def test_with_disabled_options():
cmd = CommandBuilder("ssh")
cmd.add_option("--verbose", enable=True)
cmd.add_option("-p", None)
cmd.add_option("-L", "8080:localhost:8080", enable=False)
assert cmd.to_str() == "ssh --verbose"
def __init__(self,
name: str,
docker_repo: str,
cmd: str,
gpu_enabled=False):
if gpu_enabled:
self.cmd = CommandBuilder('nvidia-docker run')
else:
self.cmd = CommandBuilder('docker run')
self.cmd.add_option('--net', 'host')
self.cmd.add_option('--name', name)
self.cmd.add_argument('-d')
self.cmd.add_argument(docker_repo)
self.cmd.add_argument(cmd)
def __init__(self,
name: str,
docker_repo: str,
docker_run_options: str,
cmd: str,
gpu_enabled=False):
if gpu_enabled:
self.cmd = CommandBuilder("nvidia-docker run")
else:
self.cmd = CommandBuilder("docker run")
self.cmd.add_option("--net", "host")
self.cmd.add_option("--name", name)
self.cmd.add_argument("-d")
self.cmd.add_argument(docker_run_options)
self.cmd.add_argument(docker_repo)
self.cmd.add_argument(cmd)
def generate_task(spark_client, container_id, application, remote=False):
resource_files = []
# The application provided is not hosted remotely and therefore must be uploaded
if not remote:
app_resource_file = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=application.application,
blob_client=spark_client.blob_client,
use_full_path=False,
)
# Upload application file
resource_files.append(app_resource_file)
application.application = "$AZ_BATCH_TASK_WORKING_DIR/" + os.path.basename(
application.application)
# Upload dependent JARS
jar_resource_file_paths = []
for jar in application.jars:
current_jar_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=jar,
blob_client=spark_client.blob_client,
use_full_path=False,
)
jar_resource_file_paths.append(current_jar_resource_file_path)
resource_files.append(current_jar_resource_file_path)
# Upload dependent python files
py_files_resource_file_paths = []
for py_file in application.py_files:
current_py_files_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=py_file,
blob_client=spark_client.blob_client,
use_full_path=False,
)
py_files_resource_file_paths.append(
current_py_files_resource_file_path)
resource_files.append(current_py_files_resource_file_path)
# Upload other dependent files
files_resource_file_paths = []
for file in application.files:
files_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=file,
blob_client=spark_client.blob_client,
use_full_path=False,
)
files_resource_file_paths.append(files_resource_file_path)
resource_files.append(files_resource_file_path)
# Upload application definition
application.jars = [os.path.basename(jar) for jar in application.jars]
application.py_files = [
os.path.basename(py_files) for py_files in application.py_files
]
application.files = [
os.path.basename(files) for files in application.files
]
application_definition_file = helpers.upload_text_to_container(
container_name=container_id,
application_name=application.name,
file_path="application.yaml",
content=yaml.dump(vars(application)),
blob_client=spark_client.blob_client,
)
resource_files.append(application_definition_file)
# create command to submit task
task_cmd = CommandBuilder("sudo docker exec")
