def _wrapper_fun(iter):
for i in iter:
executor_num = i
tb_pid = 0
tb_hdfs_path = ''
hdfs_exec_logdir = ''
t = threading.Thread(target=devices.print_periodic_gpu_utilization)
if devices.get_num_gpus() > 0:
t.start()
global local_logdir_bool
try:
#Arguments
if args_dict:
argcount = six.get_function_code(map_fun).co_argcount
names = six.get_function_code(map_fun).co_varnames
args = []
argIndex = 0
param_string = ''
while argcount > 0:
#Get args for executor and run function
param_name = names[argIndex]
param_val = args_dict[param_name][executor_num]
param_string += str(param_name) + '=' + str(
param_val) + '.'
args.append(param_val)
argcount -= 1
argIndex += 1
param_string = param_string[:-1]
val = _get_metric(param_string, app_id, generation_id, run_id)
hdfs_exec_logdir, hdfs_appid_logdir = hopshdfs.create_directories(
app_id,
run_id,
param_string,
'differential_evolution',
sub_type='generation.' + str(generation_id))
pydoop.hdfs.dump('',
os.environ['EXEC_LOGFILE'],
user=hopshdfs.project_user())
hopshdfs.init_logger()
tb_hdfs_path, tb_pid = tensorboard.register(
hdfs_exec_logdir,
hdfs_appid_logdir,
executor_num,
local_logdir=local_logdir_bool)
gpu_str = '\nChecking for GPUs in the environment' + devices.get_gpu_info(
)
hopshdfs.log(gpu_str)
print(gpu_str)
print(
'-------------------------------------------------------')
print('Started running task ' + param_string + '\n')
if val:
print('Reading returned metric from previous run: ' +
str(val))
hopshdfs.log('Started running task ' + param_string)
task_start = datetime.datetime.now()
if not val:
val = map_fun(*args)
task_end = datetime.datetime.now()
time_str = 'Finished task ' + param_string + ' - took ' + util.time_diff(
task_start, task_end)
print('\n' + time_str)
hopshdfs.log(time_str)
try:
castval = int(val)
except:
raise ValueError(
'Your function needs to return a metric (number) which should be maximized or minimized'
)
metric_file = hdfs_exec_logdir + '/metric'
fs_handle = hopshdfs.get_fs()
try:
fd = fs_handle.open_file(metric_file, mode='w')
except:
fd = fs_handle.open_file(metric_file, flags='w')
fd.write(str(float(val)).encode())
fd.flush()
fd.close()
print('Returning metric ' + str(val))
print(
'-------------------------------------------------------')
except:
#Always do cleanup
if tb_hdfs_path:
_cleanup(tb_hdfs_path)
if devices.get_num_gpus() > 0:
t.do_run = False
t.join()
raise
finally:
if local_logdir_bool:
local_tb = tensorboard.local_logdir_path
util.store_local_tensorboard(local_tb, hdfs_exec_logdir)
hopshdfs.log('Finished running')
if tb_hdfs_path:
_cleanup(tb_hdfs_path)
if devices.get_num_gpus() > 0:
t.do_run = False
t.join()
def _wrapper_fun(iter):
for i in iter:
executor_num = i
tb_hdfs_path = ''
hdfs_exec_logdir = ''
t = threading.Thread(target=devices.print_periodic_gpu_utilization)
if devices.get_num_gpus() > 0:
t.start()
try:
#Arguments
if args_dict:
argcount = six.get_function_code(map_fun).co_argcount
names = six.get_function_code(map_fun).co_varnames
args = []
argIndex = 0
param_string = ''
while argcount > 0:
#Get args for executor and run function
param_name = names[argIndex]
param_val = args_dict[param_name][executor_num]
param_string += str(param_name) + '=' + str(
param_val) + '.'
