def _mapfn(iter):
import pyspark
# Note: consuming the input iterator helps Pyspark re-use this worker,
for i in iter:
executor_id = i
# check that there are enough available GPUs (if using tensorflow-gpu) before committing reservation on this node
# note: for Spark 3+ w/ GPU allocation, the required number of GPUs should be guaranteed by the resource manager
if version.parse(pyspark.__version__).base_version < version.parse(
'3.0.0').base_version:
if gpu_info.is_gpu_available():
num_gpus = tf_args.num_gpus if 'num_gpus' in tf_args else 1
gpus_to_use = gpu_info.get_gpus(num_gpus)
# 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
logger.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 job_name in ('ps', 'evaluator'):
# 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()
logger.debug("CLASSPATH: {0}".format(hadoop_classpath))
os.environ['CLASSPATH'] = classpath + os.pathsep + hadoop_classpath
# start TensorBoard if requested, on 'worker:0' if available (for backwards-compatibility), otherwise on 'chief:0' or 'master:0'
job_names = sorted([
k for k in cluster_template.keys()
if k in ['chief', 'master', 'worker']
])
tb_job_name = 'worker' if 'worker' in job_names else job_names[0]
tb_pid = 0
tb_port = 0
if tensorboard and job_name == tb_job_name and task_index == 0:
tb_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
tb_sock.bind(('', 0))
tb_port = tb_sock.getsockname()[1]
tb_sock.close()
logdir = log_dir if log_dir else "tensorboard_%d" % executor_id
# search for tensorboard in python/bin, PATH, and PYTHONPATH
pypath = sys.executable
pydir = os.path.dirname(pypath)
sys_path = os.pathsep.join(sys.path)
search_path = os.pathsep.join([
pydir, sys_path, os.environ['PATH'], os.environ['PYTHONPATH']
])
tb_path = util.find_in_path(search_path,
'tensorboard') # executable in PATH
if not tb_path:
tb_path = util.find_in_path(search_path,
'tensorboard/main.py') # TF 1.3+
if not tb_path:
tb_path = util.find_in_path(
search_path,
'tensorflow/tensorboard/__main__.py') # TF 1.2-
if not tb_path:
raise Exception(
"Unable to find 'tensorboard' in: {}".format(search_path))
# launch tensorboard
if version.parse(TF_VERSION) >= version.parse('2.0.0'):
tb_proc = subprocess.Popen([
pypath, tb_path, "--reload_multifile=True",
"--logdir=%s" % logdir,
"--port=%d" % tb_port
],
env=os.environ)
else:
tb_proc = subprocess.Popen([
pypath, tb_path,
"--logdir=%s" % logdir,
"--port=%d" % tb_port
],
env=os.environ)
tb_pid = tb_proc.pid
# 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
}
# register node metadata with server
logger.info("TFSparkNode.reserve: {0}".format(node_meta))
client.register(node_meta)
# wait for other nodes to finish reservations
cluster_info = client.await_reservations()
client.close()
# construct a TensorFlow clusterspec from cluster_info
sorted_cluster_info = sorted(cluster_info,
key=lambda k: k['executor_id'])
cluster_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']
logger.info("node: {0}".format(node))
(njob, nhost, nport) = (node['job_name'], node['host'],
node['port'])
hosts = [] if njob not in cluster_spec else cluster_spec[njob]
hosts.append("{0}:{1}".format(nhost, nport))
cluster_spec[njob] = hosts
# update TF_CONFIG if cluster spec has a 'master' node (i.e. tf.estimator)
if 'master' in cluster_spec or 'chief' in cluster_spec:
tf_config = json.dumps({
'cluster': cluster_spec,
'task': {
'type': job_name,
'index': task_index
},
'environment': 'cloud'
})
logger.info("export TF_CONFIG: {}".format(tf_config))
os.environ['TF_CONFIG'] = tf_config
# reserve GPU(s) again, just before launching TF process (in case situation has changed)
# and setup CUDA_VISIBLE_DEVICES accordingly
if gpu_info.is_gpu_available():
gpus_to_use = None
# For Spark 3+, try to get GPU resources from TaskContext first
if version.parse(
pyspark.__version__).base_version >= version.parse(
"3.0.0").base_version:
from pyspark import TaskContext
context = TaskContext()
if 'gpu' in context.resources():
# use ALL GPUs assigned by resource manager
gpus = context.resources()['gpu'].addresses
num_gpus = len(gpus)
gpus_to_use = ','.join(gpus)
if not gpus_to_use:
