diff --git a/dffml/plugins.py b/dffml/plugins.py
index 8e4f7e2ec2..e02704efd3 100644
--- a/dffml/plugins.py
+++ b/dffml/plugins.py
@@ -32,6 +32,7 @@ def inpath(binary):
     ("model", "pytorch"),
     ("model", "spacy"),
     ("model", "daal4py"),
+    ("model", "h2o"),
 ]
 
 # Models which currently don't support Windows or MacOS
diff --git a/model/h2o/.coveragerc b/model/h2o/.coveragerc
new file mode 100644
index 0000000000..a65850eed9
--- /dev/null
+++ b/model/h2o/.coveragerc
@@ -0,0 +1,13 @@
+[run]
+source =
+    dffml_model_h2o
+    tests
+branch = True
+
+[report]
+exclude_lines =
+    no cov
+    no qa
+    noqa
+    pragma: no cover
+    if __name__ == .__main__.:
diff --git a/model/h2o/.gitignore b/model/h2o/.gitignore
new file mode 100644
index 0000000000..070ee81c83
--- /dev/null
+++ b/model/h2o/.gitignore
@@ -0,0 +1,20 @@
+*.log
+*.pyc
+.cache/
+.coverage
+.idea/
+.vscode/
+*.egg-info/
+build/
+dist/
+docs/build/
+venv/
+wheelhouse/
+*.egss
+.mypy_cache/
+*.swp
+.venv/
+.eggs/
+*.modeldir
+*.db
+htmlcov/
diff --git a/model/h2o/LICENSE b/model/h2o/LICENSE
new file mode 100644
index 0000000000..805b69fb3e
--- /dev/null
+++ b/model/h2o/LICENSE
@@ -0,0 +1,21 @@
+Copyright (c) 2020 Intel
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/model/h2o/MANIFEST.in b/model/h2o/MANIFEST.in
new file mode 100644
index 0000000000..19f3196490
--- /dev/null
+++ b/model/h2o/MANIFEST.in
@@ -0,0 +1,3 @@
+include README.md
+include LICENSE
+include setup_common.py
diff --git a/model/h2o/README.md b/model/h2o/README.md
new file mode 100644
index 0000000000..b556a0b3a7
--- /dev/null
+++ b/model/h2o/README.md
@@ -0,0 +1,10 @@
+# DFFML Model for H2O-AutoML
+
+## About
+
+H2O's AutoML models.
+
+## License
+
+H2O-AutoML models are distributed under the terms of the
+[MIT License](LICENSE).
diff --git a/model/h2o/dffml_model_autoh2o/__init__.py b/model/h2o/dffml_model_autoh2o/__init__.py
new file mode 100644
index 0000000000..8b13789179
--- /dev/null
+++ b/model/h2o/dffml_model_autoh2o/__init__.py
@@ -0,0 +1 @@
+
diff --git a/model/h2o/dffml_model_autoh2o/autoh2o.py b/model/h2o/dffml_model_autoh2o/autoh2o.py
new file mode 100644
index 0000000000..6b13607429
--- /dev/null
+++ b/model/h2o/dffml_model_autoh2o/autoh2o.py
@@ -0,0 +1,285 @@
+from typing import AsyncIterator
+import pandas as pd
+import h2o
+import os
+import sklearn
+from h2o.sklearn import H2OAutoMLClassifier, H2OAutoMLRegressor
+from .config import AutoH2OConfig
+from dffml.model.model import Model
+from dffml import (
+    Sources,
+    Record,
+    SourcesContext,
+    ModelContext,
+    ModelNotTrained,
+)
+from dffml.util.entrypoint import entrypoint
+
+
+class AutoH2OModelContext(ModelContext):
+    """
+    H2O AutoML based model context 
+    """
+
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.features = self._get_feature_names()
+
+    def _get_feature_names(self):
+        return [name for name in self.parent.config.features.names()]
+
+    async def get_test_records(self, sources: SourcesContext):
+        ret_record = []
+        async for record in sources.with_features(self.features):
+            ret_record.append(record)
+        return ret_record
+
+    async def get_predictions(self, data):
+        return self.parent.best_model.predict(h2o.H2OFrame(data)).as_data_frame().iloc[:, 0].tolist()
+
+    async def get_best_model(self):
+        if not self.is_trained:
+            raise ModelNotTrained(
+                "Train the model first before getting predictions"
+            )
+        return self.parent.clf.get_best_model()
+
+    async def accuracy_score(self, y_test, predictions):
+        return sklearn.metrics.r2_score(y_test, predictions)
+
+    async def get_probabilities(self, data):
+        return [[float("nan")] * len(data)]
+
+    async def train(self, sources: Sources):
+        all_data = []
+        async for record in sources.with_features(
+            self.features + [self.parent.config.predict.name]
+        ):
+            all_data.append(record.features())
+
+        df = pd.DataFrame(all_data)
+        y = self.parent.config.predict.name
+        self.parent.clf.fit(df[self.features], pd.DataFrame(df[y]))
+        if(self.parent.config.show_leaderboard):
