Deephyper

* Now compatible with `Python >=3.7, <3.10`
* Fixed `log_dir` argument in search
* Added logging for command line HPS/NAS

* All the search algorithms were tested to have a correct behaviour when `random_state` is set.
* Callbacks (`deephyper.evaluator.callback`) can now be used to extend the behavior of the existing `Evaluator`. A `LoggerCallback`, `ProfilingCallback`, `SearchEarlyStopping` are already available (see example below).
* All search algorithms are now importable from their `hps` or `nas` package. For example, `from deephyper.search.hps import AMBS` and `from deephyper.search.nas import AgEBO`.
* `HpProblem` and `NaProblem` do not have a `seed` parameter anymore. The `random_state` has to be set when instantiating a `Search(random_state=...)`.

**Examlpe**: `SearchEarlyStopping`

python
from deephyper.problem import HpProblem
from deephyper.search.hps import AMBS
from deephyper.evaluator import Evaluator
from deephyper.evaluator.callback import LoggerCallback, SearchEarlyStopping

problem = HpProblem()
problem.add_hyperparameter((0.0, 10.0), "x")

def f(config):
return config["x"]

evaluator = Evaluator.create(f,
method="ray",
method_kwargs={
"num_cpus": 1,
"num_cpus_per_task": 0.25,
"callbacks": [LoggerCallback(), SearchEarlyStopping(patience=10)]
})
print(f"Num. Workers {evaluator.num_workers}")

search = AMBS(problem, evaluator, filter_duplicated=False)

results = search.search(max_evals=500)


Gives the following output:

console
Num. Workers 4
[00001] -- best objective: 3.74540 -- received objective: 3.74540
[00002] -- best objective: 6.38145 -- received objective: 6.38145

This new release help us move toward a more stable version of DeepHyper.

* Refactored the [DeepHyper Documentation](http://deephyper.readthedocs.io)
* Developed notebook tutorials
* Decoupled the command line and Python interfaces
* Refactored the `Evaluator` interface with `evaluator.submit/gather`
* Added `deephyper.ensemble` for ensembles with uncertainty quantification
* Removed `deephyper.post`

General

Full API documentation

The DeepHyper API is now fully documented at [DeepHyper API](https://deephyper.readthedocs.io/en/latest/api/deephyper.html)

Tensorflow-Probability as a new dependency

[TensorFlow Probability](https://www.tensorflow.org/probability) is now part of DeepHyper default set of dependencies

Automated submission with Ray at ALCF

It is now possible to directly submit with `deephyper ray-submit ...` for DeepHyper at the ALCF. This feature is only available on ThetaGPU for now but can be extended to other systems by following [this script](https://github.com/deephyper/deephyper/blob/c7608e0c61bd805c109145744b567cbb6cf01673/deephyper/core/cli/ray_submit.py).

ThetaGPU at ALCF

* New installation documentation is available at [Installation ThetaGPU (ALCF)](https://deephyper.readthedocs.io/en/latest/install/thetagpu.html)
* A new user guide is available at [Running on ThetaGPU (ALCF)](https://deephyper.readthedocs.io/en/latest/user_guides/thetagpu.html) to understand how to run manually and automatically DeepHyper on ThetaGPU.

New documentation for auto-sklearn search with DeepHyper

The access to auto-sklearn features was changed to `deephyper.sklearn` and a new documentation is available for this feature at [User guide: AutoSklearn](https://deephyper.readthedocs.io/en/latest/user_guides/autosklearn.html)

New command lines for DeepHyper Analytics

The `deephyper-analytics` command was modified and enhanced with new features. The see the full updated documentation follow [DeepHyper Analytics Tools](https://deephyper.readthedocs.io/en/latest/user_guides/analytics.html).

The `topk` command is now available to have quick feedback from the results of an experiment:

$ deephyper-analytics topk combo_8gpu_8_agebo/infos/results.csv -k 2
'0':
arch_seq: '[229, 0, 22, 1, 1, 53, 29, 1, 119, 1, 0, 116, 123, 1, 273, 0, 1, 388]'
batch_size: 59

Minor bug corrections

* Compatible with Tensorflow 2.
* Horovod compatibility with Balsam evaluator for Theta.
* Horovod and Balsam are now optional installations.
* Update of the AMBS algorithm for Hyperparameter search for better scalability.
* Removing the PPO search for Neural Architecture Search.
* Creating the `SpaceFactory` interface for the `deepspace` package which provides ready to go neural architecture search spaces.
* Local distribution of jobs with Ray and multiprocessors CPUs.