diff --git a/.travis.yml b/.travis.yml
index 215d2cf192216b573e514b62a6cad9c510cd2b0a..bd5987b873f292c98119113fffed83867cfca901 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,13 +2,11 @@
# Read more under http://docs.travis-ci.com/user/build-configuration/
# THIS SCRIPT IS SUPPOSED TO BE AN EXAMPLE. MODIFY IT ACCORDING TO YOUR NEEDS!
-dist: xenial
+dist: bionic
sudo: false
language: python
virtualenv:
system_site_packages: false
-before_install:
- - sudo apt-get install -y ninja-build build-essential nvidia-cuda-toolkit
addons:
apt:
update: true
@@ -28,19 +26,39 @@ matrix:
env: DISTRIB="conda" PYTHON_VERSION="3.6" COVERAGE="false" LINT="false"
before_install:
- sudo apt-get install -y ninja-build build-essential nvidia-cuda-toolkit
- - name: "Documentation and Lint"
+ - name: "Lint and documentation test"
env: DISTRIB="ubuntu" TOX_PYTHON_VERSION="py36" COVERAGE="false" LINT="true"
+ - name: "Python 3.7.2 on macOS"
+ os: osx
+ osx_image: xcode10.2 # Python 3.7.2 running on macOS 10.14.3
+ language: shell # 'language: python' is an error on Travis CI macOS
+ before_install:
+ - brew update && brew upgrade python
+ - brew install ninja
+ - alias python=python3
+ - alias pip="python3 -m pip"
+ - shopt -s expand_aliases
+ before_cache:
+ - brew cleanup
+ - name: "Python 3.7.3 on Windows"
+ os: windows # Windows 10.0.17134 N/A Build 17134
+ language: shell # 'language: python' is an error on Travis CI Windows
+ before_install:
+ - choco install python
+ - python -m pip install --upgrade pip
+ env: PATH=/c/Python37:/c/Python37/Scripts:$PATH
install:
- source tests/travis_install.sh
+ - pip3 install -e .
+ - pip3 install tensorflow torch || echo "failed to install machine learning stuff"
before_script:
- - git config --global user.email "you@example.com"
- - git config --global user.name "Your Name"
+ - git config --global user.email "stephan.seitz@fau.de"
+ - git config --global user.name "Stephan Seitz"
script:
- export NO_GPU_EXECUTION=1
- - pip install -e .
- - pip install tensorflow torch
- - if [[ "$LINT" == "false" ]]; then python setup.py test; fi
- - if [[ "$LINT" == "true" ]]; then flake8 src;python setup.py doctest; fi
+ - if [[ "$LINT" == "true" ]]; then flake8 src;python setup.py doctest; exit 0; fi
+ - python setup.py test
+
after_success:
- if [[ "$COVERAGE" == "true" ]]; then coveralls || echo "failed"; codecov; fi
after_script:
@@ -49,3 +67,5 @@ cache:
pip: true
directories:
- $HOME/miniconda
+ - /c/Python37
+ - $HOME/Library/Caches/Homebrew
diff --git a/setup.cfg b/setup.cfg
index 984b9c6820c44b0fdd4ac1b59c31de590ba361ea..bb14d8e5b87504ebb4827484bf3f385acfa61733 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -42,7 +42,6 @@ test_requires =
ansi2html
pytest-cov
tensorflow
- torch
# Require a specific Python version, e.g. Python 2.7 or >= 3.4
# python_requires = >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*
@@ -63,7 +62,6 @@ testing =
ansi2html
pytest-cov
tensorflow
- torch
[options.entry_points]
# Add here console scripts like:
diff --git a/src/pystencils_autodiff/backends/_pytorch.py b/src/pystencils_autodiff/backends/_pytorch.py
index 22242763a6830c571af6d155faf6c2f76e29e598..659a437db1b1babcda44be57379df2b3f81c24f1 100644
--- a/src/pystencils_autodiff/backends/_pytorch.py
+++ b/src/pystencils_autodiff/backends/_pytorch.py
@@ -1,7 +1,10 @@
import uuid
import numpy as np
-import torch
+try:
+ import torch
+except ImportError:
+ pass
try:
import pycuda.autoinit
