Reference | Package presets for custom Python environments#

For custom Python environments, Dataiku offers makes it easy to add sets of packages commonly used together for certain kinds of machine learning tasks.

Visual Machine Learning (scikit-learn, XGBoost, LightGBM)
scikit-learn>=0.20,<0.21
scipy>=1.2,<1.3
xgboost==0.82
lightgbm>=3.2,<3.3
jinja2>=2.10,<2.11
MarkupSafe<2.1.0
itsdangerous<2.1.0
flask>=1.0,<1.1
cloudpickle>=1.3,<1.6
statsmodels>=0.10,<0.11
Visual Machine Learning with Bayesian search (scikit-learn, XGBoost, LightGBM, scikit-optimize)
scikit-optimize>=0.7,<0.8
scikit-learn>=0.20,<0.21
scipy>=1.2,<1.3
xgboost==0.82
lightgbm>=3.2,<3.3
jinja2>=2.10,<2.11
MarkupSafe<2.1.0
itsdangerous<2.1.0
flask>=1.0,<1.1
cloudpickle>=1.3,<1.6
statsmodels>=0.10,<0.11
Visual Machine Learning with sentence embedding (scikit-learn, XGBoost, LightGBM, sentence-transformers)
sentence-transformers>=2.1,<2.3
scikit-learn>=0.20,<0.21
scipy>=1.2,<1.3
xgboost==0.82
lightgbm>=3.2,<3.3
jinja2>=2.10,<2.11
MarkupSafe<2.1.0
itsdangerous<2.1.0
flask>=1.0,<1.1
cloudpickle>=1.3,<1.6
statsmodels>=0.10,<0.11
Visual Deep Learning: Tensorflow. CPU, and GPU with CUDA11.2 + cuDNN 8.1
tensorflow>=2.6.2,<3.0
scikit-learn>=0.20,<0.21
scipy>=1.2,<1.3
statsmodels>=0.10,<0.11
jinja2>=2.10,<2.11
MarkupSafe<2.1.0
itsdangerous<2.1.0
flask>=1.0,<1.1
pillow==6.2.2
cloudpickle>=1.3,<1.6
h5py==3.1.0