This lab/project shows:
- how to run distributed training (MNIST Data) on MiniKF (CPU) with Tensorflow.
- all files used in this LAB: https://github.com/omerbsezer/Fast-Kubeflow/tree/main/Project_Distributed_Training_Tensorflow
- other examples with PyTorch, Tensorflow, MXNet, XGBoost (some of them requires GPU):
- You should have Kubeflow Environment (Easiest Way: Using MiniKF)
- Create "dist-mnist.py" file and copy followings into it:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import math
import os
import sys
import tempfile
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
flags = tf.app.flags
flags.DEFINE_string("data_dir", "/tmp/mnist-data",
"Directory for storing mnist data")
flags.DEFINE_boolean("download_only", False,
"Only perform downloading of data; Do not proceed to "
"session preparation, model definition or training")
flags.DEFINE_integer("task_index", None,
"Worker task index, should be >= 0. task_index=0 is "
"the master worker task the performs the variable "
"initialization ")
flags.DEFINE_integer("num_gpus", 1, "Total number of gpus for each machine."
"If you don't use GPU, please set it to '0'")
flags.DEFINE_integer("replicas_to_aggregate", None,
"Number of replicas to aggregate before parameter update"
"is applied (For sync_replicas mode only; default: "
"num_workers)")
flags.DEFINE_integer("hidden_units", 100,
"Number of units in the hidden layer of the NN")
flags.DEFINE_integer("train_steps", 20000,
"Number of (global) training steps to perform")
flags.DEFINE_integer("batch_size", 100, "Training batch size")
flags.DEFINE_float("learning_rate", 0.01, "Learning rate")
flags.DEFINE_boolean(
"sync_replicas", False,
"Use the sync_replicas (synchronized replicas) mode, "
"wherein the parameter updates from workers are aggregated "
"before applied to avoid stale gradients")
flags.DEFINE_boolean(
"existing_servers", False, "Whether servers already exists. If True, "
"will use the worker hosts via their GRPC URLs (one client process "
"per worker host). Otherwise, will create an in-process TensorFlow "
"server.")
flags.DEFINE_string("ps_hosts", "localhost:2222",
"Comma-separated list of hostname:port pairs")
flags.DEFINE_string("worker_hosts", "localhost:2223,localhost:2224",
"Comma-separated list of hostname:port pairs")
flags.DEFINE_string("job_name", None, "job name: worker or ps")
FLAGS = flags.FLAGS
IMAGE_PIXELS = 28
# Example:
# cluster = {'ps': ['host1:2222', 'host2:2222'],
# 'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
# os.environ['TF_CONFIG'] = json.dumps(
# {'cluster': cluster,
# 'task': {'type': 'worker', 'index': 1}})
def main(unused_argv):
# Parse environment variable TF_CONFIG to get job_name and task_index
# If not explicitly specified in the constructor and the TF_CONFIG
# environment variable is present, load cluster_spec from TF_CONFIG.
tf_config = json.loads(os.environ.get('TF_CONFIG') or '{}')
task_config = tf_config.get('task', {})
task_type = task_config.get('type')
task_index = task_config.get('index')
FLAGS.job_name = task_type
FLAGS.task_index = task_index
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
if FLAGS.download_only:
sys.exit(0)
if FLAGS.job_name is None or FLAGS.job_name == "":
raise ValueError("Must specify an explicit `job_name`")
if FLAGS.task_index is None or FLAGS.task_index == "":
raise ValueError("Must specify an explicit `task_index`")
print("job name = %s" % FLAGS.job_name)
print("task index = %d" % FLAGS.task_index)
cluster_config = tf_config.get('cluster', {})
ps_hosts = cluster_config.get('ps')
worker_hosts = cluster_config.get('worker')
ps_hosts_str = ','.join(ps_hosts)
worker_hosts_str = ','.join(worker_hosts)
FLAGS.ps_hosts = ps_hosts_str
FLAGS.worker_hosts = worker_hosts_str
# Construct the cluster and start the server
ps_spec = FLAGS.ps_hosts.split(",")
worker_spec = FLAGS.worker_hosts.split(",")
# Get the number of workers.
num_workers = len(worker_spec)
cluster = tf.train.ClusterSpec({"ps": ps_spec, "worker": worker_spec})
if not FLAGS.existing_servers:
# Not using existing servers. Create an in-process server.
server = tf.train.Server(
cluster, job_name=FLAGS.job_name, task_index=FLAGS.task_index)
if FLAGS.job_name == "ps":
server.join()
is_chief = (FLAGS.task_index == 0)
if FLAGS.num_gpus > 0:
# Avoid gpu allocation conflict: now allocate task_num -> #gpu
# for each worker in the corresponding machine
gpu = (FLAGS.task_index % FLAGS.num_gpus)
worker_device = "/job:worker/task:%d/gpu:%d" % (FLAGS.task_index, gpu)
elif FLAGS.num_gpus == 0:
# Just allocate the CPU to worker server
cpu = 0
worker_device = "/job:worker/task:%d/cpu:%d" % (FLAGS.task_index, cpu)
