User Guide & Configuring Tune

These pages will demonstrate the various features and configurations of Tune.


Before you continue, be sure to have read Key Concepts.

This document provides an overview of the core concepts as well as some of the configurations for running Tune.

Resources (Parallelism, GPUs, Distributed)


To run everything sequentially, use Ray Local Mode.

Parallelism is determined by resources_per_trial (defaulting to 1 CPU, 0 GPU per trial) and the resources available to Tune (ray.cluster_resources()).

By default, Tune automatically runs N concurrent trials, where N is the number of CPUs (cores) on your machine.

# If you have 4 CPUs on your machine, this will run 4 concurrent trials at a time., num_samples=10)

You can override this parallelism with resources_per_trial. Here you can specify your resource requests using either a dictionary or a PlacementGroupFactory object. In any case, Ray Tune will try to start a placement group for each trial.

# If you have 4 CPUs on your machine, this will run 2 concurrent trials at a time., num_samples=10, resources_per_trial={"cpu": 2})

# If you have 4 CPUs on your machine, this will run 1 trial at a time., num_samples=10, resources_per_trial={"cpu": 4})

# Fractional values are also supported, (i.e., {"cpu": 0.5})., num_samples=10, resources_per_trial={"cpu": 0.5})

Tune will allocate the specified GPU and CPU from resources_per_trial to each individual trial. Even if the trial cannot be scheduled right now, Ray Tune will still try to start the respective placement group. If not enough resources are available, this will trigger autoscaling behavior if you’re using the Ray cluster launcher.

It is also possible to specify memory ("memory", in bytes) and custom resource requirements.

If your trainable function starts more remote workers, you will need to pass placement groups factory objects to request these resources. See the PlacementGroupFactory documentation for further information. This also applies if you are using other libraries making use of Ray, such as Modin. Failure to set resources correctly may result in a deadlock, “hanging” the cluster.

Using GPUs

To leverage GPUs, you must set gpu in This will automatically set CUDA_VISIBLE_DEVICES for each trial.

# If you have 8 GPUs, this will run 8 trials at once., num_samples=10, resources_per_trial={"gpu": 1})

# If you have 4 CPUs on your machine and 1 GPU, this will run 1 trial at a time., num_samples=10, resources_per_trial={"cpu": 2, "gpu": 1})

You can find an example of this in the Keras MNIST example.


If ‘gpu’ is not set, CUDA_VISIBLE_DEVICES environment variable will be set as empty, disallowing GPU access.

Troubleshooting: Occasionally, you may run into GPU memory issues when running a new trial. This may be due to the previous trial not cleaning up its GPU state fast enough. To avoid this, you can use tune.utils.wait_for_gpu - see docstring.

Concurrent samples

If using a search algorithm, you may want to limit the number of trials that are being evaluated. For example, you may want to serialize the evaluation of trials to do sequential optimization.

In this case, ray.tune.suggest.ConcurrencyLimiter to limit the amount of concurrency:

algo = BayesOptSearch(utility_kwargs={
    "kind": "ucb",
    "kappa": 2.5,
    "xi": 0.0
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()

See ConcurrencyLimiter (tune.suggest.ConcurrencyLimiter) for more details.

Distributed Tuning


This section covers how to run Tune across multiple machines. See Distributed Training for guidance in tuning distributed training jobs.

To attach to a Ray cluster, simply run ray.init before See Starting Ray via the CLI (ray start) for more information about ray.init:

# Connect to an existing distributed Ray cluster
ray.init(address=<ray_address>), num_samples=100, resources_per_trial=tune.PlacementGroupFactory([{"CPU": 2, "GPU": 1}]))

Read more in the Tune distributed experiments guide.

Tune Distributed Training

To tune distributed training jobs, Tune provides a set of DistributedTrainableCreator for different training frameworks. Below is an example for tuning distributed TensorFlow jobs:

# Please refer to full example in
from ray.tune.integration.tensorflow import DistributedTrainableCreator
tf_trainable = DistributedTrainableCreator(

Read more about tuning distributed PyTorch, TensorFlow and Horovod jobs.

Search Space (Grid/Random)

You can specify a grid search or sampling distribution via the dict passed into

parameters = {
    "qux": tune.sample_from(lambda spec: 2 + 2),
    "bar": tune.grid_search([True, False]),
    "foo": tune.grid_search([1, 2, 3]),
    "baz": "asd",  # a constant value
}, config=parameters)

By default, each random variable and grid search point is sampled once. To take multiple random samples, add num_samples: N to the experiment config. If grid_search is provided as an argument, the grid will be repeated num_samples of times.

