diff --git a/.travis.yml b/.travis.yml
index 0dbb122..c631476 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,12 +4,15 @@ sudo: false
 
 env:
     matrix:
-        - CONDA_PY=3.5
+#        - CONDA_PY=3.5
         - CONDA_PY=3.6
-
+        - CONDA_PY=3.7
+#        - CONDA_PY=3.8
 branches:
   only:
-    - master
+    - Development
+    - update_to_py37
+#    - master
 
 install:
     - source devtools/travis-ci/install_miniconda.sh
diff --git a/devtools/conda-recipe/meta.yaml b/devtools/conda-recipe/meta.yaml
index 6492571..193b11d 100644
--- a/devtools/conda-recipe/meta.yaml
+++ b/devtools/conda-recipe/meta.yaml
@@ -26,6 +26,7 @@ requirements:
     - bokeh
     - matplotlib
     - pandas
+    - joblib
 
 test:
 
@@ -33,8 +34,6 @@ test:
     - nose
     - nose-timer
     - gpy
-    - msmbuilder
-    - msmb_data
     - mdtraj
     - hyperopt
     - coverage
diff --git a/devtools/travis-ci/install_miniconda.sh b/devtools/travis-ci/install_miniconda.sh
index 0b5c760..f68231f 100755
--- a/devtools/travis-ci/install_miniconda.sh
+++ b/devtools/travis-ci/install_miniconda.sh
@@ -1,7 +1,8 @@
 #!/bin/bash
 MINICONDA=Miniconda3-latest-Linux-x86_64.sh
-MINICONDA_MD5=$(curl -s https://repo.continuum.io/miniconda/ | grep -A3 $MINICONDA | sed -n '4p' | sed -n 's/ *<td>\(.*\)<\/td> */\1/p')
-wget https://repo.continuum.io/miniconda/$MINICONDA
+MINICONDA_REPO=https://repo.anaconda.com/miniconda
+MINICONDA_MD5=$(curl $MINICONDA_REPO/ | grep -A3 $MINICONDA | sed -n '4p' | sed -n 's/ *<td>\(.*\)<\/td> */\1/p')
+wget $MINICONDA_REPO/$MINICONDA
 if [[ $MINICONDA_MD5 != $(md5sum $MINICONDA | cut -d ' ' -f 1) ]]; then
     echo "Miniconda MD5 mismatch"
     exit 1
diff --git a/docs/changelog.rst b/docs/changelog.rst
index 9d7825c..27aead7 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -20,7 +20,7 @@ New Features
 + Added ``n_jobs`` flag for ``osprey worker`` to control how many threads are used for cross-validation.
 + Added the ability to specify three different acquisition functions for the gaussian processes strategy: expected improvement `ei`, upper confidence bound, `ucb` and the original Osprey function (the default), `osprey`.
 + Refactored code: GP strategy has been replaced by a general Bayes strategy.  This will eventually allow different surrogate functions, other than Gaussian Process, to be specified.
-
++ Update Python compatability: Now compatable with Python 3.7. MSMBuilder is now an optional.
