Training (tune.Trainable, session.report)
Contents
Training (tune.Trainable, session.report)#
Training can be done with either a Function API (session.report) or Class API (tune.Trainable).
For the sake of example, let’s maximize this objective function:
def objective(x, a, b):
return a * (x ** 0.5) + b
Function API#
The Function API allows you to define a custom training function that Tune will run in parallel Ray actor processes, one for each Tune trial.
With the Function API, you can report intermediate metrics by simply calling session.report
within the function.
from ray import tune
from ray.air import session
def trainable(config: dict):
intermediate_score = 0
for x in range(20):
intermediate_score = objective(x, config["a"], config["b"])
session.report({"score": intermediate_score}) # This sends the score to Tune.
tuner = tune.Tuner(trainable, param_space={"a": 2, "b": 4})
results = tuner.fit()
Tip
Do not use session.report
within a Trainable
class.
In the previous example, we reported on every step, but this metric reporting frequency is configurable. For example, we could also report only a single time at the end with the final score:
from ray import tune
from ray.air import session
def trainable(config: dict):
final_score = 0
for x in range(20):
final_score = objective(x, config["a"], config["b"])
session.report({"score": final_score}) # This sends the score to Tune.
tuner = tune.Tuner(trainable, param_space={"a": 2, "b": 4})
results = tuner.fit()
It’s also possible to return a final set of metrics to Tune by returning them from your function:
def trainable(config: dict):
final_score = 0
for x in range(20):
final_score = objective(x, config["a"], config["b"])
return {"score": final_score} # This sends the score to Tune.
You’ll notice that Ray Tune will output extra values in addition to the user reported metrics,
such as iterations_since_restore
. See How to use log metrics in Tune? for an explanation/glossary of these values.
Function API Checkpointing#
Many Tune features rely on checkpointing, including the usage of certain Trial Schedulers and fault tolerance. You can save and load checkpoints in Ray Tune in the following manner:
from ray import tune
from ray.air import session
from ray.air.checkpoint import Checkpoint
def train_func(config):
epochs = config.get("epochs", 2)
start = 0
loaded_checkpoint = session.get_checkpoint()
if loaded_checkpoint:
last_step = loaded_checkpoint.to_dict()["step"]
start = last_step + 1
for step in range(start, epochs):
# Model training here
# ...
# Report metrics and save a checkpoint
metrics = {"metric": "my_metric"}
checkpoint = Checkpoint.from_dict({"step": step})
session.report(metrics, checkpoint=checkpoint)
tuner = tune.Tuner(train_func)
results = tuner.fit()
Note
checkpoint_frequency
and checkpoint_at_end
will not work with Function API checkpointing.
In this example, checkpoints will be saved by training iteration to <local_dir>/<exp_name>/trial_name/checkpoint_<step>
.
Tune also may copy or move checkpoints during the course of tuning. For this purpose,
it is important not to depend on absolute paths in the implementation of save
.
See here for more information on creating checkpoints.
If using framework-specific trainers from Ray AIR, see here for
references to framework-specific checkpoints such as TensorflowCheckpoint
.
Trainable Class API#
Caution
Do not use session.report
within a Trainable
class.
The Trainable class API will require users to subclass ray.tune.Trainable
. Here’s a naive example of this API:
from ray import air, tune
class Trainable(tune.Trainable):
def setup(self, config: dict):
# config (dict): A dict of hyperparameters
self.x = 0
self.a = config["a"]
self.b = config["b"]
def step(self): # This is called iteratively.
score = objective(self.x, self.a, self.b)
self.x += 1
return {"score": score}
tuner = tune.Tuner(
Trainable,
run_config=air.RunConfig(
# Train for 20 steps
stop={"training_iteration": 20},
checkpoint_config=air.CheckpointConfig(
# We haven't implemented checkpointing yet. See below!
checkpoint_at_end=False
),
),
param_space={"a": 2, "b": 4},
)
results = tuner.fit()
As a subclass of tune.Trainable
, Tune will create a Trainable
object on a
separate process (using the Ray Actor API).
setup
function is invoked once training starts.
step
is invoked multiple times. Each time, the Trainable object executes one logical iteration of training in the tuning process, which may include one or more iterations of actual training.
cleanup
is invoked when training is finished.