 # num_samples=10 repeats the 3x3 grid search 10 times, for a total of 90 trials
         "alpha": tune.uniform(100),
         "beta": tune.sample_from(lambda spec: spec.config.alpha * np.random.normal()),
         "nn_layers": [
             tune.grid_search([16, 64, 256]),
             tune.grid_search([16, 64, 256]),

Note that search spaces may not be interoperable across different search algorithms. For example, for many search algorithms, you will not be able to use a grid_search parameter. Read about this in the Search Space API page.

Auto-filled Metrics

You can log arbitrary values and metrics in both training APIs:

def trainable(config):
    for i in range(num_epochs):
        ..., metric_foo=random_metric_1, bar=metric_2)

class Trainable(tune.Trainable):
    def step(self):
        # don't call report here!
        return dict(acc=accuracy, metric_foo=random_metric_1, bar=metric_2)

During training, Tune will automatically log the below metrics in addition to the user-provided values. All of these can be used as stopping conditions or passed as a parameter to Trial Schedulers/Search Algorithms.

  • config: The hyperparameter configuration

  • date: String-formatted date and time when the result was processed

  • done: True if the trial has been finished, False otherwise

  • episodes_total: Total number of episodes (for RLLib trainables)

  • experiment_id: Unique experiment ID

  • experiment_tag: Unique experiment tag (includes parameter values)

  • hostname: Hostname of the worker

  • iterations_since_restore: The number of times has been called after restoring the worker from a checkpoint

  • node_ip: Host IP of the worker

  • pid: Process ID (PID) of the worker process

  • time_since_restore: Time in seconds since restoring from a checkpoint.

  • time_this_iter_s: Runtime of the current training iteration in seconds (i.e. one call to the trainable function or to _train() in the class API.

  • time_total_s: Total runtime in seconds.

  • timestamp: Timestamp when the result was processed

  • timesteps_since_restore: Number of timesteps since restoring from a checkpoint

  • timesteps_total: Total number of timesteps

  • training_iteration: The number of times has been called

  • trial_id: Unique trial ID

All of these metrics can be seen in the Trial.last_result dictionary.

Reproducible runs

Exact reproducibility of machine learning runs is hard to achieve. This is even more true in a distributed setting, as more non-determinism is introduced. For instance, if two trials finish at the same time, the convergence of the search algorithm might be influenced by which trial result is processed first. This depends on the searcher - for random search, this shouldn’t make a difference, but for most other searchers it will.

If you try to achieve some amount of reproducibility, there are two places where you’ll have to set random seeds:

  1. On the driver program, e.g. for the search algorithm. This will ensure that at least the initial configurations suggested by the search algorithms are the same.

  2. In the trainable (if required). Neural networks are usually initialized with random numbers, and many classical ML algorithms, like GBDTs, make use of randomness. Thus you’ll want to make sure to set a seed here so that the initialization is always the same.

Here is an example that will always produce the same result (except for trial runtimes).

import numpy as np
from ray import tune

def train(config):
    # Set seed for trainable random result.
    # If you remove this line, you will get different results
    # each time you run the trial, even if the configuration
    # is the same.
    random_result = np.random.uniform(0, 100, size=1).item()

# Set seed for Ray Tune's random search.
# If you remove this line, you will get different configurations
# each time you run the script.
        "seed": tune.randint(0, 1000)

Some searchers use their own random states to sample new configurations. These searchers usually accept a seed parameter that can be passed on initialization. Other searchers use Numpy’s np.random interface - these seeds can be then set with np.random.seed(). We don’t offer an interface to do this in the searcher classes as setting a random seed globally could have side effects. For instance, it could influence the way your dataset is split. Thus, we leave it up to the user to make these global configuration changes.

Checkpointing and synchronization

When running a hyperparameter search, Tune can automatically and periodically save/checkpoint your model. This allows you to:

  • save intermediate models throughout training

  • use pre-emptible machines (by automatically restoring from last checkpoint)

  • Pausing trials when using Trial Schedulers such as HyperBand and PBT.

Tune stores checkpoints on the node where the trials are executed. If you are training on more than one node, this means that some trial checkpoints may be on the head node and others are not.