diff --git a/src/pystencils_autodiff/backends/_torch_native.py b/src/pystencils_autodiff/backends/_torch_native.py
index 1276e734f3dfca708714614dd4c8ff26cd5fcccf..fcd1ac9cd45b47f3ba7753622fd652040eb3daa3 100644
--- a/src/pystencils_autodiff/backends/_torch_native.py
+++ b/src/pystencils_autodiff/backends/_torch_native.py
@@ -4,7 +4,6 @@ from itertools import chain
from os.path import dirname, isdir, isfile, join
import jinja2
-import torch
from appdirs import user_cache_dir
import pystencils
@@ -17,6 +16,11 @@ from pystencils.cpu.kernelcreation import create_kernel
from pystencils.gpucuda.kernelcreation import create_cuda_kernel
from pystencils_autodiff.backends._pytorch import numpy_dtype_to_torch
+try:
+ import torch
+except ImportError:
+ pass
+
def _read_file(file):
with open(file, 'r') as f:
diff --git a/tests/backends/test_torch_native_compilation.py b/tests/backends/test_torch_native_compilation.py
index f552192d02eea20a2e7eea7a60b3aec5b882983a..0ba80745995e7851747dfe4da46b8ce283fa3390 100644
--- a/tests/backends/test_torch_native_compilation.py
+++ b/tests/backends/test_torch_native_compilation.py
@@ -4,6 +4,7 @@
#
import os
+import subprocess
from os.path import dirname, isfile, join
# TODO: from pystencils.backends.cudabackend import generate_cuda
@@ -11,7 +12,6 @@ import appdirs
import jinja2
import numpy as np
import pytest
-import torch
import pystencils
import pystencils_autodiff
@@ -20,6 +20,10 @@ from pystencils.backends.cbackend import generate_c
from pystencils.gpucuda.kernelcreation import create_cuda_kernel
from pystencils_autodiff.backends._torch_native import create_autograd_function, generate_torch
+torch = pytest.importorskip('torch')
+pytestmark = pytest.mark.skipif(subprocess.call(['ninja', '--v']) != 0,
+ reason='torch compilation requires ninja')
+
PROJECT_ROOT = dirname
@@ -50,23 +54,26 @@ def test_jit():
def test_torch_native_compilation():
x, y = pystencils.fields('x, y: float32[2d]')
- assignments = pystencils.AssignmentCollection({
- y.center(): x.center()**2
- }, {})
+ assignments = pystencils.AssignmentCollection({y.center(): x.center()**2},
+ {})
autodiff = pystencils_autodiff.AutoDiffOp(assignments)
backward_assignments = autodiff.backward_assignments
print(assignments)
print(backward_assignments)
- template_string = read_file(join(dirname(__file__),
- '../../src/pystencils_autodiff/backends/torch_native_cuda.tmpl.cpp'))
+ template_string = read_file(
+ join(
+ dirname(__file__),
+ '../../src/pystencils_autodiff/backends/torch_native_cuda.tmpl.cpp'
+ ))
template = jinja2.Template(template_string)
print(template_string)
forward_kernel = create_cuda_kernel(assignments.all_assignments).body
- backward_kernel = create_cuda_kernel(backward_assignments.all_assignments).body
+ backward_kernel = create_cuda_kernel(
+ backward_assignments.all_assignments).body
forward_code = generate_c(forward_kernel)
backward_code = generate_c(backward_kernel)
@@ -74,20 +81,31 @@ def test_torch_native_compilation():
output = template.render(
forward_tensors=[f.name for f in autodiff.forward_fields],
forward_input_tensors=[f.name for f in autodiff.forward_input_fields],
- forward_output_tensors=[f.name for f in autodiff.forward_output_fields],
- backward_tensors=[f.name for f in autodiff.backward_fields + autodiff.forward_input_fields],
- backward_input_tensors=[f.name for f in autodiff.backward_input_fields],
- backward_output_tensors=[f.name for f in autodiff.backward_output_fields],
+ forward_output_tensors=[
+ f.name for f in autodiff.forward_output_fields
+ ],
+ backward_tensors=[