task_cmd.add_argument("-i")
task_cmd.add_option(
"-e", "AZ_BATCH_TASK_WORKING_DIR=$AZ_BATCH_TASK_WORKING_DIR")
task_cmd.add_option("-e",
"STORAGE_LOGS_CONTAINER={0}".format(container_id))
task_cmd.add_argument("spark /bin/bash >> output.log 2>&1")
task_cmd.add_argument(
r'-c "source ~/.bashrc; '
r"export PYTHONPATH=$PYTHONPATH:\$AZTK_WORKING_DIR; "
r"cd \$AZ_BATCH_TASK_WORKING_DIR; "
r'\$AZTK_WORKING_DIR/.aztk-env/.venv/bin/python \$AZTK_WORKING_DIR/aztk/node_scripts/submit.py"'
)
# Create task
task = batch_models.TaskAddParameter(
id=application.name,
command_line=helpers.wrap_commands_in_shell([task_cmd.to_str()]),
resource_files=resource_files,
constraints=batch_models.TaskConstraints(
max_task_retry_count=application.max_retry_count),
user_identity=batch_models.UserIdentity(
auto_user=batch_models.AutoUserSpecification(
scope=batch_models.AutoUserScope.task,
elevation_level=batch_models.ElevationLevel.admin)),
)
return task
def __docker_run_cmd(docker_repo: str = None,
gpu_enabled: bool = False,
worker_on_master: bool = True,
file_mounts = None,
plugins = None,
mixed_mode = False) -> str:
"""
Build the docker run command by setting up the environment variables
"""
if gpu_enabled:
cmd = CommandBuilder('nvidia-docker run')
else:
cmd = CommandBuilder('docker run')
cmd.add_option('--net', 'host')
cmd.add_option('--name', constants.DOCKER_SPARK_CONTAINER_NAME)
cmd.add_option('-v', '/mnt/batch/tasks:/mnt/batch/tasks')
if file_mounts:
for mount in file_mounts:
cmd.add_option('-v', '{0}:{0}'.format(mount.mount_path))
cmd.add_option('-e', 'DOCKER_WORKING_DIR=/mnt/batch/tasks/startup/wd')
cmd.add_option('-e', 'AZ_BATCH_ACCOUNT_NAME=$AZ_BATCH_ACCOUNT_NAME')
cmd.add_option('-e', 'BATCH_ACCOUNT_KEY=$BATCH_ACCOUNT_KEY')
cmd.add_option('-e', 'BATCH_SERVICE_URL=$BATCH_SERVICE_URL')
cmd.add_option('-e', 'STORAGE_ACCOUNT_NAME=$STORAGE_ACCOUNT_NAME')
cmd.add_option('-e', 'STORAGE_ACCOUNT_KEY=$STORAGE_ACCOUNT_KEY')
cmd.add_option('-e', 'STORAGE_ACCOUNT_SUFFIX=$STORAGE_ACCOUNT_SUFFIX')
cmd.add_option('-e', 'SP_TENANT_ID=$SP_TENANT_ID')
cmd.add_option('-e', 'SP_CLIENT_ID=$SP_CLIENT_ID')
cmd.add_option('-e', 'SP_CREDENTIAL=$SP_CREDENTIAL')
cmd.add_option('-e', 'SP_BATCH_RESOURCE_ID=$SP_BATCH_RESOURCE_ID')
cmd.add_option('-e', 'SP_STORAGE_RESOURCE_ID=$SP_STORAGE_RESOURCE_ID')
cmd.add_option('-e', 'AZ_BATCH_POOL_ID=$AZ_BATCH_POOL_ID')
cmd.add_option('-e', 'AZ_BATCH_NODE_ID=$AZ_BATCH_NODE_ID')
cmd.add_option(
'-e', 'AZ_BATCH_NODE_IS_DEDICATED=$AZ_BATCH_NODE_IS_DEDICATED')
if worker_on_master is not None:
cmd.add_option('-e', 'WORKER_ON_MASTER={}'.format(worker_on_master))
else:
# default to True if not specified
cmd.add_option('-e', 'WORKER_ON_MASTER={}'.format(True))
cmd.add_option('-e', 'MIXED_MODE={}'.format(mixed_mode))
cmd.add_option('-e', 'SPARK_WEB_UI_PORT=$SPARK_WEB_UI_PORT')
cmd.add_option('-e', 'SPARK_WORKER_UI_PORT=$SPARK_WORKER_UI_PORT')
cmd.add_option('-e', 'SPARK_CONTAINER_NAME=$SPARK_CONTAINER_NAME')
cmd.add_option('-e', 'SPARK_SUBMIT_LOGS_FILE=$SPARK_SUBMIT_LOGS_FILE')
cmd.add_option('-e', 'SPARK_JOB_UI_PORT=$SPARK_JOB_UI_PORT')
cmd.add_option('-p', '8080:8080') # Spark Master UI
