Change Logdir of Ray RLlib Training instead of ~/ray_results

Question

I'm using Ray & RLlib to train RL agents on an Ubuntu system. Tensorboard is used to monitor the training progress by pointing it to ~/ray_results where all the log files for all runs are stored. Ray Tune is not being used.

For example, on starting a new Ray/RLlib training run, a new directory will be created at

~/ray_results/DQN_ray_custom_env_2020-06-07_05-26-32djwxfdu1

To visualize the training progress, we need to start Tensorboard using

tensorboard --logdir=~/ray_results

Question: Is it possible to configure Ray/RLlib to change the output directory of the log files from ~/ray_results to another location?

Additionally, instead of logging to a directory named something like DQN_ray_custom_env_2020-06-07_05-26-32djwxfdu1, can this directory name by set by ourselves?

---

Failed Attempt: Tried setting

os.environ['TUNE_RESULT_DIR'] = '~/another_dir`

before running ray.init(), but the result log files were still being written to ~/ray_results.

Answer 1

Is it possible to configure Ray/RLlib to change the output directory of the log files from ~/ray_results to another location?

There is currently no way to configure this using RLib CLI tool (rllib).

If you're okay with Python API, then, as described in documentation, local_dir parameter of tune.run is responsible for specifying output directory, default is ~/ray_results.

Additionally, instead of logging to a directory named something like DQN_ray_custom_env_2020-06-07_05-26-32djwxfdu1, can this directory name by set by ourselves?

This is governed by trial_name_creator parameter of tune.run. It must be a function that accepts trial object and formats it into a string like so:

def trial_name_id(trial):
    return f"{trial.trainable_name}_{trial.trial_id}"

tune.run(...trial_name_creator=trial_name_id)

Answer 2

Without using Tune, you can change the logdir using rllib's "Trainer". The "Trainer" class takes in an optional "logger_creator" if you want to specify where to save the log (see here).

A concrete example:

1. Define your customized logger creator (you can simply modify from the default one):

def custom_log_creator(custom_path, custom_str):

    timestr = datetime.today().strftime("%Y-%m-%d_%H-%M-%S")
    logdir_prefix = "{}_{}".format(custom_str, timestr)

    def logger_creator(config):

        if not os.path.exists(custom_path):
            os.makedirs(custom_path)
        logdir = tempfile.mkdtemp(prefix=logdir_prefix, dir=custom_path)
        return UnifiedLogger(config, logdir, loggers=None)

    return logger_creator

2. Pass this logger_creator to the trainer, and start training:

trainer = PPOTrainer(config=config, env='CartPole-v0',
                     logger_creator=custom_log_creator(os.path.expanduser("~/another_ray_results/subdir"), 'custom_dir'))

for i in range(ITER_NUM):
    result = trainer.train()

You will find the training results (i.e., TensorBoard events file, params, model, ...) saved under "~/another_ray_results/subdir" with your specified naming convention.