 
 Bug Fixes
 ~~~~~~~~~
diff --git a/osprey/config.py b/osprey/config.py
index edf55b2..f533cd8 100644
--- a/osprey/config.py
+++ b/osprey/config.py
@@ -372,7 +372,7 @@ def sha1(self):
 
 
 def parse(f):
-    res = yaml.load(f)
+    res = yaml.load(f, Loader=yaml.SafeLoader)
     if res is None:
         res = {}
 
diff --git a/osprey/dataset_loaders.py b/osprey/dataset_loaders.py
index db45da4..2407dc4 100644
--- a/osprey/dataset_loaders.py
+++ b/osprey/dataset_loaders.py
@@ -219,7 +219,7 @@ class JoblibDatasetLoader(BaseDatasetLoader):
     short_name = 'joblib'
 
     def __init__(self, filenames, x_name=None, y_name=None,
-                 system_joblib=False):
+                 system_joblib=True):
         self.filenames = filenames
         self.x_name = x_name
         self.y_name = y_name
diff --git a/osprey/fit_estimator.py b/osprey/fit_estimator.py
index 7696473..d6ec4c9 100644
--- a/osprey/fit_estimator.py
+++ b/osprey/fit_estimator.py
@@ -6,13 +6,21 @@
 import numpy as np
 import sklearn
 from sklearn.base import is_classifier, clone
-from sklearn.metrics.scorer import check_scoring
-from sklearn.externals.joblib import Parallel, delayed
+try:
+    from sklearn.metrics import check_scoring
+except ModuleNotFoundError:
+    from sklearn.metrics.scorer import check_scoring
+# TODO: Remove old import? externals.joblib deprecated as of 0.23
+try:
+    from joblib import Parallel, delayed
+except ModuleNotFoundError:
+    from sklearn.externals.joblib import Parallel, delayed
+
 from sklearn.model_selection import check_cv
 from sklearn.model_selection._validation import _safe_split, _score
 
 from .utils import check_arrays, num_samples
-from .utils import short_format_time, is_msmbuilder_estimator
+from .utils import short_format_time, is_msmbuilder_estimator, is_pyemma_estimator
 
 
 if LooseVersion(sklearn.__version__) < LooseVersion('0.16.1'):
@@ -40,7 +48,6 @@ def fit_and_score_estimator(estimator, parameters, cv, X, y=None, scoring=None,
         The scores on the training and test sets, as well as the mean test set
         score.
     """
-
     scorer = check_scoring(estimator, scoring=scoring)
     n_samples = num_samples(X)
     X, y = check_arrays(X, y, allow_lists=True, sparse_format='csr',
@@ -136,9 +143,11 @@ def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
 
     scoring_time = time.time() - start_time
 
-    msmbuilder_api = is_msmbuilder_estimator(estimator)
-    n_samples_test = num_samples(X_test, is_nested=msmbuilder_api)
-    n_samples_train = num_samples(X_train, is_nested=msmbuilder_api)
+    # With PyEMMA and MSMBuilder, observations are nested in separate lists not just in a single
+    # array.
+    is_nested = is_msmbuilder_estimator(estimator) or is_pyemma_estimator(estimator)
+    n_samples_test = num_samples(X_test, is_nested=is_nested)
+    n_samples_train = num_samples(X_train, is_nested=is_nested)
     if verbose > 2:
         msg += ", score=%f" % test_score
     if verbose > 1:
diff --git a/osprey/plot.py b/osprey/plot.py
index 6e3ee78..6398bc7 100644
--- a/osprey/plot.py
+++ b/osprey/plot.py
@@ -24,7 +24,7 @@ def nonconstant_parameters(data):
     assert len(data) > 0
     df = pd.DataFrame([d['parameters'] for d in data])
     # http://stackoverflow.com/a/20210048/1079728
-    filtered = df.loc[:, (df != df.ix[0]).any()]
+    filtered = df.loc[:, (df != df.iloc[0]).any()]
     return filtered
 
 
diff --git a/osprey/plugins/tests/test_plugin_pylearn2.py b/osprey/plugins/tests/test_plugin_pylearn2.py
index 6c48a6b..99bde52 100644
--- a/osprey/plugins/tests/test_plugin_pylearn2.py
+++ b/osprey/plugins/tests/test_plugin_pylearn2.py
@@ -6,7 +6,12 @@
 import unittest
 from argparse import Namespace
 import numpy as np
-from numpy.testing.decorators import skipif
+# TODO remove compat with py<=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
 
 try:
     import pylearn2
diff --git a/osprey/tests/test_cli_skeleton.py b/osprey/tests/test_cli_skeleton.py
index eaedfa2..20ef85d 100644
--- a/osprey/tests/test_cli_skeleton.py
+++ b/osprey/tests/test_cli_skeleton.py
@@ -18,11 +18,11 @@ def test_1():
 
     try:
         os.chdir(dirname)
-        subprocess.check_call([OSPREY_BIN, 'skeleton', '-t', 'msmbuilder',
+        subprocess.check_call([OSPREY_BIN, 'skeleton', '-t', 'sklearn',
                               '-f', 'config.yaml'])
         assert os.path.exists('config.yaml')
         with open('config.yaml', 'rb') as f:
-            yaml.load(f)
+            yaml.load(f, Loader=yaml.SafeLoader)
         Config('config.yaml')
 
     finally:
diff --git a/osprey/tests/test_cli_worker_and_dump.py b/osprey/tests/test_cli_worker_and_dump.py
index 47a0ca0..d9ec606 100644
--- a/osprey/tests/test_cli_worker_and_dump.py
+++ b/osprey/tests/test_cli_worker_and_dump.py
@@ -7,10 +7,16 @@
 import subprocess
 import tempfile
 from distutils.spawn import find_executable
-from numpy.testing.decorators import skipif
-from nose.plugins.skip import SkipTest
+# TODO remove compat with py<=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
 
+# from nose.plugins.skip import SkipTest
 
+# TODO need Pyemma analogues of these tests.