When trials are restored (e.g. after a failure or when the experiment was paused), they may be scheduled on different nodes, but still would need access to the latest checkpoint. To make sure this works, Ray Tune comes with facilities to synchronize trial checkpoints between nodes.

Generally we consider three cases:

  1. When using a shared directory (e.g. via NFS)

  2. When using cloud storage (e.g. S3 or GS)

  3. When using neither

The default option here is 3, which will be automatically used if nothing else is configured.

Using a shared directory

If all Ray nodes have access to a shared filesystem, e.g. via NFS, they can all write to this directory. In this case, we don’t need any synchronization at all, as it is implicitly done by the operating system.

For this case, we only need to tell Ray Tune not to do any syncing at all (as syncing is the default):

from ray import tune
        syncer=None  # Disable syncing

Note that the driver (on the head node) will have access to all checkpoints locally (in the shared directory) for further processing.

Using cloud storage

If all nodes have access to cloud storage, e.g. S3 or GS, the remote trials can automatically synchronize their checkpoints. For the filesyste, we end up with a similar situation as in the first case, only that the consolidated directory including all logs and checkpoints lives on cloud storage.

This approach is especially useful when training a large number of distributed trials, as logs and checkpoints are otherwise synchronized via SSH, which quickly can become a performance bottleneck.

For this case, we tell Ray Tune to use an upload_dir to store checkpoints at. This will automatically store both the experiment state and the trial checkpoints at that directory:

from ray import tune

We don’t have to provide a syncer here as it will be automatically detected. However, you can provide a string if you want to use a custom command:

from ray import tune
        syncer="aws s3 sync {source} {target}",  # Custom sync command

If a string is provided, then it must include replacement fields {source} and {target}, as demonstrated in the example above.

The consolidated data will live be available in the cloud bucket. This means that the driver (on the head node) will not have access to all checkpoints locally. If you want to process e.g. the best checkpoint further, you will first have to fetch it from the cloud storage.

Default syncing (no shared/cloud storage)

If you’re using neither a shared filesystem nor cloud storage, Ray Tune will resort to the default syncing mechanisms, which utilizes rsync (via SSH) to synchronize checkpoints across nodes.

Please note that this approach is likely the least efficient one - you should always try to use shared or cloud storage if possible when training on a multi node cluster.

For the syncing to work, the head node must be able to SSH into the worker nodes. If you are using the Ray cluster launcher this is usually the case (note that Kubernetes is an exception, but see here for more details).

If you don’t provide a tune.SyncConfig at all, rsync-based syncing will be used.

If you want to customize syncing behavior, you can again specify a custom sync template:

from ray import tune
        # Do not specify an upload dir here
        syncer="rsync -savz -e "ssh -i ssh_key.pem" {source} {target}",  # Custom sync command

Alternatively, a function can be provided with the following signature:

def custom_sync_func(source, target):
    sync_cmd = "rsync {source} {target}".format(
    sync_process = subprocess.Popen(sync_cmd, shell=True)
        sync_period=60  # Synchronize more often

When syncing results back to the driver, the source would be a path similar to ubuntu@, and the target would be a local path.

Note that we adjusted the sync period in the example above. Setting this to a lower number will pull checkpoints from remote nodes more often. This will lead to more robust trial recovery, but it will also lead to more synchronization overhead (as SHH is usually slow).

As in the first case, the driver (on the head node) will have access to all checkpoints locally for further processing.

Checkpointing examples

Let’s cover how to configure your checkpoints storage location, checkpointing frequency, and how to resume from a previous run.

A simple (cloud) checkpointing example

Cloud storage-backed Tune checkpointing is the recommended best practice for both performance and reliability reasons. It also enables checkpointing if using Ray on Kubernetes, which does not work out of the box with rsync-based sync, which relies on SSH. If you’d rather checkpoint locally or use rsync based checkpointing, see here.

Prerequisites to use cloud checkpointing in Ray Tune for the example below:

Your my_trainable is either a:

  1. Model with an existing Ray integration

  1. Custom training function

  • All this means is that your function has to expose a checkpoint_dir argument in the function signature, and call tune.checkpoint_dir. See this example, it’s quite simple to do.