Tip
As a rule of thumb, the execution time of step
should be large enough to avoid overheads
(i.e. more than a few seconds), but short enough to report progress periodically (i.e. at most a few minutes).
You’ll notice that Ray Tune will output extra values in addition to the user reported metrics,
such as iterations_since_restore
.
See How to use log metrics in Tune? for an explanation/glossary of these values.
Class API Checkpointing#
You can also implement checkpoint/restore using the Trainable Class API:
import os
import torch
from torch import nn
from ray import air, tune
class MyTrainableClass(tune.Trainable):
def setup(self, config):
self.model = nn.Sequential(
nn.Linear(config.get("input_size", 32), 32), nn.ReLU(), nn.Linear(32, 10)
)
def step(self):
return {}
def save_checkpoint(self, tmp_checkpoint_dir):
checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.pth")
torch.save(self.model.state_dict(), checkpoint_path)
return tmp_checkpoint_dir
def load_checkpoint(self, tmp_checkpoint_dir):
checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.pth")
self.model.load_state_dict(torch.load(checkpoint_path))
tuner = tune.Tuner(
MyTrainableClass,
param_space={"input_size": 64},
run_config=air.RunConfig(
stop={"training_iteration": 2},
checkpoint_config=air.CheckpointConfig(checkpoint_frequency=2),
),
)
tuner.fit()
You can checkpoint with three different mechanisms: manually, periodically, and at termination.
Manual Checkpointing: A custom Trainable can manually trigger checkpointing by returning should_checkpoint: True
(or tune.result.SHOULD_CHECKPOINT: True
) in the result dictionary of step
.
This can be especially helpful in spot instances:
def step(self):
# training code
result = {"mean_accuracy": accuracy}
if detect_instance_preemption():
result.update(should_checkpoint=True)
return result
Periodic Checkpointing: periodic checkpointing can be used to provide fault-tolerance for experiments.
This can be enabled by setting checkpoint_frequency=<int>
and max_failures=<int>
to checkpoint trials
every N iterations and recover from up to M crashes per trial, e.g.:
tuner = tune.Tuner(
my_trainable,
run_config=air.RunConfig(
checkpoint_config=air.CheckpointConfig(checkpoint_frequency=10),
failure_config=air.FailureConfig(max_failures=5))
)
results = tuner.fit()
Checkpointing at Termination: The checkpoint_frequency may not coincide with the exact end of an experiment.
If you want a checkpoint to be created at the end of a trial, you can additionally set the checkpoint_at_end=True
:
tuner = tune.Tuner(
my_trainable,
run_config=air.RunConfig(
checkpoint_config=air.CheckpointConfig(checkpoint_frequency=10, checkpoint_at_end=True),
failure_config=air.FailureConfig(max_failures=5))
)
results = tuner.fit()
Use validate_save_restore
to catch save_checkpoint
/load_checkpoint
errors before execution.
from ray.tune.utils import validate_save_restore
# both of these should return
validate_save_restore(MyTrainableClass)
validate_save_restore(MyTrainableClass, use_object_store=True)
Advanced: Reusing Actors#
Note
This feature is only for the Trainable Class API.
Your Trainable can often take a long time to start.
To avoid this, you can do tune.TuneConfig(reuse_actors=True)
(which is taken in by Tuner
) to reuse the same Trainable Python process and
object for multiple hyperparameters.
This requires you to implement Trainable.reset_config
, which provides a new set of hyperparameters.
