CICD (#20)

.github/codecov.yml

@@ -6,3 +6,13 @@ ignore:
  - "Pyrado/pyrado/environments/mujoco"
  - "Pyrado/scripts"
  - "Pyrado/tests"
+comment:
+ layout: "reach, diff, flags, files"
+ behavior: default
+ require_changes: false # if true: only post the comment if coverage changes
+ require_base: no # [yes :: must have a base report to post]
+ require_head: yes # [yes :: must have a head report to post]
+ branches: # branch names that can post comment
+ - "master"
+github_checks:
+ annotations: false

.github/workflows/publish_docs.yml

@@ -0,0 +1,39 @@
+name: Deploy Documentation
+
+on:
+ workflow_run:
+ workflows: ["Publish Docker Images"]
+ branches: [cicd]
+ types:
+ - completed
+ - requested
+
+jobs:
+ build-docs:
+ runs-on: ubuntu-latest
+ needs: build-images
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ submodules: true
+ - name: Login to GitHub Container Registry
+ uses: docker/login-action@v1
+ with:
+ registry: ghcr.io
+ username: ${{ github.repository_owner }}
+ password: ${{ secrets.PAT }}
+ - name: Build Docs
+ continue-on-error: true
+ run: |
+ mkdir public
+ export PUBLIC=$(realpath ./public)
+ docker run --rm -u 1000:1000 -v $PUBLIC:/var/www ghcr.io/famura/simurlacra:sacher conda run -n pyrado bash -c "./build_docs.sh;sudo mkdir /var/www/pyrado; sudo cp -r Pyrado/doc/build/* /var/www/pyrado;mv RcsPySim/build/doc/html RcsPySim/build/doc/rcspysim; sudo cp -r RcsPySim/build/doc/rcspysim /var/www"
+ echo $PUBLIC
+ ls $PUBLIC quietioet
+ - name: Deploy
+ continue-on-error: true
+ uses: JamesIves/github-pages-deploy-action@3.5.9
+ with:
+ GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ BRANCH: gh-pages
+ FOLDER: public

.github/workflows/run_tests.yml

@@ -12,14 +12,16 @@ jobs:
  - uses: actions/checkout@v2
  with:
  submodules: true
+ - name: Build Image
+ run: |
+ docker pull simurlacra/simurlacra:sacher
+ docker build . -t simurlacra:test --build-arg OPTION=sacher
  - name: Execute tests
  env:
  CC_TEST_REPORTER_ID: ${{ secrets.CC_TEST_REPORTER_ID }}
  run: |
- docker pull simurlacra/simurlacra:sacher
- docker build . -t simurlacra:test --build-arg OPTION=sacher --cache-from simurlacra/simurlacra:sacher
  export COVPATH=$(realpath .)
- docker run --rm -i -v $COVPATH:/cov simurlacra:test conda run -n pyrado bash -c "pytest Pyrado/tests --cov=./Pyrado --cov-report=xml -v -m 'not visualization' -n 4; sudo cp ./coverage.xml /cov"
+ docker run --rm -i -v $COVPATH:/cov simurlacra:test bash -c "pytest Pyrado --cov=./Pyrado --cov-report=xml --verbose -n 2 -m 'not visualization'; sudo cp ./coverage.xml /cov"
  sudo mkdir /home/user/
  sudo mkdir /home/user/SimuRLacra
  sudo ln -s $COVPATH /home/user/SimuRLacra