Let’s assume for this example you’re running this script from your laptop, and connecting to your remote Ray cluster via ray.init(), making your script on your laptop the “driver”.

import ray
from ray import tune
from your_module import my_trainable

ray.init(address="<cluster-IP>:<port>")  # set `address=None` to train on laptop

# configure how checkpoints are sync'd to the scheduler/sampler
# we recommend cloud storage checkpointing as it survives the cluster when
# instances are terminated, and has better performance
sync_config = tune.syncConfig(
    upload_dir="s3://my-checkpoints-bucket/path/",  # requires AWS credentials

# this starts the run!

    # name of your experiment

    # a directory where results are stored before being
    # sync'd to head node/cloud storage

    # see above! we will sync our checkpoints to S3 directory

    # we'll keep the best five checkpoints at all times
    # checkpoints (by AUC score, reported by the trainable, descending)

    # a very useful trick! this will resume from the last run specified by
    # sync_config (if one exists), otherwise it will start a new tuning run

In this example, checkpoints will be saved:

  • Locally: not saved! Nothing will be sync’d to the driver (your laptop) automatically (because cloud syncing is enabled)

  • S3: s3://my-checkpoints-bucket/path/my-tune-exp/<trial_name>/checkpoint_<step>

  • On head node: ~/ray-results/my-tune-exp/<trial_name>/checkpoint_<step> (but only for trials done on that node)

  • On workers nodes: ~/ray-results/my-tune-exp/<trial_name>/checkpoint_<step> (but only for trials done on that node)

If your run stopped for any reason (finished, errored, user CTRL+C), you can restart it any time by running the script above again – note with resume="AUTO", it will detect the previous run so long as the sync_config points to the same location.

If, however, you prefer not to use resume="AUTO" (or are on an older version of Ray) you can resume manaully:

# Restored previous trial from the given checkpoint
    # our same trainable as before

    # The name can be different from your original name

    # our same config as above!

A simple local/rsync checkpointing example

Local or rsync checkpointing can be a good option if:

  1. You want to tune on a single laptop Ray cluster

  2. You aren’t using Ray on Kubernetes (rsync doesn’t work with Ray on Kubernetes)

  3. You don’t want to use S3

Let’s take a look at an example:

import ray
from ray import tune
from your_module import my_trainable

ray.init(address="<cluster-IP>:<port>")  # set `address=None` to train on laptop

# configure how checkpoints are sync'd to the scheduler/sampler
sync_config = tune.syncConfig()  # the default mode is to use use rsync

# this starts the run!

    # name of your experiment

    # a directory where results are stored before being
    # sync'd to head node/cloud storage

    # sync our checkpoints via rsync
    # you don't have to pass an empty sync config - but we
    # do it here for clarity and comparison

    # we'll keep the best five checkpoints at all times
    # checkpoints (by AUC score, reported by the trainable, descending)

    # a very useful trick! this will resume from the last run specified by
    # sync_config (if one exists), otherwise it will start a new tuning run

Distributed Checkpointing

On a multinode cluster, Tune automatically creates a copy of all trial checkpoints on the head node. This requires the Ray cluster to be started with the cluster launcher and also requires rsync to be installed.

Note that you must use the tune.checkpoint_dir API to trigger syncing (or use a model type with a built-in Ray Tune integration as described here). See custom_func_checkpointing for an example.

If you are running Ray Tune on Kubernetes, you should usually use a cloud checkpointing or a shared filesystem for checkpoint sharing. Please see here for best practices for running Tune on Kubernetes.

If you do not use the cluster launcher, you should set up a NFS or global file system and disable cross-node syncing:

sync_config = tune.SyncConfig(syncer=None), sync_config=sync_config)

Stopping and resuming a tuning run

Ray Tune periodically checkpoints the experiment state so that it can be restarted when it fails or stops. The checkpointing period is dynamically adjusted so that at least 95% of the time is used for handling training results and scheduling.

If you send a SIGINT signal to the process running (which is usually what happens when you press Ctrl+C in the console), Ray Tune shuts down training gracefully and saves a final experiment-level checkpoint. You can then call with resume=True to continue this run in the future:
    # ...

# This is interrupted e.g. by sending a SIGINT signal
# Next time, continue the run like so:
    # ...

You will have to pass a name if you are using resume=True so that Ray Tune can detect the experiment folder (which is usually stored at e.g. ~/ray_results/my_experiment). If you forgot to pass a name in the first call, you can still pass the name when you resume the run. Please note that in this case it is likely that your experiment name has a date suffix, so if you ran, the name might look like something like this: my_trainable_2021-01-29_10-16-44.

