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'' })