.. _integrations-optuna: Neptune-Optuna Integration ========================== |Run on Colab| What will you get with this integration? ---------------------------------------- |optuna-tour| |Optuna| is an open source hyperparameter optimization framework to automate hyperparameter search. With Neptune integration, you can: * see the experiment as it is running, * see charts of logged run scores, * log the parameters tried at every run, * log the best parameters after training, * log and explore interactive optuna visualizations like plot_contour, plot_slice, plot_parallel_coordinate, and optimization_history, * save the ``optuna.study`` object itself. .. note:: This integration is tested with ``optuna==2.3.0``, ``neptune-client==0.4.132``, and ``neptune-contrib==0.25.0``. Where to start? --------------- To get started with this integration, follow the :ref:`quickstart ` below. You can also skip the basics and take a look at how to log Optuna visualizations and the ``optuna.study`` object during or after the sweep in the :ref:`advanced options ` section. If you want to try things out and focus only on the code you can either: #. Open the Colab notebook (badge-link below) with quickstart code and run it as an anonymous user "`neptuner`" - zero setup, it just works, #. View quickstart code as a plain Python script on |script|. .. _optuna-quickstart: Quickstart ---------- This quickstart will show you how to: * Install the necessary neptune packages * Connect Neptune to your Optuna hyperparameter tuning code and create the first experiment * Log metrics, figures, and artifacts from your first Optuna sweep to Neptune, and * Explore them in the Neptune UI. |Run on Colab| .. _optuna-before-you-start-basic: Before you start ^^^^^^^^^^^^^^^^ You have ``Python 3.x`` and following libraries installed: * ``neptune-client``, and ``neptune-contrib==0.24.7``. See :ref:`neptune-client installation guide `. * ``optuna``. See |optuna-install|. .. note:: The integration should work on newer versions of ``neptune-client`` and ``neptune-contrib`` too. You also need minimal familiarity with Optuna. Have a look at the |optuna-guide| guide to get started. .. code-block:: bash pip install --quiet optuna neptune-client neptune-contrib['monitoring'] Step 1: Initialize Neptune ^^^^^^^^^^^^^^^^^^^^^^^^^^ Run the code below: .. code-block:: python3 import neptune neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/optuna-integration') .. tip:: You can also use your personal API token. Read more about how to :ref:`securely set the Neptune API token `. Step 2: Create an Experiment ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Run the code below to create a Neptune experiment: .. code-block:: python3 neptune.create_experiment('optuna-sweep') This also creates a link to the experiment. Open the link in a new tab. The charts will currently be empty, but keep the window open. You will be able to see live metrics once logging starts. Step 3: Create the Neptune Callback ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: python3 import neptunecontrib.monitoring.optuna as opt_utils neptune_callback = opt_utils.NeptuneCallback() Step 4: Run Optuna with the Neptune callback ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Pass the ``neptune_callback`` as a callback to ``study.optimize()`` to monitor the metrics and parameters checked at each run. .. code-block:: python3 study = optuna.create_study(direction='maximize') study.optimize(objective, n_trials=100, callbacks=[neptune_callback]) Step 5: Monitor your Optuna training in Neptune ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Now you can switch to the Neptune tab which you had opened previously to watch the optimization live! Check out this |example experiment|. |optuna-basic-logs| .. _optuna-advanced-options: Advanced Options ---------------- Log charts and study object during sweep ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ While creating the Neptune Callback, you can set ``log_study=True`` and ``log_charts=True`` to log interactive charts from ``optuna.visualization`` and the study object itself after every iteration. .. code-block:: python3 neptune_callback = opt_utils.NeptuneCallback(log_study=True, log_charts=True) .. warning:: Depending on the size of the ``optuna.study`` object and the charts, this might add some overhead to the sweep. To avoid this, you can log the study object and charts after the sweep. Log charts and study object after sweep ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ You can log the ``optuna.study`` object and charts after the sweep has completed by running: .. code-block:: python3 opt_utils.log_study_info(study) Check out this |advance experiment| with advanced logging. |optuna-advanced-logs| How to ask for help? -------------------- Please visit the :ref:`Getting help ` page. Everything regarding support is there. What's next ----------- Now that you know how to integrate Neptune with Optuna, you can check: * Other :ref:`Hyperparameter Optimization Integrations with Neptune ` * :ref:`Downloading experiment data from Neptune ` .. External links .. |Run on Colab| raw:: html
Run in Google Colab View source on GitHub
.. |optuna-tour| raw:: html
.. |Optuna| raw:: html Optuna .. |script| raw:: html GitHub .. |optuna-install| raw:: html Optuna installation guide .. |optuna-guide| raw:: html Optuna tutorial .. |neptune-client| raw:: html neptune-client .. |neptune-contrib| raw:: html neptune-contrib .. |Neptune| raw:: html Neptune .. |example experiment| raw:: html example experiment .. |optuna-basic-logs| raw:: html
.. |advance experiment| raw:: html example experiment .. |optuna-advanced-logs| raw:: html