CI env var (#1667)

Make CIRCLECI env var checks also apply on github actions

Co-authored-by: bottler <[email protected]>

nevergrad/benchmark/test_experiments.py

@@ -10,20 +10,23 @@
 import typing as tp
 from pathlib import Path
 from unittest import SkipTest
-import pytest
-import numpy as np
-from nevergrad.optimization import registry as optregistry
+
 import nevergrad.functions.base as fbase
-from nevergrad.functions.mlda import datasets
-from nevergrad.functions import rl
-from nevergrad.common import testing
+import numpy as np
+import pytest
 
 # from nevergrad.common.tools import Selector
-from nevergrad.common import tools
-from .xpbase import Experiment
+from nevergrad.common import testing, tools
+from nevergrad.functions import rl
+from nevergrad.functions.mlda import datasets
+from nevergrad.optimization import registry as optregistry
+
+from . import experiments, optgroups
 from .utils import Selector
-from . import experiments
-from . import optgroups
+from .xpbase import Experiment
+
+
+CI = bool(os.environ.get("CIRCLECI", False)) or bool(os.environ.get("CI", False))
 
 
 @testing.parametrized(
@@ -48,17 +51,17 @@ def test_experiments_registry(name: str, maker: tp.Callable[[], tp.Iterator[expe
         raise SkipTest("Compiler/emulator stuff too heavy for CircleCI.")
 
     # Our PGAN is not well accepted by circleci.
-    if "_pgan" in name and os.environ.get("CIRCLECI", False):
-        raise SkipTest("Too slow in CircleCI")
+    if "_pgan" in name and CI:
+        raise SkipTest("Too slow in CI")
 
-    if "gym" in name and os.environ.get("CIRCLECI", False):
-        raise SkipTest("Too slow in CircleCI")
+    if "gym" in name and CI:
+        raise SkipTest("Too slow in CI")
 
     if "yawideb" in name or "_quality" in name:
         raise SkipTest("I should have a look at this test.")
 
     # mixsimulator is not accepted by circleci pytest.
-    if "mixsimulator" in name and os.environ.get("CIRCLECI", False):
+    if "mixsimulator" in name and CI:
         raise SkipTest("Sigkill in CircleCI")
 
     # Our IQAs and our ScikitLearn are not well guaranteed on Windows.
@@ -72,7 +75,7 @@ def test_experiments_registry(name: str, maker: tp.Callable[[], tp.Iterator[expe
             check_maker(maker)  # this is to extract the function for reuse if other external packages need it
 
     # Some tests are skipped on CircleCI (but they do work well locally, if memory enough).
-    if os.environ.get("CIRCLECI", False):
+    if CI:
         if any(x in name for x in ["image", "mlda", "realworld", "adversarial_attack"]):
             raise SkipTest("Too slow in CircleCI")
 
@@ -145,7 +148,11 @@ def check_experiment(maker: tp.Any, short: bool = False, skip_seed: bool = False
         xps = list(itertools.islice(maker(seed), 0, 1 if short else 2))
         simplified = [
             Experiment(
-                xp.function, algo, budget=2, num_workers=min(2, xp.optimsettings.num_workers), seed=xp.seed
+                xp.function,
+                algo,
+                budget=2,
+                num_workers=min(2, xp.optimsettings.num_workers),
+                seed=xp.seed,
             )
             for xp in xps
         ]

nevergrad/functions/images/core.py

@@ -3,22 +3,23 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import os
 import itertools
+import os
 from pathlib import Path
 
 import cv2
+
+import nevergrad as ng
+import nevergrad.common.typing as tp
 import numpy as np
 import PIL.Image
-import torch.nn as nn
 import torch
+import torch.nn as nn
 import torchvision
-from torchvision.models import resnet50
 import torchvision.transforms as tr
-
-import nevergrad as ng
-import nevergrad.common.typing as tp
 from nevergrad.common import errors
+from torchvision.models import resnet50
+
 from .. import base
 from . import imagelosses
 
@@ -102,7 +103,8 @@ def _generate_images(self, x: np.ndarray) -> np.ndarray:
             .numpy()[:, :, :, [2, 1, 0]]
         )
 