It is up to the user to correctly update the hyperparameters of your trainable.
class PytorchTrainble(tune.Trainable):
"""Train a Pytorch ConvNet."""
def setup(self, config):
self.train_loader, self.test_loader = get_data_loaders()
self.model = ConvNet()
self.optimizer = optim.SGD(
self.model.parameters(),
lr=config.get("lr", 0.01),
momentum=config.get("momentum", 0.9))
def reset_config(self, new_config):
for param_group in self.optimizer.param_groups:
if "lr" in new_config:
param_group["lr"] = new_config["lr"]
if "momentum" in new_config:
param_group["momentum"] = new_config["momentum"]
self.model = ConvNet()
self.config = new_config
return True
Comparing the Function API and Class API#
Here are a few key concepts and what they look like for the Function and Class API’s.
Concept |
Function API |
Class API |
---|---|---|
Training Iteration |
Increments on each |
Increments on each |
Report metrics |
|
Return metrics from |
Saving a checkpoint |
|
|
Loading a checkpoint |
|
|
Accessing config |
Passed as an argument |
Passed through |
Advanced Resource Allocation#
Trainables can themselves be distributed. If your trainable function / class creates further Ray actors or tasks
that also consume CPU / GPU resources, you will want to add more bundles to the PlacementGroupFactory
to reserve extra resource slots.
For example, if a trainable class requires 1 GPU itself, but also launches 4 actors, each using another GPU,
then you should use this:
tuner = tune.Tuner(
tune.with_resources(my_trainable, tune.PlacementGroupFactory([
{"CPU": 1, "GPU": 1},
{"GPU": 1},
{"GPU": 1},
{"GPU": 1},
{"GPU": 1}
])),
run_config=air.RunConfig(name="my_trainable")
)
The Trainable
also provides the default_resource_requests
interface to automatically
declare the resources per trial based on the given configuration.
It is also possible to specify memory ("memory"
, in bytes) and custom resource requirements.
session (Function API)#
- ray.air.session.report(metrics: Dict, *, checkpoint: Optional[ray.air.checkpoint.Checkpoint] = None) None [source]
Report metrics and optionally save a checkpoint.
Each invocation of this method will automatically increment the underlying iteration number. The physical meaning of this “iteration” is defined by user (or more specifically the way they call
report
). It does not necessarily map to one epoch.This API is the canonical way to report metrics from Tune and Train, and replaces the legacy
tune.report
,with tune.checkpoint_dir
,train.report
andtrain.save_checkpoint
calls.Note on directory checkpoints: AIR will take ownership of checkpoints passed to
report()
by moving them to a new path. The original directory will no longer be accessible to the caller after the report call.Example
- Parameters
metrics – The metrics you want to report.
checkpoint – The optional checkpoint you want to report.
- ray.air.session.get_checkpoint() Optional[ray.air.checkpoint.Checkpoint] [source]
Access the session’s last checkpoint to resume from if applicable.
- Returns
- Checkpoint object if the session is currently being resumed.
Otherwise, return None.
######## Using it in the *per worker* train loop (TrainSession) ###### from ray.air import session from ray.air.checkpoint import Checkpoint from ray.air.config import ScalingConfig def train_func(): ckpt = session.get_checkpoint() if ckpt: with ckpt.as_directory() as loaded_checkpoint_dir: import tensorflow as tf model = tf.keras.models.load_model(loaded_checkpoint_dir) else: model = build_model() model.save("my_model", overwrite=True) session.report( metrics={"iter": 1}, checkpoint=Checkpoint.from_directory("my_model") ) scaling_config = ScalingConfig(num_workers=2) trainer = TensorflowTrainer( train_loop_per_worker=train_func, scaling_config=scaling_config ) result = trainer.fit() # trainer2 will pick up from the checkpoint saved by trainer1. trainer2 = TensorflowTrainer( train_loop_per_worker=train_func, scaling_config=scaling_config, # this is ultimately what is accessed through # ``Session.get_checkpoint()`` resume_from_checkpoint=result.checkpoint, ) result2 = trainer2.fit()
- ray.air.session.get_trial_name() str [source]
Trial name for the corresponding trial.
