ray-project/ray ray-0.6.0

on GitHub

Breaking Changes

• Renamed _submit to _remote. #3321
• Object store memory capped at 20GB by default. #3243
• Now ray.global_state.client_table() returns a list instead of a dictionary.
• Renamed ray.global_state.dump_catapult_trace to ray.global_state.chrome_tracing_dump.

Known Issues

• The Plasma TensorFlow operator leaks memory. #3404
• Object broadcasts on large clusters are inefficient. #2945
• Ape-X leaks memory. #3452
• Action clipping can impede learning (please set clip_actions: False as a workaround) #3496

Core

• New raylet backend on by default and legacy backend removed. #3020 #3121
• Support for Python 3.7. #2546
• Support for fractional resources (e.g., GPUs).
• Added ray stack for improved debugging (to get stack traces of Python processes on current node). #3213
• Better error messages for low-memory conditions. #3323
• Log file names reorganized under /tmp/ray/. #2862
• Improved timeline visualizations. #2306 #3255

Modin

• Modin is shipped with Ray. After running import ray you can run import modin. #3109

RLlib

• Multi agent support for Ape-X and IMPALA. #3147
• Multi GPU support for IMPALA. #2766
• TD3 optimizations for DDPG. #3353
• Support for Dict and Tuple observation spaces. #3051
• Support for parametric and variable-length action spaces. #3384
• Support batchnorm layers. #3369
• Support custom metrics. #3144

Autoscaler

• Added ray submit for submitting scripts to clusters. #3312
• Added --new flag for ray attach. #2973
• Added option to allow private IPs only. #3270

Tune

• Support for fractional GPU allocations for trials. #3169
• Better checkpointing and setup. #2889
• Memory tracking and notification. #3298
• Bug fixes for SearchAlgorithms. #3081
• Add a raise_on_failed_trial flag in run_experiments. #2915
• Better handling of node failures. #3238

Training

• Experimental support for distributed SGD. #2858 #3033