'AbortedError' in TensorFlow: Causes and How to Fix

Diagnose the Context

• Check for resource contention, as multiple processes trying to access the same resource can lead to an AbortedError. Use profiling to check for such conflicts.

 

• Consult the TensorFlow logs for more detailed error information. This might give insights on which part of the computation caused the error.

Optimize Resource Usage

• Reduce the batch size in your training or inference pipeline if memory contention is suspected.

 

• Limit the number of parallel operations using the tf.data.Options. Enable the experimental optimization flags such as deterministic=False to allow the API to relax ordering constraints, potentially reducing resource contention.

import tensorflow as tf

options = tf.data.Options()
options.experimental_deterministic = False
dataset = dataset.with_options(options)

Adjust Session Configuration

• If using a session in TensorFlow 1.x, explicitly configure GPU options to allow TensorFlow to retry operations in cases of failure. Allocate memory as needed using allow\_growth.

 

• Control the intra_op and inter_op parallelism threads appropriately based on your hardware setup. This helps manage the compute resources across multiple processes.

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.intra_op_parallelism_threads = 2
config.inter_op_parallelism_threads = 2
session = tf.Session(config=config)

Utilize Distributed Strategy

• Leverage tf.distribute.Strategy if running computations on multiple devices. This can help mitigate AbortedError by effectively distributing workload and managing resources between devices.

 

• Ensure all necessary operations are compiled correctly with distributed execution in mind, avoiding any device resource conflicts.

strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    model = tf.keras.Sequential([...])
    # Compile and fit in the strategy scope to ensure resources are managed correctly

model.compile(...)

Review Custom Operations

• Inspect any custom operations or layers integrated into the TensorFlow model, as improper handling can cause AbortedError.

 

• Ensure all custom ops are compatible with TensorFlow's graph execution environment, and test them independently to verify resource management efficiency.

Implement Retry Logic for Robustness

• If encountering AbortedError sporadically, implement retry logic for idempotent operations. This allows the program to retry the failed operation without side effects.

 

• Use a retry mechanism with exponential backoff to avoid overwhelming the system upon repeated failures.

import time

def retry_operation(fn, retries=3, delay=5):
    for i in range(retries):
        try:
            return fn()
        except tf.errors.AbortedError as e:
            print(f"Retrying operation after failure: {e}")
            time.sleep(delay * (2 ** i))
    raise RuntimeError("Operation could not be completed after retries.")

Keep TensorFlow Updated

• Always use the latest stable version of TensorFlow, as newer versions come with better handling for errors, optimizations, and additional logging information.

 

• Stay abreast of patches and fixes related to common errors encountered in previous versions.