+            print(h2o.automl.get_leaderboard(self.parent.clf.estimator, extra_columns = "ALL"))
+        self.is_trained = True
+
+    async def predict(self, sources: SourcesContext) -> AsyncIterator[Record]:
+        if not self.is_trained:
+            raise ModelNotTrained(
+                "Train the model first before getting predictions"
+            )
+        test_records = await self.get_test_records(sources)
+        x_test = pd.DataFrame(
+            [record.features(self.features) for record in test_records]
+        )
+        predictions = await self.get_predictions(x_test)
+        probability = await self.get_probabilities(x_test)
+        target = self.parent.config.predict.name
+        for record, predict, prob in zip(
+            test_records, predictions, probability
+        ):
+            record.predicted(target, predict, max(prob))
+            yield record
+    
+
+
+@entrypoint("h2o")
+class AutoH2OModel(Model):
+
+    r"""
+    ``h2o`` / ``AutoH2OModel`` will use ``H2O.ai's`` AutoML Python API
+    to train a model for you.
+
+    This is AutoML, it will train and tune hyperparameters from a list of models,
+    and return the best model.
+
+    Implemented using ``H2O``'s AutoML Python API (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html).
+
+    In this version, you must specify the ML task that you wish to perform (either "regression" or "classification").
+
+    Here, we will show a small example using regression. First we create the training and testing datasets:
+
+    **train.csv**
+
+    .. code-block::
+        :test:
+        :filepath: train.csv
+
+        Feature1,Feature2,TARGET
+        0.93,0.68,3.89
+        0.24,0.42,1.75
+        0.36,0.68,2.75
+        0.53,0.31,2.00
+        0.29,0.25,1.32
+        0.29,0.52,2.14
+
+    **test.csv**
+
+    .. code-block::
+        :test:
+        :filepath: test.csv
+
+        Feature1,Feature2,TARGET
+        0.57,0.84,3.65
+        0.95,0.19,2.46
+        0.23,0.15,0.93
+
+    Train the model
+
+    .. code-block:: console
+        :test:
+
+        $ dffml train \
+            -model h2o \
+            -model-predict TARGET:float:1 \
+            -model-clstype int \
+            -sources f=csv \
+            -source-filename train.csv \
+            -model-features \
+              Feature1:float:1 \
+              Feature2:float:1 \
+            -model-location tempdir \
+            -log debug
+
+    Assess the accuracy
+
+    .. code-block:: console
+        :test:
+
+        $ dffml accuracy \
+            -model h2o \
+            -model-predict TARGET:float:1 \
+            -model-location tempdir \
+            -features TARGET:float:1 \
+            -sources f=csv \
+            -source-filename test.csv \
+            -model-features \
+              Feature1:float:1 \
+              Feature2:float:1 \
+            -scorer mse \
+            -log critical
+        0.9961211434899032
+
+    Make a file containing the data to predict on
+
+    **predict.csv**
+
+    .. code-block::
+        :test:
+        :filepath: predict.csv
+
+        Feature1,Feature2
+        0.57,0.84
+
+    Make a prediction
+
+    .. code-block:: console
+        :test:
+
+        $ dffml predict all \
+            -model h2o \
+            -model-location tempdir \
+            -model-predict TARGET:float:1 \
+            -sources iris=csv \
+            -model-features \
+              Feature1:float:1 \
+              Feature2:float:1 \
+            -source-filename predict.csv
+        [
+            {
+                "extra": {},
+                "features": {
+                    "Feature1": 0.57,
+                    "Feature2": 0.84
+                },
+                "key": "0",
+                "last_updated": "2020-11-23T05:52:13Z",
+                "prediction": {
+                    "TARGET": {
+                        "confidence": NaN,
+                        "value": 3.566799074411392
+                    }
+                }
+            }
+        ]
+
+    The model can be trained on large datasets to get better accuracy
+    output. The example shown above is to demonstrate the command line usage
+    of the model.