 try:
     __import__('msmbuilder.example_datasets')
     HAVE_MSMBUILDER = True
@@ -20,12 +26,13 @@
 OSPREY_BIN = find_executable('osprey')
 
 
-# @skipif(not HAVE_MSMBUILDER, 'this test requires MSMBuilder')
+@skipif(not HAVE_MSMBUILDER, 'this test requires MSMBuilder')
 def test_msmbuilder_skeleton():
-    try:
-        from msmbuilder.example_datasets import FsPeptide
-    except ImportError as e:
-        raise SkipTest(e)
+    from msmbuilder.example_datasets import FsPeptide
+    # try:
+    #     from msmbuilder.example_datasets import FsPeptide
+    # except ImportError as e:
+    #     raise SkipTest(e)
 
     assert OSPREY_BIN is not None
     cwd = os.path.abspath(os.curdir)
@@ -48,12 +55,13 @@ def test_msmbuilder_skeleton():
         shutil.rmtree(dirname)
 
 
-# @skipif(not HAVE_MSMBUILDER, 'this test requires MSMBuilder')
+@skipif(not HAVE_MSMBUILDER, 'this test requires MSMBuilder')
 def test_msmb_feat_select_skeleton():
-    try:
-        from msmbuilder.example_datasets import FsPeptide
-    except ImportError as e:
-        raise SkipTest(e)
+    from msmbuilder.example_datasets import FsPeptide
+    # try:
+    #     from msmbuilder.example_datasets import FsPeptide
+    # except ImportError as e:
+    #     raise SkipTest(e)
 
     assert OSPREY_BIN is not None
     cwd = os.path.abspath(os.curdir)
diff --git a/osprey/tests/test_config.py b/osprey/tests/test_config.py
index 57eccb9..dd3090e 100644
--- a/osprey/tests/test_config.py
+++ b/osprey/tests/test_config.py
@@ -14,6 +14,7 @@
 from osprey.search_space import IntVariable, FloatVariable, EnumVariable
 from osprey.strategies import RandomSearch, HyperoptTPE, Bayes
 
+import warnings
 
 os.environ['OSPREYRC'] = ' '
 
@@ -24,21 +25,24 @@ def test_estimator_pickle():
     with tempfile.NamedTemporaryFile('w+b', 0) as f:
 
         cPickle.dump(KMeans(), f)
-
         config = Config.fromdict({
             'estimator': {'pickle': f.name}
         }, check_fields=False)
+
         assert isinstance(config.estimator(), KMeans)
 
 
 def test_estimator_eval_1():
-    config = Config.fromdict({
-        'estimator': {
-            'eval': 'KMeans()',
-            'eval_scope': 'sklearn',
-        }
-    }, check_fields=False)
-    assert isinstance(config.estimator(), KMeans)
+    # TODO: why is this the only call to sklearn that produces the FutureWarnings?  What about eval_1?