cmd.add_option('-p', '7077:7077') # Spark Master
cmd.add_option('-p', '7337:7337') # Spark Shuffle Service
cmd.add_option('-p', '4040:4040') # Job UI
cmd.add_option('-p', '18080:18080') # Spark History Server UI
cmd.add_option('-p', '3022:3022') # Docker SSH
if plugins:
for plugin in plugins:
for port in plugin.ports:
cmd.add_option('-p', '{0}:{1}'.format(port.internal, port.internal)) # Jupyter UI
cmd.add_option('-d', docker_repo)
cmd.add_argument('/bin/bash /mnt/batch/tasks/startup/wd/docker_main.sh')
return cmd.to_str()
class DockerCmd:
"""
Class helping to write a docker command
"""
def __init__(self,
name: str,
docker_repo: str,
docker_run_options: str,
cmd: str,
gpu_enabled=False):
if gpu_enabled:
self.cmd = CommandBuilder("nvidia-docker run")
else:
self.cmd = CommandBuilder("docker run")
self.cmd.add_option("--net", "host")
self.cmd.add_option("--name", name)
self.cmd.add_argument("-d")
self.cmd.add_argument(docker_run_options)
self.cmd.add_argument(docker_repo)
self.cmd.add_argument(cmd)
def add_env(self, env: str, value: str):
self.cmd.add_option("-e", "{0}={1}".format(env, value))
def pass_env(self, env: str):
"""
Give the value of an environment variable in the main process to the docker image
"""
self.cmd.add_option("-e", "{0}".format(env))
def share_folder(self, folder: str):
self.cmd.add_option("-v", "{0}:{0}".format(folder))
def open_port(self, port: int):
self.cmd.add_option("-p", "{0}:{0}".format(port)) # Spark Master UI
def to_str(self):
return self.cmd.to_str()
def __app_cmd():
docker_exec = CommandBuilder("sudo docker exec")
docker_exec.add_argument("-i")
docker_exec.add_option(
"-e", "AZ_BATCH_TASK_WORKING_DIR=$AZ_BATCH_TASK_WORKING_DIR")
docker_exec.add_option("-e", "AZ_BATCH_JOB_ID=$AZ_BATCH_JOB_ID")
docker_exec.add_argument(
"spark /bin/bash >> output.log 2>&1 -c \"python \$DOCKER_WORKING_DIR/job_submission.py\""
)
return docker_exec.to_str()
def __app_cmd(scheduling_target=None, resource_files=None):
resource_file_sas_list = None
if resource_files:
resource_file_sas_list = ' '.join([
'\\\"{}\\\"'.format(task_def.blob_source)
for task_def in resource_files
])
docker_exec = CommandBuilder("sudo docker exec")
docker_exec.add_argument("-i")
docker_exec.add_option(
"-e", "AZ_BATCH_TASK_WORKING_DIR=$AZ_BATCH_TASK_WORKING_DIR")
docker_exec.add_option("-e", "AZ_BATCH_JOB_ID=$AZ_BATCH_JOB_ID")
docker_exec.add_argument(
r'spark /bin/bash >> output.log 2>&1 -c "'
r"source ~/.bashrc; "
r"export PYTHONPATH=$PYTHONPATH:\$AZTK_WORKING_DIR; "
r"cd \$AZ_BATCH_TASK_WORKING_DIR; "
r'\$AZTK_WORKING_DIR/.aztk-env/.venv/bin/python \$AZTK_WORKING_DIR/aztk/node_scripts/scheduling/job_submission.py {0} {1}"'
.format(scheduling_target if scheduling_target else "",
resource_file_sas_list if resource_file_sas_list else ""))
return docker_exec.to_str()
def __docker_run_cmd(docker_repo: str = None, gpu_enabled: bool = False, file_mounts = []) -> str:
"""
Build the docker run command by setting up the environment variables
"""
if gpu_enabled:
cmd = CommandBuilder('nvidia-docker run')
else:
cmd = CommandBuilder('docker run')
cmd.add_option('--net', 'host')
cmd.add_option('--name', constants.DOCKER_SPARK_CONTAINER_NAME)