-    def interpolate(self, base_image: np.ndarray, target: np.ndarray, k: int, num_images: int) -> np.ndarray:
+    @staticmethod
+    def interpolate(base_image: np.ndarray, target: np.ndarray, k: int, num_images: int) -> np.ndarray:
         if num_images == 1:
             return target
         coef1 = k / (num_images - 1)
@@ -132,7 +134,10 @@ def _loss_with_pgan(self, x: np.ndarray, export_string: str = "") -> float:
             image = self._generate_images(base_image).squeeze(0)
             image = cv2.resize(image, dsize=(226, 226), interpolation=cv2.INTER_NEAREST)
             if export_string:
-                cv2.imwrite(f"{export_string}_image{i}_{num_total_images}_{self.num_images}.jpg", image)
+                cv2.imwrite(
+                    f"{export_string}_image{i}_{num_total_images}_{self.num_images}.jpg",
+                    image,
+                )
             assert image.shape == (226, 226, 3), f"{x.shape} != {(226, 226, 3)}"
             loss += self.loss_function(image)
         return loss
@@ -202,7 +207,12 @@ def __init__(
             mutable_sigma=True,
         ).set_name("")
         array.set_mutation(sigma=self.epsilon / 10)
-        array.set_bounds(lower=-self.epsilon, upper=self.epsilon, method="clipping", full_range_sampling=True)
+        array.set_bounds(
+            lower=-self.epsilon,
+            upper=self.epsilon,
+            method="clipping",
+            full_range_sampling=True,
+        )
         max_size = ng.p.Scalar(lower=1, upper=200).set_integer_casting()
         array = ng.p.mutation.Crossover(axis=(1, 2), max_size=max_size)(array)
         super().__init__(self._loss, array)
@@ -250,7 +260,10 @@ def make_folder_functions(
             an experiment function corresponding to 1 of the image of the provided folder dataset.
         """
         assert model in {"resnet50", "test"}
-        tags = {"folder": "#FAKE#" if folder is None else Path(folder).name, "model": model}
+        tags = {
+            "folder": "#FAKE#" if folder is None else Path(folder).name,
+            "model": model,
+        }
         classifier: tp.Any = Resnet50() if model == "resnet50" else TestClassifier()
         classifier.eval()
 
@@ -271,7 +284,11 @@ def make_folder_functions(
             _, pred = torch.max(classifier(data), axis=1)
             if pred == target:
                 func = cls(
-                    classifier=classifier, image=data[0], label=int(target), targeted=False, epsilon=0.05
+                    classifier=classifier,
+                    image=data[0],
+                    label=int(target),
+                    targeted=False,
+                    epsilon=0.05,
                 )
                 func.add_descriptors(**tags)
                 yield func
@@ -310,7 +327,8 @@ def __init__(
         if not torch.cuda.is_available():
             use_gpu = False
         # Storing high level information..
-        if os.environ.get("CIRCLECI", False):
+        CI = bool(os.environ.get("CIRCLECI", False)) or bool(os.environ.get("CI", False))
+        if CI:
             raise errors.UnsupportedExperiment("ImageFromPGAN is not well supported in CircleCI")
         self.pgan_model = torch.hub.load(
             "facebookresearch/pytorch_GAN_zoo:hub",

nevergrad/functions/photonics/test_core.py

@@ -4,14 +4,17 @@
 # LICENSE file in the root directory of this source tree.
 
 import os
-from unittest import SkipTest
-import warnings
 import typing as tp
-import pytest
+import warnings
+from unittest import SkipTest
+
 import numpy as np
+import pytest
 from nevergrad.common import testing
-from . import core
-from . import photonics
+
+from . import core, photonics
+
+CI = bool(os.environ.get("CIRCLECI", False)) or bool(os.environ.get("CI", False))
 