args.append(param_val)
argcount -= 1
argIndex += 1
param_string = param_string[:-1]
hdfs_exec_logdir, hdfs_appid_logdir = hopshdfs.create_directories(
app_id, run_id, param_string, 'grid_search')
pydoop.hdfs.dump('',
os.environ['EXEC_LOGFILE'],
user=hopshdfs.project_user())
hopshdfs.init_logger()
tb_hdfs_path, tb_pid = tensorboard.register(
hdfs_exec_logdir,
hdfs_appid_logdir,
executor_num,
local_logdir=local_logdir)
gpu_str = '\nChecking for GPUs in the environment' + devices.get_gpu_info(
)
hopshdfs.log(gpu_str)
print(gpu_str)
print(
'-------------------------------------------------------')
print('Started running task ' + param_string + '\n')
hopshdfs.log('Started running task ' + param_string)
task_start = datetime.datetime.now()
retval = map_fun(*args)
task_end = datetime.datetime.now()
_handle_return(retval, hdfs_exec_logdir)
time_str = 'Finished task ' + param_string + ' - took ' + util.time_diff(
task_start, task_end)
print('\n' + time_str)
print(
'-------------------------------------------------------')
hopshdfs.log(time_str)
except:
#Always do cleanup
_cleanup(tb_hdfs_path)
if devices.get_num_gpus() > 0:
t.do_run = False
t.join()
raise
finally:
if local_logdir:
local_tb = tensorboard.local_logdir_path
util.store_local_tensorboard(local_tb, hdfs_exec_logdir)
_cleanup(tb_hdfs_path)
if devices.get_num_gpus() > 0:
t.do_run = False
t.join()
def _mapfn(iter):
# Note: consuming the input iterator helps Pyspark re-use this worker,
for i in iter:
executor_id = i
# assign TF job/task based on provided cluster_spec template (or use default/null values)
job_name = 'default'
task_index = -1
cluster_id = cluster_meta['id']
cluster_template = cluster_meta['cluster_template']
for jobtype in cluster_template:
nodes = cluster_template[jobtype]
if executor_id in nodes:
job_name = jobtype
task_index = nodes.index(executor_id)
break
# get unique key (hostname, executor_id) for this executor
host = util.get_ip_address()
util.write_executor_id(executor_id)
port = 0
# check for existing TFManagers
if TFSparkNode.mgr is not None and str(
TFSparkNode.mgr.get('state')) != "'stopped'":
if TFSparkNode.cluster_id == cluster_id:
# raise an exception to force Spark to retry this "reservation" task on another executor
raise Exception(
"TFManager already started on {0}, executor={1}, state={2}"
.format(host, executor_id,
str(TFSparkNode.mgr.get("state"))))
else:
# old state, just continue with creating new manager
logging.warn(
"Ignoring old TFManager with cluster_id {0}, requested cluster_id {1}"
.format(TFSparkNode.cluster_id, cluster_id))
gpu_present = gpu_info.detect_gpu_present()
client = reservation.Client(cluster_meta['server_addr'])
logging.info("TFSparkNode.run register: {0}".format(gpu_present))
client.register_gpu_presence(gpu_present)
gpus_are_present_on_executors = client.await_gpu_check()
logging.info("TFSparkNode.run await_gpu_check: {0}".format(
gpus_are_present_on_executors))
# check for existing TFManagers
if TFSparkNode.mgr is not None and str(
TFSparkNode.mgr.get('state')) != "'stopped'":
if TFSparkNode.cluster_id == cluster_id:
# raise an exception to force Spark to retry this "reservation" task on another executor
raise Exception(
"TFManager already started on {0}, state={1}".format(
host, str(TFSparkNode.mgr.get("state"))))
else:
# old state, just continue with creating new manager
logging.warn(
"Ignoring old TFManager with cluster_id {0}, requested cluster_id {1}"
.format(TFSparkNode.cluster_id, cluster_id))
# start a TFManager and get a free port
# use a random uuid as the authkey
authkey = uuid.uuid4().bytes
addr = None
if (gpus_are_present_on_executors):
#Valid PS, does not have GPUs, will be started as a PS
if job_name == 'ps' and gpu_present == False:
# PS nodes must be remotely accessible in order to shutdown from Spark driver.
TFSparkNode.mgr = TFManager.start(authkey,
['control', 'error'],
'remote')
addr = (host, TFSparkNode.mgr.address[1])
#Invalid worker, all workers should have GPUs, this one will assume role as PS
elif job_name == 'worker' and gpu_present == False:
