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or local link. Must use full URL if want to link to
cran-comments.md.Solaris: pandoc (>= 2.0), qpdf ( >= 7.0);. Getting
now two notes and a warrning.Solaris: pkgutil -y -i qpdf, pkgutil -y -i pandoc"
to SysReqs.skip_on_cran() in
test_r_torch_share_objects.R, test_types.R, and
test-install_rtorch_dryrun.R. Causing errors in Fedora. It
doesn’t want to install numpy but now errors went away in
Fedora and Solaris because is nos tested on numpy.numpy at the top of test
fileqpdf, pandoc (>= 2.7.2) on Solaris".numpy ( >= 1.14.0) for Fedora.solaris-x86-patched platform to rhub.SystemRequirements: "conda (python=3.6 pytorch torchvision cpuonly matplotlib pandas -c pytorch), Python (>=3.6), pytorch (>=1.6), torchvision, numpy".
It makes no difference in Fedora.fedora-clang-devel in rhub. Still
throwing error ModuleNotFoundNo module named 'numpy'.
failed. It seems tha Fedora cannot install numpy as
painless as in Debian or Ubuntu.test_types.R. Put quotes
at the beginning of the function parentheses in Python code.rhub to
debug.sign, abs,
sqrt, floor, ceil,
round, sin, cos,
tan, asin, acos,
atan in generics.R. Documented.tests/testthat/rhub-tests.R that sends rTorch
for testing on three different platforms. Use it as well in addition to
Travis and Appveyor. Closer to CRAN tests.data.table and R6 from
Imports. Not used yet.torch_config does
not update output after issuing a install_pytorch()
command; they return the previous installed PyTorch info. The purpose of
these test was initially see if the installation performed as initially
planned. It works outside unit tests. But we cannot enable this test for
CRAN because it will take longer time and may not work due to the
PyTorch installation process. The major problem I found with these tests
is that the torch_config objects do not update after
issuing a new install_pytorch.reticulate.R from rTorch code. Some functions
were previously customized to accept channel. Now the
reticulate package accepts channel as a
function parameter.SystemRequirements because CRAN is not
passing.skip_if_no_python() so CRAN doesn’t throw error in
unit test test-install_rtorch_dryrun.Rutils.R by
helper_utils.R. We also have utils.R under the
R folder.test-install_rtorch_dryrun.R in two files.
The second one test-install_rtorch_parse_version.R will
only perform the parsing of what is being sent to
install_pytorch().skip_if_no_python() in case there is no way Python
is installed at the testing point.rhub for testing before releasing to
CRAN.skip_if_no_torch() in tests in cases where PyTorch
cannot be installed in CRAN.skip_if_no_python().\dontest to \dontrun in
examples.0.4.0-fix_examples_problem_in_cran.tests/testthat/utils.R to include
skip_on_cran()PyTorch
versions 1.4, 1.5, 1.6.Python 3.6, 3.7 and 3.8. A
testing matrix was implemented in Travis and
Appveyor to test version combinations of Python, PyTorch
and R. The R versions tested were R-3.4.3,
R-3.5.3, R-3.6.3, and
R-4.0.2.travis.yml by bringing
- PYTHON_V="3.7" PYTORCH_V="1.6" near
env: metrix. Maybe some space or alignment was preventing
ennvronment variables being passed to Travis containers.is_rtorch_env_name() and
env_name object to torch_config() to live unit
test install_pytorch()install_pytorch() to vignettepython_version to function
conda_install()Roxygen: list(markdown = TRUE)PYTORCH_VERSION and
PYTHON_VERSION in Travis.and, or and
not to be boolean or uint8 as their inputs.equal and not equal.uint8 types. Currently, AND (“!”) and OR (“|”) return
booleans while NOT and others don’t; they return
uint8. We should fix this lack of consistency.uint8. Newer PyTorch versions make the conversion of the
comparison and return boolean values. In 1.1 they return
uint8.torch$eq() and torch$ne()
to validate boolean inputs.eq() and ne() in PyTorch
1.1. They return tensor(True, dtype=torch.bool) or
tensor(False, dtype=torch.bool).generics.R and
properties.Ruint8 types as
original PyTorch functions in generics.R.torch$all, torch$any
and some generic logicals in test-tensor_comparison.R.!.torch.Tensor to return boolean if
input is boolean, otherwise return opriginal type. Fix tests in
test_generics.R and test_numpy_logical.R.R-4.0.2 and
Python 3.7,install_pytorch(conda_python_version = "3.8", version = "1.2").
Tests failed. But not because of PyTorch but conflict during the conda
installation.install_pytorch(conda_python_version = "3.8", version = "1.4")
with tests passed.numpy version to printout of
rtorch_config().Python 3.8
because they fail with all PyTorch versions.PYTHON_VERSION to conda in
build_script of appveyor. Twelve (12) passed.Python 3.6 and Python 3.7, and
3.8 for only PyTorch 1.6, a total of 36 tests.
All passed.Python 3.6 and Python 3.7, a total
of 36 tests. All passed.develop branch with Travis and Appveyor. All
tests passed.0.0.3.9010-fix-appveyor-cpu from pytorch and
torchvision packages from appveyor.yml. still
failing because of python version is 3.6.1.appveyor.yml.
Passed.f0nzie/r-appveyor requires some changes.appveyor-tool.ps1 in
r-appveyor repo. changes related to Rtools4.pytorch=1.1.0. Will change to
pytorch=1.4.python=3.7 and
pytorch=1.4. All R versions passed.DESCRIPTION. remove ctb. will credit them in
README.python=3.7 and
pytorch=1.6. All R versions passed.pytorch=1.4 and R-4.0.2.