- ray.air.session.get_trial_id() str [source]
Trial id for the corresponding trial.
- ray.air.session.get_trial_resources() PlacementGroupFactory [source]
Trial resources for the corresponding trial.
- ray.air.session.get_trial_dir() str [source]
Log directory corresponding to the trial directory for a Tune session. If calling from a Train session, this will give the trial directory of its parent Tune session.
from ray import tune from ray.air import session def train_func(): # Example: # >>> session.get_trial_dir() # ~/ray_results/<exp-name>/<trial-dir> tuner = tune.Tuner(train_func) tuner.fit()
tune.Trainable (Class API)#
- class ray.tune.Trainable(config: Dict[str, Any] = None, logger_creator: Callable[[Dict[str, Any]], Logger] = None, remote_checkpoint_dir: Optional[str] = None, custom_syncer: Optional[ray.tune.syncer.Syncer] = None, sync_timeout: Optional[int] = None)[source]#
Abstract class for trainable models, functions, etc.
A call to
train()
on a trainable will execute one logical iteration of training. As a rule of thumb, the execution time of one train call should be large enough to avoid overheads (i.e. more than a few seconds), but short enough to report progress periodically (i.e. at most a few minutes).Calling
save()
should save the training state of a trainable to disk, andrestore(path)
should restore a trainable to the given state.Generally you only need to implement
setup
,step
,save_checkpoint
, andload_checkpoint
when subclassing Trainable.Other implementation methods that may be helpful to override are
log_result
,reset_config
,cleanup
, and_export_model
.Tune will convert this class into a Ray actor, which runs on a separate process. By default, Tune will also change the current working directory of this process to its corresponding trial-level log directory
self.logdir
. This is designed so that different trials that run on the same physical node won’t accidently write to the same location and overstep each other.The behavior of changing the working directory can be disabled by setting the flag
chdir_to_trial_dir=False
intune.TuneConfig
. This allows access to files in the original working directory, but relative paths should be used for read only purposes, and you must make sure that the directory is synced on all nodes if running on multiple machines.The
TUNE_ORIG_WORKING_DIR
environment variable was the original workaround for accessing paths relative to the original working directory. This environment variable is deprecated, and thechdir_to_trial_dir
flag described above should be used instead.This class supports checkpointing to and restoring from remote storage.
PublicAPI: This API is stable across Ray releases.
- _checkpoint_cls#
alias of
ray.air.checkpoint.Checkpoint
- _create_logger(config: Dict[str, Any], logger_creator: Callable[[Dict[str, Any]], Logger] = None)[source]#
Create logger from logger creator.
Sets _logdir and _result_logger.
_logdir
is the per trial directory for the Trainable.
- _export_model(export_formats: List[str], export_dir: str)[source]#
Subclasses should override this to export model.
- Parameters
export_formats – List of formats that should be exported.
export_dir – Directory to place exported models.
- Returns
A dict that maps ExportFormats to successfully exported models.
- _storage_path(local_path)[source]#
Converts a
local_path
to be based off ofself.remote_checkpoint_dir
.
- cleanup()[source]#
Subclasses should override this for any cleanup on stop.
If any Ray actors are launched in the Trainable (i.e., with a RLlib trainer), be sure to kill the Ray actor process here.
This process should be lightweight. Per default,
You can kill a Ray actor by calling
ray.kill(actor)
on the actor or removing all references to it and waiting for garbage collectionNew in version 0.8.7.
- classmethod default_resource_request(config: Dict[str, Any]) Optional[Union[ray.tune.resources.Resources, ray.tune.execution.placement_groups.PlacementGroupFactory]] [source]#
Provides a static resource requirement for the given configuration.
This can be overridden by sub-classes to set the correct trial resource allocation, so the user does not need to.