# PS nodes must be remotely accessible in order to shutdown from Spark driver.
TFSparkNode.mgr = TFManager.start(authkey,
['control', 'error'],
'remote')
addr = (host, TFSparkNode.mgr.address[1])
#Correct worker
else:
# worker nodes only need to be locally accessible within the executor for data feeding
TFSparkNode.mgr = TFManager.start(authkey, queues)
addr = TFSparkNode.mgr.address
else:
if job_name == 'ps':
# PS nodes must be remotely accessible in order to shutdown from Spark driver.
TFSparkNode.mgr = TFManager.start(authkey,
['control', 'error'],
'remote')
addr = (host, TFSparkNode.mgr.address[1])
else:
# worker nodes only need to be locally accessible within the executor for data feeding
TFSparkNode.mgr = TFManager.start(authkey, queues)
addr = TFSparkNode.mgr.address
# initialize mgr state
TFSparkNode.mgr.set('state', 'running')
TFSparkNode.cluster_id = cluster_id
# expand Hadoop classpath wildcards for JNI (Spark 2.x)
if 'HADOOP_PREFIX' in os.environ:
classpath = os.environ['CLASSPATH']
hadoop_path = os.path.join(os.environ['HADOOP_PREFIX'], 'bin',
'hadoop')
hadoop_classpath = subprocess.check_output(
[hadoop_path, 'classpath', '--glob']).decode()
logging.debug("CLASSPATH: {0}".format(hadoop_classpath))
os.environ['CLASSPATH'] = classpath + os.pathsep + hadoop_classpath
# start TensorBoard if requested
tb_pid = 0
tb_port = 0
# check server to see if this task is being retried (i.e. already reserved)
client = reservation.Client(cluster_meta['server_addr'])
cluster_info = client.get_reservations()
tmp_sock = None
node_meta = None
for node in cluster_info:
(nhost, nexec) = (node['host'], node['executor_id'])
if nhost == host and nexec == executor_id:
node_meta = node
port = node['port']
# if not already done, register everything we need to set up the cluster
if node_meta is None:
# first, find a free port for TF
tmp_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tmp_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tmp_sock.bind(('', port))
port = tmp_sock.getsockname()[1]
node_meta = {
'executor_id': executor_id,
'host': host,
'job_name': job_name,
'task_index': task_index,
'port': port,
'tb_pid': tb_pid,
'tb_port': tb_port,
'addr': addr,
'authkey': authkey,
'gpu_present': gpu_present
}
# register node metadata with server
logging.info("TFSparkNode.run register: {0}".format(node_meta))
client.register(node_meta)
# wait for other nodes to finish reservations
cluster_info = client.await_reservations()
logging.info(
"TFSparkNode.run await_reservations: {0}".format(cluster_info))
client.close()
# construct a TensorFlow clusterspec from cluster_info
sorted_cluster_info = sorted(cluster_info,
key=lambda k: k['executor_id'])
spec = {}
last_executor_id = -1
for node in sorted_cluster_info:
if (node['executor_id'] == last_executor_id):
raise Exception("Duplicate worker/task in cluster_info")
last_executor_id = node['executor_id']
logging.info("node: {0}".format(node))
(njob, nhost, nport) = (node['job_name'], node['host'],
node['port'])
hosts = [] if njob not in spec else spec[njob]
hosts.append("{0}:{1}".format(nhost, nport))
spec[njob] = hosts
for node in cluster_info:
if ((node_meta['host'] == node['host'])
and (node_meta['authkey'] == node['authkey'])):
job_name = node['job_name']
task_index = node['task_index']
executor_id = node['executor_id']
break
hdfs_exec_logdir = ''
if gpus_are_present_on_executors and gpu_present and job_name == 'worker' and task_index == 0:
# When running with GPUs
hdfs_exec_logdir, hdfs_appid_logdir = hdfs.create_directories(
app_id, run_id, None, 'tensorflowonspark')
tb_proc = tensorboard.register(hdfs_exec_logdir,
hdfs_appid_logdir,
0,
local_logdir=local_logdir)
elif not gpus_are_present_on_executors and job_name == 'worker' and task_index == 0:
# When running with no GPUs
hdfs_exec_logdir, hdfs_appid_logdir = hdfs.create_directories(
app_id, run_id, None, 'tensorflowonspark')
tb_proc = tensorboard.register(hdfs_exec_logdir,
hdfs_appid_logdir,
0,
local_logdir=local_logdir)
# construct a TensorFlow clusterspec from cluster_info
sorted_cluster_info = sorted(cluster_info,
key=lambda k: k['executor_id'])
spec = {}
for node in sorted_cluster_info:
logging.info("node: {0}".format(node))
(njob, nhost, nport) = (node['job_name'], node['host'],
node['port'])
hosts = [] if njob not in spec else spec[njob]
hosts.append("{0}:{1}".format(nhost, nport))
spec[njob] = hosts
# update TF_CONFIG and reserve GPU for tf.estimator based code
# Note: this will execute but be ignored by non-tf.estimator code
tf_config = json.dumps({
'cluster': spec,
'task': {
'type': job_name,
'index': task_index
},
'environment': 'cloud'
})
os.environ['TF_CONFIG'] = tf_config
# create a context object to hold metadata for TF
ctx = TFNodeContext(executor_id, job_name, task_index, spec,
cluster_meta['default_fs'],
cluster_meta['working_dir'], TFSparkNode.mgr)
# release port reserved for TF as late as possible
if tmp_sock is not None:
tmp_sock.close()
