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issue commentfacebookresearch/moco

AssertionError: Default process group is not initialized

The default #GPU in the code uses 8 GPUs. You may need to change the number of GPUs, batch size, number of training iterations, and lr to make it run on 4 GPUs.

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issue commentfacebookresearch/moco

AssertionError: Default process group is not initialized

i have the same problem....how to fix?

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pull request commentoptuna/optuna

Refactor a unittest in test_median.py

Codecov Report

Merging #2644 (ef50fe2) into master (211b1a9) will increase coverage by 0.00%. The diff coverage is n/a.

@@           Coverage Diff           @@
##           master    #2644   +/-   ##
=======================================
  Coverage   91.70%   91.70%           
=======================================
  Files         138      138           
  Lines       11497    11499    +2     
=======================================
+ Hits        10543    10545    +2     
  Misses        954      954

Impacted Files	Coverage Δ
...a/visualization/matplotlib/_intermediate_values.py	`100.00% <0.00%> (ø)`

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Pull request review commentoptuna/optuna

Refactor a unittest in test_median.py

  def test_median_pruner_with_one_trial() -> None: -    study = optuna.study.create_study()-    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))-    trial.report(1, 1)     pruner = optuna.pruners.MedianPruner(0, 0)+    study = optuna.study.create_study(pruner=pruner)+    trial = study.ask()+    trial.report(1, 1)      # A pruner is not activated at a first trial.-    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))+    assert not trial.should_prune()   @pytest.mark.parametrize("direction_value", [("minimize", 2), ("maximize", 0.5)]) def test_median_pruner_intermediate_values(direction_value: Tuple[str, float]) -> None:      direction, intermediate_value = direction_value     pruner = optuna.pruners.MedianPruner(0, 0)-    study = optuna.study.create_study(direction=direction)+    study = optuna.study.create_study(direction=direction, pruner=pruner) -    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))+    trial = study.ask()     trial.report(1, 1)-    study._storage.set_trial_state(trial._trial_id, TrialState.COMPLETE)+    study.tell(trial, 1) -    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))+    trial = study.ask()     # A pruner is not activated if a trial has no intermediate values.-    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))+    assert not trial.should_prune()      trial.report(intermediate_value, 1)     # A pruner is activated if a trial has an intermediate value.-    assert pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))+    assert trial.should_prune()   def test_median_pruner_intermediate_values_nan() -> None:      pruner = optuna.pruners.MedianPruner(0, 0)-    study = optuna.study.create_study()+    study = optuna.study.create_study(pruner=pruner) -    trial = optuna.trial.Trial(study, study._storage.create_new_trial(study._study_id))+    trial = study.ask()     trial.report(float("nan"), 1)     # A pruner is not activated if the study does not have any previous trials.-    assert not pruner.prune(study=study, trial=study._storage.get_trial(trial._trial_id))-    study._storage.set_trial_state(trial._trial_id, TrialState.COMPLETE)+    assert not trial.should_prune()+    study.tell(trial, -1)

It also makes sense for me to use -1 here.

keisuke-umezawa

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issue commentYannDubs/Neural-Process-Family

Why using posterior sampling for evaluation

Hi @xuesongwang thanks for the kind words but I don't completely understand what you are saying. Are you essentially suggesting to mask Y_trgt but still use X_trgt during evaluation ? If so I don't really see how that can help, the decoder already has access to X_trgt ...

xuesongwang

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PR closed optuna/optuna

Move examples to sub directories example optuna.integration stale

Motivation

A successive work of #2302 and #2458. Some examples for ML libraries are in the root directory of examples. I'd like to create sub directories for them.

Description of the changes

catboost_simple.py --> catboost/catboost_simple.py
dask_ml_simple.py --> dask_ml/dask_ml_simple.py
haiku_simple.py --> haiku/haiku_simple.py
ray-joblib.py --> ray/ray-joblib.py
skimage_lbp_simple.py --> skimage/skimage_lbp_simple.py
tensorboard_simple.py --> tensorboard/tensorboard_simple.py

+9 -9

2 commit in toshihikoyanase/optuna/tree/example-dir optuna/optuna/tree/master

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toshihikoyanase

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pull request commentoptuna/optuna

Move examples to sub directories

@keisuke-umezawa @HideakiImamura As mentioned in #2654, I'd like to close this PR and create a new repository for the Optuna examples. Thank you for your reviews.

toshihikoyanase

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PR opened optuna/optuna

[WIP] Move examples to a new repository example

Motivation

This PR is related to #2654.

Description of the changes

Remove examples. They will be placed in a new repository by using git subtree split command. The prototype repository is https://github.com/toshihikoyanase/optuna-examples.

TODO

[ ] Update README.md
[ ] Update links in the references

Opinion Wanted

Do we move all examples? Or do we keep some basic examples like quadratic_simple.py?

+15 -5784

1 commit in toshihikoyanase/optuna/tree/move-examples-to-independent-repository optuna/optuna/tree/master

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68 changed files

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issue openedoptuna/optuna

[RFC] Create `oputna/examples` repository and move existing examples

Motivation

Currently, the optuna/optuna repository has more than 60 example files, thanks to the great efforts of the contributors. It provides the usage of Optuna for various kinds of ML libraries such as PyTorch, LightGBM and etc.