@classmethod def default_resource_request(cls, config): return PlacementGroupFactory([{"CPU": 1}, {"CPU": 1}]])
- Parameters
config[Dict[str – The Trainable’s config dict.
Any]] – The Trainable’s config dict.
- Returns
- A Resources object or
PlacementGroupFactory consumed by Tune for queueing.
- Return type
Union[Resources, PlacementGroupFactory]
- delete_checkpoint(checkpoint_path: Union[str, ray.air.checkpoint.Checkpoint])[source]#
Deletes local copy of checkpoint.
- Parameters
checkpoint_path – Path to checkpoint.
- export_model(export_formats: Union[List[str], str], export_dir: Optional[str] = None)[source]#
Exports model based on export_formats.
Subclasses should override _export_model() to actually export model to local directory.
- Parameters
export_formats – Format or list of (str) formats that should be exported.
export_dir – Optional dir to place the exported model. Defaults to self.logdir.
- Returns
A dict that maps ExportFormats to successfully exported models.
- get_auto_filled_metrics(now: Optional[datetime.datetime] = None, time_this_iter: Optional[float] = None, debug_metrics_only: bool = False) dict [source]#
Return a dict with metrics auto-filled by the trainable.
If
debug_metrics_only
is True, only metrics that don’t require at least one iteration will be returned (ray.tune.result.DEBUG_METRICS
).
- load_checkpoint(checkpoint: Union[Dict, str])[source]#
Subclasses should override this to implement restore().
Warning
In this method, do not rely on absolute paths. The absolute path of the checkpoint_dir used in
Trainable.save_checkpoint
may be changed.If
Trainable.save_checkpoint
returned a prefixed string, the prefix of the checkpoint string returned byTrainable.save_checkpoint
may be changed. This is because trial pausing depends on temporary directories.The directory structure under the checkpoint_dir provided to
Trainable.save_checkpoint
is preserved.See the examples below.
Example
>>> import os >>> from ray.tune.trainable import Trainable >>> class Example(Trainable): ... def save_checkpoint(self, checkpoint_path): ... my_checkpoint_path = os.path.join(checkpoint_path, "my/path") ... return my_checkpoint_path ... def load_checkpoint(self, my_checkpoint_path): ... print(my_checkpoint_path) >>> trainer = Example() >>> # This is used when PAUSED. >>> obj = trainer.save_to_object() <logdir>/tmpc8k_c_6hsave_to_object/checkpoint_0/my/path >>> # Note the different prefix. >>> trainer.restore_from_object(obj) <logdir>/tmpb87b5axfrestore_from_object/checkpoint_0/my/path
If
Trainable.save_checkpoint
returned a dict, then Tune will directly pass the dict data as the argument to this method.Example
>>> from ray.tune.trainable import Trainable >>> class Example(Trainable): ... def save_checkpoint(self, checkpoint_path): ... return {"my_data": 1} ... def load_checkpoint(self, checkpoint_dict): ... print(checkpoint_dict["my_data"])
New in version 0.8.7.
- Parameters
checkpoint – If dict, the return value is as returned by
save_checkpoint
. If a string, then it is a checkpoint path that may have a different prefix than that returned bysave_checkpoint
. The directory structure underneath thecheckpoint_dir
fromsave_checkpoint
is preserved.
- log_result(result: Dict)[source]#
Subclasses can optionally override this to customize logging.
The logging here is done on the worker process rather than the driver.
New in version 0.8.7.
- Parameters
result – Training result returned by step().
- reset(new_config, logger_creator=None)[source]#
Resets trial for use with new config.
Subclasses should override reset_config() to actually reset actor behavior for the new config.
- reset_config(new_config: Dict)[source]#
Resets configuration without restarting the trial.
This method is optional, but can be implemented to speed up algorithms such as PBT, and to allow performance optimizations such as running experiments with reuse_actors=True.
- Parameters
new_config – Updated hyperparameter configuration for the trainable.