# Background mode relies reuse of python worker in Spark.
if background:
# However, reuse of python worker can't work on Windows, we need to check if the current
# script runs on Windows or not.
if os.name == 'nt' or platform.system() == 'Windows':
raise Exception("Background mode is not supported on Windows.")
# Check if the config of reuse python worker is enabled on Spark.
if not os.environ.get("SPARK_REUSE_WORKER"):
raise Exception(
"Background mode relies reuse of python worker on Spark. This config 'spark.python.worker.reuse' is not enabled on Spark. Please enable it before using background."
)
def wrapper_fn(args, context):
"""Wrapper function that sets the sys.argv of the executor."""
if isinstance(args, list):
sys.argv = args
fn(args, context)
def wrapper_fn_background(args, context):
"""Wrapper function that signals exceptions to foreground process."""
errq = TFSparkNode.mgr.get_queue('error')
try:
wrapper_fn(args, context)
except Exception:
errq.put(traceback.format_exc())
errq.join()
if job_name == 'ps' or background:
# invoke the TensorFlow main function in a background thread
logging.info(
"Starting TensorFlow {0}:{1} as {2} on cluster node {3} on background process"
.format(job_name, task_index, job_name, executor_id))
p = multiprocessing.Process(target=wrapper_fn_background,
args=(tf_args, ctx))
if job_name == 'ps':
p.daemon = True
p.start()
# for ps nodes only, wait indefinitely in foreground thread for a "control" event (None == "stop")
if job_name == 'ps':
queue = TFSparkNode.mgr.get_queue('control')
equeue = TFSparkNode.mgr.get_queue('error')
done = False
while not done:
while (queue.empty() and equeue.empty()):
time.sleep(1)
if (not equeue.empty()):
e_str = equeue.get()
equeue.task_done()
raise Exception("exception in ps:\n" + e_str)
msg = queue.get(block=True)
logging.info("Got msg: {0}".format(msg))
if msg == None:
logging.info("Terminating PS")
TFSparkNode.mgr.set('state', 'stopped')
done = True
queue.task_done()
else:
t = threading.Thread(target=devices.print_periodic_gpu_utilization)
if devices.get_num_gpus() > 0:
t.start()
# otherwise, just run TF function in the main executor/worker thread
logging.info(
"Starting TensorFlow {0}:{1} on cluster node {2} on foreground thread"
.format(job_name, task_index, executor_id))
try:
wrapper_fn(tf_args, ctx)
except:
raise
finally:
if local_logdir:
if gpus_are_present_on_executors and gpu_present and job_name == 'worker' and task_index == 0:
# When running with GPUs
local_tb = tensorboard.local_logdir_path
hopsutil.store_local_tensorboard(
local_tb, hdfs_exec_logdir)
elif not gpus_are_present_on_executors and job_name == 'worker' and task_index == 0:
# When running with no GPUs
local_tb = tensorboard.local_logdir_path
hopsutil.store_local_tensorboard(
local_tb, hdfs_exec_logdir)
if devices.get_num_gpus() > 0:
t.do_run = False
t.join()
logging.info(
"Finished TensorFlow {0}:{1} on cluster node {2}".format(
job_name, task_index, executor_id))
def _wrapper_fun(iter):
for i in iter:
executor_num = i
client = coordination_server.Client(server_addr)
node_meta = {
'host': get_ip_address(),
'executor_cwd': os.getcwd(),
