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[tune] Fix up examples (#9201)
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Richard Liaw
@@ -3,81 +3,85 @@
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A Basic Tune Tutorial
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=====================
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.. image:: /images/tune-api.svg
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This tutorial will walk you through the process of setting up Tune. Specifically, we'll leverage early stopping and Bayesian Optimization (via HyperOpt) to optimize your PyTorch model.
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This tutorial will walk you through the following process to setup a Tune experiment using Pytorch. Specifically, we'll leverage ASHA and Bayesian Optimization (via HyperOpt) via the following steps:
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1. Integrating Tune into your workflow
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2. Specifying a TrialScheduler
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3. Adding a SearchAlgorithm
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4. Getting the best model and analyzing results
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.. tip:: If you have suggestions as to how to improve this tutorial, please `let us know <https://github.com/ray-project/ray/issues/new/choose>`_!
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.. note::
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To run this example, you will need to install the following:
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To run this example, you will need to install the following:
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.. code-block:: bash
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.. code-block:: bash
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$ pip install ray torch torchvision
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$ pip install ray torch torchvision
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Pytorch Model Setup
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~~~~~~~~~~~~~~~~~~~
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We first run some imports:
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To start off, let's first import some dependencies:
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __tutorial_imports_begin__
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:end-before: __tutorial_imports_end__
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Then, let's define the PyTorch model that we'll be training.
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Below, we have some boiler plate code for a PyTorch training function.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __model_def_begin__
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:end-before: __model_def_end__
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Below, we have some boiler plate code for training and evaluating your model in Pytorch. :ref:`Skip ahead to the Tune usage <tutorial-tune-setup>`.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __train_def_begin__
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:end-before: __train_def_end__
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.. _tutorial-tune-setup:
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Setting up Tune
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~~~~~~~~~~~~~~~
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Below, we define a function that trains the Pytorch model for multiple epochs. This function will be executed on a separate :ref:`Ray Actor (process) <actor-guide>` underneath the hood, so we need to communicate the performance of the model back to Tune (which is on the main Python process).
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To do this, we call :ref:`tune.report <tune-function-docstring>` in our training function, which sends the performance value back to Tune.
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.. tip:: Since the function is executed on the separate process, make sure that the function is :ref:`serializable by Ray <serialization-guide>`.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __train_func_begin__
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:end-before: __train_func_end__
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Notice that there's a couple helper functions in the above training script. You can take a look at these functions in the imported module `examples/mnist_pytorch <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/mnist_pytorch.py>`__; there's no black magic happening. For example, ``train`` is simply a for loop over the data loader.
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.. code:: python
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EPOCH_SIZE = 20
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def train(model, optimizer, train_loader):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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if batch_idx * len(data) > EPOCH_SIZE:
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return
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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Let's run 1 trial, randomly sampling from a uniform distribution for learning rate and momentum.
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Let's run 1 trial by calling :ref:`tune.run <tune-run-ref>` and :ref:`randomly sample <tune-sample-docs>` from a uniform distribution for learning rate and momentum.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __eval_func_begin__
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:end-before: __eval_func_end__
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We can then plot the performance of this trial.
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``tune.run`` returns an :ref:`Analysis object <tune-analysis-docs>`. You can use this to plot the performance of this trial.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __plot_begin__
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:end-before: __plot_end__
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.. note:: Tune will automatically run parallel trials across all available cores/GPUs on your machine or cluster. To limit the number of cores that Tune uses, you can call ``ray.init(num_cpus=<int>, num_gpus=<int>)`` before ``tune.run``.
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.. note:: Tune will automatically run parallel trials across all available cores/GPUs on your machine or cluster. To limit the number of cores that Tune uses, you can call ``ray.init(num_cpus=<int>, num_gpus=<int>)`` before ``tune.run``. If you're using a Search Algorithm like Bayesian Optimization, you'll want to use the :ref:`ConcurrencyLimiter <limiter>`.
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Early Stopping with ASHA
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~~~~~~~~~~~~~~~~~~~~~~~~
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Let's integrate a Trial Scheduler to our search - ASHA, a scalable algorithm for principled early stopping.
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Let's integrate early stopping into our optimization process. Let's use :ref:`ASHA <tune-scheduler-hyperband>`, a scalable algorithm for `principled early stopping`_.
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How does it work? On a high level, it terminates trials that are less promising and
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allocates more time and resources to more promising trials. See `this blog post <https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/>`__ for more details.
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.. _`principled early stopping`: https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/
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We can afford to **increase the search space by 5x**, by adjusting the parameter ``num_samples``. See :ref:`tune-schedulers` for more details of available schedulers and library integrations.
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On a high level, ASHA terminates trials that are less promising and allocates more time and resources to more promising trials. As our optimization process becomes more efficient, we can afford to **increase the search space by 5x**, by adjusting the parameter ``num_samples``.
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ASHA is implemented in Tune as a "Trial Scheduler". These Trial Schedulers can early terminate bad trials, pause trials, clone trials, and alter hyperparameters of a running trial. See :ref:`the TrialScheduler documentation <tune-schedulers>` for more details of available schedulers and library integrations.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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@@ -95,7 +99,7 @@ You can run the below in a Jupyter notebook to visualize trial progress.
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:scale: 50%
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:align: center
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You can also use Tensorboard for visualizing results.
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You can also use :ref:`Tensorboard <tensorboard>` for visualizing results.
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.. code:: bash
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@@ -105,18 +109,21 @@ You can also use Tensorboard for visualizing results.
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Search Algorithms in Tune
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~~~~~~~~~~~~~~~~~~~~~~~~~
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With Tune you can combine powerful hyperparameter search libraries such as `HyperOpt <https://github.com/hyperopt/hyperopt>`_ and `Ax <https://ax.dev>`_ with state-of-the-art algorithms such as HyperBand without modifying any model training code. Tune allows you to use different search algorithms in combination with different trial schedulers. See :ref:`tune-search-alg` for more details of available algorithms and library integrations.
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In addition to :ref:`TrialSchedulers <tune-schedulers>`, you can further optimize your hyperparameters by using an intelligent search technique like Bayesian Optimization. To do this, you can use a Tune :ref:`Search Algorithm <tune-search-alg>`. Search Algorithms leverage optimization algorithms to intelligently navigate the given hyperparameter space.
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Note that each library has a specific way of defining the search space.
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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:start-after: __run_searchalg_begin__
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:end-before: __run_searchalg_end__
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.. note:: Tune allows you to use some search algorithms in combination with different trial schedulers. See :ref:`this page for more details <tune-schedulers>`.
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Evaluate your model
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~~~~~~~~~~~~~~~~~~~
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You can evaluate best trained model using the Analysis object to retrieve the best model:
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You can evaluate best trained model using the :ref:`Analysis object <tune-analysis-docs>` to retrieve the best model:
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.. literalinclude:: /../../python/ray/tune/tests/tutorial.py
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:language: python
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@@ -126,4 +133,7 @@ You can evaluate best trained model using the Analysis object to retrieve the be
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Next Steps
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----------
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Take a look at the :ref:`tune-user-guide` for a more comprehensive overview of Tune's features.
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* Take a look at the :ref:`tune-user-guide` for a more comprehensive overview of Tune's features.
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* Browse our :ref:`gallery of examples <tune-general-examples>` to see how to use Tune with PyTorch, XGBoost, Tensorflow, etc.
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* `Let us know <https://github.com/ray-project/ray/issues>`__ if you ran into issues or have any questions by opening an issue on our Github.
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