[air][tune] Aim logger (ray-project#32041)

Co-authored-by: Justin Yu <justinvyu@anyscale.com>
Co-authored-by: angelinalg <122562471+angelinalg@users.noreply.github.com>
Co-authored-by: tmynn <hovhannes.tamoyan@gmail.com>
Co-authored-by: Justin Yu <justinvyu@berkeley.edu>
Closes ray-project#30537
Fixes ray-project#30674

doc/source/_toc.yml

@@ -202,12 +202,14 @@ parts:
                     title: "Huggingface Example"
               - file: tune/examples/experiment-tracking
                 sections:
-                  - file: tune/examples/tune-comet
-                    title: "Comet Example"
                   - file: tune/examples/tune-wandb
                     title: "Weights & Biases Example"
                   - file: tune/examples/tune-mlflow
                     title: "MLflow Example"
+                  - file: tune/examples/tune-aim
+                    title: "Aim Example"
+                  - file: tune/examples/tune-comet
+                    title: "Comet Example"
               - file: tune/examples/hpo-frameworks
                 sections:
                   - file: tune/examples/ax_example

doc/source/tune/api/logging.rst

@@ -10,28 +10,33 @@ If you need to log something lower level like model weights or gradients,
 see :ref:`Trainable Logging <trainable-logging>`.
 
 .. note::
+
     Tune's per-trial ``Logger`` classes have been deprecated. They can still be used, but we encourage you
-    to use our new interface with the ``LoggerCallback`` class instead.
+    to use the ``LoggerCallback`` interface instead.
 
 
-Viskit
-------
+.. currentmodule:: ray
 
-Tune automatically integrates with `Viskit <https://github.com/vitchyr/viskit>`_ via the ``CSVLoggerCallback`` outputs.
-To use VisKit (you may have to install some dependencies), run:
+.. _logger-interface:
 
-.. code-block:: bash
+LoggerCallback Interface (tune.logger.LoggerCallback)
+-----------------------------------------------------
 
-    $ git clone https://github.com/rll/rllab.git
-    $ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
+.. autosummary::
+    :toctree: doc/
+    :template: autosummary/class_with_autosummary.rst
 
-The non-relevant metrics (like timing stats) can be disabled on the left to show only the
-relevant ones (like accuracy, loss, etc.).
+    ~tune.logger.LoggerCallback
 
-.. image:: ../images/ray-tune-viskit.png
+.. autosummary::
+    :toctree: doc/
 
+    ~tune.logger.LoggerCallback.log_trial_start
+    ~tune.logger.LoggerCallback.log_trial_restore
+    ~tune.logger.LoggerCallback.log_trial_save
+    ~tune.logger.LoggerCallback.log_trial_result
+    ~tune.logger.LoggerCallback.log_trial_end
 
-.. currentmodule:: ray
 
 Tune Built-in Loggers
 ---------------------
@@ -44,46 +49,59 @@ Tune Built-in Loggers
     tune.logger.TBXLoggerCallback
 
 
-MLFlow Integration: MLFlowLoggerCallback
-----------------------------------------
+MLFlow Integration
+------------------
 
 Tune also provides a logger for `MLflow <https://mlflow.org>`_.
 You can install MLflow via ``pip install mlflow``.
-You can see the :doc:`tutorial here </tune/examples/tune-mlflow>`.
+See the :doc:`tutorial here </tune/examples/tune-mlflow>`.
 
 .. autosummary::
     :toctree: doc/
 
     air.integrations.mlflow.MLflowLoggerCallback
 
-Wandb Integration: WandbLoggerCallback
---------------------------------------
+Wandb Integration
+-----------------
 
 Tune also provides a logger for `Weights & Biases <https://www.wandb.ai/>`_.
 You can install Wandb via ``pip install wandb``.
-You can see the :doc:`tutorial here </tune/examples/tune-wandb>`.
+See the :doc:`tutorial here </tune/examples/tune-wandb>`.
 
 .. autosummary::
     :toctree: doc/
 
     air.integrations.wandb.WandbLoggerCallback
 
-.. _logger-interface:
+Aim Integration
+---------------
 
-LoggerCallback Interface (tune.logger.LoggerCallback)
------------------------------------------------------
+Tune also provides a logger for the `Aim <https://aimstack.io/>`_ experiment tracker.
+You can install Aim via ``pip install aim``.
+See the :doc:`tutorial here </tune/examples/tune-aim>`
 
 .. autosummary::
     :toctree: doc/
-    :template: autosummary/class_with_autosummary.rst
 
-    ~tune.logger.LoggerCallback
+    ~tune.logger.aim.AimLoggerCallback
 
-.. autosummary::
-    :toctree: doc/
 
-    ~tune.logger.LoggerCallback.log_trial_start
-    ~tune.logger.LoggerCallback.log_trial_restore
-    ~tune.logger.LoggerCallback.log_trial_save
-    ~tune.logger.LoggerCallback.log_trial_result
-    ~tune.logger.LoggerCallback.log_trial_end
+Other Integrations
+------------------
+
+Viskit
+~~~~~~
+
+Tune automatically integrates with `Viskit <https://github.com/vitchyr/viskit>`_ via the ``CSVLoggerCallback`` outputs.
+To use VisKit (you may have to install some dependencies), run:
+
+.. code-block:: bash
+
+    $ git clone https://github.com/rll/rllab.git
+    $ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
+
+The non-relevant metrics (like timing stats) can be disabled on the left to show only the
+relevant ones (like accuracy, loss, etc.).
+
+.. image:: ../images/ray-tune-viskit.png
+

doc/source/tune/examples/experiment-tracking.rst

@@ -11,6 +11,14 @@ to use Ray Tune with Tensorboard, you can find more information in our
     :column: col-md-4 px-2 py-2
     :img-top-cls: pt-5 w-75 d-block mx-auto
 
+    ---
+    :img-top: /images/aim_logo.png
+
+    +++
+    .. link-button:: tune-aim-ref
+        :type: ref
+        :text: Using Aim with Ray Tune For Experiment Management
+        :classes: btn-link btn-block stretched-link
     ---
     :img-top: /images/comet_logo_full.png