'cuda_visible_devices_ordinals':
devices.get_minor_gpu_device_numbers()
}
client.register(node_meta)
t_gpus = threading.Thread(
target=devices.print_periodic_gpu_utilization)
if devices.get_num_gpus() > 0:
t_gpus.start()
# Only spark executor with index 0 should create necessary HDFS directories and start mpirun
# Other executors simply block until index 0 reports mpirun is finished
clusterspec = client.await_reservations()
#pydoop.hdfs.dump('', os.environ['EXEC_LOGFILE'], user=hopshdfs.project_user())
#hopshdfs.init_logger()
#hopshdfs.log('Starting Spark executor with arguments')
gpu_str = '\n\nChecking for GPUs in the environment\n' + devices.get_gpu_info(
)
#hopshdfs.log(gpu_str)
print(gpu_str)
mpi_logfile_path = os.getcwd() + '/mpirun.log'
if os.path.exists(mpi_logfile_path):
os.remove(mpi_logfile_path)
mpi_logfile = open(mpi_logfile_path, 'w')
py_runnable = localize_scripts(nb_path, clusterspec)
# non-chief executor should not do mpirun
if not executor_num == 0:
client.await_mpirun_finished()
else:
hdfs_exec_logdir, hdfs_appid_logdir = hopshdfs.create_directories(
app_id, run_id, param_string='Horovod')
tb_hdfs_path, tb_pid = tensorboard.register(
hdfs_exec_logdir, hdfs_appid_logdir, 0)
mpi_cmd = 'HOROVOD_TIMELINE=' + tensorboard.logdir() + '/timeline.json' + \
' TENSORBOARD_LOGDIR=' + tensorboard.logdir() + \
' mpirun -np ' + str(get_num_ps(clusterspec)) + ' --hostfile ' + get_hosts_file(clusterspec) + \
' -bind-to none -map-by slot ' + \
' -x LD_LIBRARY_PATH ' + \
' -x HOROVOD_TIMELINE ' + \
' -x TENSORBOARD_LOGDIR ' + \
' -x NCCL_DEBUG=INFO ' + \
' -mca pml ob1 -mca btl ^openib ' + \
os.environ['PYSPARK_PYTHON'] + ' ' + py_runnable
mpi = subprocess.Popen(mpi_cmd,
shell=True,
stdout=mpi_logfile,
stderr=mpi_logfile,
preexec_fn=util.on_executor_exit('SIGTERM'))
t_log = threading.Thread(target=print_log)
t_log.start()
mpi.wait()
client.register_mpirun_finished()
if devices.get_num_gpus() > 0:
t_gpus.do_run = False
t_gpus.join()
return_code = mpi.returncode
if return_code != 0:
cleanup(tb_hdfs_path)
t_log.do_run = False
t_log.join()
raise Exception(
'mpirun FAILED, look in the logs for the error')
cleanup(tb_hdfs_path)
t_log.do_run = False
t_log.join()
def _wrapper_fun(iter):
for i in iter:
executor_num = i
hdfs_exec_logdir, hdfs_appid_logdir = hopshdfs.create_directories(
app_id, run_id, None, 'horovod')
tb_pid = 0
tb_hdfs_path = ''
pydoop.hdfs.dump('',
os.environ['EXEC_LOGFILE'],
user=hopshdfs.project_user())
hopshdfs.init_logger()
hopshdfs.log('Starting Spark executor with arguments')
if executor_num == 0:
tb_hdfs_path, tb_pid = tensorboard.register(
hdfs_exec_logdir,
hdfs_appid_logdir,
0,
local_logdir=local_logdir)
gpu_str = '\n\nChecking for GPUs in the environment\n' + devices.get_gpu_info(
)
hopshdfs.log(gpu_str)
print(gpu_str)
#1. Download notebook file
fs_handle = hopshdfs.get_fs()
try:
fd = fs_handle.open_file(nb_path, flags='r')
except:
fd = fs_handle.open_file(nb_path, mode='r')
notebook = ''
for line in fd:
notebook += line
path, filename = os.path.split(nb_path)
f_nb = open(filename, "w+")
f_nb.write(notebook)
f_nb.flush()
f_nb.close()
