OSError: [Errno 9] Bad file descriptor in tensorflow Estimater When deploying model on Multiple GPUs using tensorflow mirror strategy

Question

I am trying to deploying deep learning model on two GPUs with single machine. I am utilizing TensorFlow mirror strategy. I am getting the following error:

Traceback (most recent call last):

Code

 from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import os
import json
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
tf.logging.set_verbosity(tf.logging.INFO)
from tensorflow.keras.datasets import mnist



def cnn_model_fn(features, labels, mode):
  
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    input_layer = tf.cast(input_layer, tf.float32)
    labels = tf.cast(labels, tf.int32)
   
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)


    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)

    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(
        inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

    logits = tf.layers.dense(inputs=dropout, units=10)

    predictions = {
        # Generate predictions (for PREDICT and EVAL mode)
        "classes": tf.argmax(input=logits, axis=1),
        # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
        # `logging_hook`.
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # Calculate Loss (for both TRAIN and EVAL modes)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
        train_op = optimizer.minimize(
            loss=loss,
            global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

    # Add evaluation metrics (for EVAL mode)
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(
            labels=labels, predictions=predictions["classes"])}
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)


def per_device_batch_size(batch_size, num_gpus):
    if num_gpus <= 1:
        return batch_size

    remainder = batch_size % num_gpus
    if remainder:
        err = ('When running with multiple GPUs, batch size '
               'must be a multiple of the number of available GPUs. Found {} '
               'GPUs with a batch size of {}; try --batch_size={} instead.'
               ).format(num_gpus, batch_size, batch_size - remainder)
        raise ValueError(err)
    return int(batch_size / num_gpus)


class InputFnProvider:
    def __init__(self, train_batch_size):
        self.train_batch_size = train_batch_size
        self.__load_data()

    def __load_data(self):
        # Load training and eval data

        (X_train, Y_train), (X_test, Y_test) = mnist.load_data()
        self.train_data = X_train # Returns np.array
        self.train_labels = Y_train
        self.eval_data = X_test  # Returns np.array
        self.eval_labels = Y_test

    def train_input_fn(self):
        dataset = tf.data.Dataset.from_tensor_slices(({"x": self.train_data}, self.train_labels))
        dataset = dataset.shuffle(1000).repeat().batch(self.train_batch_size)
        return dataset

    def eval_input_fn(self):
        """An input function for evaluation or prediction"""
        dataset = tf.data.Dataset.from_tensor_slices(({"x": self.eval_data}, self.eval_labels))
        dataset = dataset.batch(1)
        return dataset


def main(unused_argv):
    batch_size = 100
    num_gpus = 2

    input_fn_provider = InputFnProvider(per_device_batch_size(batch_size, num_gpus))


    if num_gpus > 1:
        distribution = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"],
                                          cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())
    else:
        distribution = None
    # Pass to RunConfig
    config = tf.estimator.RunConfig(
        train_distribute=distribution,
        model_dir="/tmp/mnist_convnet_model")

    mnist_classifier = tf.estimator.Estimator(
        model_fn=cnn_model_fn,
        config=config)

    # Train the model
    mnist_classifier.train(
        input_fn=input_fn_provider.train_input_fn,
        steps=1000)

    eval_results = mnist_classifier.evaluate(input_fn=input_fn_provider.eval_input_fn)
    print(eval_results)


if __name__ == "__main__":
    tf.app.run()

Surprisingly when I deploy the model on a single GPU using the same code, it works; however, when I try to deploy the model on two GPUs via changing a bit in code, I face the above-given error. I do not know about this error. Can anyone help?

Answer 1

Refer to this link to see how multiprocessing thread pools can be created in python: https://docs.python.org/zh-tw/3/library/concurrent.futures.html

In concurrent.futures.threadpoolexecutor, the context manager closes the thread pool when the code block is exited.

Coming back to our problem, mirroredStrategy also creates a multiprocessing threadpool. However, it does not automatically close the thread pool before ending of the program. So we have to explicitly close the pool on exit, using:

import atexit

strategy = tf.distribute.MirroredStrategy()

atexit.register(strategy._extended._collective_ops._pool.close)

Refer to the most voted answer here: https://github.com/tensorflow/tensorflow/issues/50487