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Given a model and a list of its dependencies and packages, an environment needs to be present that has these requirements readily available so as to use the model. To make sure that different dependencies for different models (or projects) don't clash, virtual environments are used.
MLEM can create a python virtual environment using requirements gathered from a
model. This naturally extends the functionality of the
requirements builder.
$ mlem build venv -m model --target newenv
⏳️ Loading model from model.mlem
💼 Creating virtual env newenv...
💼 Installing the required packages...
Collecting scikit-learn==1.0.2
Using cached scikit_learn-1.0.2-cp39-cp39-macosx_12_0_arm64.whl (6.9 MB)
Collecting pandas==1.4.2
Using cached pandas-1.4.2-cp39-cp39-macosx_11_0_arm64.whl (10.1 MB)
Collecting numpy==1.22.3
Using cached numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl (12.8 MB)
Collecting joblib>=0.11
Using cached joblib-1.2.0-py3-none-any.whl (297 kB)
Collecting scipy>=1.1.0
Using cached scipy-1.9.2-cp39-cp39-macosx_12_0_arm64.whl (28.6 MB)
Collecting threadpoolctl>=2.0.0
Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting pytz>=2020.1
Using cached pytz-2022.4-py2.py3-none-any.whl (500 kB)
Collecting python-dateutil>=2.8.1
Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
Collecting six>=1.5
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: pytz, threadpoolctl, six, numpy, joblib, scipy, python-dateutil, scikit-learn, pandas
Successfully installed joblib-1.2.0 numpy-1.22.3 pandas-1.4.2 python-dateutil-2.8.2 pytz-2022.4 scikit-learn-1.0.2 scipy-1.9.2 six-1.16.0 threadpoolctl-3.1.0
✅ virtual environment `newenv` is ready, activate with `source newenv/bin/activate`If the target is not passed, the default name for the new environment is
venv.
Other options include using:
--no_cache True — to disable caching while fetching packages in creation of
the environment.--current_env True — whether to install the requirements in a currently
activated virtual environment.