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License: MIT License
Cookiecutter for evaluating algorithms and training models in nussl
License: MIT License
We shouldn't need requirements.txt if we're using poetry. I know this is for docker, but maybe change this name to avoid confusion. requirements.txt
==> freeze_requirements.txt
Currently links to https://cookiecutter.readthedocs.io/en/latest/readme.html which 404's.
should it link to this:
https://cookiecutter.readthedocs.io/en/latest/
?
On this page (https://pseeth.github.io/cookiecutter-nussl/getting_started.html#the-makefile) the first link to Makefile points to https://pseeth.github.io/cookiecutter-nussl/Makefile, which 404s
make install
fails with the following output:
(slakh_models) emanilow@gpubox1:~/slakh_models/slakh-models$ make install
curl -sSL https://raw.githubusercontent.com/sdispater/poetry/master/get-poetry.py | python
Retrieving Poetry metadata
Latest version already installed.
poetry update
Updating dependencies
Resolving dependencies... (9.6s)
No dependencies to install or update
poetry install
Installing dependencies from lock file
No dependencies to install or update
apt-get install redis
E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)
E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?
Makefile:18: recipe for target 'install' failed
make: *** [install] Error 100
I try running again with sudo
:
(slakh_models) emanilow@gpubox1:~/slakh_models/slakh-models$ sudo make install
curl -sSL https://raw.githubusercontent.com/sdispater/poetry/master/get-poetry.py | python
Retrieving Poetry metadata
Latest version already installed.
poetry update
/bin/bash: poetry: command not found
Makefile:18: recipe for target 'install' failed
make: *** [install] Error 127
This will crash instead of logging the exception gracefully because this variable is undefined.
Is this supposed to be event.source_file
?
Many commands don't really "make" anything, so alias them so that running an experiment doesn't look like:
make pipeline yml=path/to/experiment/pipeline.yml
Maybe something like run
instead?
You can train an OVA model by only specifying one source folder in dataset_config: source_folders: ['s1']
, but for eval we need another source to calculate reliable SIR values.
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