..tip:: To run everything sequentially, use :ref:`Ray Local Mode <tune-debugging>`.
Parallelism is determined by ``resources_per_trial`` (defaulting to 1 CPU, 0 GPU per trial) and the resources available to Tune (``ray.cluster_resources()``).
Tune will allocate the specified GPU and CPU from ``resources_per_trial`` to each individual trial. A trial will not be scheduled unless at least that amount of resources is available, preventing the cluster from being overloaded.
By default, Tune automatically runs N concurrent trials, where N is the number of CPUs (cores) on your machine.
..code-block:: python
# If you have 4 CPUs on your machine, this will run 4 concurrent trials at a time.
tune.run(trainable, num_samples=10)
You can override this parallelism with ``resources_per_trial``:
..code-block:: python
# If you have 4 CPUs on your machine, this will run 2 concurrent trials at a time.
..warning:: If you use a Search Algorithm, you will need to use a different search space API.
You can specify a grid search or random search via the dict passed into ``tune.run(config=)``.
..code-block:: python
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
}
tune.run(trainable, 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.
..code-block:: python
:emphasize-lines:13
# num_samples=10 repeats the 3x3 grid search 10 times, for a total of 90 trials
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.
You can restore a single trial checkpoint by using ``tune.run(restore=<checkpoint_dir>)`` By doing this, you can change whatever experiments' configuration such as the experiment's name:
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 :ref:`cluster launcher <ref-automatic-cluster>` and also requires rsync to be installed.
Note that you must use the ``tune.checkpoint_dir`` API to trigger syncing. Also, if running Tune on Kubernetes, be sure to use the :ref:`KubernetesSyncer <tune-kubernetes>` to transfer files between different pods.
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 ``pin_in_object_store`` utility function that can be used to broadcast such large objects. Objects pinned in this way will never be evicted from the Ray object store while the driver process is running, and can be efficiently retrieved from any task via ``get_pinned_object``.
..code-block:: python
import ray
from ray import tune
from ray.tune.utils import pin_in_object_store, get_pinned_object
If a dictionary is passed in, the keys may be any field in the return result of ``tune.report`` 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**.
..code-block:: python
# training_iteration is an auto-filled metric by Tune.
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).
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:
..code-block:: python
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()
tune.run(my_trainable, 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. See the :ref:`tune-stop-ref` 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 :ref:`Trainable Logging <trainable-logging>`.
**Learn more about logging and customizations here**: :ref:`loggers-docstring`.
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:
You can use a :ref:`Reporter <tune-reporter-doc>` object to customize the console output.
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 URIs.
..code-block:: python
tune.run(
MyTrainableClass,
local_dir="~/ray_results",
upload_dir="s3://my-log-dir"
)
You can customize this to specify arbitrary storages with the ``sync_to_cloud`` argument in ``tune.run``. This argument supports either strings with the same replacement fields OR arbitrary functions.
..code-block:: python
tune.run(
MyTrainableClass,
upload_dir="s3://my-log-dir",
sync_to_cloud=custom_sync_str_or_func,
)
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:
By default, syncing occurs every 300 seconds. To change the frequency of syncing, set the ``TUNE_CLOUD_SYNC_S`` environment variable in the driver 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 ``global_checkpoint_period`` argument. So the true upload period is given by ``max(TUNE_CLOUD_SYNC_S, global_checkpoint_period)``.
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 ``tune.run``.
..code-block:: python
ray.init(local_mode=True)
Local mode with multiple configuration evaluations will interleave computation, so it is most naturally used when running a single configuration evaluation.
Stopping after the first failure
--------------------------------
By default, ``tune.run`` will continue executing until all trials have terminated or errored. To stop the entire Tune run as soon as **any** trial errors:
..code-block:: python
tune.run(trainable, fail_fast=True)
This is useful when you are trying to setup a large hyperparameter experiment.
Further Questions or Issues?
----------------------------
You can post questions or issues or feedback through the following channels:
1.`StackOverflow`_: For questions about how to use Ray.
2.`GitHub Issues`_: For bug reports and feature requests.