+ f.name
+ for f in autodiff.backward_fields + autodiff.forward_input_fields
+ ],
+ backward_input_tensors=[
+ f.name for f in autodiff.backward_input_fields
+ ],
+ backward_output_tensors=[
+ f.name for f in autodiff.backward_output_fields
+ ],
forward_kernel=forward_code,
backward_kernel=backward_code,
dimensions=range(2),
kernel_name="square",
- dtype="float"
- )
+ dtype="float")
print(output)
- template_string = read_file(join(dirname(__file__),
- '../../src/pystencils_autodiff/backends/torch_native_cuda.tmpl.cu'))
+ template_string = read_file(
+ join(
+ dirname(__file__),
+ '../../src/pystencils_autodiff/backends/torch_native_cuda.tmpl.cu')
+ )
template = jinja2.Template(template_string)
print(template_string)
@@ -96,7 +114,9 @@ def test_torch_native_compilation():
forward_tensors=[f for f in autodiff.forward_fields],
forward_input_tensors=[f for f in autodiff.forward_input_fields],
forward_output_tensors=[f for f in autodiff.forward_output_fields],
- backward_tensors=[f for f in autodiff.backward_fields + autodiff.forward_input_fields],
+ backward_tensors=[
+ f for f in autodiff.backward_fields + autodiff.forward_input_fields
+ ],
backward_input_tensors=[f for f in autodiff.backward_input_fields],
backward_output_tensors=[f for f in autodiff.backward_output_fields],
forward_kernel=forward_code,
@@ -106,12 +126,14 @@ def test_torch_native_compilation():
forward_blocks=str({1, 1, 1}),
forward_threads=str({1, 1, 1}),
kernel_name="square",
- dimensions=range(2)
- )
+ dimensions=range(2))
print(output)
- template_string = read_file(join(dirname(__file__),
- '../../src/pystencils_autodiff/backends/torch_native_cpu.tmpl.cpp'))
+ template_string = read_file(
+ join(
+ dirname(__file__),
+ '../../src/pystencils_autodiff/backends/torch_native_cpu.tmpl.cpp')
+ )
template = jinja2.Template(template_string)
print(template_string)
@@ -119,16 +141,24 @@ def test_torch_native_compilation():
output = template.render(
forward_tensors=[f.name for f in autodiff.forward_fields],
forward_input_tensors=[f.name for f in autodiff.forward_input_fields],
- forward_output_tensors=[f.name for f in autodiff.forward_output_fields],
- backward_tensors=[f.name for f in autodiff.backward_fields + autodiff.forward_input_fields],
- backward_input_tensors=[f.name for f in autodiff.backward_input_fields],
- backward_output_tensors=[f.name for f in autodiff.backward_output_fields],
+ forward_output_tensors=[
+ f.name for f in autodiff.forward_output_fields
+ ],
+ backward_tensors=[
+ f.name
+ for f in autodiff.backward_fields + autodiff.forward_input_fields
+ ],
+ backward_input_tensors=[
+ f.name for f in autodiff.backward_input_fields
+ ],
+ backward_output_tensors=[
+ f.name for f in autodiff.backward_output_fields
+ ],
forward_kernel=forward_code,
backward_kernel=backward_code,
kernel_name="square",
dtype="float",
- dimensions=range(2)
- )
+ dimensions=range(2))
print(output)
@@ -136,33 +166,36 @@ def test_torch_native_compilation():
def test_generate_torch_gpu():
x, y = pystencils.fields('x, y: float32[2d]')
- assignments = pystencils.AssignmentCollection({
- y.center(): x.center()**2
- }, {})
+ assignments = pystencils.AssignmentCollection({y.center(): x.center()**2},
+ {})
autodiff = pystencils_autodiff.AutoDiffOp(assignments)
- op_cuda = generate_torch(appdirs.user_cache_dir('pystencils'), autodiff, is_cuda=True, dtype=np.float32)
+ op_cuda = generate_torch(appdirs.user_cache_dir('pystencils'),
+ autodiff,
+ is_cuda=True,
+ dtype=np.float32)
assert op_cuda is not None