You can see which name you need to pass by taking a look at the results table of your original tuning run:

== Status ==
Memory usage on this node: 11.0/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 1/16 CPUs, 0/0 GPUs, 0.0/4.69 GiB heap, 0.0/1.61 GiB objects
Result logdir: /Users/ray/ray_results/my_trainable_2021-01-29_10-16-44
Number of trials: 1/1 (1 RUNNING)

Handling Large Datasets

You often will want to compute a large object (e.g., training data, model weights) on the driver and use that object within each trial.

Tune provides a wrapper function tune.with_parameters() that allows you to broadcast large objects to your trainable. Objects passed with this wrapper will be stored on the Ray object store and will be automatically fetched and passed to your trainable as a parameter.


If the objects are small in size or already exist in the Ray Object Store, there’s no need to use tune.with_parameters(). You can use partials or pass in directly to config instead.

from ray import tune

import numpy as np

def f(config, data=None):
    # use data

data = np.random.random(size=100000000), data=data))

Stopping Trials

You can control when trials are stopped early by passing the stop argument to This argument takes, a dictionary, a function, or a Stopper class as an argument.

If a dictionary is passed in, the keys may be any field in the return result of in the Function API or step() (including the results from step and auto-filled metrics).

In the example below, each trial will be stopped either when it completes 10 iterations OR when it reaches a mean accuracy of 0.98. These metrics are assumed to be increasing.

# training_iteration is an auto-filled metric by Tune.
    stop={"training_iteration": 10, "mean_accuracy": 0.98}

For more flexibility, you can pass in a function instead. If a function is passed in, it must take (trial_id, result) as arguments and return a boolean (True if trial should be stopped and False otherwise).

def stopper(trial_id, result):
    return result["mean_accuracy"] / result["training_iteration"] > 5, stop=stopper)

Finally, you can implement the Stopper abstract class for stopping entire experiments. For example, the following example stops all trials after the criteria is fulfilled by any individual trial, and prevents new ones from starting:

from ray.tune import Stopper

class CustomStopper(Stopper):
    def __init__(self):
        self.should_stop = False

    def __call__(self, trial_id, result):
        if not self.should_stop and result['foo'] > 10:
            self.should_stop = True
        return self.should_stop

    def stop_all(self):
        """Returns whether to stop trials and prevent new ones from starting."""
        return self.should_stop

stopper = CustomStopper(), stop=stopper)

Note that in the above example the currently running trials will not stop immediately but will do so once their current iterations are complete.

Ray Tune comes with a set of out-of-the-box stopper classes. See the Stopper documentation.


Tune by default will log results for Tensorboard, CSV, and JSON formats. If you need to log something lower level like model weights or gradients, see Trainable Logging.

Learn more about logging and customizations here: Loggers (tune.logger).

Tune will log the results of each trial to a subfolder under a specified local dir, which defaults to ~/ray_results.

# This logs to 2 different trial folders:
# ~/ray_results/trainable_name/trial_name_1 and ~/ray_results/trainable_name/trial_name_2
# trainable_name and trial_name are autogenerated., num_samples=2)

You can specify the local_dir and trainable_name:

# This logs to 2 different trial folders:
# ./results/test_experiment/trial_name_1 and ./results/test_experiment/trial_name_2
# Only trial_name is autogenerated., num_samples=2, local_dir="./results", name="test_experiment")

To specify custom trial folder names, you can pass use the trial_name_creator argument to This takes a function with the following signature:

def trial_name_string(trial):
        trial (Trial): A generated trial object.

        trial_name (str): String representation of Trial.
    return str(trial)

See the documentation on Trials: Trial.

Tensorboard (Logging)

Tune automatically outputs Tensorboard files during To visualize learning in tensorboard, install tensorboardX:

$ pip install tensorboardX

Then, after you run an experiment, you can visualize your experiment with TensorBoard by specifying the output directory of your results.

$ tensorboard --logdir=~/ray_results/my_experiment

If you are running Ray on a remote multi-user cluster where you do not have sudo access, you can run the following commands to make sure tensorboard is able to write to the tmp directory:

$ export TMPDIR=/tmp/$USER; mkdir -p $TMPDIR; tensorboard --logdir=~/ray_results

If using TF2, Tune also automatically generates TensorBoard HParams output, as shown below:
        "lr": tune.grid_search([1e-5, 1e-4]),
        "momentum": tune.grid_search([0, 0.9])

Console Output

User-provided fields will be outputted automatically on a best-effort basis. You can use a Reporter object to customize the console output.