+
+
+    """
+ 
+
+    def __init__(self, config) -> None:
+        super().__init__(config)
+        h2o.init()
+        self.clf = None
+        self.best_model = None
+        
+        
+        
+
+    async def __aenter__(self) -> "AutoH2OModel":
+        await super().__aenter__()
+        self.path =  os.path.abspath(self.filepath(self.location, "trained_model"))
+        self.load_model()
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback):
+        if self.clf.estimator and self.clf.estimator.leader:
+            self.clf.estimator.leader.save_mojo(self.path.__str__())
+        await super().__aexit__(exc_type, exc_value, traceback)
+
+    def filepath(self, location, file):
+        return location / file
+
+    def load_model(self):
+     
+
+        if(os.path.isdir(self.path)):
+    
+            self.best_model = h2o.import_mojo(self.path.__str__())
+            self.is_trained = True
+        
+        #TO-DO: split into separate regression/classification Models
+
+        if(self.config.task == "regression"):
+            self.clf = H2OAutoMLRegressor(
+                max_models = self.config.max_models,
+                seed=1,
+                max_runtime_secs = self.config.max_runtime_secs,
+                nfolds = self.config.nfolds,
+                max_runtime_secs_per_model = self.config.max_runtime_secs_per_model,
+                stopping_metric=self.config.stopping_metric,
+                stopping_tolerance=self.config.stopping_tolerance,
+                stopping_rounds=self.config.stopping_rounds,
+                sort_metric=self.config.sort_metric,
+                exclude_algos=self.config.exclude_algos,
+                include_algos=self.config.include_algos,
+                verbosity=self.config.verbosity,        
+            )
+        else:
+            self.clf = H2OAutoMLClassifier(
+                max_models = self.config.max_models,
+                seed=1,
+                max_runtime_secs = self.config.max_runtime_secs,
+                nfolds = self.config.nfolds,
+                balance_classes = self.config.balance_classes,
+                max_after_balance_size = self.config.max_after_balance_size,
+                max_runtime_secs_per_model = self.config.max_runtime_secs_per_model,
+                stopping_metric=self.config.stopping_metric,
+                stopping_tolerance=self.config.stopping_tolerance,
+                stopping_rounds=self.config.stopping_rounds,
+                sort_metric=self.config.sort_metric,
+                exclude_algos=self.config.exclude_algos,
+                include_algos=self.config.include_algos,
+                verbosity=self.config.verbosity,
+        
+
+            )
+        
+
+    CONFIG = AutoH2OConfig
+    CONTEXT = AutoH2OModelContext
\ No newline at end of file
diff --git a/model/h2o/dffml_model_autoh2o/config.py b/model/h2o/dffml_model_autoh2o/config.py
new file mode 100644
index 0000000000..e1a136a61d
--- /dev/null
+++ b/model/h2o/dffml_model_autoh2o/config.py
@@ -0,0 +1,67 @@
+import pathlib
+from pathlib import Path
+from typing import AsyncIterator, List
+from dffml.base import config, field
+
+from dffml import (
+    Feature,
+    Features,
+    field,
+
+)
+
+@config
+class AutoH2OConfig:
+    features: Features
+    predict: Feature = field("Feature to predict")
+    location: Path = field("Location where state should be saved")
+    #TO-DO: split into separate regression/classification models
+    task: str = field(
+        "Task to perform, possible values are `classification`, `regression`",
+        default="regression",
+    )
+    max_runtime_secs: int = field(
+        "The maximum time that the AutoML process will run", default=0
+    )
+    max_models: int = field(