def test_generate_torch_cpu():
x, y = pystencils.fields('x, y: float32[2d]')
- assignments = pystencils.AssignmentCollection({
- y.center(): x.center()**2
- }, {})
+ assignments = pystencils.AssignmentCollection({y.center(): x.center()**2},
+ {})
autodiff = pystencils_autodiff.AutoDiffOp(assignments)
- op_cpp = generate_torch(appdirs.user_cache_dir('pystencils'), autodiff, is_cuda=False, dtype=np.float32)
+ op_cpp = generate_torch(appdirs.user_cache_dir('pystencils'),
+ autodiff,
+ is_cuda=False,
+ dtype=np.float32)
assert op_cpp is not None
def test_execute_torch():
x, y = pystencils.fields('x, y: float64[32,32]')
- assignments = pystencils.AssignmentCollection({
- y.center(): 5 + x.center()
- }, {})
+ assignments = pystencils.AssignmentCollection({y.center(): 5 + x.center()},
+ {})
autodiff = pystencils_autodiff.AutoDiffOp(assignments)
x_tensor = pystencils_autodiff.torch_tensor_from_field(x, 1, cuda=False)
@@ -174,13 +207,13 @@ def test_execute_torch():
assert op_cpp is not None
-@pytest.mark.skipif('NO_GPU_EXECUTION' in os.environ, reason='Skip GPU execution tests')
+@pytest.mark.skipif('NO_GPU_EXECUTION' in os.environ,
+ reason='Skip GPU execution tests')
def test_execute_torch_gpu():
x, y = pystencils.fields('x, y: float64[32,32]')
- assignments = pystencils.AssignmentCollection({
- y.center(): 5 + x.center()
- }, {})
+ assignments = pystencils.AssignmentCollection({y.center(): 5 + x.center()},
+ {})
autodiff = pystencils_autodiff.AutoDiffOp(assignments)
x_tensor = pystencils_autodiff.torch_tensor_from_field(x, 3, cuda=True)
@@ -193,4 +226,3 @@ def test_execute_torch_gpu():
rtn = op_cuda.forward()
print(y_tensor)
print(rtn)
-
diff --git a/tests/test_autodiff.py b/tests/test_autodiff.py
index 4d917e98c08654422f596bfa1f2eed16b4db3bec..3c530eb7451bd3b145bd97c329fb01f0fdb6d367 100644
--- a/tests/test_autodiff.py
+++ b/tests/test_autodiff.py
@@ -49,4 +49,3 @@ def test_simple_2d_check_raw_assignments():
for diff_mode in DiffModes:
pystencils_autodiff.create_backward_assignments(
forward_assignments, diff_mode=diff_mode)
-
diff --git a/tests/test_tfmad.py b/tests/test_tfmad.py
index 8b1820945601e21951226a83c8590e088247611d..87efa0c974ccb342144decc7c53efa5e6cbb38d3 100644
--- a/tests/test_tfmad.py
+++ b/tests/test_tfmad.py
@@ -4,8 +4,6 @@ import os
import numpy as np
import pytest
import sympy as sp
-import tensorflow as tf
-import torch
import pystencils as ps
import pystencils_autodiff
@@ -46,8 +44,8 @@ def test_tfmad_two_stencils():
print(assignment_collection)
print('Backward')
- auto_diff = pystencils_autodiff.AutoDiffOp(
- assignment_collection, diff_mode='transposed-forward')
+ auto_diff = pystencils_autodiff.AutoDiffOp(assignment_collection,
+ diff_mode='transposed-forward')
backward = auto_diff.backward_assignments
print(backward)
print('Forward output fields (to check order)')
@@ -56,13 +54,15 @@ def test_tfmad_two_stencils():
print(auto_diff)
-@pytest.mark.skipif("NO_TENSORFLOW_TEST" in os.environ, reason="Requires Tensorflow")
@pytest.mark.skipif("TRAVIS" in os.environ, reason="Temporary skip")
def test_tfmad_gradient_check():
+ tf = pytest.importorskip('tensorflow')
+
a, b, out = ps.fields("a, b, out: double[21,13]")
print(a.shape)
- cont = ps.fd.Diff(a, 0) - ps.fd.Diff(a, 1) - ps.fd.Diff(b, 0) + ps.fd.Diff(b, 1)
+ cont = ps.fd.Diff(a, 0) - ps.fd.Diff(a, 1) - ps.fd.Diff(b, 0) + ps.fd.Diff(
+ b, 1)
discretize = ps.fd.Discretization2ndOrder(dx=1)
discretization = discretize(cont)
@@ -72,8 +72,8 @@ def test_tfmad_gradient_check():