== Status ==
Memory usage on this node: 11.4/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 4/12 CPUs, 0/0 GPUs, 0.0/3.17 GiB heap, 0.0/1.07 GiB objects
Result logdir: /Users/foo/ray_results/myexp
Number of trials: 4 (4 RUNNING)
| Trial name           | status   | loc                 |    param1 | param2 |    acc | total time (s) |  iter |
| MyTrainable_a826033a | RUNNING  | | 0.303706  | 0.0761 | 0.1289 |        7.54952 |    15 |
| MyTrainable_a8263fc6 | RUNNING  | | 0.929276  | 0.158  | 0.4865 |        7.0501  |    14 |
| MyTrainable_a8267914 | RUNNING  | | 0.068426  | 0.0319 | 0.9585 |        7.0477  |    14 |
| MyTrainable_a826b7bc | RUNNING  | | 0.729127  | 0.0748 | 0.1797 |        7.05715 |    14 |

Uploading Results

If an upload directory is provided, Tune will automatically sync results from the local_dir to the given directory, natively supporting standard S3/gsutil/HDFS URIs.

You can customize this to specify arbitrary storages with the syncer argument in tune.SyncConfig. This argument supports either strings with the same replacement fields OR arbitrary functions.

If a string is provided, then it must include replacement fields {source} and {target}, like s3 sync {source} {target}. Alternatively, a function can be provided with the following signature:

def custom_sync_func(source, target):
    # do arbitrary things inside
    sync_cmd = "s3 {source} {target}".format(
    sync_process = subprocess.Popen(sync_cmd, shell=True)

By default, syncing occurs every 300 seconds. To change the frequency of syncing, set the sync_period attribute of the sync config to the desired syncing period.

Note that uploading only happens when global experiment state is collected, and the frequency of this is determined by the sync period. So the true upload period is given by max(sync period, TUNE_GLOBAL_CHECKPOINT_S).

Make sure that worker nodes have the write access to the cloud storage. Failing to do so would cause error messages like Error message (1): fatal error: Unable to locate credentials. For AWS set up, this involves adding an IamInstanceProfile configuration for worker nodes. Please see here for more tips.

Using Tune with Docker

Tune automatically syncs files and checkpoints between different remote containers as needed.

To make this work in your Docker cluster, e.g. when you are using the Ray autoscaler with docker containers, you will need to pass a DockerSyncer to the syncer argument of tune.SyncConfig.

from ray.tune.integration.docker import DockerSyncer
sync_config = tune.SyncConfig(
    syncer=DockerSyncer), sync_config=sync_config)

Using Tune with Kubernetes

Ray Tune automatically synchronizes files and checkpoints between different remote nodes as needed. This usually happens via SSH, but this can be a performance bottleneck, especially when running many trials in parallel.

Instead you should use shared storage for checkpoints so that no additional synchronization across nodes is necessary. There are two main options.

First, you can use the SyncConfig to store your logs and checkpoints on cloud storage, such as AWS S3 or Google Cloud Storage:

from ray import tune
    # ...,

Second, you can set up a shared file system like NFS. If you do this, disable automatic trial syncing:

from ray import tune
    # ...,
        # Do not sync because we are on shared storage

Lastly, if you still want to use ssh for trial synchronization, but are not running on the Ray cluster launcher, you might need to pass a KubernetesSyncer to the syncer argument of tune.SyncConfig. You have to specify your Kubernetes namespace explicitly:

from ray.tune.integration.kubernetes import NamespacedKubernetesSyncer
sync_config = tune.SyncConfig(
), sync_config=sync_config)

Please note that we strongly encourage you to use one of the other two options instead, as they will result in less overhead and don’t require pods to SSH into each other.

Redirecting stdout and stderr to files

The stdout and stderr streams are usually printed to the console. For remote actors, Ray collects these logs and prints them to the head process.

However, if you would like to collect the stream outputs in files for later analysis or troubleshooting, Tune offers an utility parameter, log_to_file, for this.

