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Multi-worker training with Estimator

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Overview

This tutorial demonstrates how tf.distribute.Strategy can be used for distributed multi-worker training with tf.estimator. If you write your code using tf.estimator, and you're interested in scaling beyond a single machine with high performance, this tutorial is for you.

Before getting started, please read the distribution strategy guide. The multi-GPU training tutorial is also relevant, because this tutorial uses the same model.

Setup

First, setup TensorFlow and the necessary imports.

import tensorflow_datasets as tfds
import tensorflow as tf
tfds.disable_progress_bar()

import os, json

Input function

This tutorial uses the MNIST dataset from TensorFlow Datasets. The code here is similar to the multi-GPU training tutorial with one key difference: when using Estimator for multi-worker training, it is necessary to shard the dataset by the number of workers to ensure model convergence. The input data is sharded by worker index, so that each worker processes 1/num_workers distinct portions of the dataset.

BUFFER_SIZE = 10000
BATCH_SIZE = 64

def input_fn(mode, input_context=None):
  datasets, info = tfds.load(name='mnist',
                                with_info=True,
                                as_supervised=True)
  mnist_dataset = (datasets['train'] if mode == tf.estimator.ModeKeys.TRAIN else
                   datasets['test'])

  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255
    return image, label

  if input_context:
    mnist_dataset = mnist_dataset.shard(input_context.num_input_pipelines,
                                        input_context.input_pipeline_id)
  return mnist_dataset.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)

Another reasonable approach to achieve convergence would be to shuffle the dataset with distinct seeds at each worker.

Multi-worker configuration

One of the key differences in this tutorial (compared to the multi-GPU training tutorial) is the multi-worker setup. The TF_CONFIG environment variable is the standard way to specify the cluster configuration to each worker that is part of the cluster.

There are two components of TF_CONFIG: cluster and task. cluster provides information about the entire cluster, namely the workers and parameter servers in the cluster. task provides information about the current task. In this example, the task type is worker and the task index is 0.

For illustration purposes, this tutorial shows how to set a TF_CONFIG with 2 workers on localhost. In practice, you would create multiple workers on an external IP address and port, and set TF_CONFIG on each worker appropriately, i.e. modify the task index.

os.environ['TF_CONFIG'] = json.dumps({
    'cluster': {
        'worker': ["localhost:12345", "localhost:23456"]
    },
    'task': {'type': 'worker', 'index': 0}
})

Define the model

Write the layers, the optimizer, and the loss function for training. This tutorial defines the model with Keras layers, similar to the multi-GPU training tutorial.

LEARNING_RATE = 1e-4
def model_fn(features, labels, mode):
  model = tf.keras.Sequential([
      tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(10)
  ])
  logits = model(features, training=False)

  if mode == tf.estimator.ModeKeys.PREDICT:
    predictions = {'logits': logits}
    return tf.estimator.EstimatorSpec(labels=labels, predictions=predictions)

  optimizer = tf.compat.v1.train.GradientDescentOptimizer(
      learning_rate=LEARNING_RATE)
  loss = tf.keras.losses.SparseCategoricalCrossentropy(
      from_logits=True, reduction=tf.keras.losses.Reduction.NONE)(labels, logits)
  loss = tf.reduce_sum(loss) * (1. / BATCH_SIZE)
  if mode == tf.estimator.ModeKeys.EVAL:
    return tf.estimator.EstimatorSpec(mode, loss=loss)

  return tf.estimator.EstimatorSpec(
      mode=mode,
      loss=loss,
      train_op=optimizer.minimize(
          loss, tf.compat.v1.train.get_or_create_global_step()))

MultiWorkerMirroredStrategy

To train the model, use an instance of tf.distribute.experimental.MultiWorkerMirroredStrategy. MultiWorkerMirroredStrategy creates copies of all variables in the model's layers on each device across all workers. It uses CollectiveOps, a TensorFlow op for collective communication, to aggregate gradients and keep the variables in sync. The tf.distribute.Strategy guide has more details about this strategy.

strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
INFO:tensorflow:Using MirroredStrategy with devices ('/device:GPU:0',)
INFO:tensorflow:Single-worker MultiWorkerMirroredStrategy with local_devices = ('/device:GPU:0',), communication = CollectiveCommunication.AUTO

Train and evaluate the model

Next, specify the distribution strategy in the RunConfig for the estimator, and train and evaluate by invoking tf.estimator.train_and_evaluate. This tutorial distributes only the training by specifying the strategy via train_distribute. It is also possible to distribute the evaluation via eval_distribute.

config = tf.estimator.RunConfig(train_distribute=strategy)

classifier = tf.estimator.Estimator(
    model_fn=model_fn, model_dir='/tmp/multiworker', config=config)
tf.estimator.train_and_evaluate(
    classifier,
    train_spec=tf.estimator.TrainSpec(input_fn=input_fn),
    eval_spec=tf.estimator.EvalSpec(input_fn=input_fn)
)
INFO:tensorflow:Initializing RunConfig with distribution strategies.
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Using config: {'_model_dir': '/tmp/multiworker', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': <tensorflow.python.distribute.collective_all_reduce_strategy.CollectiveAllReduceStrategy object at 0x7f5476dcb7b8>, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_distribute_coordinator_mode': None}
INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.
INFO:tensorflow:The `input_fn` accepts an `input_context` which will be given by DistributionStrategy
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Create CheckpointSaverHook.

INFO:tensorflow:Create CheckpointSaverHook.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Saving checkpoints for 0 into /tmp/multiworker/model.ckpt.

INFO:tensorflow:Saving checkpoints for 0 into /tmp/multiworker/model.ckpt.

INFO:tensorflow:loss = 2.2904248, step = 0

INFO:tensorflow:loss = 2.2904248, step = 0

INFO:tensorflow:global_step/sec: 275.729

INFO:tensorflow:global_step/sec: 275.729

INFO:tensorflow:loss = 2.3041787, step = 100 (0.365 sec)

INFO:tensorflow:loss = 2.3041787, step = 100 (0.365 sec)

INFO:tensorflow:global_step/sec: 300.166

INFO:tensorflow:global_step/sec: 300.166

INFO:tensorflow:loss = 2.3031883, step = 200 (0.333 sec)

INFO:tensorflow:loss = 2.3031883, step = 200 (0.333 sec)

INFO:tensorflow:global_step/sec: 291.067

INFO:tensorflow:global_step/sec: 291.067

INFO:tensorflow:loss = 2.2956822, step = 300 (0.343 sec)

INFO:tensorflow:loss = 2.2956822, step = 300 (0.343 sec)

INFO:tensorflow:global_step/sec: 306.264

INFO:tensorflow:global_step/sec: 306.264

INFO:tensorflow:loss = 2.3059468, step = 400 (0.326 sec)

INFO:tensorflow:loss = 2.3059468, step = 400 (0.326 sec)

INFO:tensorflow:global_step/sec: 309.681

INFO:tensorflow:global_step/sec: 309.681

INFO:tensorflow:loss = 2.2891643, step = 500 (0.323 sec)

INFO:tensorflow:loss = 2.2891643, step = 500 (0.323 sec)

INFO:tensorflow:global_step/sec: 305.06

INFO:tensorflow:global_step/sec: 305.06

INFO:tensorflow:loss = 2.3045273, step = 600 (0.328 sec)

INFO:tensorflow:loss = 2.3045273, step = 600 (0.328 sec)

INFO:tensorflow:global_step/sec: 303.993

INFO:tensorflow:global_step/sec: 303.993

INFO:tensorflow:loss = 2.2795393, step = 700 (0.329 sec)

INFO:tensorflow:loss = 2.2795393, step = 700 (0.329 sec)

INFO:tensorflow:global_step/sec: 344.642

INFO:tensorflow:global_step/sec: 344.642

INFO:tensorflow:loss = 2.2893176, step = 800 (0.289 sec)

INFO:tensorflow:loss = 2.2893176, step = 800 (0.289 sec)

INFO:tensorflow:global_step/sec: 739.177

INFO:tensorflow:global_step/sec: 739.177

INFO:tensorflow:loss = 2.2893682, step = 900 (0.135 sec)

INFO:tensorflow:loss = 2.2893682, step = 900 (0.135 sec)

INFO:tensorflow:Saving checkpoints for 938 into /tmp/multiworker/model.ckpt.