# The device setter will automatically place Variables ops on separate
# parameter servers (ps). The non-Variable ops will be placed on the workers.
# The ps use CPU and workers use corresponding GPU
with tf.device(
tf.train.replica_device_setter(
worker_device=worker_device,
ps_device="/job:ps/cpu:0",
cluster=cluster)):
global_step = tf.Variable(0, name="global_step", trainable=False)
# Variables of the hidden layer
hid_w = tf.Variable(
tf.truncated_normal(
[IMAGE_PIXELS * IMAGE_PIXELS, FLAGS.hidden_units],
stddev=1.0 / IMAGE_PIXELS),
name="hid_w")
hid_b = tf.Variable(tf.zeros([FLAGS.hidden_units]), name="hid_b")
# Variables of the softmax layer
sm_w = tf.Variable(
tf.truncated_normal(
[FLAGS.hidden_units, 10],
stddev=1.0 / math.sqrt(FLAGS.hidden_units)),
name="sm_w")
sm_b = tf.Variable(tf.zeros([10]), name="sm_b")
# Ops: located on the worker specified with FLAGS.task_index
x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS * IMAGE_PIXELS])
y_ = tf.placeholder(tf.float32, [None, 10])
hid_lin = tf.nn.xw_plus_b(x, hid_w, hid_b)
hid = tf.nn.relu(hid_lin)
y = tf.nn.softmax(tf.nn.xw_plus_b(hid, sm_w, sm_b))
cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y, 1e-10, 1.0)))
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
if FLAGS.sync_replicas:
if FLAGS.replicas_to_aggregate is None:
replicas_to_aggregate = num_workers
else:
replicas_to_aggregate = FLAGS.replicas_to_aggregate
opt = tf.train.SyncReplicasOptimizer(
opt,
replicas_to_aggregate=replicas_to_aggregate,
total_num_replicas=num_workers,
name="mnist_sync_replicas")
train_step = opt.minimize(cross_entropy, global_step=global_step)
if FLAGS.sync_replicas:
local_init_op = opt.local_step_init_op
if is_chief:
local_init_op = opt.chief_init_op
ready_for_local_init_op = opt.ready_for_local_init_op
# Initial token and chief queue runners required by the sync_replicas mode
chief_queue_runner = opt.get_chief_queue_runner()
sync_init_op = opt.get_init_tokens_op()
init_op = tf.global_variables_initializer()
train_dir = tempfile.mkdtemp()
if FLAGS.sync_replicas:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
local_init_op=local_init_op,
ready_for_local_init_op=ready_for_local_init_op,
recovery_wait_secs=1,
global_step=global_step)
else:
sv = tf.train.Supervisor(
is_chief=is_chief,
logdir=train_dir,
init_op=init_op,
recovery_wait_secs=1,
global_step=global_step)
sess_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
device_filters=["/job:ps",
"/job:worker/task:%d" % FLAGS.task_index])
# The chief worker (task_index==0) session will prepare the session,
# while the remaining workers will wait for the preparation to complete.
if is_chief:
print("Worker %d: Initializing session..." % FLAGS.task_index)
else:
print("Worker %d: Waiting for session to be initialized..." %
FLAGS.task_index)
if FLAGS.existing_servers:
server_grpc_url = "grpc://" + worker_spec[FLAGS.task_index]
print("Using existing server at: %s" % server_grpc_url)
sess = sv.prepare_or_wait_for_session(server_grpc_url, config=sess_config)
else:
sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
print("Worker %d: Session initialization complete." % FLAGS.task_index)
if FLAGS.sync_replicas and is_chief:
# Chief worker will start the chief queue runner and call the init op.
sess.run(sync_init_op)
sv.start_queue_runners(sess, [chief_queue_runner])
# Perform training
time_begin = time.time()
print("Training begins @ %f" % time_begin)
local_step = 0
while True:
# Training feed
batch_xs, batch_ys = mnist.train.next_batch(FLAGS.batch_size)
train_feed = {x: batch_xs, y_: batch_ys}
_, step = sess.run([train_step, global_step], feed_dict=train_feed)
local_step += 1
now = time.time()
print("%f: Worker %d: training step %d done (global step: %d)" %
(now, FLAGS.task_index, local_step, step))
if step >= FLAGS.train_steps:
break
time_end = time.time()
print("Training ends @ %f" % time_end)
training_time = time_end - time_begin
print("Training elapsed time: %f s" % training_time)
# Validation feed
val_feed = {x: mnist.validation.images, y_: mnist.validation.labels}
val_xent = sess.run(cross_entropy, feed_dict=val_feed)
print("After %d training step(s), validation cross entropy = %g" %
(FLAGS.train_steps, val_xent))
if __name__ == "__main__":
tf.app.run()
- Create Dockerfile and copy followings into it:
FROM tensorflow/tensorflow:1.5.0
ADD . /var/tf_dist_mnist
ENTRYPOINT ["python", "/var/tf_dist_mnist/dist_mnist.py"]
- Create docker image and push it to DockerHub:
docker build -t {DockerUsername}/tf-dist-mnist-test:1.0 .
e.g. docker build -t omerbsezer/tf-dist-mnist-test:1.0 .
docker push {DockerUsername}/tf-dist-mnist-test:1.0
e.g. docker push omerbsezer/tf-dist-mnist-test:1.0
- Create "tf_job_mnist.yaml" and copy the followings (you can increase the number of worker if you run on the real cluster, have more resources (CPU, RAM))
apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
name: dist-mnist
spec:
tfReplicaSpecs:
PS:
replicas: 1
restartPolicy: Never
template:
spec:
containers:
- name: tensorflow
image: omerbsezer/tf-dist-mnist-test:1.0
Worker:
replicas: 1
restartPolicy: Never
template:
spec:
containers:
- name: tensorflow
image: omerbsezer/tf-dist-mnist-test:1.0
- Run containers on K8s cluster:
kubectl apply -f tf_job_mnist.yaml
-
It creates TFJob and containers (PS: parameter_service container and Worker):
-
After training, worker node is not deleted because of the TFJob:
-
After training, logs can be viewed with following command:
kubectl logs dist-mnist-worker-0