By passing log_to_file=True to, stdout and stderr will be logged to trial_logdir/stdout and trial_logdir/stderr, respectively:

If you would like to specify the output files, you can either pass one filename, where the combined output will be stored, or two filenames, for stdout and stderr, respectively:
    log_to_file=("my_stdout.log", "my_stderr.log"))

The file names are relative to the trial’s logdir. You can pass absolute paths, too.

If log_to_file is set, Tune will automatically register a new logging handler for Ray’s base logger and log the output to the specified stderr output file.


Ray Tune supports callbacks that are called during various times of the training process. Callbacks can be passed as a parameter to, and the submethod will be invoked automatically.

This simple callback just prints a metric each time a result is received:

from ray import tune
from ray.tune import Callback

class MyCallback(Callback):
    def on_trial_result(self, iteration, trials, trial, result, **info):
        print(f"Got result: {result['metric']}")

def train(config):
    for i in range(10):

For more details and available hooks, please see the API docs for Ray Tune callbacks.


By default, Tune will run hyperparameter evaluations on multiple processes. However, if you need to debug your training process, it may be easier to do everything on a single process. You can force all Ray functions to occur on a single process with local_mode by calling the following before


Local mode with multiple configuration evaluations will interleave computation, so it is most naturally used when running a single configuration evaluation.

Note that local_mode has some known issues, so please read these tips for more info.

Stopping after the first failure

By default, will continue executing until all trials have terminated or errored. To stop the entire Tune run as soon as any trial errors:, fail_fast=True)

This is useful when you are trying to setup a large hyperparameter experiment.

Environment variables

Some of Ray Tune’s behavior can be configured using environment variables. These are the environment variables Ray Tune currently considers:

  • TUNE_CLUSTER_SSH_KEY: SSH key used by the Tune driver process to connect to remote cluster machines for checkpoint syncing. If this is not set, ~/ray_bootstrap_key.pem will be used.

  • TUNE_DISABLE_AUTO_CALLBACK_LOGGERS: Ray Tune automatically adds a CSV and JSON logger callback if they haven’t been passed. Setting this variable to 1 disables this automatic creation. Please note that this will most likely affect analyzing your results after the tuning run.

  • TUNE_DISABLE_AUTO_CALLBACK_SYNCER: Ray Tune automatically adds a Syncer callback to sync logs and checkpoints between different nodes if none has been passed. Setting this variable to 1 disables this automatic creation. Please note that this will most likely affect advanced scheduling algorithms like PopulationBasedTraining.

  • TUNE_DISABLE_AUTO_INIT: Disable automatically calling ray.init() if not attached to a Ray session.

  • TUNE_DISABLE_DATED_SUBDIR: Ray Tune automatically adds a date string to experiment directories when the name is not specified explicitly or the trainable isn’t passed as a string. Setting this environment variable to 1 disables adding these date strings.

  • TUNE_DISABLE_STRICT_METRIC_CHECKING: When you report metrics to Tune via and passed a metric parameter to, a scheduler, or a search algorithm, Tune will error if the metric was not reported in the result. Setting this environment variable to 1 will disable this check.

  • TUNE_DISABLE_SIGINT_HANDLER: Ray Tune catches SIGINT signals (e.g. sent by Ctrl+C) to gracefully shutdown and do a final checkpoint. Setting this variable to 1 will disable signal handling and stop execution right away. Defaults to 0.

  • TUNE_FORCE_TRIAL_CLEANUP_S: By default, Ray Tune will gracefully terminate trials, letting them finish the current training step and any user-defined cleanup. Setting this variable to a non-zero, positive integer will cause trials to be forcefully terminated after a grace period of that many seconds. Defaults to 0.

  • TUNE_FUNCTION_THREAD_TIMEOUT_S: Time in seconds the function API waits for threads to finish after instructing them to complete. Defaults to 2.

  • TUNE_GLOBAL_CHECKPOINT_S: Time in seconds that limits how often Tune’s experiment state is checkpointed. If not set this will default to 10.

  • TUNE_MAX_LEN_IDENTIFIER: Maximum length of trial subdirectory names (those with the parameter values in them)

  • TUNE_MAX_PENDING_TRIALS_PG: Maximum number of pending trials when placement groups are used. Defaults to auto, which will be updated to max(16, cluster_cpus * 1.1) for random/grid search and 1 for any other search algorithms.

  • TUNE_PLACEMENT_GROUP_CLEANUP_DISABLED: Ray Tune cleans up existing placement groups with the _tune__ prefix in their name before starting a run. This is used to make sure that scheduled placement groups are removed when multiple calls to are done in the same script. You might want to disable this if you run multiple Tune runs in parallel from different scripts. Set to 1 to disable.

