Merge pull request #695 from Epistimio/release-v0.2.0rc1

Release v0.2.0rc1

Author: bouthilx

LICENSE

@@ -1,6 +1,6 @@
 Software License Agreement (BSD License)
 
- Copyright (c) 2017-2020, Epistímio.
+ Copyright (c) 2017-2021, Epistímio.
  All rights reserved.
 
  Redistribution and use in source and binary forms, with or without

README.rst

@@ -118,7 +118,7 @@ If you use Oríon for published work, please cite our work using the following b
 
 .. code-block:: bibtex
 
-   @software{xavier_bouthillier_2021_0_1_15,
+   @software{xavier_bouthillier_2021_0_2_0,
      author       = {Xavier Bouthillier and
                      Christos Tsirigotis and
                      François Corneau-Tremblay and
@@ -142,10 +142,10 @@ If you use Oríon for published work, please cite our work using the following b
                      Pascal Lamblin and
                      Christopher Beckham},
      title        = {{Epistimio/orion: Asynchronous Distributed Hyperparameter Optimization}},
-     month        = may,
+     month        = nov,
      year         = 2021,
      publisher    = {Zenodo},
-     version      = {v0.1.17},
+     version      = {v0.2.0},
      doi          = {10.5281/zenodo.3478592},
      url          = {https://doi.org/10.5281/zenodo.3478592}
    }

ROADMAP.md

@@ -1,25 +1,35 @@
 # Roadmap
-Last update Sep 14th, 2021
+Last update Nov 23rd, 2021
 
 ## Next releases - Short-Term
 
-### v0.2
+### v0.2.1
 
-#### Generic `Optimizer` interface supporting various types of algorithms
+- New master process to enhance parallelisation efficiency.
+- [PBT](https://arxiv.org/abs/1711.09846)
 
-Change interface to support trial object instead of curated lists. This is necessary to support algorithms such as PBT.
+### v0.2.2
+
+- Use shared algo serialization instead of replications to enhance parallelisation efficiency.
+- [DEBH](https://arxiv.org/abs/2105.09821)
+
+### v0.2.3
+
+- [HEBO](https://github.com/huawei-noah/HEBO/tree/master/HEBO/archived_submissions/hebo)
+
+### v0.2.4
 
-#### More Optimizers
-- [PBT](https://arxiv.org/abs/1711.09846)
 - [BOHB](https://ml.informatik.uni-freiburg.de/papers/18-ICML-BOHB.pdf)
 
+## Next releases - Mid-Term
+
 #### Simple dashboard specific to monitoring and benchmarking of Black-Box optimization
 - Specific to hyper parameter optimizations
 - Provide status of experiments
 
 #### Leveraging previous experiences
 Leveraging the knowledge base contained in the EVC of previous trials to optimize and drive new
- trials.
+trials.
 
 ## Next releases - Long-Term
 

docs/src/code/algo/asha.rst

@@ -3,6 +3,6 @@ Asynchronous Successive Halving Algorithm
 
 Can't build documentation because of import order.
 Sphinx is loading ``orion.algo.asha`` before ``orion.algo`` and therefore
-there is a cycle between the definition of ``OptimizationAlgorithm`` and
+there is a cycle between the definition of ``BaseAlgorithm`` and
 ``ASHA`` as the meta-class ``Factory`` is trying to import ``ASHA``.
 `PR #135 <https://github.com/Epistimio/orion/pull/135/files>`_ should get rid of this problem.

docs/src/code/core/io/database.rst

@@ -12,5 +12,3 @@ Databases
 .. automodule:: orion.core.io.database
    :members:
    :show-inheritance:
-
-

docs/src/code/core/utils.rst

@@ -9,7 +9,6 @@ Utilities
    utils/format_trials
    utils/format_terminal
    utils/singleton
-   utils/points
 
 .. automodule:: orion.core.utils
    :members:

docs/src/code/core/utils/points.rst

@@ -55,7 +55,7 @@ For the previous example, we would run
 
 .. code-block:: console
 
-   $ orion hunt -n <experiment name> script.py --lr~'loguniform(1e-5, 1.0)'
+   $ orion hunt -n <experiment name> --max-trials 10 python script.py --lr~'loguniform(1e-5, 1.0)'
 
 This is going to start the optimization process using the default optimization algorithm and sample
 the values for the ``lr`` hyper-parameter in a log uniform distribution between 0.00001 et 1.0. Each

docs/src/install/gettingstarted.rst

@@ -110,7 +110,7 @@ Full Example of Global Configuration
         heartbeat: 120
         interrupt_signal_code: 130
         max_broken: 10
-        max_idle_time: 60
+        reservation_timeout: 60
         max_trials: 1000000000
         user_script_config: config
 
@@ -365,7 +365,7 @@ Worker
         heartbeat: 120
         interrupt_signal_code: 130
         max_broken: 10
-        max_idle_time: 60
+        reservation_timeout: 60
         max_trials: 1000000000
         user_script_config: config
 
@@ -464,21 +464,37 @@ max_broken
     Maximum number of broken trials before worker stops.
 
 
+.. _config_worker_reservation_timeout:
+
+reservation_timeout
+~~~~~~~~~~~~~~~~~~~
+
+:Type: int
+:Default: 60
+:Env var: ORION_RESERVATION_TIMEOUT
+:Description:
+    Maximum time the experiment can spend trying to reserve a new suggestion. Such timeout are
+    generally caused by slow database, large number of concurrent workers leading to many race
+    conditions or small search spaces with integer/categorical dimensions that may be fully
+    explored.
+
 
 .. _config_worker_max_idle_time:
 
 max_idle_time
 ~~~~~~~~~~~~~
 
+.. warning::
+
+   **DEPRECATED.** This argument will be removed in v0.3.
+   Use :ref:`config_worker_reservation_timeout` instead.
+
 :Type: int
 :Default: 60
 :Env var: ORION_MAX_IDLE_TIME
 :Description:
-    Maximum time the producer can spend trying to generate a new suggestion.Such timeout are
-    generally caused by slow database, large number of concurrent workers leading to many race
-    conditions or small search spaces with integer/categorical dimensions that may be fully
-    explored.
-
+    (DEPRECATED) This argument will be removed in v0.3. Use :ref:`config_worker_reservation_timeout`
+    instead.
 
 
 .. _config_worker_interrupt_signal_code:

docs/src/user/config.rst

@@ -42,7 +42,7 @@ Executor backends
 It is also possible to execute multiple workers using the argument ``--n-workers`` in commandline
 or ``experiment.workon(n_workers)`` using the python API. The workers will work together
 using the same mechanisms explained above, but an
-:class:`orion.executor.base.Executor` backend will be used in addition
+:class:`orion.executor.base.BaseExecutor` backend will be used in addition
 to spawn the workers and maintain them alive. The default backend is :ref:`executor-joblib`.
 
 You can configure it