 
 @testing.parametrized(
@@ -66,7 +69,7 @@ def test_photonics_bragg_recombination() -> None:
 
 
 def test_photonics_custom_mutation() -> None:
-    if os.environ.get("CIRCLECI", False):
+    if CI:
         raise SkipTest("Skipping in CI because way too slow on their machine (weird)")
     func = core.Photonics("morpho", 16, rolling=True)
     param = func.parametrization.spawn_child()
@@ -102,7 +105,7 @@ def test_no_warning(name: str, method: str) -> None:
     bragg=("bragg", 2.5, 0.93216),
 )
 def test_photonics_values(name: str, value: float, expected: float) -> None:
-    if name == "morpho" and os.environ.get("CIRCLECI", False):
+    if name == "morpho" and CI:
         raise SkipTest("Too slow in CircleCI")
     photo = core.Photonics(name, 16)
     np.testing.assert_almost_equal(photo(value * np.ones(16)), expected, decimal=4)
@@ -169,13 +172,21 @@ def test_photonics_values(name: str, value: float, expected: float) -> None:
 @testing.parametrized(
     morpho=("morpho", 1.127904, None),
     chirped=("chirped", 0.594587, None),
-    good_chirped=("chirped", 0.275923, np.array([GOOD_CHIRPED])),  # supposed to be better
+    good_chirped=(
+        "chirped",
+        0.275923,
+        np.array([GOOD_CHIRPED]),
+    ),  # supposed to be better
     bragg=("bragg", 0.96776, None),
-    photosic_realistic=("cf_photosic_realistic", 0.0860257, np.array(EPS_AND_D).reshape((2, -1))),
+    photosic_realistic=(
+        "cf_photosic_realistic",
+        0.0860257,
+        np.array(EPS_AND_D).reshape((2, -1)),
+    ),
     photosic_reference=("cf_photosic_reference", 0.431072, None),
 )
 def test_photonics_values_random(name: str, expected: float, data: tp.Optional[np.ndarray]) -> None:
-    if name == "morpho" and os.environ.get("CIRCLECI", False):
+    if name == "morpho" and CI:
         raise SkipTest("Too slow in CircleCI")
     size = data.size if data is not None else (16 if name != "morpho" else 4)
     photo = core.Photonics(name, size)

nevergrad/optimization/test_optimizerlib.py

@@ -44,6 +44,9 @@
 np.random.seed(0)
 
 
+CI = bool(os.environ.get("CIRCLECI", False)) or bool(os.environ.get("CI", False))
+
+
 def long_name(s: str):
     if s[-1] in "0123456789":
         return True
@@ -55,9 +58,7 @@ def long_name(s: str):
         return True
     if "DS" in s or "AX" in s or "BO" in s or any(x in s for x in [str(i) for i in range(10)]):
         return True
-    return len(s.replace("DiscreteOnePlusOne", "D1+1").replace("Tuned", "")) > 2 and os.environ.get(
-        "CIRCLECI", False
-    )
+    return len(s.replace("DiscreteOnePlusOne", "D1+1").replace("Tuned", "")) > 2 and CI
 
 
 short_registry = [r for r in registry if not long_name(r)]
@@ -206,8 +207,8 @@ def test_ngopt(dim: int, budget_multiplier: int, num_workers: int, bounded: bool
 @pytest.mark.parametrize("name", short_registry)  # type: ignore
 @testing.suppress_nevergrad_warnings()  # hides bad loss
 def test_infnan(name: str) -> None:
-    if any(x in name for x in ["SMAC", "BO", "AX"]) and os.environ.get("CIRCLECI", False):
-        raise SkipTest("too slow for CircleCI!")
+    if any(x in name for x in ["SMAC", "BO", "AX"]) and CI:
+        raise SkipTest("too slow for CI!")
     if "Force" in name:
         raise SkipTest("Forced methods not tested for infnan")
 