INFO:tensorflow:Saving checkpoints for 938 into /tmp/multiworker/model.ckpt.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Done calling model_fn.

INFO:tensorflow:Starting evaluation at 2020-03-28T01:32:56Z

INFO:tensorflow:Starting evaluation at 2020-03-28T01:32:56Z

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Graph was finalized.

INFO:tensorflow:Restoring parameters from /tmp/multiworker/model.ckpt-938

INFO:tensorflow:Restoring parameters from /tmp/multiworker/model.ckpt-938

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Done running local_init_op.

INFO:tensorflow:Evaluation [10/100]

INFO:tensorflow:Evaluation [10/100]

INFO:tensorflow:Evaluation [20/100]

INFO:tensorflow:Evaluation [20/100]

INFO:tensorflow:Evaluation [30/100]

INFO:tensorflow:Evaluation [30/100]

INFO:tensorflow:Evaluation [40/100]

INFO:tensorflow:Evaluation [40/100]

INFO:tensorflow:Evaluation [50/100]

INFO:tensorflow:Evaluation [50/100]

INFO:tensorflow:Evaluation [60/100]

INFO:tensorflow:Evaluation [60/100]

INFO:tensorflow:Evaluation [70/100]

INFO:tensorflow:Evaluation [70/100]

INFO:tensorflow:Evaluation [80/100]

INFO:tensorflow:Evaluation [80/100]

INFO:tensorflow:Evaluation [90/100]

INFO:tensorflow:Evaluation [90/100]

INFO:tensorflow:Evaluation [100/100]

INFO:tensorflow:Evaluation [100/100]

INFO:tensorflow:Inference Time : 0.81122s

INFO:tensorflow:Inference Time : 0.81122s

INFO:tensorflow:Finished evaluation at 2020-03-28-01:32:57

INFO:tensorflow:Finished evaluation at 2020-03-28-01:32:57

INFO:tensorflow:Saving dict for global step 938: global_step = 938, loss = 2.2886915

INFO:tensorflow:Saving dict for global step 938: global_step = 938, loss = 2.2886915

INFO:tensorflow:Saving 'checkpoint_path' summary for global step 938: /tmp/multiworker/model.ckpt-938

INFO:tensorflow:Saving 'checkpoint_path' summary for global step 938: /tmp/multiworker/model.ckpt-938

INFO:tensorflow:Loss for final step: 1.1468834.

INFO:tensorflow:Loss for final step: 1.1468834.

({'loss': 2.2886915, 'global_step': 938}, [])

Optimize training performance

You now have a model and a multi-worker capable Estimator powered by tf.distribute.Strategy. You can try the following techniques to optimize performance of multi-worker training:

  • Increase the batch size: The batch size specified here is per-GPU. In general, the largest batch size that fits the GPU memory is advisable.
  • Cast variables: Cast the variables to tf.float if possible. The official ResNet model includes an example of how this can be done.
  • Use collective communication: MultiWorkerMirroredStrategy provides multiple collective communication implementations.

    • RING implements ring-based collectives using gRPC as the cross-host communication layer.
    • NCCL uses Nvidia's NCCL to implement collectives.
    • AUTO defers the choice to the runtime.

    The best choice of collective implementation depends upon the number and kind of GPUs, and the network interconnect in the cluster. To override the automatic choice, specify a valid value to the communication parameter of MultiWorkerMirroredStrategy's constructor, e.g. communication=tf.distribute.experimental.CollectiveCommunication.NCCL.

Visit the Performance section in the guide to learn more about other strategies and tools you can use to optimize the performance of your TensorFlow models.

Other code examples

  1. End to end example for multi worker training in tensorflow/ecosystem using Kubernetes templates. This example starts with a Keras model and converts it to an Estimator using the tf.keras.estimator.model_to_estimator API.
  2. Official models, many of which can be configured to run multiple distribution strategies.