cmd.add_option('-v', '/mnt/batch/tasks:/batch')
if file_mounts:
for mount in file_mounts:
cmd.add_option('-v', '{0}:{0}'.format(mount.mount_path))
cmd.add_option('-e', 'DOCKER_WORKING_DIR=/batch/startup/wd')
cmd.add_option('-e', 'AZ_BATCH_ACCOUNT_NAME=$AZ_BATCH_ACCOUNT_NAME')
cmd.add_option('-e', 'BATCH_ACCOUNT_KEY=$BATCH_ACCOUNT_KEY')
cmd.add_option('-e', 'BATCH_ACCOUNT_URL=$BATCH_ACCOUNT_URL')
cmd.add_option('-e', 'STORAGE_ACCOUNT_NAME=$STORAGE_ACCOUNT_NAME')
cmd.add_option('-e', 'STORAGE_ACCOUNT_KEY=$STORAGE_ACCOUNT_KEY')
cmd.add_option('-e', 'STORAGE_ACCOUNT_SUFFIX=$STORAGE_ACCOUNT_SUFFIX')
cmd.add_option('-e', 'AZ_BATCH_POOL_ID=$AZ_BATCH_POOL_ID')
cmd.add_option('-e', 'AZ_BATCH_NODE_ID=$AZ_BATCH_NODE_ID')
cmd.add_option(
'-e', 'AZ_BATCH_NODE_IS_DEDICATED=$AZ_BATCH_NODE_IS_DEDICATED')
cmd.add_option('-e', 'SPARK_WEB_UI_PORT=$SPARK_WEB_UI_PORT')
cmd.add_option('-e', 'SPARK_WORKER_UI_PORT=$SPARK_WORKER_UI_PORT')
cmd.add_option('-e', 'SPARK_JUPYTER_PORT=$SPARK_JUPYTER_PORT')
cmd.add_option('-e', 'SPARK_JOB_UI_PORT=$SPARK_JOB_UI_PORT')
cmd.add_option('-p', '8080:8080') # Spark Master UI
cmd.add_option('-p', '7077:7077') # Spark Master
cmd.add_option('-p', '4040:4040') # Job UI
cmd.add_option('-p', '8888:8888') # Jupyter UI
cmd.add_option('-p', '8787:8787') # Rstudio Server
cmd.add_option('-p', '18080:18080') # Spark History Server UI
cmd.add_option('-p', '3022:3022') # Docker SSH
cmd.add_option('-p', '8020:8020') # Namenode IPC: ClientProtocol
cmd.add_option('-p', '9000:9000') # Namenode IPC: ClientProtocol
cmd.add_option('-p', '50010:50010') # Datanode http data transfer
cmd.add_option('-p', '50020:50020') # Datanode IPC metaata operations
cmd.add_option('-p', '50070:50070') # Namenode WebUI
cmd.add_option('-p', '50075:50075') # DataNode WebUI
cmd.add_option('-p', '50090:50090') # Secondary NameNode http address
cmd.add_option('-d', docker_repo)
cmd.add_argument('/bin/bash /batch/startup/wd/docker_main.sh')
return cmd.to_str()
def test_only_command():
cmd = CommandBuilder("ssh")
assert cmd.to_str() == "ssh"
def test_with_arg_and_option():
cmd = CommandBuilder("ssh")
cmd.add_argument("[email protected]")
cmd.add_option("-p", "2020")
assert cmd.to_str() == "ssh -p 2020 [email protected]"
def test_with_multiple_options():
cmd = CommandBuilder("ssh")
cmd.add_option("-L", "8080:localhost:8080")
cmd.add_option("-p", "2020")
assert cmd.to_str() == "ssh -L 8080:localhost:8080 -p 2020"
def __docker_run_cmd(docker_repo: str = None,
gpu_enabled: bool = False,
worker_on_master: bool = True,
file_mounts = None,
mixed_mode = False) -> str:
"""
Build the docker run command by setting up the environment variables
"""
if gpu_enabled:
cmd = CommandBuilder('nvidia-docker run')
else:
cmd = CommandBuilder('docker run')
cmd.add_option('--net', 'host')
cmd.add_option('--name', constants.DOCKER_SPARK_CONTAINER_NAME)
cmd.add_option('-v', '/mnt/batch/tasks:/mnt/batch/tasks')
if file_mounts:
for mount in file_mounts:
cmd.add_option('-v', '{0}:{0}'.format(mount.mount_path))
cmd.add_option('-e', 'DOCKER_WORKING_DIR=/mnt/batch/tasks/startup/wd')
cmd.add_option('-e', 'AZ_BATCH_ACCOUNT_NAME=$AZ_BATCH_ACCOUNT_NAME')