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", category=FutureWarning)
+        config = Config.fromdict({
+            'estimator': {
+                'eval': 'KMeans()',
+                'eval_scope': 'sklearn',
+            }
+        }, check_fields=False)
+        assert isinstance(config.estimator(), KMeans)
 
 
 def test_estimator_eval_2():
diff --git a/osprey/tests/test_dataset_loader.py b/osprey/tests/test_dataset_loader.py
index ee55e9e..a2d6ece 100644
--- a/osprey/tests/test_dataset_loader.py
+++ b/osprey/tests/test_dataset_loader.py
@@ -6,8 +6,17 @@
 
 import numpy as np
 import sklearn.datasets
-from sklearn.externals.joblib import dump
-from numpy.testing.decorators import skipif
+# TODO remove old import?
+try:
+    from joblib import dump
+except ModuleNotFoundError:
+    from sklearn.externals.joblib import dump
+# TODO remove compat with py<=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
 
 from osprey.dataset_loaders import (DSVDatasetLoader, FilenameDatasetLoader,
                                     JoblibDatasetLoader, HDF5DatasetLoader,
@@ -177,7 +186,7 @@ def test_MDTrajDatasetLoader_1():
     finally:
         shutil.rmtree(dirname)
 
-
+@skipif(not HAVE_MSMBUILDER, 'this test requires MSMBuilder')
 def test_MSMBuilderDatasetLoader_1():
     # TODO Why does this work when other msmbuilder imports don't?
     from msmbuilder.dataset import dataset
diff --git a/osprey/tests/test_fit_estimator.py b/osprey/tests/test_fit_estimator.py
index 607bc53..d2b8f1c 100644
--- a/osprey/tests/test_fit_estimator.py
+++ b/osprey/tests/test_fit_estimator.py
@@ -9,9 +9,27 @@
 
 from osprey.fit_estimator import fit_and_score_estimator
 
+try:
+    __import__('msmbuilder.msm')
+    HAVE_MSMBUILDER = True
+except:
+    HAVE_MSMBUILDER = False
+
+try:
+    __import__('pyemma.msm')
+    HAVE_PYEMMA = True
+except:
+    HAVE_PYEMMA = False
+
+# TODO remove compat with py<=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
 
 def test_1():
-    X, y = make_regression(n_features=10)
+    X, y = make_regression(n_samples=300, n_features=10)
 
     lasso = Lasso()
     params = {'alpha': 2}
@@ -29,12 +47,9 @@ def test_1():
         [g.cv_results_['split{}_test_score'.format(i)] for i in range(cv)])
     assert np.all(out['test_scores'] == test_scores)
 
-
+@skipif(not HAVE_MSMBUILDER, "this test requires MSMBuilder")
 def test_2():
-    try:
-        from msmbuilder.msm import MarkovStateModel
-    except ImportError as e:
-        raise SkipTest(e)
+    from msmbuilder.msm import MarkovStateModel
 
     X = [np.random.randint(2, size=10), np.random.randint(2, size=11)]
     out = fit_and_score_estimator(MarkovStateModel(), {'verbose': False},
@@ -44,3 +59,17 @@ def test_2():
                                   verbose=0)
     np.testing.assert_array_equal(out['n_train_samples'], [11, 10])
     np.testing.assert_array_equal(out['n_test_samples'], [10, 11])
+
+
+@skipif(not HAVE_PYEMMA, "this test requires PyEMMA")
+def test_3():
+    from pyemma.msm import MaximumLikelihoodMSM
+
+    X = [np.random.randint(2, size=10), np.random.randint(2, size=11)]
+    out = fit_and_score_estimator(MaximumLikelihoodMSM(), {'score_k': 2},
+                                  cv=2,
+                                  X=X,
+                                  y=None,
+                                  verbose=0)
+    np.testing.assert_array_equal(out['n_train_samples'], [11, 10])
+    np.testing.assert_array_equal(out['n_test_samples'], [10, 11])
diff --git a/osprey/tests/test_search_space.py b/osprey/tests/test_search_space.py
index 945a85f..25d42d0 100644
--- a/osprey/tests/test_search_space.py
+++ b/osprey/tests/test_search_space.py
@@ -4,11 +4,18 @@
 import numpy as np
 import scipy.stats
 from six.moves import xrange
-from numpy.testing.decorators import skipif
+# backwards compat with python <=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
+
 try:
     from hyperopt import pyll
+    HAVE_HYPEROPT = True
 except ImportError:
-    pass
+    HAVE_HYPEROPT = False
 
 from osprey.search_space import SearchSpace
 from osprey.search_space import IntVariable, FloatVariable, EnumVariable
@@ -50,8 +57,9 @@ def test_2_1():
     values = [s.rvs()['a'] for _ in xrange(100)]
     _run_chi2_test(values, bin_edges=range(5))
 
-
-@skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+# TODO : why does old skipif check in sys.modules rather than test import?