  • TUNE_PLACEMENT_GROUP_PREFIX: Prefix for placement groups created by Ray Tune. This prefix is used e.g. to identify placement groups that should be cleaned up on start/stop of the tuning run. This is initialized to a unique name at the start of the first run.

  • TUNE_PLACEMENT_GROUP_RECON_INTERVAL: How often to reconcile placement groups. Reconcilation is used to make sure that the number of requested placement groups and pending/running trials are in sync. In normal circumstances these shouldn’t differ anyway, but reconcilation makes sure to capture cases when placement groups are manually destroyed. Reconcilation doesn’t take much time, but it can add up when running a large number of short trials. Defaults to every 5 (seconds).

  • TUNE_PLACEMENT_GROUP_WAIT_S: Default time the trial executor waits for placement groups to be placed before continuing the tuning loop. Setting this to a float will block for that many seconds. This is mostly used for testing purposes. Defaults to -1, which disables blocking.

  • TUNE_RESULT_DIR: Directory where Ray Tune trial results are stored. If this is not set, ~/ray_results will be used.

  • TUNE_RESULT_BUFFER_LENGTH: Ray Tune can buffer results from trainables before they are passed to the driver. Enabling this might delay scheduling decisions, as trainables are speculatively continued. Setting this to 0 disables result buffering. Defaults to 1000 (results), or to 1 (no buffering) if used with checkpoint_at_end.

  • TUNE_RESULT_DELIM: Delimiter used for nested entries in ExperimentAnalysis dataframes. Defaults to . (but will be changed to / in future versions of Ray).

  • TUNE_RESULT_BUFFER_MAX_TIME_S: Similarly, Ray Tune buffers results up to number_of_trial/10 seconds, but never longer than this value. Defaults to 100 (seconds).

  • TUNE_RESULT_BUFFER_MIN_TIME_S: Additionally, you can specify a minimum time to buffer results. Defaults to 0.

  • TUNE_SYNCER_VERBOSITY: Amount of command output when using Tune with Docker Syncer. Defaults to 0.

  • TUNE_TRIAL_RESULT_WAIT_TIME_S: Amount of time Ray Tune will block until a result from a running trial is received. Defaults to 1 (second).

  • TUNE_TRIAL_STARTUP_GRACE_PERIOD: Amount of time after starting a trial that Ray Tune checks for successful trial startups. After the grace period, Tune will block for up to TUNE_TRIAL_RESULT_WAIT_TIME_S seconds until a result from a running trial is received. Can be disabled by setting this to lower or equal to 0.

  • TUNE_WARN_THRESHOLD_S: Threshold for logging if an Tune event loop operation takes too long. Defaults to 0.5 (seconds).

  • TUNE_WARN_INSUFFICENT_RESOURCE_THRESHOLD_S: Threshold for throwing a warning if no active trials are in RUNNING state for this amount of seconds. If the Ray Tune job is stuck in this state (most likely due to insufficient resources), the warning message is printed repeatedly every this amount of seconds. Defaults to 60 (seconds).

  • TUNE_WARN_INSUFFICENT_RESOURCE_THRESHOLD_S_AUTOSCALER: Threshold for throwing a warning, when the autoscaler is enabled, if no active trials are in RUNNING state for this amount of seconds. If the Ray Tune job is stuck in this state (most likely due to insufficient resources), the warning message is printed repeatedly every this amount of seconds. Defaults to 60 (seconds).

  • TUNE_STATE_REFRESH_PERIOD: Frequency of updating the resource tracking from Ray. Defaults to 10 (seconds).

  • TUNE_SYNC_DISABLE_BOOTSTRAP: Disable bootstrapping the autoscaler config for Docker syncing.

There are some environment variables that are mostly relevant for integrated libraries:

  • SIGOPT_KEY: SigOpt API access key.

  • WANDB_API_KEY: Weights and Biases API key. You can also use wandb login instead.

Further Questions or Issues?

You can post questions or issues or feedback through the following channels:

  1. Discussion Board: For questions about Ray usage or feature requests.

  2. GitHub Issues: For bug reports.

  3. Ray Slack: For getting in touch with Ray maintainers.

  4. StackOverflow: Use the [ray] tag questions about Ray.