+        "Maximum number of models to build in AutoML run", default=10
+    )
+    nfolds: int = field(
+        "Number of folds for k-fold cross-validation", default=5
+    )
+    balance_classes: bool = field(
+        "Oversampling on minority classes should be performed or not",
+        default=False,
+    )
+    max_after_balance_size: int = field(
+        "Maximum relative size of training set after performing oversampling",
+        default=5,
+    )
+    max_runtime_secs_per_model: int = field(
+        "Maximum time to train individual model in AutoML", default=0
+    )
+    stopping_metric: str = field(
+        "Metric used for stopping criteria", default="AUTO"
+    )
+    stopping_tolerance: float = field(
+        "Specifies the relative tolerance for the metric-based stopping",
+        default=0.001,
+    )
+    stopping_rounds: int = field(
+        "Stop training when metric doesn't improve max of stopping_rounds",
+        default=3,
+    )
+    sort_metric: str = field(
+        "Metric used to sort the leaderboard", default="AUTO"
+    )
+    exclude_algos: List[str] = field(
+        "Algorithm to skip during training", default=None
+    )
+    include_algos: List[str] = field(
+        "Algorithm to be used during training", default=None
+    )
+    verbosity: str = field("Print the backend messages", default=None)
+    show_leaderboard: bool = field(
+        "Print the leaderboard after the building the models in AutoML",
+        default=True,
+    )
diff --git a/model/h2o/dffml_model_autoh2o/version.py b/model/h2o/dffml_model_autoh2o/version.py
new file mode 100644
index 0000000000..1cf6267ae5
--- /dev/null
+++ b/model/h2o/dffml_model_autoh2o/version.py
@@ -0,0 +1 @@
+VERSION = "0.1.0"
diff --git a/model/h2o/examples/accuracy.sh b/model/h2o/examples/accuracy.sh
new file mode 100644
index 0000000000..618a2490c0
--- /dev/null
+++ b/model/h2o/examples/accuracy.sh
@@ -0,0 +1,10 @@
+dffml accuracy \
+  -model h2o \
+  -scorer clf \
+  -model-features x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 \
+  -model-predict response \
+  -model-location tempdir \
+  -model-task classification \
+  -sources f=csv \
+  -source-filename higgs_test_5k.csv \
+  -features response
\ No newline at end of file
diff --git a/model/h2o/examples/predict.sh b/model/h2o/examples/predict.sh
new file mode 100644
index 0000000000..d4e3accd55
--- /dev/null
+++ b/model/h2o/examples/predict.sh
@@ -0,0 +1,8 @@
+dffml predict all \
+  -model h2o \
+  -model-features x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 \
+  -model-predict response \
+  -model-location tempdir \
+  -model-task classification \
+  -sources f=csv \
+  -source-filename higgs_test_5k.csv
\ No newline at end of file
diff --git a/model/h2o/examples/predict_data.sh b/model/h2o/examples/predict_data.sh
new file mode 100644
index 0000000000..1b0bb11162
--- /dev/null
+++ b/model/h2o/examples/predict_data.sh
@@ -0,0 +1,2 @@
+wget --user-agent="Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36" https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv
+sed -i "" *.csv
\ No newline at end of file
diff --git a/model/h2o/examples/regression/accuracy.sh b/model/h2o/examples/regression/accuracy.sh
new file mode 100644
index 0000000000..edaa8b548e
--- /dev/null
+++ b/model/h2o/examples/regression/accuracy.sh
@@ -0,0 +1,12 @@
+dffml accuracy \
+    -model h2o \
+    -model-predict TARGET:float:1 \
+    -model-location tempdir \
+    -features TARGET:float:1 \
+    -sources f=csv \
+    -source-filename test.csv \
+    -model-features \
+        Feature1:float:1 \
+        Feature2:float:1 \
+    -scorer mse \
+    -log critical
\ No newline at end of file
diff --git a/model/h2o/examples/regression/predict.sh b/model/h2o/examples/regression/predict.sh
new file mode 100644