- Returns
True if reset was successful else False.
- classmethod resource_help(config: Dict)[source]#
Returns a help string for configuring this trainable’s resources.
- Parameters
config – The Trainer’s config dict.
- restore(checkpoint_path: Union[str, ray.air.checkpoint.Checkpoint], checkpoint_node_ip: Optional[str] = None, fallback_to_latest: bool = False)[source]#
Restores training state from a given model checkpoint.
These checkpoints are returned from calls to save().
Subclasses should override
load_checkpoint()
instead to restore state. This method restores additional metadata saved with the checkpoint.checkpoint_path
should match with the return fromsave()
.checkpoint_path
can be/ray_results/exp/MyTrainable_abc/ checkpoint_00000/checkpoint
. Or,/ray_results/exp/MyTrainable_abc/checkpoint_00000
.self.logdir
should generally be corresponding tocheckpoint_path
, for example,/ray_results/exp/MyTrainable_abc
.self.remote_checkpoint_dir
in this case, is something like,REMOTE_CHECKPOINT_BUCKET/exp/MyTrainable_abc
- Parameters
checkpoint_path – Path to restore checkpoint from. If this path does not exist on the local node, it will be fetched from external (cloud) storage if available, or restored from a remote node.
checkpoint_node_ip – If given, try to restore checkpoint from this node if it doesn’t exist locally or on cloud storage.
fallback_to_latest – If True, will try to recover the latest available checkpoint if the given
checkpoint_path
could not be found.
- restore_from_object(obj)[source]#
Restores training state from a checkpoint object.
These checkpoints are returned from calls to save_to_object().
- save(checkpoint_dir: Optional[str] = None, prevent_upload: bool = False) str [source]#
Saves the current model state to a checkpoint.
Subclasses should override
save_checkpoint()
instead to save state. This method dumps additional metadata alongside the saved path.If a remote checkpoint dir is given, this will also sync up to remote storage.
- Parameters
checkpoint_dir – Optional dir to place the checkpoint.
prevent_upload – If True, will not upload the saved checkpoint to cloud.
- Returns
The given or created checkpoint directory.
Note the return path should match up with what is expected of
restore()
.
- save_checkpoint(checkpoint_dir: str) Optional[Union[str, Dict]] [source]#
Subclasses should override this to implement
save()
.Warning
Do not rely on absolute paths in the implementation of
Trainable.save_checkpoint
andTrainable.load_checkpoint
.Use
validate_save_restore
to catchTrainable.save_checkpoint
/Trainable.load_checkpoint
errors before execution.>>> from ray.tune.utils import validate_save_restore >>> MyTrainableClass = ... >>> validate_save_restore(MyTrainableClass) >>> validate_save_restore( ... MyTrainableClass, use_object_store=True)
New in version 0.8.7.
- Parameters
checkpoint_dir – The directory where the checkpoint file must be stored. In a Tune run, if the trial is paused, the provided path may be temporary and moved.
- Returns
A dict or string. If string, the return value is expected to be prefixed by
checkpoint_dir
. If dict, the return value will be automatically serialized by Tune. In both cases, the return value is exactly what will be passed toTrainable.load_checkpoint()
upon restore.
Example
>>> trainable, trainable1, trainable2 = ... >>> print(trainable1.save_checkpoint("/tmp/checkpoint_1")) "/tmp/checkpoint_1" >>> print(trainable2.save_checkpoint("/tmp/checkpoint_2")) {"some": "data"} >>> trainable.save_checkpoint("/tmp/bad_example") "/tmp/NEW_CHECKPOINT_PATH/my_checkpoint_file" # This will error.
- save_to_object()[source]#
Saves the current model state to a Python object.
It also saves to disk but does not return the checkpoint path. It does not save the checkpoint to cloud storage.
- Returns
Object holding checkpoint data.
- setup(config: Dict)[source]#
Subclasses should override this for custom initialization.