print(assignment_collection)
print('Backward')
- auto_diff = pystencils_autodiff.AutoDiffOp(
- assignment_collection, diff_mode='transposed-forward')
+ auto_diff = pystencils_autodiff.AutoDiffOp(assignment_collection,
+ diff_mode='transposed-forward')
backward = auto_diff.backward_assignments
print(backward)
print('Forward output fields (to check order)')
@@ -87,7 +87,11 @@ def test_tfmad_gradient_check():
sess.run(tf.global_variables_initializer())
gradient_error = compute_gradient_error_without_border(
- [a_tensor, b_tensor], [a.shape, b.shape], out_tensor, out.shape, num_border_pixels=2, ndim=2)
+ [a_tensor, b_tensor], [a.shape, b.shape],
+ out_tensor,
+ out.shape,
+ num_border_pixels=2,
+ ndim=2)
print('error: %s' % gradient_error.max_error)
assert any(e < 1e-4 for e in gradient_error.values())
@@ -100,30 +104,31 @@ def check_tfmad_vector_input_data(args):
# create arrays
c_arr = np.zeros(domain_shape)
- v_arr = np.zeros(domain_shape + (ndim,))
+ v_arr = np.zeros(domain_shape + (ndim, ))
# create fields
- c, v, c_next = ps.fields("c, v(2), c_next: % s[ % i, % i]" % ("float" if dtype == np.float32 else "double",
- domain_shape[0],
- domain_shape[1]),
+ c, v, c_next = ps.fields("c, v(2), c_next: % s[ % i, % i]" %
+ ("float" if dtype == np.float32 else "double",
+ domain_shape[0], domain_shape[1]),
c=c_arr,
v=v_arr,
c_next=c_arr)
# write down advection diffusion pde
# the equation is represented by a single term and an implicit "=0" is assumed.
- adv_diff_pde = ps.fd.transient(
- c) - ps.fd.diffusion(c, sp.Symbol("D")) + ps.fd.advection(c, v)
+ adv_diff_pde = ps.fd.transient(c) - ps.fd.diffusion(
+ c, sp.Symbol("D")) + ps.fd.advection(c, v)
discretize = ps.fd.Discretization2ndOrder(args.dx, args.dt)
discretization = discretize(adv_diff_pde)
- discretization = discretization.subs(
- sp.Symbol("D"), args.diffusion_coefficient)
+ discretization = discretization.subs(sp.Symbol("D"),
+ args.diffusion_coefficient)
forward_assignments = ps.AssignmentCollection(
[ps.Assignment(c_next.center(), discretization)], [])
autodiff = pystencils_autodiff.AutoDiffOp(
- forward_assignments, diff_mode='transposed-forward') # , constant_fields=[v]
+ forward_assignments,
+ diff_mode='transposed-forward') # , constant_fields=[v]
print('Forward assignments:')
print(autodiff.forward_assignments)
@@ -133,32 +138,31 @@ def check_tfmad_vector_input_data(args):
def test_tfmad_vector_input_data():
parser = argparse.ArgumentParser()
- parser.add_argument(
- '--domain_shape', default=(100, 30), nargs=2, type=int, help="")
- parser.add_argument(
- '--dx', default=1, type=float, help="")
- parser.add_argument(
- '--dt', default=0.01, type=float, help="")
- parser.add_argument(
- '--diffusion_coefficient', default=1, type=float, help="")
- parser.add_argument(
- '--num_total_time_steps', default=100, type=int)
- parser.add_argument(
- '--num_time_steps_for_op', default=1, type=int)
- parser.add_argument(
- '--learning_rate', default=1e-2, type=float)
- parser.add_argument(
- '--dtype', default=np.float64, type=np.dtype)
- parser.add_argument(
- '--num_optimization_steps', default=2000, type=int)
+ parser.add_argument('--domain_shape',
+ default=(100, 30),
+ nargs=2,
+ type=int,
+ help="")
+ parser.add_argument('--dx', default=1, type=float, help="")
+ parser.add_argument('--dt', default=0.01, type=float, help="")
+ parser.add_argument('--diffusion_coefficient',
+ default=1,
+ type=float,
+ help="")
+ parser.add_argument('--num_total_time_steps', default=100, type=int)