+# @skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+@skipif(not HAVE_HYPEROPT, "this test requires hyperopt")
 def test_2_2():
     s = SearchSpace()
     s.add_int('a', 0, 3)
@@ -68,8 +76,8 @@ def test_3_1():
     assert all(-2 < v < 2 for v in values)
     _run_chi2_test(values, bin_edges=np.linspace(-2, 2, 10))
 
-
-@skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+# @skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+@skipif(not HAVE_HYPEROPT, "this test requires hyperopt")
 def test_3_2():
     s = SearchSpace()
     s.add_float('b', -2, 2)
@@ -88,8 +96,8 @@ def test_4_1():
     assert all(v in [True, False] for v in values)
     _run_chi2_test(np.array(values, dtype=int), bin_edges=range(3))
 
-
-@skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+# @skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+@skipif(not HAVE_HYPEROPT, "this test requires hyperopt")
 def test_4_2():
     s = SearchSpace()
     s.add_enum('c', [True, False])
@@ -114,7 +122,8 @@ def test_5_1():
     _run_chi2_test(values, bin_edges)
 
 
-@skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+# @skipif('hyperopt.pyll' not in sys.modules, 'this test requires hyperopt')
+@skipif(not HAVE_HYPEROPT, "this test requires hyperopt")
 def test_5_2():
     s = SearchSpace()
     s.add_float('a', 1e-5, 1, warp='log')
@@ -157,6 +166,7 @@ def test_gp_3():
     assert 10.0 == v.point_from_unit(v.point_to_unit(10.1))
 
 
+# TODO : enum variables should map to corners of hypercube
 def test_gp_4():
     v = EnumVariable('name', ['a', 'b', 'c'])
     assert 'a' == v.point_from_unit(v.point_to_unit('a'))
diff --git a/osprey/tests/test_strategies.py b/osprey/tests/test_strategies.py
index eb357d7..ce8b687 100644
--- a/osprey/tests/test_strategies.py
+++ b/osprey/tests/test_strategies.py
@@ -3,7 +3,12 @@
 import sys
 from six import iteritems
 import numpy as np
-from numpy.testing.decorators import skipif
+# TODO remove compat with py<=3.6
+try:
+    from numpy.testing import dec
+    skipif = dec.skipif
+except ModuleNotFoundError:
+    from numpy.testing.decorators import skipif
 
 from osprey.search_space import SearchSpace
 from osprey.search_space import IntVariable, EnumVariable, FloatVariable
@@ -11,8 +16,15 @@
 
 try:
     from hyperopt import hp, fmin, tpe, Trials
+    HAVE_HYPEROPT = True
+except:
+    HAVE_HYPEROPT = False
+
+try:
+    import gpy
+    HAVE_GPY = True
 except:
-    pass
+    HAVE_GPY = False
 
 
 def test_random():
@@ -60,6 +72,7 @@ def test_check_repeated_params():
         assert not grid_search4.is_repeated_suggestion(params, history)
 
 
+@skipif(not HAVE_HYPEROPT, "this test requires Hyperopt")
 def hyperopt_x2_iterates(n_iters=100):
     iterates = []
     trials = Trials()
@@ -77,6 +90,7 @@ def fn(params):
     return np.array(iterates)
 
 
+@skipif(not HAVE_HYPEROPT, "this test requires Hyperopt")
 def our_x2_iterates(n_iters=100):
     history = []
     searchspace = SearchSpace()