cmd.add_option('-e', 'BATCH_ACCOUNT_KEY=$BATCH_ACCOUNT_KEY')
cmd.add_option('-e', 'BATCH_SERVICE_URL=$BATCH_SERVICE_URL')
cmd.add_option('-e', 'STORAGE_ACCOUNT_NAME=$STORAGE_ACCOUNT_NAME')
cmd.add_option('-e', 'STORAGE_ACCOUNT_KEY=$STORAGE_ACCOUNT_KEY')
cmd.add_option('-e', 'STORAGE_ACCOUNT_SUFFIX=$STORAGE_ACCOUNT_SUFFIX')
cmd.add_option('-e', 'SP_TENANT_ID=$SP_TENANT_ID')
cmd.add_option('-e', 'SP_CLIENT_ID=$SP_CLIENT_ID')
cmd.add_option('-e', 'SP_CREDENTIAL=$SP_CREDENTIAL')
cmd.add_option('-e', 'SP_BATCH_RESOURCE_ID=$SP_BATCH_RESOURCE_ID')
cmd.add_option('-e', 'SP_STORAGE_RESOURCE_ID=$SP_STORAGE_RESOURCE_ID')
cmd.add_option('-e', 'AZ_BATCH_POOL_ID=$AZ_BATCH_POOL_ID')
cmd.add_option('-e', 'AZ_BATCH_NODE_ID=$AZ_BATCH_NODE_ID')
cmd.add_option(
'-e', 'AZ_BATCH_NODE_IS_DEDICATED=$AZ_BATCH_NODE_IS_DEDICATED')
if worker_on_master is not None:
cmd.add_option('-e', 'WORKER_ON_MASTER={}'.format(worker_on_master))
else:
# default to True if not specified
cmd.add_option('-e', 'WORKER_ON_MASTER={}'.format(True))
cmd.add_option('-e', 'MIXED_MODE={}'.format(mixed_mode))
cmd.add_option('-e', 'SPARK_WEB_UI_PORT=$SPARK_WEB_UI_PORT')
cmd.add_option('-e', 'SPARK_WORKER_UI_PORT=$SPARK_WORKER_UI_PORT')
cmd.add_option('-e', 'SPARK_CONTAINER_NAME=$SPARK_CONTAINER_NAME')
cmd.add_option('-e', 'SPARK_SUBMIT_LOGS_FILE=$SPARK_SUBMIT_LOGS_FILE')
cmd.add_option('-e', 'SPARK_JUPYTER_PORT=$SPARK_JUPYTER_PORT')
cmd.add_option('-e', 'SPARK_JOB_UI_PORT=$SPARK_JOB_UI_PORT')
cmd.add_option('-p', '8080:8080') # Spark Master UI
cmd.add_option('-p', '7077:7077') # Spark Master
cmd.add_option('-p', '7337:7337') # Spark Shuffle Service
cmd.add_option('-p', '4040:4040') # Job UI
cmd.add_option('-p', '8888:8888') # Jupyter UI
cmd.add_option('-p', '8787:8787') # Rstudio Server
cmd.add_option('-p', '18080:18080') # Spark History Server UI
cmd.add_option('-p', '3022:3022') # Docker SSH
cmd.add_option('-p', '8020:8020') # Namenode IPC: ClientProtocol
cmd.add_option('-p', '9000:9000') # Namenode IPC: ClientProtocol
cmd.add_option('-p', '50010:50010') # Datanode http data transfer
cmd.add_option('-p', '50020:50020') # Datanode IPC metaata operations
cmd.add_option('-p', '50070:50070') # Namenode WebUI
cmd.add_option('-p', '50075:50075') # DataNode WebUI
cmd.add_option('-p', '50090:50090') # Secondary NameNode http address
cmd.add_option('-d', docker_repo)
cmd.add_argument('/bin/bash /mnt/batch/tasks/startup/wd/docker_main.sh')
return cmd.to_str()
def __app_submit_cmd(
spark_client,
cluster_id: str,
name: str,
app: str,
app_args: str,
main_class: str,
jars: List[str],
py_files: List[str],
files: List[str],
driver_java_options: str,
driver_library_path: str,
driver_class_path: str,
driver_memory: str,
executor_memory: str,
driver_cores: str,
executor_cores: str):
cluster = spark_client.get_cluster(cluster_id)
master_id = cluster.master_node_id
master_ip = __get_node(spark_client, master_id, cluster_id).ip_address
spark_home = constants.DOCKER_SPARK_HOME
# set file paths to correct path on container
files_path = '/batch/workitems/{0}/{1}/{2}/wd/'.format(cluster_id, "job-1", name)
jars = [files_path + jar for jar in jars]
py_files = [files_path + py_file for py_file in py_files]
