How to set RAY_DEDUP_LOGS=0

Ray Core
1

How severe does this issue affect your experience of using Ray?

  • Low: It annoys or frustrates me for a moment.

Very new to Ray, and I’m been very impressed but I am having some trouble setting a variable referenced in the docs. I am using Ray Tune to build a PyTorch model (similar to this tutorial). I am receiving the “Ray deduplicates logs by default…” message after it deduplicated a few of my print statements. The docs say that I can stop the deduplication by setting RAY_DEDUP_LOGS=0 but I am not sure which object controls this or how to access it. When I google that variable name I can only find two references; the aforementioned doc and an unrelated GitHub query.

Any help would be very appreciated, thanks!

1 Like
2

Hi @ReuvenB

I suffer from the same issue as I’m trying to inspect the seedings of my custom env when running on single machine as well as on a cluster. The goal is that each env has its own specific seed. I have tried starting the local cluster (in this case only one node, i.e. the head) like this:

env RAY_DEDUP_LOGS=0 ray start --head

and/or this:

os.environ["RAY_DEDUP_LOGS"] = "0"

in the python file prior to ray.init()

None of the combinations seems to work.

BR

Jorgen

1 Like
3

@ericl is there anything special for these environment variables?

4

I have managed to disable this feature by setting os.environ["RAY_DEDUP_LOGS"] = "0" before ray.init. @Jorgen_Svane can you share your script? I can reproduce on my end.

5

Hi @cade

Sorry for my late reply.

I’ve just been running the code below:

import os
import numpy as np
import gymnasium
from gymnasium.spaces import Discrete, Box
from gymnasium.wrappers import ResizeObservation
import ray
from ray.tune.registry import register_env
import ray.rllib.algorithms.impala as impala
from ray.tune.logger import pretty_print

config = {
        "observation_space": Box(
            low=0,
            high=255,
            shape=(72, 128, 3),
            dtype=np.uint8),
        "action_space": Discrete(5),
        "p_terminated": 1e-4, # to prevent early termination - decrease if needed
        "max_episode_len":495,
        # "sleeping":1.0 # to mimic slow env - increase if needed
        }

def env_creator(env_config):
    kwargs = {}
    env = gymnasium.make('my_random:my_random/MyRandomEnv-v0',config=env_config,**kwargs)
    env = ResizeObservation(env, 84)
    env = gymnasium.wrappers.FrameStack(env, 4)
    return env

os.environ["RAY_DEDUP_LOGS"] = "0"

ray.init()

register_env("myrandomenv", env_creator)


algo = (
    impala.ImpalaConfig()
    .training(
        lr=5e-4,
        lr_schedule=[[0, 5e-4],[200e6, 0.0]],
        vf_loss_coeff=0.5,
        train_batch_size=600, #1200,
        entropy_coeff=5e-3,
        entropy_coeff_schedule=[[0, 5e-3],[100e6, 1e-3],[200e6, 5e-5]],
        grad_clip=40.0) 
    .environment(env="myrandomenv",env_config=config)
    .framework(framework="tf2", eager_tracing=True)
    .rollouts(
              num_rollout_workers=6, # 6 for single machine
              num_envs_per_worker=4,
              rollout_fragment_length=100,
              remote_worker_envs=True,
              remote_env_batch_wait_ms=10,
              preprocessor_pref=None,
              )
    .resources(num_gpus=1,num_cpus_per_worker=5)
    .fault_tolerance(recreate_failed_workers=True, restart_failed_sub_environments=True)
    
    .build()
)

for i in range(10):
    result = algo.train()
    print(pretty_print(result))

    if i % 5 == 0:
        checkpoint_dir = algo.save("./ray_30_test")
        print(f"Checkpoint saved in directory {checkpoint_dir}")

Using the environment found here.

And I get this output:

2023-05-25 10:02:12,852	INFO worker.py:1616 -- Started a local Ray instance. View the dashboard at 127.0.0.1:8265 
2023-05-25 10:02:13,333	INFO algorithm_config.py:3307 -- Executing eagerly (framework='tf2'), with eager_tracing=True. For production workloads, make sure to set eager_tracing=True  in order to match the speed of tf-static-graph (framework='tf'). For debugging purposes, `eager_tracing=False` is the best choice.
2023-05-25 10:02:13,340	INFO algorithm.py:527 -- Current log_level is WARN. For more information, set 'log_level': 'INFO' / 'DEBUG' or use the -v and -vv flags.
(RolloutWorker pid=42162) ###################################
(RolloutWorker pid=42162) Seeding succeded using env_config
(RolloutWorker pid=42162) ###################################
(RolloutWorker pid=42162) I'm now being reset by a worker
(RolloutWorker pid=42162) I'm now being reset by a worker
(RolloutWorker pid=42162) /home/novelty/miniconda3/envs/ray240/lib/python3.9/site-packages/gymnasium/spaces/box.py:230: UserWarning: WARN: Casting input x to numpy array.
(RolloutWorker pid=42162)   gym.logger.warn("Casting input x to numpy array.")
(_RemoteSingleAgentEnv pid=43065) ################################### [repeated 58x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
(_RemoteSingleAgentEnv pid=43065) Seeding succeded using env_config [repeated 29x across cluster]
(_RemoteSingleAgentEnv pid=42950) I'm now being reset by a worker [repeated 83x across cluster]
(_RemoteSingleAgentEnv pid=43041) I'm now being reset by a worker [repeated 3x across cluster]
agent_timesteps_total: 7200
connector_metrics:
  StateBufferConnector_ms: 0.011156002680460611
  ViewRequirementAgentConnector_ms: 0.2080609401067098
counters:
  num_agent_steps_sampled: 7200
  num_agent_steps_trained: 7200
  num_env_steps_sampled: 7200
  num_env_steps_trained: 7200
  num_samples_added_to_queue: 7200
  num_training_step_calls_since_last_synch_worker_weights: 1171
  num_weight_broadcasts: 13

I’ve also tried setting

env RAY_DEDUP_LOGS=0 ray start --head

which doesn’t fix it either.

BR

Jorgen

6

Experiencing the same issue with output from Ray Workflow tasks.

None of the following works for me:

  • setting ENV variable before calling ray.init()
  • setting ENV variable as runtime_env parameter of ray.init()
  • setting ENV variable using RuntimeEnv from ray.runtime _env

Is there a solution for this issue? It is making debugging challenging for us due to missing (similar but not same) logs.