@@ -264,8 +265,8 @@ def doint(s):  # Converting a string into an int.
 @pytest.mark.parametrize("name", short_registry)  # type: ignore
 def test_optimizers(name: str) -> None:
     """Checks that each optimizer is able to converge on a simple test case"""
-    if any(x in name for x in ["Chain", "SMAC", "BO", "AX"]) and os.environ.get("CIRCLECI", False):
-        raise SkipTest("too slow for CircleCI!")
+    if any(x in name for x in ["Chain", "SMAC", "BO", "AX"]) and CI:
+        raise SkipTest("too slow for CI!")
     if "BO" in name or "Chain" in name or "Tiny" in name or "Micro" in name:  # moo issues :-(
         return
     if any(x in name for x in ["Tiny", "Vast"]):
@@ -289,7 +290,7 @@ def doint(s):  # Converting a string into an int.
         ]
         or "Tiny" in name
         or "Micro" in name
-    ) and os.environ.get("CIRCLECI", False):
+    ) and CI:
         raise SkipTest("Too expensive: we randomly skip 3/4 of these tests.")
     if name in ["CMAbounded", "NEWUOA"]:  # Not a general purpose optimization method.
         return
@@ -322,8 +323,8 @@ def doint(s):  # Converting a string into an int.
 @pytest.mark.parametrize("name", short_registry)  # type: ignore
 def test_optimizers_minimal(name: str) -> None:
     optimizer_cls = registry[name]
-    if any(x in name for x in ["SMAC", "BO", "AX"]) and os.environ.get("CIRCLECI", False):
-        raise SkipTest("too slow for CircleCI!")
+    if any(x in name for x in ["SMAC", "BO", "AX"]) and CI:
+        raise SkipTest("too slow for CI!")
     if optimizer_cls.one_shot or name in [
         "CM",
         "NLOPT_LN_PRAXIS",
@@ -480,8 +481,8 @@ def recomkeeper() -> tp.Generator[RecommendationKeeper, None, None]:
 # pylint: disable=redefined-outer-name
 @pytest.mark.parametrize("name", short_registry)  # type: ignore
 def test_optimizers_recommendation(name: str, recomkeeper: RecommendationKeeper) -> None:
-    if any(x in name for x in ["SMAC", "BO", "AX"]) and os.environ.get("CIRCLECI", False):
-        raise SkipTest("too slow for CircleCI!")
+    if any(x in name for x in ["SMAC", "BO", "AX"]) and CI:
+        raise SkipTest("too slow for CI!")
     if (
         name in UNSEEDABLE
         or "BAR" in name
@@ -906,10 +907,8 @@ def constraint(i: tp.Any) -> tp.Union[bool, float]:
 def test_parametrization_offset(name: str) -> None:
     if long_name(name):
         return
-    if any(x in name for x in ["SMAC", "BO", "AX"]) and os.environ.get(
-        "CIRCLECI", False
-    ):  # Outside CircleCI, only the big.
-        raise SkipTest("too slow for CircleCI!")
+    if any(x in name for x in ["SMAC", "BO", "AX"]) and CI:
+        raise SkipTest("too slow for CI!")
     if sum([ord(c) for c in name]) % 4 > 0:
         raise SkipTest("Randomly skipping 75% of these tests.")
     if "PSO" in name or "BO" in name:
@@ -1212,13 +1211,13 @@ def test_smoother() -> None:
 @pytest.mark.parametrize("n", [5, 10, 15, 25, 40])  # type: ignore
 @pytest.mark.parametrize("b_per_dim", [10, 20])  # type: ignore
 def test_voronoide(n, b_per_dim) -> None:
-    if n < 25 or b_per_dim < 1 and not os.environ.get("CIRCLECI", False):  # Outside CircleCI, only the big.
-        raise SkipTest("Only big things outside CircleCI.")
+    if n < 25 or b_per_dim < 1 and not CI:  # Outside CircleCI, only the big.
+        raise SkipTest("Only big things outside CI.")
 
     list_optims = ["CMA", "DE", "PSO", "RandomSearch", "TwoPointsDE", "OnePlusOne"]
-    if os.environ.get("CIRCLECI", False) and (n > 10 or n * b_per_dim > 100):  # In CircleCI, only the small.
-        raise SkipTest("Topology optimization too slow in CircleCI")
-    if os.environ.get("CIRCLECI", False) or (n < 10 or b_per_dim < 20):
+    if CI and (n > 10 or n * b_per_dim > 100):  # In CircleCI, only the small.
+        raise SkipTest("Topology optimization too slow in CI")
+    if CI or (n < 10 or b_per_dim < 20):
         list_optims = ["CMA", "PSO", "OnePlusOne"]
     if n > 20:
         list_optims = ["DE", "TwoPointsDE"]