+ parser.add_argument('--num_time_steps_for_op', default=1, type=int)
+ parser.add_argument('--learning_rate', default=1e-2, type=float)
+ parser.add_argument('--dtype', default=np.float64, type=np.dtype)
+ parser.add_argument('--num_optimization_steps', default=2000, type=int)
parser.add_argument('vargs', nargs='*')
args = parser.parse_args()
check_tfmad_vector_input_data(args)
-@pytest.mark.skipif("NO_TORCH_TEST" in os.environ, reason="Requires PyTorch")
def test_tfmad_gradient_check_torch():
+ torch = pytest.importorskip('torch')
+
a, b, out = ps.fields("a, b, out: float[21,13]")
cont = ps.fd.Diff(a, 0) - ps.fd.Diff(a, 1) - \
@@ -172,8 +176,8 @@ def test_tfmad_gradient_check_torch():
print(assignment_collection)
print('Backward')
- auto_diff = pystencils_autodiff.AutoDiffOp(
- assignment_collection, diff_mode='transposed-forward')
+ auto_diff = pystencils_autodiff.AutoDiffOp(assignment_collection,
+ diff_mode='transposed-forward')
backward = auto_diff.backward_assignments
print(backward)
print('Forward output fields (to check order)')
@@ -182,7 +186,11 @@ def test_tfmad_gradient_check_torch():
a_tensor = torch.zeros(*a.shape, dtype=torch.float64, requires_grad=True)
b_tensor = torch.zeros(*b.shape, dtype=torch.float64, requires_grad=True)
- function = auto_diff.create_tensorflow_op({a: a_tensor, b: b_tensor}, backend='torch')
+ function = auto_diff.create_tensorflow_op({
+ a: a_tensor,
+ b: b_tensor
+ },
+ backend='torch')
torch.autograd.gradcheck(function.apply, [a_tensor, b_tensor])
@@ -230,20 +238,21 @@ def get_curl(input_field: ps.Field, curl_field: ps.Field):
def test_tfmad_two_outputs():
domain_shape = (20, 30)
- vector_shape = domain_shape + (2,)
+ vector_shape = domain_shape + (2, )
- curl_input_for_u = ps.Field.create_fixed_size(
- field_name='curl_input', shape=domain_shape, index_dimensions=0)
- u_field = ps.Field.create_fixed_size(
- field_name='curl', shape=vector_shape, index_dimensions=1)
+ curl_input_for_u = ps.Field.create_fixed_size(field_name='curl_input',
+ shape=domain_shape,
+ index_dimensions=0)
+ u_field = ps.Field.create_fixed_size(field_name='curl',
+ shape=vector_shape,
+ index_dimensions=1)
- curl_op = pystencils_autodiff.AutoDiffOp(get_curl(
- curl_input_for_u, u_field), diff_mode="transposed-forward")
+ curl_op = pystencils_autodiff.AutoDiffOp(get_curl(curl_input_for_u,
+ u_field),
+ diff_mode="transposed-forward")
print('Forward')
print(curl_op.forward_assignments)
print('Backward')
print(curl_op.backward_assignments)
-
-
diff --git a/tests/travis_install.sh b/tests/travis_install.sh
index 88388647ec2647dbf4f803fc6a8cfe486326c585..d37350e625281e087fce8aaa9654ad98d2a53b73 100644
--- a/tests/travis_install.sh
+++ b/tests/travis_install.sh
@@ -38,13 +38,15 @@ if [[ "$DISTRIB" == "conda" ]]; then
# (prefer local venv, since the miniconda folder is cached)
conda create -p ./.venv --yes python=${PYTHON_VERSION} pip virtualenv
source activate ./.venv
+ alias pip3='python -m pip'
+ shopt -s expand_aliases
fi
# for all
-pip install -U pip setuptools
-pip install tox
-pip install codecov
-pip install sphinx
+pip3 install -U pip wheel setuptools
+pip3 install tox
+pip3 install codecov
+pip3 install sphinx
if [[ -z "$PYSTENCIL_FROM_PIP" ]]; then
pip install git+https://github.com/mabau/pystencils.git
@@ -54,7 +56,7 @@ fi
pip install flake8
if [[ "$COVERAGE" == "true" ]]; then
- pip install -U pytest-cov pytest-virtualenv coverage coveralls flake8
+ pip3 install -U pytest-cov pytest-virtualenv coverage coveralls flake8
fi