This repository has been archived by the owner on Jun 14, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 14
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
5 changed files
with
83 additions
and
60 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,75 @@ | ||
Develop a model using DEEP UC template | ||
====================================== | ||
|
||
Prepare DEEP UC environment | ||
--------------------------- | ||
|
||
Install cookiecutter (if not yet done) | ||
:: | ||
$ pip install cookiecutter | ||
Run the DEEP UC cookiecutter template | ||
:: | ||
$ cookiecutter https://github.com/indigo-dc/cookiecutter-data-science | ||
Answer all questions from DEEP UC cookiecutter template with attentions to | ||
``repo_name`` i.e. the name of your github repositories, etc. | ||
|
||
This creates two project directories: | ||
:: | ||
~/DEEP-OC-your_project | ||
~/your_project | ||
Go to ``github.com/your_account`` and | ||
create corresponding repositories: ``DEEP-OC-your_project`` and ``your_project`` | ||
|
||
Do ``git push origin master`` in both created directories. This puts your initial code to ``github``. | ||
|
||
|
||
Develop a model according to DEEP UC template | ||
--------------------------------------------- | ||
|
||
The structure of ``your_project`` created using | ||
`DEEP UC template <https://github.com/indigo-dc/cookiecutter-data-science>`__ contains | ||
the following core items needed to develop a model | ||
:: | ||
requirements.txt | ||
data/ | ||
models/ | ||
{{cookiecutter.repo_name}}/dataset/make_dataset.py | ||
{{cookiecutter.repo_name}}/features/build_features.py | ||
{{cookiecutter.repo_name}}/models/model.py | ||
**Installing development requirements** | ||
|
||
Modify ``requirements.txt`` according to your needs (e.g. add more libraries) then run | ||
:: | ||
$ pip install -r requirements.txt | ||
|
||
**Improve the initial code** | ||
|
||
You can modify as well as add more source files and put them accordingly into the directory structure. | ||
|
||
**1. Make datasets:** source files in this directory aim to manipulate with raw dataset(s). | ||
The output of this step is raw data, which can be cleaned and/or pre-formatted. | ||
:: | ||
{{cookiecutter.repo_name}}/dataset/make_dataset.py | ||
{{cookiecutter.repo_name}}/dataset/ | ||
|
||
**2. Build features** takes the output from the previous step (Make datasets) and | ||
creates ML train, test as well as validation data from raw data. | ||
The concrete realisation is depend on concrete UC, the aim of the application as well as | ||
technological background (e.g. high-performance supports). | ||
:: | ||
{{cookiecutter.repo_name}}/features/build_features.py | ||
{{cookiecutter.repo_name}}/features/ | ||
|
||
**3. Develop models** dealing with the most interesting ML core i.e. modelling. | ||
The most important thing in the ``model.py`` are implementations for DEEP entry points, | ||
which are defined according to :ref:`API methods <user/overview/api:Methods>`. | ||
You don't need to implement all the methods, just the ones you need. | ||
:: | ||
{{cookiecutter.repo_name}}/models/model.py | ||
{{cookiecutter.repo_name}}/models/ | ||
|
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file was deleted.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters