name, storage_account_key)
files.create_share('jobshare', fail_on_exist=False)
blobs = BlockBlobService(storage_account.name, storage_account_key)
blobs.create_container('jobcontainer', fail_on_exist=False)
job = self.client.jobs.create(
resource_group.name,
job_name,
parameters=models.JobCreateParameters(
location=location,
cluster=models.ResourceId(id=cluster.id),
node_count=1,
mount_volumes=models.
MountVolumes(azure_file_shares=[
models.AzureFileShareReference(
account_name=storage_account.name,
azure_file_url='https://{0}.file.core.windows.net/{1}'.
format(storage_account.name, 'jobshare'),
relative_mount_path='job_afs',
credentials=models.AzureStorageCredentialsInfo(
account_key=storage_account_key),
)
],
azure_blob_file_systems=[
models.AzureBlobFileSystemReference(
account_name=storage_account.name,
container_name='jobcontainer',
relative_mount_path='job_bfs',
credentials=models.
AzureStorageCredentialsInfo(
account_key=storage_account_key),
)
]),
# Put standard output on cluster level AFS to check that the job has access to it.
std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
AZURE_FILES_MOUNTING_PATH),
# Create two output directories on job level AFS and blobfuse.
output_directories=[
models.OutputDirectory(
id='OUTPUT1',
path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/job_afs'),
models.OutputDirectory(
id='OUTPUT2',
path_prefix='$AZ_BATCHAI_JOB_MOUNT_ROOT/job_bfs')
],
# Check that the job preparation has access to job level file systems.
job_preparation=models.JobPreparation(
command_line=
'echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/prep_afs.txt; '
'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/prep_bfs.txt; '
'echo done'),
# Check that the job has access to job
custom_toolkit_settings=models.CustomToolkitSettings(
command_line=
'echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/job_afs.txt; '
'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/job_bfs.txt; '
'mkdir $AZ_BATCHAI_OUTPUT_OUTPUT1/afs; '
'echo afs > $AZ_BATCHAI_OUTPUT_OUTPUT1/afs/job_afs.txt; '
'mkdir $AZ_BATCHAI_OUTPUT_OUTPUT2/bfs; '
'echo bfs > $AZ_BATCHAI_OUTPUT_OUTPUT2/bfs/job_bfs.txt; '
'echo done'))).result()
self.assertEqual(
wait_for_job_completion(self.is_live, self.client,
resource_group.name, job.name, MINUTE),
models.ExecutionState.succeeded)
job = self.client.jobs.get(resource_group.name, job.name)
# Assert job and job prep standard output is populated on cluster level filesystem
assert_job_files_are(
self, self.client, resource_group.name, job.name,
STANDARD_OUTPUT_DIRECTORY_ID, {
u'stdout.txt': u'done\n',
u'stderr.txt': u'',
u'stdout-job_prep.txt': u'done\n',
u'stderr-job_prep.txt': u''
})
# Assert files are generated on job level AFS
assert_job_files_are(self, self.client, resource_group.name, job.name,
'OUTPUT1', {
u'job_afs.txt': u'afs\n',
u'prep_afs.txt': u'afs\n',
u'afs': None
})
# Assert files are generated on job level blobfuse
assert_job_files_are(self, self.client, resource_group.name, job.name,
'OUTPUT2', {
u'job_bfs.txt': u'bfs\n',
u'prep_bfs.txt': u'bfs\n',
u'bfs': None
})
# Assert subfolders are available via API
assert_job_files_in_path_are(self, self.client, resource_group.name,
job.name, 'OUTPUT1', 'afs',
{u'job_afs.txt': u'afs\n'})
assert_job_files_in_path_are(self, self.client, resource_group.name,
job.name, 'OUTPUT2', 'bfs',
{u'job_bfs.txt': u'bfs\n'})
# Assert that we can access the output files created on job level mount volumes directly in storage using path
# segment returned by the server.
if storage_account.name != FAKE_STORAGE.name:
files = FileService(storage_account.name, storage_account_key)
self.assertTrue(
files.exists(
'jobshare', job.job_output_directory_path_segment + '/' +
OUTPUT_DIRECTORIES_FOLDER_NAME, 'job_afs.txt'))
blobs = BlockBlobService(storage_account.name, storage_account_key)
self.assertTrue(
blobs.exists(
'jobcontainer', job.job_output_directory_path_segment +
'/' + OUTPUT_DIRECTORIES_FOLDER_NAME + '/job_bfs.txt'))
# After the job is done the filesystems should be unmounted automatically, check this by submitting a new job.
checker = self.client.jobs.create(
resource_group.name,
'checker',
parameters=models.JobCreateParameters(
location=location,
cluster=models.ResourceId(id=cluster.id),
node_count=1,
std_out_err_path_prefix='$AZ_BATCHAI_MOUNT_ROOT/{0}'.format(
AZURE_FILES_MOUNTING_PATH),
custom_toolkit_settings=models.CustomToolkitSettings(
command_line='echo job; df | grep -E "job_bfs|job_afs"'))
).result()
# Check the job failed because there are not job level mount volumes anymore
self.assertEqual(
wait_for_job_completion(self.is_live, self.client,
resource_group.name, checker.name, MINUTE),
models.ExecutionState.failed)
# Check that the cluster level AFS was still mounted
assert_job_files_are(self, self.client, resource_group.name,
checker.name, STANDARD_OUTPUT_DIRECTORY_ID, {
u'stdout.txt': u'job\n',
u'stderr.txt': u''
})