RLlib Table of Contents

Troubleshooting

If you encounter errors like blas_thread_init: pthread_create: Resource temporarily unavailable when using many workers, try setting OMP_NUM_THREADS=1. Similarly, check configured system limits with ulimit -a for other resource limit errors.

For debugging unexpected hangs or performance problems, you can run ray stack to dump the stack traces of all Ray workers on the current node, ray timeline to dump a timeline visualization of tasks to a file, and ray memory to list all object references in the cluster.

TensorFlow 2.0

RLlib currently runs in tf.compat.v1 mode. This means eager execution is disabled by default, and RLlib imports TF with import tensorflow.compat.v1 as tf; tf.disable_v2_behaviour(). Eager execution can be enabled manually by calling tf.enable_eager_execution() or setting the "eager": True trainer config.