However, I saw some problems in the CI of the examples as follows:

The GitHub Actions workflow file is getting complicated. For example, some libraries does not support Python 3.6, and we need to exclude them in the workflow file. Such exception handling will increase when we add new libraries.
The conflicts of library versions. For example, we wanted to test PyTorch examples with PyTorch v1.8.0, but AllenNLP did not support it.
It takes more than 4 hours to execute all examples sequentially. Due to the execution time, we cannot run the CI jobs when submitting pull requests.

Some of these problems can be solved by splitting the CI workflow, but I think that this is a good opportunity to create an independent repository for Optuna examples. This is partly because we had already had enough contents, and partly because we may want to employ different policies (code review (1 approval or 2 approvals) and coding style(#2240)) from the optuna/optuna repository.

Description

I propose to create a new repository to host the Optuna examples. I created a prototype repository and please take a look: https://github.com/toshihikoyanase/optuna-examples

It has an independent workflow file for each ML libraries (18 workflows in total). These workflows can be executed in parallel, and the CI jobs finish in an hour. With this change, we can execute CI with
We can preserves the git history by using the git subtree split command.

Alternatives (optional)

We can just divide the examples.yml in the optuna/optuna repository. I tried this approach in #2587, and I created about 20 workflows for the CI of examples.

However, I and reviewers found that this approach had potential risk to deteriorate the productivity. This is because it doubled the workflows and it may annoy developers/reviewers when they edit/review the workflow. If we can nest the workflow like .github/workflow/examples/allennlp, we can employ this approach, but GitHub does not recognize the yaml files in subdirectories as workflows.

created time in 2 days

Pull request review commentoptuna/optuna

Add a queue-like data structure to manage updated trials

+from collections import deque+import threading+from typing import Any+from typing import Deque+from typing import Dict+from typing import List+from typing import Tuple+from typing import TYPE_CHECKING+++if TYPE_CHECKING:+    from optuna import Study+    from optuna.trial import FrozenTrial+    from optuna.trial import TrialState+++class UpdatedTrialsQueue(object):+    """A virtual queue of trials in the specified states.++    This class imitates a queue that trial is added when it goes into specified states.++    It is not supposed to be directly accessed by library users except to write user-defined+    samplers.++    Note that the ``states`` argument should consist of the same stage states.+    """++    def __init__(self, study: "Study", states: Tuple["TrialState", ...]) -> None:+        for state in states:+            if state < states[0] or states[0] < state:+                raise RuntimeError("The states should be in the same stage.")++        self._study = study+        self._states = states++        self._queue: Deque[int] = deque()+        self._watching_trial_indices: List[int] = []+        self._next_min_trial_index = 0++        self._lock = threading.Lock()++    def __getstate__(self) -> Dict[Any, Any]:++        state = self.__dict__.copy()+        del state["_lock"]+        return state++    def __setstate__(self, state: Dict[Any, Any]) -> None:++        self.__dict__.update(state)+        self._lock = threading.Lock()++    def _fetch_trials(self, deepcopy: bool) -> List["FrozenTrial"]:+        trials = self._study.get_trials(deepcopy=deepcopy)

Here, we're still constructing all trials. How about taking some benchmarks before going forward to understand in what practical use cases this feature still becomes beneficial?

not522

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Pull request review commentoptuna/optuna

Add a queue-like data structure to manage updated trials

+from collections import deque+import threading+from typing import Any+from typing import Deque+from typing import Dict+from typing import List+from typing import Tuple+from typing import TYPE_CHECKING+++if TYPE_CHECKING:+    from optuna import Study+    from optuna.trial import FrozenTrial+    from optuna.trial import TrialState+++class UpdatedTrialsQueue(object):+    """A virtual queue of trials in the specified states.++    This class imitates a queue that trial is added when it goes into specified states.++    It is not supposed to be directly accessed by library users except to write user-defined+    samplers.++    Note that the ``states`` argument should consist of the same stage states.+    """++    def __init__(self, study: "Study", states: Tuple["TrialState", ...]) -> None:+        for state in states:+            if state < states[0] or states[0] < state:+                raise RuntimeError("The states should be in the same stage.")++        self._study = study+        self._states = states++        self._queue: Deque[int] = deque()+        self._watching_trial_indices: List[int] = []+        self._next_min_trial_index = 0++        self._lock = threading.Lock()++    def __getstate__(self) -> Dict[Any, Any]:

Maybe I missed something but when are __{get,set}state__ called?

not522

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Pull request review commentoptuna/optuna

Add a queue-like data structure to manage updated trials

 def __repr__(self) -> str:     def is_finished(self) -> bool:          return self != TrialState.RUNNING and self != TrialState.WAITING++    def __lt__(self, state: "TrialState") -> bool:

I think it's clearer to implement this as a method/function with a name. E.g.

    def is_promotable_to(self, state: "TrialState") -> bool:

not522

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pull request commentoptuna/optuna

Use `command` to check the existence of the libraries to avoid partially matching

Codecov Report

Merging #2653 (d0f0c5c) into master (b9103a0) will increase coverage by 0.00%. The diff coverage is n/a.

@@           Coverage Diff           @@
##           master    #2653   +/-   ##
=======================================
  Coverage   91.70%   91.70%           
=======================================
  Files         138      138           
  Lines       11497    11499    +2     
=======================================
+ Hits        10543    10545    +2     
  Misses        954      954

Impacted Files	Coverage Δ
...a/visualization/matplotlib/_intermediate_values.py	`100.00% <0.00%> (ø)`

Continue to review full report at Codecov.

Legend - Click here to learn more Δ = absolute <relative> (impact), ø = not affected, ? = missing data Powered by Codecov. Last update b9103a0...d0f0c5c. Read the comment docs.

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