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move JPEG reference test into separate CI job #5184
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💊 CI failures summary and remediationsAs of commit c070c6b (more details on the Dr. CI page): 💚 💚 Looks good so far! There are no failures yet. 💚 💚 This comment was automatically generated by Dr. CI (expand for details).Please report bugs/suggestions to the (internal) Dr. CI Users group. |
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Thanks a lot for the PR @pmeier , I took a quick look, LMK what you think
@@ -359,6 +359,33 @@ jobs: | |||
- run_tests_selective: | |||
file_or_dir: test/test_prototype_*.py | |||
|
|||
unittest_jpeg_ref: | |||
docker: | |||
- image: condaforge/mambaforge |
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Any chance we could stick to conda instead of mamba, so as to stick to the current tools that we use in the rest of the job?
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We probably can (there is most likely a docker image that gives us conda
), but I'm not sure we should. mamba
is a re-implementation of the protocol, but faster. So we effectively waste CI resources by using conda
.
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Shouldn't we rely on the same image as the other unittest
jobs here, to avoid any risk of deviating from our "baseline" testing infra? I believe they all rely on conda
as well so there must be a way to install conda, even if we don't choose condaforge/mambaforge
as the image
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Besides our CPU workflows on Linux, none of the other workflows even use docker
:
Lines 719 to 722 in d675c0c
unittest_linux_cpu: | |
<<: *binary_common | |
docker: | |
- image: "pytorch/manylinux-cuda102" |
Lines 758 to 761 in d675c0c
unittest_linux_gpu: | |
<<: *binary_common | |
machine: | |
image: ubuntu-1604-cuda-10.2:202012-01 |
Lines 808 to 811 in d675c0c
unittest_windows_cpu: | |
<<: *binary_common | |
executor: | |
name: windows-cpu |
Lines 846 to 849 in d675c0c
unittest_windows_gpu: | |
<<: *binary_common | |
executor: | |
name: windows-gpu |
Lines 893 to 896 in d675c0c
unittest_macos_cpu: | |
<<: *binary_common | |
macos: | |
xcode: "12.0" |
Apart from that, they all download the miniconda
installer:
wget -O miniconda.sh "http://repo.continuum.io/miniconda/Miniconda3-latest-${os}-x86_64.sh" |
curl --output miniconda.exe https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe -O |
There is a container for that, but again, I see no point. It makes no difference if you install the environment with conda
or mamba
other than the latter is significantly faster.
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I'm happy to switch all of our jobs to mamba in another PR, but could we please stick to the same workflow that the other jobs are using? There is value in avoiding discrepancies with the rest of our CI jobs.
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What other workflow? Manually installing conda
and setting things up or can I at least use a docker image with conda
pre-installed?
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Sorry I'm a bit confused: what prevents us from staying as close as possible to unittest_linux_cpu:
?
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
…into jpeg-reference-ci
# against libjpeg. By installing Pillow from conda-forge, it uses whatever jpeg library is already present instead of | ||
# packaging one. |
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, it uses whatever jpeg library is already present instead of
packaging one.
I'm not sure I understand what this means. Should we just say "By installing Pillow from conda-forge, we make sure that pillow is built against libjpeg" ?
@@ -359,6 +359,33 @@ jobs: | |||
- run_tests_selective: | |||
file_or_dir: test/test_prototype_*.py | |||
|
|||
unittest_jpeg_ref: | |||
docker: | |||
- image: condaforge/mambaforge |
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Sorry I'm a bit confused: what prevents us from staying as close as possible to unittest_linux_cpu:
?
Addresses #5162 (comment). This is the newly added CI job.
cc @seemethere