This repository gathers CLI and classes needed to run luigi
based tasks for the ROMI project.
Alone, this library does not do much...
To run a "meaningful" task you need to install other ROMI libraries like plantdb
and plant-3d-vision
or plant-imager
.
Note that both plant-3d-vision
& plant-imager
ROMI libraries have romitask
& plantdb
as git submodules.
We strongly advise to create isolated environments to install the ROMI libraries.
We often use conda
as an environment and python package manager.
If you do not yet have miniconda3
installed on your system, have a look here.
The romitask
package is available from the romi-eu
channel.
To install the romitask
conda package in an existing environment, first activate it, then proceed as follows:
conda install romitask -c romi-eu
To install the romitask
conda package in a new environment, here named romi
, proceed as follows:
conda create -n romi romitask -c romi-eu
To install this library, simply clone the repo and use pip
to install it and the required dependencies.
Again, we strongly advise to create a conda
environment.
All this can be done as follows:
git clone https://github.com/romi/romitask.git -b dev # git clone the 'dev' branch of romitask
cd romitask
conda create -n romi 'python =3.10'
conda activate romi # do not forget to activate your environment!
python -m pip install -e . # install the sources
Note that the -e
option is to install the romitask
sources in "developer mode".
That is, if you make changes to the source code of romitask
you will not have to pip install
it again.
You may want to install the plantdb
sources to perform the DummyTask
test example below:
conda activate romi # do not forget to activate your environment!
python -m pip install git+https://github.com/romi/plantdb.git@dev#egg=plantdb # install the `plantdb` sources from 'dev' branch
This will install the required ROMI library plantdb
, but not in "developer mode".
To quickly create a dummy database, let's use the temporary folder /tmp
:
mkdir -p /tmp/dummy_db/dummy_dataset # create dummy database and dataset
touch /tmp/dummy_db/romidb # add the romidb marker (empty file)
export DB_LOCATION='/tmp/dummy_db' # add database location as an environment variable, 'DB_LOCATION', to current shell
To test the CLI romi_run_task
:
romi_run_task DummyTask $DB_LOCATION/dummy_dataset
You should get a "Luigi Execution Summary" similar to this:
===== Luigi Execution Summary =====
Scheduled 1 tasks of which:
* 1 ran successfully:
- 1 DummyTask(scan_id=)
This progress looks :) because there were no failed tasks or missing dependencies
===== Luigi Execution Summary =====
As no TOML configuration file was provided, you should get a pipeline.toml
with only a retcode
and a version
sections at the root of the dummy_dataset/
directory.
The dummy_database
tree structure should look like this:
dummy_database/
├── dummy_dataset/
│ ├── DummyTask__**********/
│ ├── files.json
│ ├── metadata/
│ │ └── DummyTask__**********.json
│ └── pipeline.toml
└── romidb
You first have to install the library from sources as explained here.
Start by installing the required conda-build
& anaconda-client
conda packages in the base
environment as follows:
conda install -n base conda-build anaconda-client
To build the romitask
conda package, from the root directory of the repository and the base
conda environment, run:
conda build conda/recipe/ -c conda-forge --user romi-eu
If you are struggling with some of the modifications you made to the recipe, notably when using environment variables or Jinja2 stuffs, you can always render the recipe with:
conda render conda/recipe/
The official documentation for conda-render
can be found here.
To upload the built packages, you need a valid account (here romi-eu
) on anaconda.org & to log ONCE
with anaconda login
, then:
anaconda upload ~/miniconda3/conda-bld/linux-64/romitask*.tar.bz2 --user romi-eu
To clean the source and build intermediates:
conda build purge
To clean ALL the built packages & build environments:
conda build purge-all
We use mkdocs
to build the documentation with mkdocs-material
theme.
The API documentation is generated by mkdocstrings
.
Also, we use markdown-exec
to execute some code snippets to generate output parsed in Markdown.
Finally, you may want to check the pymdown-extensions
documentation.
To install the requirements for documentation edition, simply run:
python -m pip install -e .[doc]
You can edit the documentation, mkdocs.yaml
and the docs
directory, and see the changes live by using:
mkdocs serve
You should see some logging and this message, indicating where to view it:
INFO - [14:39:21] Serving on http://127.0.0.1:8000/