New in version 0.8.7.
- Parameters
config – Hyperparameters and other configs given. Copy of
self.config
.
- step()[source]#
Subclasses should override this to implement train().
The return value will be automatically passed to the loggers. Users can also return
tune.result.DONE
ortune.result.SHOULD_CHECKPOINT
as a key to manually trigger termination or checkpointing of this trial. Note that manual checkpointing only works when subclassing Trainables.New in version 0.8.7.
- Returns
A dict that describes training progress.
- stop()[source]#
Releases all resources used by this trainable.
Calls
Trainable.cleanup
internally. Subclasses should overrideTrainable.cleanup
for custom cleanup procedures.
- train()[source]#
Runs one logical iteration of training.
Calls
step()
internally. Subclasses should overridestep()
instead to return results. This method automatically fills the following fields in the result:done
(bool): training is terminated. Filled only if not provided.time_this_iter_s
(float): Time in seconds this iteration took to run. This may be overridden in order to override the system-computed time difference.time_total_s
(float): Accumulated time in seconds for this entire experiment.experiment_id
(str): Unique string identifier for this experiment. This id is preserved across checkpoint / restore calls.training_iteration
(int): The index of this training iteration, e.g. call to train(). This is incremented afterstep()
is called.pid
(str): The pid of the training process.date
(str): A formatted date of when the result was processed.timestamp
(str): A UNIX timestamp of when the result was processed.hostname
(str): Hostname of the machine hosting the training process.node_ip
(str): Node ip of the machine hosting the training process.- Returns
A dict that describes training progress.
- train_buffered(buffer_time_s: float, max_buffer_length: int = 1000)[source]#
Runs multiple iterations of training.
Calls
train()
internally. Collects and combines multiple results. This function will runself.train()
repeatedly until one of the following conditions is met: 1) the maximum buffer length is reached, 2) the maximum buffer time is reached, or 3) a checkpoint was created. Even if the maximum time is reached, it will always block until at least one result is received.- Parameters
buffer_time_s – Maximum time to buffer. The next result received after this amount of time has passed will return the whole buffer.
max_buffer_length – Maximum number of results to buffer.
- property iteration#
Current training iteration.
This value is automatically incremented every time
train()
is called and is automatically inserted into the training result dict.
- property logdir#
Directory of the results and checkpoints for this Trainable.
Tune will automatically sync this folder with the driver if execution is distributed.
Note that the current working directory will also be changed to this.
- property training_iteration#
Current training iteration (same as
self.iteration
).This value is automatically incremented every time
train()
is called and is automatically inserted into the training result dict.
- property trial_id#
Trial ID for the corresponding trial of this Trainable.
This is not set if not using Tune.
trial_id = self.trial_id
- property trial_name#
Trial name for the corresponding trial of this Trainable.
This is not set if not using Tune.
name = self.trial_name
- property trial_resources: Union[ray.tune.resources.Resources, ray.tune.execution.placement_groups.PlacementGroupFactory]#
Resources currently assigned to the trial of this Trainable.
This is not set if not using Tune.
trial_resources = self.trial_resources
Utilities#
- ray.tune.utils.wait_for_gpu(gpu_id: Optional[Union[int, str]] = None, target_util: float = 0.01, retry: int = 20, delay_s: int = 5, gpu_memory_limit: Optional[float] = None)[source]#
Checks if a given GPU has freed memory.
Requires
gputil
to be installed:pip install gputil
.- Parameters
gpu_id – GPU id or uuid to check. Must be found within GPUtil.getGPUs(). If none, resorts to the first item returned from
ray.get_gpu_ids()
.target_util – The utilization threshold to reach to unblock. Set this to 0 to block until the GPU is completely free.
retry – Number of times to check GPU limit. Sleeps
delay_s
seconds between checks.delay_s – Seconds to wait before check.
- Returns
True if free.