.github/workflows/run_tests_future.yml

@@ -0,0 +1,60 @@
+name: Run Tests (future)
+
+on:
+ push:
+ branches: [cicd_future]
+ pull_request:
+ branches: [cicd_future]
+
+jobs:
+ build-images:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ flavor: [sacher]
+ steps:
+ - uses: actions/checkout@v2
+ with:
+ submodules: true
+ - name: Set up Docker Buildx
+ uses: docker/setup-buildx-action@v1
+ - name: Build
+ id: docker_build
+ uses: docker/build-push-action@v2
+ with:
+ context: .
+ outputs: |
+ type=docker
+ tags: |
+ simurlacra:test
+ build-args: |
+ OPTION=${{ matrix.flavor }}
+ cache-from: type=registry,ref=simurlacra/simurlacra:${{ matrix.flavor }}
+ - name: Inspect
+ run: |
+
+ docker image inspect simurlacra:test
+ - name: Execute tests
+ env:
+ CC_TEST_REPORTER_ID: ${{ secrets.CC_TEST_REPORTER_ID }}
+ run: |
+ export COVPATH=$(realpath .)
+ docker run --rm -i -v $COVPATH:/cov simurlacra:test conda run -n pyrado bash -c "pytest Pyrado/tests --cov=./Pyrado --cov-report=xml -n 4 --verbose -m 'not visualization'; sudo cp ./coverage.xml /cov"
+ sudo mkdir /home/user/
+ sudo mkdir /home/user/SimuRLacra
+ sudo ln -s $COVPATH /home/user/SimuRLacra
+ cd Pyrado
+ - name: Send Coverage to Codecov
+ uses: codecov/codecov-action@v1
+ with:
+ file: coverage.xml # optional
+ flags: unittests # optional
+ name: codecov-umbrella # optional
+ fail_ci_if_error: true
+ - name: Test & publish code coverage
+ uses: paambaati/codeclimate-action@v2.7.5
+ env:
+ CC_TEST_REPORTER_ID: ${{ secrets.CC_TEST_REPORTER_ID }}
+ with:
+ coverageLocations: |
+ coverage.xml:cobertura

Dockerfile

@@ -52,9 +52,8 @@ USER user
 WORKDIR /home/user
 
 # Setup conda
-RUN echo "export PATH=/home/user/miniconda3/bin:$PATH" >> ~/.bashrc
-
-RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
+RUN echo "export PATH=/home/user/miniconda3/bin:$PATH" >> ~/.bashrc \
+ && wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
  && bash Miniconda3-latest-Linux-x86_64.sh -b \
  && rm Miniconda3-latest-Linux-x86_64.sh
 
@@ -71,7 +70,7 @@ RUN conda create -n pyrado python=3.7 blas cmake lapack libgcc-ng mkl mkl-includ
 SHELL ["conda", "run", "-n", "pyrado", "/bin/bash", "-c"]
 
 RUN pip install argparse black box2d colorama coverage cython glfw gym joblib prettyprinter matplotlib numpy optuna pandas pycairo pytest pytest-cov pytest-xdist pyyaml scipy seaborn sphinx sphinx-math-dollar sphinx_rtd_theme tabulate tensorboard tqdm vpython git+https://github.com/Xfel/init-args-serializer.git@master; \
- conda init bashL; rm -fr /home/user/.cache
+ conda init bash ; rm -fr /home/user/.cache
 
 # Add env variables
 ENV PATH /home/user/miniconda3/envs/pyrado/bin:$PATH

Pyrado/pyrado/algorithms/meta/adr.py

@@ -26,14 +26,12 @@
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 # POSSIBILITY OF SUCH DAMAGE.
 
+from typing import Optional, Sequence
+
 import numpy as np
+import pyrado
 import torch as to
 from init_args_serializer import Serializable
-from torch import nn as nn
-from tqdm import tqdm
-from typing import Sequence, Optional
-
-import pyrado
 from pyrado.algorithms.base import Algorithm
 from pyrado.algorithms.step_based.svpg import SVPG
 from pyrado.domain_randomization.domain_parameter import DomainParam
@@ -48,6 +46,8 @@
 from pyrado.sampling.step_sequence import StepSequence
 from pyrado.spaces.box import BoxSpace
 from pyrado.utils.data_types import EnvSpec
+from torch import nn as nn
+from tqdm import tqdm
 
 
 class ADR(Algorithm):

Pyrado/pyrado/algorithms/meta/bayrn.py

@@ -26,37 +26,37 @@
 # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 # POSSIBILITY OF SUCH DAMAGE.
 