files = [files_path + f for f in files]
# 2>&1 redirect stdout and stderr to be in the same file
spark_submit_cmd = CommandBuilder(
'{0}/bin/spark-submit'.format(spark_home))
spark_submit_cmd.add_option(
'--master', 'spark://{0}:7077'.format(master_ip))
spark_submit_cmd.add_option('--name', name)
spark_submit_cmd.add_option('--class', main_class)
spark_submit_cmd.add_option('--jars', jars and ','.join(jars))
spark_submit_cmd.add_option('--py-files', py_files and ','.join(py_files))
spark_submit_cmd.add_option('--files', files and ','.join(files))
spark_submit_cmd.add_option('--driver-java-options', driver_java_options)
spark_submit_cmd.add_option('--driver-library-path', driver_library_path)
spark_submit_cmd.add_option('--driver-class-path', driver_class_path)
spark_submit_cmd.add_option('--driver-memory', driver_memory)
spark_submit_cmd.add_option('--executor-memory', executor_memory)
spark_submit_cmd.add_option('--driver-cores', driver_cores)
spark_submit_cmd.add_option('--executor-cores', executor_cores)
spark_submit_cmd.add_argument(
'/batch/workitems/{0}/{1}/{2}/wd/'.format(cluster_id, "job-1", name) +
app + ' ' + ' '.join(['\'' + app_arg + '\'' for app_arg in app_args if app_args]))
if cluster.gpu_enabled:
docker_exec_cmd = CommandBuilder('sudo nvidia-docker exec')
else:
docker_exec_cmd = CommandBuilder('sudo docker exec')
docker_exec_cmd.add_option('-i', constants.DOCKER_SPARK_CONTAINER_NAME)
docker_exec_cmd.add_argument('/bin/bash >> {0} 2>&1 -c \"cd '.format(
constants.SPARK_SUBMIT_LOGS_FILE) + files_path + '; ' + spark_submit_cmd.to_str() + '\"')
return [
docker_exec_cmd.to_str()
]
def test_with_option():
cmd = CommandBuilder("ssh")
cmd.add_option("-L", "8080:localhost:8080")
assert cmd.to_str() == "ssh -L 8080:localhost:8080"
def __app_cmd():
docker_exec = CommandBuilder("sudo docker exec")
docker_exec.add_argument("-i")
docker_exec.add_option("-e", "AZ_BATCH_TASK_WORKING_DIR=$AZ_BATCH_TASK_WORKING_DIR")
docker_exec.add_option("-e", "AZ_BATCH_JOB_ID=$AZ_BATCH_JOB_ID")
docker_exec.add_argument(
r'spark /bin/bash >> output.log 2>&1 -c "'
r"source ~/.bashrc; "
r"export PYTHONPATH=$PYTHONPATH:\$AZTK_WORKING_DIR; "
r"cd \$AZ_BATCH_TASK_WORKING_DIR; "
r'\$AZTK_WORKING_DIR/.aztk-env/.venv/bin/python \$AZTK_WORKING_DIR/aztk/node_scripts/job_submission.py"')
return docker_exec.to_str()
def generate_task(spark_client, container_id, application):
resource_files = []
app_resource_file = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=application.application,
blob_client=spark_client.blob_client,
use_full_path=False)
# Upload application file
resource_files.append(app_resource_file)
# Upload dependent JARS
jar_resource_file_paths = []
for jar in application.jars:
current_jar_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=jar,
blob_client=spark_client.blob_client,
use_full_path=False)
jar_resource_file_paths.append(current_jar_resource_file_path)
resource_files.append(current_jar_resource_file_path)
# Upload dependent python files
py_files_resource_file_paths = []
for py_file in application.py_files:
current_py_files_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=py_file,
blob_client=spark_client.blob_client,