@@ -95,7 +109,8 @@ def fn(params):
     return np.array([h[0]['x'] for h in history])
 
 
-@skipif('hyperopt.fmin' not in sys.modules, 'this test requires hyperopt')
+# @skipif('hyperopt.fmin' not in sys.modules, 'this test requires hyperopt')
+@skipif(not HAVE_HYPEROPT, "this test requires Hyperopt")
 def test_1():
     ours = our_x2_iterates(25)
     ref = hyperopt_x2_iterates(25)
@@ -103,7 +118,8 @@ def test_1():
     np.testing.assert_array_equal(ref, ours)
 
     
-@skipif('GPy' not in sys.modules, 'this test requires GPy')
+# @skipif('GPy' not in sys.modules, 'this test requires GPy')
+@skipif(not HAVE_GPY, "this test requires GPy")
 def test_gp():
     searchspace = SearchSpace()
     searchspace.add_float('x', -10, 10)
diff --git a/osprey/tests/test_trials.py b/osprey/tests/test_trials.py
index 8d10a5d..2d58ef8 100644
--- a/osprey/tests/test_trials.py
+++ b/osprey/tests/test_trials.py
@@ -7,6 +7,7 @@
 
 from osprey.trials import make_session, Trial
 
+# TODO : create test that pulls single project from db of multiple projects
 
 def test_1():
     cwd = os.path.abspath(os.curdir)
diff --git a/osprey/tests/test_utils.py b/osprey/tests/test_utils.py
index 02fee63..ffbd8bd 100644
--- a/osprey/tests/test_utils.py
+++ b/osprey/tests/test_utils.py
@@ -9,6 +9,7 @@
 import numpy as np
 from sklearn.base import BaseEstimator
 
+
 def test_is_json_serializable():
     a = BaseEstimator()
     b = np.array([1, 2, 3], dtype=int)
@@ -21,6 +22,7 @@ def test_is_json_serializable():
     for obj in [e, f, g, h, i]:
         assert(is_json_serializable(obj))
 
+
 def test_dict_merge_1():
     base = {'a': 1}
     top = {'a': 2}
diff --git a/osprey/utils.py b/osprey/utils.py
index b4dee9c..685ec92 100644
--- a/osprey/utils.py
+++ b/osprey/utils.py
@@ -166,18 +166,34 @@ def expand_path(path, base='.'):
     return path
 
 
+def estimator_in_package(estimator, package):
+    """
+    checks to see if estimator is in package. If estimator is a skl Pipeline
+    then it checks each step
+    """
+    package_estimators = import_all_estimators(package).values()
+
+    out = estimator.__class__ in package_estimators
+    if isinstance(estimator, Pipeline):
+        out = any(step.__class__ in package_estimators
+                  for name, step in estimator.steps)
+    return out
+
+
 def is_msmbuilder_estimator(estimator):
     try:
         import msmbuilder
     except ImportError:
         return False
-    msmbuilder_estimators = import_all_estimators(msmbuilder).values()
+    return estimator_in_package(estimator, msmbuilder)
 
-    out = estimator.__class__ in msmbuilder_estimators
-    if isinstance(estimator, Pipeline):
-        out = any(step.__class__ in msmbuilder_estimators
-                  for name, step in estimator.steps)
-    return out
+
+def is_pyemma_estimator(estimator):
+    try:
+        import pyemma
+    except ImportError:
+        return False
+    return estimator_in_package(estimator, pyemma)
 
 
 def _assert_all_finite(X):