Passed.pytorch=1.2, 1.4, 1.6 and
R-4.0.2. Passed.R: 4.0.2 and
3.6.3 over pytorch, 1.1, 1.2, 1.4, and 1.6. using appveyor
variable R_VERSION.python=3.8 and pytorch=1.1
failing for all R versions. Rest of tests
passed.R:
4.0.2, 3.6.3 and 3.5.3 over
pytorch, 1.1, 1.2, 1.4, and 1.6. using appveyor variable
R_VERSION.0.0.3.9008-implement-todo-itemstest_numpy_logical.R to check sample
tensorstest_info.R add test to check the version
three componentsmake_copy function0.0.3.9007-fix-auto-load-torchpackage.Ron_load()make_copy() to consider when an object
have multiple classes. Use any for the logical
selectioninstall_pytorch() and
parse_torch_version().install_pytorch() and
parse_torch_version()install_pytorch() and
parse_torch_version()dry_run option to
install_pytorch() to use output values in unit teststest-install_commands.Rtensor_dim_ to
tensor_ndimmake_copy() moved from unit test
utilities.torch$index (not
applicable).message()
instead of print()examples.RuntimeError: Expected object of scalar type Byte but got scalar type Long
in [.torch.Tensor at generics functions.(all(y[all_dims(), 1] == y[,,,,1]) == torch$tensor(1L))$numpy().
In older versions of R it works. We could change the test to something
like
as.logical((all(y[all_dims(), 1] == y[,,,,1]))$numpy()) == TRUE.
Tested in R-3.6.3 locally and PASSED. Will test via Travis.test_torch_core.R:211: warning: narrow the condition has length > 1 and only the first element will be used
but all test passed.- if [ "$TRAVIS_OS_NAME" = "osx" ]; then conda install nomkl;fi
in .travis.yml to be able to get rid off an error related to
OMP003.9004-fix-examples-torch-byte-to-long
with develop.> rTorch:::install_conda(package="pytorch=1.4", envname="r-torch", conda="auto", conda_python_version = "3.6", pip=FALSE, channel="pytorch", extra_packages=c("torchvision", "cpuonly", "matplotlib", "pandas"))fix-readme-add-tests.checkRd: (5) rTorch.Rd:0-7: Must have a \description. Also
stops in travis-ci.org.1.6 to 1.1 to debug
error in rTorch.Rd#' PyTorch bindings for R. The problem originated by the
new R version.rTorch:::install_conda(package="pytorch=1.6", envname="r-torch", conda="auto", conda_python_version = "3.6", pip=FALSE, channel="pytorch", extra_packages=c("torchvision", "cpuonly", "matplotlib", "pandas")).
Run tests. Run devtools::check(). All passed.--run-donttest option to check() arguments. Getting
errors.all_dims() examples in generics.R.logical_not() examples in generics.R.[.torch.Tensor examples in extract.R.torch_extract_opts examples in
extract.R.dontrun examples that
passed in the local machine. What is different is the PyTorch version
specified in .travis.yml. Changing variable from “1.1”” to
PYTORCH_VERSION="1.6".pytorch-cpu==1.6 in command
'rTorch::install_pytorch(method="conda", version=Sys.getenv("PYTORCH_VERSION"), channel="pytorch", conda_python_version="3.6")'.
We need to modify function install_pytorch().R version 4.0.0 (2020-04-24) -- "Arbor Day"rTorch::pytorch_install(). Use instead
rTorch:::conda_install().libstdc++.so.6 in the Linux
installation. This is confusing Python.LD_LIBRARY_PATH=${TRAVIS_HOME}/miniconda/lib.Rscript -e 'install.packages(c("logging", "reticulate", "jsonlite", "R6", "rstudioapi", "data.table"))test_tensor_dim.R because
throwing error due to lack of memory.* checking for future file timestamps ... NOTE. unable to verify current time.fix-elimination-cpu-suffix to address
removal of suffix by developer.rTorch:::install_conda(package="pytorch=1.1", envname="r-torch", conda="auto", conda_python_version = "3.6", pip=FALSE, channel="pytorch", extra_packages=c("torchvision", "cpuonly", "matplotlib", "pandas"))test_types.R. Minor changes in
reticulate makes it more sensitive.mnist dataset until internal tests
are resolvedVERSIONS <- c("1.1", "1.0", "1.2", "1.3", "1.4", "1.5", "1.6")
in test_info.Rextract syntaxsys:1: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /opt/conda/conda-bld/pytorch_1595629417679/work/torch/csrc/utils/tensor_numpy.cpp:141.).expect_all_true() in
utils.R that shortens a test with multiple TRUE returning
from conditionr_to_py to R array
and then copying with r_to_py(r_array)$copy() before
converting to tensortensor_logical_and() and
tensor_logical_or() in generics.R -which use
NumPy logical functions - to make a copy before converting the numpy
array to a tensoras_tensor() function in tensor_functions.R with
torch$as_tensor(). Use make_copy() to prevent PyTorch
warning.\dontrun by \donttest where
applicable\value to all functions with
@returncran-comments.mdappveyor.ymlappveyor scripts now are called
from this repo. Original source is at krlmlr/r-appveyor.travis.ymlpytorch channel in
reticulate.R.install.Rrsuite.a %% btest_generics.R to use new
function expect_true_tensorany and all. Add
examplesmnist_fashion_inference.Rmd:mnist_fashion_inference.Rmd.simple_linear_regression.Rmd.linear_regression_rainfall_builtins.Rmdlinear_regression_rainfall.Rmdpng_images_minist_digits.Rmd. It uses PBG
images in a local folder instead of downloading MNIST idx format
images.idx_images_minist_digits.Rmdtwo_layer_neural_network.Rmd. Had some
problem with the tensor types. Fixed by using shorter generic version of
the tensor gradient operation.py_run_string("import torch")rpystats-apollo11NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.