use_full_path=False)
py_files_resource_file_paths.append(
current_py_files_resource_file_path)
resource_files.append(current_py_files_resource_file_path)
# Upload other dependent files
files_resource_file_paths = []
for file in application.files:
files_resource_file_path = helpers.upload_file_to_container(
container_name=container_id,
application_name=application.name,
file_path=file,
blob_client=spark_client.blob_client,
use_full_path=False)
files_resource_file_paths.append(files_resource_file_path)
resource_files.append(files_resource_file_path)
# Upload application definition
application.application = os.path.basename(application.application)
application.jars = [os.path.basename(jar) for jar in application.jars]
application.py_files = [
os.path.basename(py_files) for py_files in application.py_files
]
application.files = [
os.path.basename(files) for files in application.files
]
application_definition_file = helpers.upload_text_to_container(
container_name=container_id,
application_name=application.name,
file_path='application.yaml',
content=yaml.dump(vars(application)),
blob_client=spark_client.blob_client)
resource_files.append(application_definition_file)
# create command to submit task
task_cmd = CommandBuilder('sudo docker exec')
task_cmd.add_argument('-i')
task_cmd.add_option(
'-e', 'AZ_BATCH_TASK_WORKING_DIR=$AZ_BATCH_TASK_WORKING_DIR')
task_cmd.add_option('-e',
'STORAGE_LOGS_CONTAINER={0}'.format(container_id))
task_cmd.add_argument('spark /bin/bash >> output.log 2>&1')
task_cmd.add_argument('-c "source ~/.bashrc; '\
'cd $AZ_BATCH_TASK_WORKING_DIR; ' \
'\$(pyenv root)/versions/\$AZTK_PYTHON_VERSION/bin/python ' \
'\$DOCKER_WORKING_DIR/aztk/node_scripts/submit.py"')
# Create task
task = batch_models.TaskAddParameter(
id=application.name,
command_line=helpers.wrap_commands_in_shell([task_cmd.to_str()]),
resource_files=resource_files,
constraints=batch_models.TaskConstraints(
max_task_retry_count=application.max_retry_count),
user_identity=batch_models.UserIdentity(
auto_user=batch_models.AutoUserSpecification(
scope=batch_models.AutoUserScope.task,
elevation_level=batch_models.ElevationLevel.admin)))
return task
def __app_submit_cmd(
name: str,
app: str,
app_args: List[str],
main_class: str,
jars: List[str],
py_files: List[str],
files: List[str],
driver_java_options: str,
driver_library_path: str,
driver_class_path: str,
driver_memory: str,
executor_memory: str,
driver_cores: int,
executor_cores: int,
):
spark_home = os.environ["SPARK_HOME"]
with open(os.path.join(spark_home, "conf", "master")) as f:
master_ip = f.read().rstrip()
# set file paths to correct path on container
files_path = os.environ["AZ_BATCH_TASK_WORKING_DIR"]
jars = [os.path.join(files_path, os.path.basename(jar)) for jar in jars]
py_files = [os.path.join(files_path, os.path.basename(py_file)) for py_file in py_files]
files = [os.path.join(files_path, os.path.basename(f)) for f in files]
# 2>&1 redirect stdout and stderr to be in the same file
spark_submit_cmd = CommandBuilder("{0}/bin/spark-submit".format(spark_home))
spark_submit_cmd.add_option("--master", "spark://{0}:7077".format(master_ip))