index 0000000000..478077d56d
--- /dev/null
+++ b/model/h2o/examples/regression/predict.sh
@@ -0,0 +1,9 @@
+dffml predict all \
+    -model h2o \
+    -model-location tempdir \
+    -model-predict TARGET:float:1 \
+    -sources iris=csv \
+    -model-features \
+        Feature1:float:1 \
+        Feature2:float:1 \
+    -source-filename predict.csv
\ No newline at end of file
diff --git a/model/h2o/examples/regression/predict_data.sh b/model/h2o/examples/regression/predict_data.sh
new file mode 100644
index 0000000000..34ee4566fc
--- /dev/null
+++ b/model/h2o/examples/regression/predict_data.sh
@@ -0,0 +1,4 @@
+cat > predict.csv << EOF
+Feature1,Feature2
+0.57,0.84
+EOF
\ No newline at end of file
diff --git a/model/h2o/examples/regression/test_data.sh b/model/h2o/examples/regression/test_data.sh
new file mode 100644
index 0000000000..ad902ed4cf
--- /dev/null
+++ b/model/h2o/examples/regression/test_data.sh
@@ -0,0 +1,6 @@
+cat > test.csv << EOF
+Feature1,Feature2,TARGET
+0.57,0.84,3.65
+0.95,0.19,2.46
+0.23,0.15,0.93
+EOF
\ No newline at end of file
diff --git a/model/h2o/examples/regression/train.sh b/model/h2o/examples/regression/train.sh
new file mode 100644
index 0000000000..7659dc6d9c
--- /dev/null
+++ b/model/h2o/examples/regression/train.sh
@@ -0,0 +1,10 @@
+dffml train \
+    -model h2o \
+    -model-predict TARGET:float:1 \
+    -model-clstype int \
+    -sources f=csv \
+    -source-filename train.csv \
+    -model-features \
+        Feature1:float:1 \
+        Feature2:float:1 \
+    -model-location tempdir 
\ No newline at end of file
diff --git a/model/h2o/examples/regression/train_data.sh b/model/h2o/examples/regression/train_data.sh
new file mode 100644
index 0000000000..b309577db3
--- /dev/null
+++ b/model/h2o/examples/regression/train_data.sh
@@ -0,0 +1,9 @@
+cat > train.csv << EOF
+Feature1,Feature2,TARGET
+0.93,0.68,3.89
+0.24,0.42,1.75
+0.36,0.68,2.75
+0.53,0.31,2.00
+0.29,0.25,1.32
+0.29,0.52,2.14
+EOF
\ No newline at end of file
diff --git a/model/h2o/examples/test_data.sh b/model/h2o/examples/test_data.sh
new file mode 100644
index 0000000000..8f76f3c270
--- /dev/null
+++ b/model/h2o/examples/test_data.sh
@@ -0,0 +1,2 @@
+wget --user-agent="Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36" https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv 
+sed -i "" *.csv
\ No newline at end of file
diff --git a/model/h2o/examples/train.sh b/model/h2o/examples/train.sh
new file mode 100644
index 0000000000..3edd29af02
--- /dev/null
+++ b/model/h2o/examples/train.sh
@@ -0,0 +1,8 @@
+dffml train \
+  -model h2o \
+  -model-features x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 \
+  -model-predict response \
+  -model-location tempdir \
+  -model-task classification \
+  -sources f=csv \
+  -source-filename higgs_train_10k.csv
\ No newline at end of file
diff --git a/model/h2o/examples/train_data.sh b/model/h2o/examples/train_data.sh
new file mode 100644
index 0000000000..4adcc62a60
--- /dev/null
+++ b/model/h2o/examples/train_data.sh
@@ -0,0 +1,2 @@
+wget --user-agent="Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.103 Safari/537.36" https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv 
+sed -i "" *.csv
\ No newline at end of file
diff --git a/model/h2o/pyproject.toml b/model/h2o/pyproject.toml
new file mode 100644
index 0000000000..8b9d32fa10
--- /dev/null
+++ b/model/h2o/pyproject.toml
@@ -0,0 +1,20 @@
+[tool.black]
+line-length = 79
+target-version = ['py37']
+
+exclude = '''
+(
+  /(
+      \.eggs         # exclude a few common directories in the
+    | \.git          # root of the project
+    | \.hg
+    | \.mypy_cache
+    | \.tox
+    | \.venv
+    | _build
+    | buck-out
+    | build
+    | dist
+  )
+)
+'''
diff --git a/model/h2o/setup.cfg b/model/h2o/setup.cfg
new file mode 100644