- Return type
bool
- Raises
RuntimeError – If GPUtil is not found, if no GPUs are detected or if the check fails.
Example:
def tune_func(config): tune.util.wait_for_gpu() train() tuner = tune.Tuner( tune.with_resources( tune_func, resources={"gpu": 1} ), tune_config=tune.TuneConfig(num_samples=10) ) tuner.fit()
PublicAPI (beta): This API is in beta and may change before becoming stable.
- ray.tune.utils.diagnose_serialization(trainable: Callable)[source]#
Utility for detecting why your trainable function isn’t serializing.
- Parameters
trainable – The trainable object passed to tune.Tuner(trainable). Currently only supports Function API.
- Returns
bool | set of unserializable objects.
Example:
import threading # this is not serializable e = threading.Event() def test(): print(e) diagnose_serialization(test) # should help identify that 'e' should be moved into # the `test` scope. # correct implementation def test(): e = threading.Event() print(e) assert diagnose_serialization(test) is True
DeveloperAPI: This API may change across minor Ray releases.
- ray.tune.utils.validate_save_restore(trainable_cls: Type, config: Optional[Dict] = None, num_gpus: int = 0, use_object_store: bool = False)[source]#
Helper method to check if your Trainable class will resume correctly.
- Parameters
trainable_cls – Trainable class for evaluation.
config – Config to pass to Trainable when testing.
num_gpus – GPU resources to allocate when testing.
use_object_store – Whether to save and restore to Ray’s object store. Recommended to set this to True if planning to use algorithms that pause training (i.e., PBT, HyperBand).
DeveloperAPI: This API may change across minor Ray releases.
tune.with_parameters#
- ray.tune.with_parameters(trainable: Union[Type[Trainable], Callable], **kwargs)[source]#
Wrapper for trainables to pass arbitrary large data objects.
This wrapper function will store all passed parameters in the Ray object store and retrieve them when calling the function. It can thus be used to pass arbitrary data, even datasets, to Tune trainables.
This can also be used as an alternative to
functools.partial
to pass default arguments to trainables.When used with the function API, the trainable function is called with the passed parameters as keyword arguments. When used with the class API, the
Trainable.setup()
method is called with the respective kwargs.If the data already exists in the object store (are instances of ObjectRef), using
tune.with_parameters()
is not necessary. You can instead pass the object refs to the training function via theconfig
or use Python partials.- Parameters
trainable – Trainable to wrap.
**kwargs – parameters to store in object store.
Function API example:
from ray import tune from ray.air import session def train(config, data=None): for sample in data: loss = update_model(sample) session.report(loss=loss) data = HugeDataset(download=True) tuner = Tuner( tune.with_parameters(train, data=data), # ... ) tuner.fit()
Class API example:
from ray import tune class MyTrainable(tune.Trainable): def setup(self, config, data=None): self.data = data self.iter = iter(self.data) self.next_sample = next(self.iter) def step(self): loss = update_model(self.next_sample) try: self.next_sample = next(self.iter) except StopIteration: return {"loss": loss, done: True} return {"loss": loss} data = HugeDataset(download=True) tuner = Tuner( tune.with_parameters(MyTrainable, data=data), # ... )
Note
When restoring a Tune experiment, you need to re-specify the trainable wrapped with
tune.with_parameters
. The reasoning behind this is as follows:1.
tune.with_parameters
stores parameters in the object store and attaches object references to the trainable, but the objects they point to may not exist anymore upon restore.2. The attached objects could be arbitrarily large, so Tune does not save the object data along with the trainable.
To restore, Tune allows the trainable to be re-specified in
Tuner.restore(overwrite_trainable=...)
. Continuing from the previous examples, here’s an example of restoration:from ray.tune import Tuner data = HugeDataset(download=True) tuner = Tuner.restore( "/path/to/experiment/", overwrite_trainable=tune.with_parameters(MyTrainable, data=data) )
PublicAPI (beta): This API is in beta and may change before becoming stable.