diff --git a/README.md b/README.md
index 999c5c2..90c12b8 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,12 @@ If you are a gaussian process expert, you might be better served by [gpytorch](h
 
 ## Usage
 
+You can install our librarie with:
+
+```
+pip install git+https://github.com/nestordemeure/tabularGP.git
+```
+
 Our API was built to be compatible with [fastai V1's tabular models](https://docs.fast.ai/tabular.html) and should be familiar to fastai's users:
 
 ```python
diff --git a/setup.py b/setup.py
index 0e543c7..cbf2b4b 100644
--- a/setup.py
+++ b/setup.py
@@ -1,3 +1,4 @@
+# https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 from distutils.core import setup
 
 setup(
@@ -9,7 +10,7 @@
   author = 'NestorDemeure',
 #  author_email = 'your.email@domain.com',
   url = 'https://github.com/nestordemeure/tabularGP',
-  download_url = 'https://github.com/nestordemeure/tabularGP/archive/v1.0.tar.gz',
+  download_url = 'https://github.com/nestordemeure/tabularGP/archive/v1.1.tar.gz',
   keywords = ['gaussian-processes', 'tabular-data', 'deep-learning', 'pytorch', 'fastai'],
   install_requires=[
           'numpy',
diff --git a/tabularGP/kernel.py b/tabularGP/kernel.py
index 912a652..e7e84aa 100644
--- a/tabularGP/kernel.py
+++ b/tabularGP/kernel.py
@@ -10,8 +10,8 @@
 from torch import nn
 import torch
 # my imports
-from utils import Scale
-from universalCombinator import PositiveMultiply, PositiveProductOfSum
+from tabularGP.utils import Scale
+from tabularGP.universalCombinator import PositiveMultiply, PositiveProductOfSum
 
 __all__ = ['CategorialKernel', 'ContinuousKernel', 'TabularKernel',
            'IndexKernelSingle', 'IndexKernel', 'HammingKernel',
diff --git a/tabularGP/tabularGP.py b/tabularGP/tabularGP.py
index 76e7b5c..ef293ed 100644
--- a/tabularGP/tabularGP.py
+++ b/tabularGP/tabularGP.py
@@ -7,11 +7,11 @@
 from torch import nn, Tensor
 from fastai.tabular import DataBunch, ListSizes, ifnone, Learner
 # my imports
-from loss_functions import gp_gaussian_marginal_log_likelihood, gp_is_greater_log_likelihood, gp_metric_wrapper
-from utils import psd_safe_cholesky, freeze, unfreeze
-from kernel import ProductOfSumsKernel, TabularKernel
-from trainset_selection import select_trainset
-from prior import ConstantPrior
+from tabularGP.loss_functions import gp_gaussian_marginal_log_likelihood, gp_is_greater_log_likelihood, gp_metric_wrapper
+from tabularGP.utils import psd_safe_cholesky, freeze, unfreeze
+from tabularGP.kernel import ProductOfSumsKernel, TabularKernel
+from tabularGP.trainset_selection import select_trainset
+from tabularGP.prior import ConstantPrior
 
 __all__ = ['TabularGPModel', 'TabularGPLearner', 'tabularGP_learner']
 
diff --git a/tabularGP/universalCombinator.py b/tabularGP/universalCombinator.py
index 413b25c..619b223 100644
--- a/tabularGP/universalCombinator.py
+++ b/tabularGP/universalCombinator.py
@@ -9,7 +9,7 @@
 import torch.nn.functional as F
 import torch
 # my imports
-from utils import soft_clamp_max, magnitude, magnitude_reciprocal
+from tabularGP.utils import soft_clamp_max, magnitude, magnitude_reciprocal
 
 __all__ = ['PositiveLinear', 'PositiveMultiply', 'Multiply', 'Polynomial', 'PositiveProductOfSum']