index 0000000000..04e4f6830c
--- /dev/null
+++ b/model/h2o/setup.cfg
@@ -0,0 +1,13 @@
+[options]
+zip_safe = False
+include_package_data = True
+packages = find:
+
+install_requires =
+    future >=0.15.2
+    tabulate >=0.7.5
+    requests >=2.10
+    h2o >= 3.2.0
+    pandas>=0.25.0
+    dffml>=0.4.0
+    scikit-learn>=0.21.2
diff --git a/model/h2o/setup.py b/model/h2o/setup.py
new file mode 100644
index 0000000000..8f1c68ce5d
--- /dev/null
+++ b/model/h2o/setup.py
@@ -0,0 +1,23 @@
+import os
+import sys
+import site
+import importlib.util
+from setuptools import setup
+
+# See https://github.com/pypa/pip/issues/7953
+site.ENABLE_USER_SITE = "--user" in sys.argv[1:]
+
+# Boilerplate to load commonalities
+spec = importlib.util.spec_from_file_location(
+    "setup_common", os.path.join(os.path.dirname(__file__), "setup_common.py")
+)
+common = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(common)
+
+common.KWARGS["entry_points"] = {
+    "dffml.model": [
+        "h2o = dffml_model_autoh2o.autoh2o:AutoH2OModel",
+    ]
+}
+
+setup(**common.KWARGS)
diff --git a/model/h2o/setup_common.py b/model/h2o/setup_common.py
new file mode 100644
index 0000000000..9a02471672
--- /dev/null
+++ b/model/h2o/setup_common.py
@@ -0,0 +1,55 @@
+import os
+import sys
+import ast
+from pathlib import Path
+
+ORG = "dffml"
+NAME = "dffml-model-autoh2o"
+DESCRIPTION = "DFFML model dffml-model-autoh2o"
+AUTHOR_NAME = "Edison Siow"
+AUTHOR_EMAIL = "edisonsiowxiong@gmail.com"
+
+IMPORT_NAME = (
+    NAME
+    if "replace_package_name".upper() != NAME
+    else "replace_import_package_name".upper()
+).replace("-", "_")
+
+SELF_PATH = Path(sys.argv[0]).parent.resolve()
+if not (SELF_PATH / Path(IMPORT_NAME, "version.py")).is_file():
+    SELF_PATH = os.path.dirname(os.path.realpath(__file__))
+
+VERSION = ast.literal_eval(
+    Path(SELF_PATH, IMPORT_NAME, "version.py")
+    .read_text()
+    .split("=")[-1]
+    .strip()
+)
+
+README = Path(SELF_PATH, "README.md").read_text()
+
+KWARGS = dict(
+    name=NAME,
+    version=VERSION,
+    description=DESCRIPTION,
+    long_description=README,
+    long_description_content_type="text/markdown",
+    author=AUTHOR_NAME,
+    author_email=AUTHOR_EMAIL,
+    maintainer=AUTHOR_NAME,
+    maintainer_email=AUTHOR_EMAIL,
+    url=f"https://github.com/{ORG}/{NAME}",
+    license="MIT",
+    keywords=["dffml"],
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "License :: OSI Approved :: MIT License",
+        "Natural Language :: English",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3 :: Only",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: Implementation :: CPython",
+        "Programming Language :: Python :: Implementation :: PyPy",
+    ],
+)
diff --git a/model/h2o/tests/__init__.py b/model/h2o/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/model/h2o/tests/test_h2o.py b/model/h2o/tests/test_h2o.py
new file mode 100644
index 0000000000..9f1fbb4686
--- /dev/null
+++ b/model/h2o/tests/test_h2o.py
@@ -0,0 +1,102 @@
+import os
+import tempfile
+import contextlib
+import subprocess
+
+from dffml.cli.cli import CLI
+from dffml.util.os import chdir
+from dffml.util.asynctestcase import AsyncTestCase
+
+
+@contextlib.contextmanager
+def directory_with_csv_files():
+    with tempfile.TemporaryDirectory() as tempdir:
+        with chdir(tempdir):
+            subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "train_data.sh",
+                    ),
+                ]
+            )
+            subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "test_data.sh",
+                    ),
+                ]
+            )
+            subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "predict_data.sh",
+                    ),
+                ]
+            )
+            yield tempdir
