mirror of
https://github.com/vale981/ray
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213 lines
7.2 KiB
ReStructuredText
213 lines
7.2 KiB
ReStructuredText
.. _trainable-docs:
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Training (tune.Trainable, tune.report)
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======================================
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Training can be done with either a **Class API** (``tune.Trainable``) or **function-based API** (``tune.report``).
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You can use the **function-based API** for fast prototyping. On the other hand, the ``tune.Trainable`` interface supports checkpoint/restore functionality and provides more control for advanced algorithms.
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For the sake of example, let's maximize this objective function:
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.. code-block:: python
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def objective(x, a, b):
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return a * (x ** 0.5) + b
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.. _tune-function-api:
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Function-based API
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------------------
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.. code-block:: python
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def trainable(config):
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# config (dict): A dict of hyperparameters.
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for x in range(20):
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score = objective(x, config["a"], config["b"])
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tune.report(score=score) # This sends the score to Tune.
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analysis = tune.run(
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trainable,
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config={
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"a": 2,
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"b": 4
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})
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print("best config: ", analysis.get_best_config(metric="score", mode="max"))
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.. tip:: Do not use ``tune.track.log`` within a ``Trainable`` class.
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Tune will run this function on a separate thread in a Ray actor process. Note that this API is not checkpointable, since the thread will never return control back to its caller.
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.. note:: If you want to pass in a Python lambda, you will need to first register the function: ``tune.register_trainable("lambda_id", lambda x: ...)``. You can then use ``lambda_id`` in place of ``my_trainable``.
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.. _tune-class-api:
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Trainable Class API
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-------------------
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.. caution:: Do not use ``tune.track.log`` within a ``Trainable`` class.
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The Trainable **class API** will require users to subclass ``ray.tune.Trainable``. Here's a naive example of this API:
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.. code-block:: python
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from ray import tune
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class Trainable(tune.Trainable):
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def _setup(self, config):
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# config (dict): A dict of hyperparameters
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self.x = 0
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self.a = config["a"]
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self.b = config["b"]
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def _train(self): # This is called iteratively.
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score = objective(self.x, self.a, self.b)
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self.x += 1
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return {"score": score}
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analysis = tune.run(
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Trainable,
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stop={"training_iteration": 20},
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config={
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"a": 2,
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"b": 4
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})
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print('best config: ', analysis.get_best_config(metric="score", mode="max"))
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As a subclass of ``tune.Trainable``, Tune will create a ``Trainable`` object on a separate process (using the :ref:`Ray Actor API <actor-guide>`).
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1. ``_setup`` function is invoked once training starts.
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2. ``_train`` 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.
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3. ``_stop`` is invoked when training is finished.
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.. tip:: As a rule of thumb, the execution time of ``_train`` 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).
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In this example, we only implemented the ``_setup`` and ``_train`` methods for simplification. Next, we'll implement ``_save`` and ``_restore`` for checkpoint and fault tolerance.
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.. _tune-trainable-save-restore:
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Save and Restore
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~~~~~~~~~~~~~~~~
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Many Tune features rely on ``_save``, and ``_restore``, including the usage of certain Trial Schedulers, fault tolerance, and checkpointing.
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.. code-block:: python
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class MyTrainableClass(Trainable):
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def _save(self, tmp_checkpoint_dir):
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checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.pth")
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torch.save(self.model.state_dict(), checkpoint_path)
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return tmp_checkpoint_dir
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def _restore(self, tmp_checkpoint_dir):
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checkpoint_path = os.path.join(tmp_checkpoint_dir, "model.pth")
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self.model.load_state_dict(torch.load(checkpoint_path))
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Checkpoints will be saved by training iteration to ``local_dir/exp_name/trial_name/checkpoint_<iter>``. You can restore a single trial checkpoint by using ``tune.run(restore=<checkpoint_dir>)``.
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Tune also generates temporary checkpoints for pausing and switching between trials. For this purpose, it is important not to depend on absolute paths in the implementation of ``save``.
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Use ``validate_save_restore`` to catch ``_save``/``_restore`` errors before execution.
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.. code-block:: python
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from ray.tune.utils import validate_save_restore
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# both of these should return
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validate_save_restore(MyTrainableClass)
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validate_save_restore(MyTrainableClass, use_object_store=True)
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Advanced Resource Allocation
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----------------------------
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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 set ``extra_cpu`` or ``extra_gpu`` inside ``tune.run`` 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 set ``"gpu": 1, "extra_gpu": 4``.
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.. code-block:: python
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:emphasize-lines: 4-8
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tune.run(
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my_trainable,
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name="my_trainable",
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resources_per_trial={
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"cpu": 1,
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"gpu": 1,
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"extra_gpu": 4
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}
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)
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The ``Trainable`` also provides the ``default_resource_requests`` interface to automatically declare the ``resources_per_trial`` based on the given configuration.
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Advanced: Reusing Actors
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~~~~~~~~~~~~~~~~~~~~~~~~
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Your Trainable can often take a long time to start. To avoid this, you can do ``tune.run(reuse_actors=True)`` to reuse the same Trainable Python process and object for multiple hyperparameters.
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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.
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.. code-block:: python
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class PytorchTrainble(tune.Trainable):
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"""Train a Pytorch ConvNet."""
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def _setup(self, config):
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self.train_loader, self.test_loader = get_data_loaders()
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self.model = ConvNet()
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self.optimizer = optim.SGD(
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self.model.parameters(),
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lr=config.get("lr", 0.01),
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momentum=config.get("momentum", 0.9))
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def reset_config(self, new_config):
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for param_group in self.optimizer.param_groups:
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if "lr" in new_config:
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param_group["lr"] = new_config["lr"]
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if "momentum" in new_config:
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param_group["momentum"] = new_config["momentum"]
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self.model = ConvNet()
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self.config = new_config
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return True
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tune.Trainable
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--------------
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.. autoclass:: ray.tune.Trainable
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:member-order: groupwise
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:private-members:
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:members:
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tune.DurableTrainable
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---------------------
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.. autoclass:: ray.tune.DurableTrainable
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.. _track-docstring:
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tune.track
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----------
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.. automodule:: ray.tune.track
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:members:
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:exclude-members: init,
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KerasCallback
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-------------
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.. automodule:: ray.tune.integration.keras
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:members:
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StatusReporter
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--------------
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.. autoclass:: ray.tune.function_runner.StatusReporter
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:members: __call__, logdir
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