spark_submit_cmd.add_option("--name", name)
spark_submit_cmd.add_option("--class", main_class)
spark_submit_cmd.add_option("--jars", jars and ",".join(jars))
spark_submit_cmd.add_option("--py-files", py_files and ",".join(py_files))
spark_submit_cmd.add_option("--files", files and ",".join(files))
spark_submit_cmd.add_option("--driver-java-options", driver_java_options)
spark_submit_cmd.add_option("--driver-library-path", driver_library_path)
spark_submit_cmd.add_option("--driver-class-path", driver_class_path)
spark_submit_cmd.add_option("--driver-memory", driver_memory)
spark_submit_cmd.add_option("--executor-memory", executor_memory)
if driver_cores:
spark_submit_cmd.add_option("--driver-cores", str(driver_cores))
if executor_cores:
spark_submit_cmd.add_option("--executor-cores", str(executor_cores))
spark_submit_cmd.add_argument(
os.path.expandvars(app) + " " + " ".join(["'" + str(app_arg) + "'" for app_arg in (app_args or [])]))
with open("spark-submit.txt", mode="w", encoding="UTF-8") as stream:
stream.write(spark_submit_cmd.to_str())
return spark_submit_cmd
def __app_submit_cmd(name: str, app: str, app_args: List[str], main_class: str,
jars: List[str], py_files: List[str], files: List[str],
driver_java_options: str, driver_library_path: str,
driver_class_path: str, driver_memory: str,
executor_memory: str, driver_cores: int,
executor_cores: int):
cluster_id = os.environ['AZ_BATCH_POOL_ID']
spark_home = os.environ['SPARK_HOME']
with open(os.path.join(spark_home, 'conf', 'master')) as f:
master_ip = f.read().rstrip()
# set file paths to correct path on container
files_path = os.environ['AZ_BATCH_TASK_WORKING_DIR']
jars = [os.path.join(files_path, os.path.basename(jar)) for jar in jars]
py_files = [
os.path.join(files_path, os.path.basename(py_file))
for py_file in py_files
]
files = [os.path.join(files_path, os.path.basename(f)) for f in files]
# 2>&1 redirect stdout and stderr to be in the same file
spark_submit_cmd = CommandBuilder(
'{0}/bin/spark-submit'.format(spark_home))
spark_submit_cmd.add_option('--master',
'spark://{0}:7077'.format(master_ip))
spark_submit_cmd.add_option('--name', name)
spark_submit_cmd.add_option('--class', main_class)
spark_submit_cmd.add_option('--jars', jars and ','.join(jars))
spark_submit_cmd.add_option('--py-files', py_files and ','.join(py_files))
spark_submit_cmd.add_option('--files', files and ','.join(files))
spark_submit_cmd.add_option('--driver-java-options', driver_java_options)
spark_submit_cmd.add_option('--driver-library-path', driver_library_path)
spark_submit_cmd.add_option('--driver-class-path', driver_class_path)
spark_submit_cmd.add_option('--driver-memory', driver_memory)
spark_submit_cmd.add_option('--executor-memory', executor_memory)
if driver_cores:
spark_submit_cmd.add_option('--driver-cores', str(driver_cores))
if executor_cores:
spark_submit_cmd.add_option('--executor-cores', str(executor_cores))
spark_submit_cmd.add_argument(
os.path.expandvars(app) + ' ' +
' '.join(['\'' + str(app_arg) + '\'' for app_arg in (app_args or [])]))
with open("spark-submit.txt", mode="w", encoding="UTF-8") as stream:
stream.write(spark_submit_cmd.to_str())
return spark_submit_cmd