-import numpy as np
 import os
 import os.path as osp
+from typing import Optional, Union
+
+import numpy as np
+import pyrado
 import torch as to
-from botorch.models import SingleTaskGP
+from botorch.acquisition import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
 from botorch.fit import fit_gpytorch_model
-from botorch.acquisition import UpperConfidenceBound, ExpectedImprovement, ProbabilityOfImprovement, PosteriorMean
+from botorch.models import SingleTaskGP
 from botorch.optim import optimize_acqf
 from gpytorch.constraints import GreaterThan
 from gpytorch.mlls import ExactMarginalLogLikelihood
-from tabulate import tabulate
-from typing import Optional, Union
-
-import pyrado
 from pyrado.algorithms.base import Algorithm, InterruptableAlgorithm
 from pyrado.algorithms.utils import until_thold_exceeded
-from pyrado.logger.step import StepLogger
-from pyrado.spaces import BoxSpace
 from pyrado.environment_wrappers.base import EnvWrapper
 from pyrado.environment_wrappers.domain_randomization import MetaDomainRandWrapper
 from pyrado.environment_wrappers.utils import inner_env, typed_env
 from pyrado.environments.real_base import RealEnv
 from pyrado.environments.sim_base import SimEnv
+from pyrado.logger.step import StepLogger
 from pyrado.policies.base import Policy
 from pyrado.sampling.bootstrapping import bootstrap_ci
 from pyrado.sampling.parallel_rollout_sampler import ParallelRolloutSampler
 from pyrado.sampling.rollout import rollout
-from pyrado.utils.order import natural_sort
+from pyrado.spaces import BoxSpace
+from pyrado.utils.data_processing import standardize
 from pyrado.utils.input_output import print_cbt
 from pyrado.utils.math import UnitCubeProjector
-from pyrado.utils.data_processing import standardize
+from pyrado.utils.order import natural_sort
+from tabulate import tabulate
 
 
 class BayRn(InterruptableAlgorithm):
@@ -159,7 +159,8 @@ def __init__(
  self._subrtn.save_name = "subrtn"
  self.ddp_space = ddp_space
  self.ddp_projector = UnitCubeProjector(
- to.from_numpy(self.ddp_space.bound_lo), to.from_numpy(self.ddp_space.bound_up)
+ to.from_numpy(self.ddp_space.bound_lo).to(dtype=to.get_default_dtype()),
+ to.from_numpy(self.ddp_space.bound_up).to(dtype=to.get_default_dtype()),
  )
  self.cands = None # called x in the context of GPs
  self.cands_values = None # called y in the context of GPs
@@ -405,13 +406,16 @@ def step(self, snapshot_mode: str = "latest", meta_info: dict = None):
  raise pyrado.ValueErr(given=self.acq_fcn_type, eq_constraint="'UCB', 'EI', 'PI'")
 
  # Optimize acquisition function and get new candidate point
- cand_norm, acq_value = optimize_acqf(
+ cand_norm, _ = optimize_acqf(
  acq_function=acq_fcn,
- bounds=to.stack([to.zeros(self.ddp_space.flat_dim), to.ones(self.ddp_space.flat_dim)]),
+ bounds=to.stack([to.zeros(self.ddp_space.flat_dim), to.ones(self.ddp_space.flat_dim)]).to(
+ dtype=to.float32
+ ),
  q=1,
  num_restarts=self.acq_restarts,
  raw_samples=self.acq_samples,
  )
+ cand_norm = cand_norm.to(dtype=to.get_default_dtype())
  next_cand = self.ddp_projector.project_back(cand_norm)
  print_cbt(f"Found the next candidate: {next_cand.numpy()}", "g")
  self.cands = to.cat([self.cands, next_cand], dim=0)
@@ -484,7 +488,10 @@ def argmax_posterior_mean(
  raise pyrado.TypeErr(given=ddp_space, expected_type=BoxSpace)
 
  # Normalize the input data and standardize the output data
- uc_projector = UnitCubeProjector(to.from_numpy(ddp_space.bound_lo), to.from_numpy(ddp_space.bound_up))
+ uc_projector = UnitCubeProjector(
+ to.from_numpy(ddp_space.bound_lo).to(dtype=to.get_default_dtype()),
+ to.from_numpy(ddp_space.bound_up).to(dtype=to.get_default_dtype()),
+ )
  cands_norm = uc_projector.project_to(cands)
  cands_values_stdized = standardize(cands_values)
 
@@ -503,14 +510,15 @@ def argmax_posterior_mean(
  fit_gpytorch_model(mll)
 
  # Find position with maximal posterior mean
- cand_norm, acq_value = optimize_acqf(
+ cand_norm, _ = optimize_acqf(
  acq_function=PosteriorMean(gp),
- bounds=to.stack([to.zeros(ddp_space.flat_dim), to.ones(ddp_space.flat_dim)]),
+ bounds=to.stack([to.zeros(ddp_space.flat_dim), to.ones(ddp_space.flat_dim)]).to(dtype=to.float32),
  q=1,
  num_restarts=num_restarts,
  raw_samples=num_samples,
  )
 
+ cand_norm = cand_norm.to(dtype=to.get_default_dtype())
  cand = uc_projector.project_back(cand_norm.detach())
  print_cbt(f"Converged to argmax of the posterior mean: {cand.numpy()}", "g", bright=True)
  return cand