# compute my index relative to other nodes on the same host (for GPU allocation)
my_addr = cluster_spec[job_name][task_index]
my_host = my_addr.split(':')[0]
flattened = [
v for sublist in cluster_spec.values() for v in sublist
]
local_peers = [p for p in flattened if p.startswith(my_host)]
my_index = local_peers.index(my_addr)
# default to one GPU if not specified explicitly
num_gpus = tf_args.num_gpus if 'num_gpus' in tf_args else 1
gpus_to_use = gpu_info.get_gpus(num_gpus, my_index)
gpu_str = "GPUs" if num_gpus > 1 else "GPU"
logger.info(
"Requested {} {}, setting CUDA_VISIBLE_DEVICES={}".format(
num_gpus, gpu_str, gpus_to_use))
os.environ['CUDA_VISIBLE_DEVICES'] = gpus_to_use
# create a context object to hold metadata for TF
ctx = TFNodeContext(executor_id, job_name, task_index, cluster_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())
if job_name in ('ps', 'evaluator') or background:
# invoke the TensorFlow main function in a background thread
logger.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 in ('ps', 'evaluator'):
p.daemon = True
p.start()
# for ps and evaluator nodes, wait indefinitely in foreground thread for a "control" event (None == "stop")
if job_name in ('ps', 'evaluator'):
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()
raise Exception("Exception in " + job_name + ":\n" +
e_str)
msg = queue.get(block=True)
logger.info("Got msg: {0}".format(msg))
if msg is None:
logger.info("Terminating {}".format(job_name))
TFSparkNode.mgr.set('state', 'stopped')
done = True
queue.task_done()
else:
# otherwise, just run TF function in the main executor/worker thread
logger.info(
"Starting TensorFlow {0}:{1} on cluster node {2} on foreground thread"
.format(job_name, task_index, executor_id))
wrapper_fn(tf_args, ctx)
logger.info(
"Finished TensorFlow {0}:{1} on cluster node {2}".format(
job_name, task_index, executor_id))
def _get_gpus(cluster_spec=None):
gpus = []
is_k8s = 'SPARK_EXECUTOR_POD_IP' in os.environ
# handle explicitly configured tf_args.num_gpus
if 'num_gpus' in tf_args:
requested_gpus = tf_args.num_gpus
user_requested = True
else:
requested_gpus = 0
user_requested = False
# first, try Spark 3 resources API, returning all visible GPUs
# note: num_gpus arg is only used (if supplied) to limit/truncate visible devices
if _has_spark_resource_api():
from pyspark import TaskContext
context = TaskContext()
resources = context.resources()
if resources and 'gpu' in resources:
# get all GPUs assigned by resource manager
gpus = context.resources()['gpu'].addresses
logger.info("Spark gpu resources: {}".format(gpus))
if user_requested:
if requested_gpus < len(gpus):
# override/truncate list, if explicitly configured
logger.warn(
"Requested {} GPU(s), but {} available".format(
requested_gpus, len(gpus)))
gpus = gpus[:requested_gpus]
else:
# implicitly requested by Spark 3
requested_gpus = len(gpus)
# if not in K8s pod and GPUs available, just use original allocation code (defaulting to 1 GPU if available)
# Note: for K8s, there is a bug with the Nvidia device_plugin which can show GPUs for non-GPU pods
# that are hosted on GPU nodes
if not is_k8s and gpu_info.is_gpu_available() and not gpus:
# default to one GPU if not specified explicitly
requested_gpus = max(
1,
requested_gpus) if not user_requested else requested_gpus
if requested_gpus > 0:
if cluster_spec:
# compute my index relative to other nodes on the same host (for GPU allocation)
my_addr = cluster_spec[job_name][task_index]
my_host = my_addr.split(':')[0]
flattened = [
v for sublist in cluster_spec.values()
for v in sublist
]
local_peers = [
p for p in flattened if p.startswith(my_host)
]
my_index = local_peers.index(my_addr)
else:
my_index = 0
# try to allocate a GPU
gpus = gpu_info.get_gpus(requested_gpus,
my_index,
format=gpu_info.AS_LIST)
if user_requested and len(gpus) < requested_gpus:
raise Exception(
"Unable to allocate {} GPU(s) from available GPUs: {}".
format(requested_gpus, gpus))
gpus_to_use = ','.join(gpus)
if gpus:
logger.info(
"Requested {} GPU(s), setting CUDA_VISIBLE_DEVICES={}".
format(requested_gpus if user_requested else len(gpus),
gpus_to_use))
os.environ['CUDA_VISIBLE_DEVICES'] = gpus_to_use
