Installation¶
Installation with conda
is recommended. conda
environment files for Python 3.6, 3.7, 3.8 and 3.9 are available in the repository. To use models under the inference.tf
module (e.g. DragonNet
), additional dependency of tensorflow
is required. For detailed instructions, see below.
Install using conda
¶
This will create a new conda
virtual environment named causalml-[tf-]py3x
, where x
is in [6, 7, 8, 9]
. e.g. causalml-py37
or causalml-tf-py38
. If you want to change the name of the environment, update the relevant YAML file in envs/
.
$ git clone https://github.com/uber/causalml.git
$ cd causalml/envs/
$ conda env create -f environment-py38.yml # for the virtual environment with Python 3.8 and CausalML
$ conda activate causalml-py38
(causalml-py38)
To install causalml
with tensorflow
using conda
, use a relevant causalml-[tf-]py3x
environment file as follows:
$ git clone https://github.com/uber/causalml.git
$ cd causalml/envs/
$ conda env create -f environment-tf-py38.yml # for the virtual environment with Python 3.8 and CausalML
$ conda activate causalml-tf-py38
(causalml-tf-py38) pip install -U numpy # this step is necessary to fix [#338](https://github.com/uber/causalml/issues/338)
Install using pip
¶
$ git clone https://github.com/uber/causalml.git
$ cd causalml
$ pip install -r requirements.txt
$ pip install causalml
To install causalml
with tensorflow
using pip
, use causalml[tf]
as follows:
$ git clone https://github.com/uber/causalml.git
$ cd causalml
$ pip install -r requirements-tf.txt
$ pip install causalml[tf]
$ pip install -U numpy # this step is necessary to fix [#338](https://github.com/uber/causalml/issues/338)
Install from source¶
$ git clone https://github.com/uber/causalml.git
$ cd causalml
$ pip install -r requirements.txt
$ python setup.py build_ext --inplace
$ python setup.py install