'FailedPreconditionError' in TensorFlow: Causes and How to Fix

Check Model Initialization

• Ensure that the TensorFlow session or environment has been correctly initialized before invoking operations. Using eager execution can help detect problems related to uninitialized variables.

import tensorflow as tf

# Ensure eager execution is enabled
tf.compat.v1.disable_eager_execution()
print('Eager execution disabled - to enable use tf.compat.v1.enable_eager_execution()')

Correct Use of Placeholders

• Ensure that all placeholders are being fed with appropriate data. Failing to feed data into a placeholder can cause a FailedPreconditionError.

import tensorflow as tf

# Define a placeholder
x = tf.compat.v1.placeholder(tf.float32, shape=[None, 784])

# Correctly feed the placeholder
with tf.compat.v1.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    result = sess.run(x, feed_dict={x: [[0.0]*784]})
    print('Placeholder fed successfully')

Initialize Variables

• Ensure all variables are initialized before running the session. Use `tf.compat.v1.global_variables_initializer()` to initialize variables.

import tensorflow as tf

# Define variables
var1 = tf.Variable(1)
var2 = tf.Variable(2)

# Initialize variables before use
with tf.compat.v1.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    print('Variables initialized:', sess.run([var1, var2]))

Fix SavedModel Issues

• When loading models using `tf.saved_model.load`, ensure the model was saved correctly with all necessary components.

import tensorflow as tf

# Load the saved model properly
loaded_model = tf.saved_model.load('path/to/saved_model')
print('Model loaded successfully:', loaded_model.signatures.keys())

Correct Graph Mode Execution

• When using TensorFlow in graph mode, make sure to correctly manage sessions and not mix eager execution where it's not supposed to be used.

import tensorflow as tf

graph = tf.Graph()

# Use a managed session inside the `with` block
with graph.as_default():
    a = tf.constant(5)
    b = tf.constant(2)
    c = a + b
  
    with tf.compat.v1.Session() as sess:
        result = sess.run(c)
        print('Graph executed successfully:', result)

Handle File Path & Permissions

• If the error pertains to file operations, ensure the file paths are correct and that TensorFlow has the necessary permissions.

import os

file_path = 'correct/path/to/file'
assert os.path.exists(file_path), 'File path is incorrect or file does not exist'
print('File path verified')