# 2. Convert notebook to py file
jupyter_runnable = os.path.abspath(
os.path.join(os.environ['PYSPARK_PYTHON'], os.pardir)) + '/jupyter'
conversion_cmd = jupyter_runnable + ' nbconvert --to python ' + filename
conversion = subprocess.Popen(conversion_cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
conversion.wait()
stdout, stderr = conversion.communicate()
print(stdout)
print(stderr)
# 3. Make py file runnable
py_runnable = os.getcwd() + '/' + filename.split('.')[0] + '.py'
st = os.stat(py_runnable)
os.chmod(py_runnable, st.st_mode | stat.S_IEXEC)
t_gpus = threading.Thread(
target=devices.print_periodic_gpu_utilization)
if devices.get_num_gpus() > 0:
t_gpus.start()
mpi_logfile_path = os.getcwd() + '/mpirun.log'
if os.path.exists(mpi_logfile_path):
os.remove(mpi_logfile_path)
mpi_logfile = open(mpi_logfile_path, 'w')
# 4. Run allreduce
mpi_np = os.environ['MPI_NP']
mpi_cmd = 'HOROVOD_TIMELINE=' + tensorboard.logdir() + '/timeline.json' + \
' TENSORBOARD_LOGDIR=' + tensorboard.logdir() + \
' mpirun -np ' + str(mpi_np) + \
' -bind-to none -map-by slot ' + \
' -x HOROVOD_TIMELINE ' + \
' -x TENSORBOARD_LOGDIR ' + \
' -x NCCL_DEBUG=INFO ' + \
os.environ['PYSPARK_PYTHON'] + ' ' + py_runnable
mpi = subprocess.Popen(mpi_cmd,
shell=True,
stdout=mpi_logfile,
stderr=mpi_logfile,
preexec_fn=util.on_executor_exit('SIGTERM'))
t_log = threading.Thread(target=print_log)
t_log.start()
mpi.wait()
if devices.get_num_gpus() > 0:
t_gpus.do_run = False
t_gpus.join()
return_code = mpi.returncode
if local_logdir:
local_tb = tensorboard.local_logdir_path
pydoop.hdfs.put(local_tb, hdfs_exec_logdir)
if return_code != 0:
cleanup(tb_hdfs_path)
t_log.do_run = False
t_log.join()
raise Exception('mpirun FAILED, look in the logs for the error')
cleanup(tb_hdfs_path)
t_log.do_run = False
t_log.join()
hopshdfs.kill_logger()
def begin(spark,
name='no-name',
local_logdir=False,
versioned_resources=None,
description=None):
""" Start an experiment
Args:
:spark_session: SparkSession object
:name: (optional) name of the job
"""
global running
if running:
raise RuntimeError(
"An experiment is currently running. Please call experiment.stop() to stop it."
)
try:
global app_id
global experiment_json
global elastic_id
global run_id
global driver_tensorboard_hdfs_path
running = True
sc = spark.sparkContext
app_id = str(sc.applicationId)
run_id = run_id + 1
versioned_path = util.version_resources(versioned_resources,
get_logdir(app_id))
experiment_json = None
experiment_json = util.populate_experiment(sc, name,
'experiment', 'begin',
get_logdir(app_id), None,
versioned_path, description)
util.version_resources(versioned_resources, get_logdir(app_id))
util.put_elastic(hopshdfs.project_name(), app_id, elastic_id,
experiment_json)
hdfs_exec_logdir, hdfs_appid_logdir = hopshdfs.create_directories(
app_id, run_id, None, 'begin')
pydoop.hdfs.dump('',
os.environ['EXEC_LOGFILE'],
user=hopshdfs.project_user())
hopshdfs.init_logger()
driver_tensorboard_hdfs_path, _ = tensorboard.register(
hdfs_exec_logdir,
hdfs_appid_logdir,
0,
local_logdir=local_logdir,
tensorboard_driver=True)
except:
exception_handler()
raise
return