+
+
+class TestH2OClassificationModel(AsyncTestCase):
+    async def test_run(self):
+        self.required_plugins("dffml-model-autoh2o")
+ 
+        def clean_args(fd, directory):
+            cmnd = " ".join(fd.readlines()).split("\\\n")
+            cmnd = " ".join(cmnd).split()
+            for idx, word in enumerate(cmnd):
+                cmnd[idx] = word.strip()
+            cmnd[cmnd.index("-model-location") + 1] = directory
+            return cmnd
+
+        with directory_with_csv_files() as tempdir:
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "train.sh",
+                ),
+                "r",
+            ) as f:
+                train_cmnd = clean_args(f, tempdir)
+            await CLI.cli(*train_cmnd[1:])
+
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "accuracy.sh",
+                ),
+                "r",
+            ) as f:
+                accuracy_cmnd = clean_args(f, tempdir)
+            await CLI.cli(*accuracy_cmnd[1:])
+
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "predict.sh",
+                ),
+                "r",
+            ) as f:
+                predict_cmnd = clean_args(f, tempdir)
+                results = await CLI._main(*predict_cmnd[1:])
+                self.assertTrue(isinstance(results, list))
+                self.assertTrue(results)
+                results = results[0].export()
+                self.assertIn("prediction", results)
+                results = results["prediction"]
+                self.assertIn("response", results)
+                results = results["response"]
+                self.assertIn("value", results)
+                self.assertEqual(results["value"], 0)
+      
diff --git a/model/h2o/tests/test_h2o_regression.py b/model/h2o/tests/test_h2o_regression.py
new file mode 100644
index 0000000000..30688a49de
--- /dev/null
+++ b/model/h2o/tests/test_h2o_regression.py
@@ -0,0 +1,111 @@
+import os
+import tempfile
+import contextlib
+import subprocess
+
+from dffml.cli.cli import CLI
+from dffml.util.os import chdir
+from dffml.util.asynctestcase import AsyncTestCase
+
+
+@contextlib.contextmanager
+def directory_with_csv_files():
+    with tempfile.TemporaryDirectory() as tempdir:
+        with chdir(tempdir):
+            p = subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "regression",
+                        "train_data.sh",
+                    ),
+                ]
+            )
+   
+            subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "regression",
+                        "test_data.sh",
+                    ),
+                ]
+            )
+      
+            subprocess.check_output(
+                [
+                    "bash",
+                    os.path.join(
+                        os.path.dirname(os.path.dirname(__file__)),
+                        "examples",
+                        "regression",
+                        "predict_data.sh",
+                    ),
+                ]
+            )
+
+            yield tempdir
+
+
+class TestH2ORegressionModel(AsyncTestCase):
+    async def test_run(self):
+        self.required_plugins("dffml-model-autoh2o")
+ 
+        def clean_args(fd, directory):
+            cmnd = " ".join(fd.readlines()).split("\\\n")
+            cmnd = " ".join(cmnd).split()
+            for idx, word in enumerate(cmnd):
+                cmnd[idx] = word.strip()
+            cmnd[cmnd.index("-model-location") + 1] = directory
+            return cmnd
+
+        with directory_with_csv_files() as tempdir:
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "regression",
+                    "train.sh",
+                ),
+                "r",
+            ) as f:
+                train_cmnd = clean_args(f, tempdir)
+            await CLI.cli(*train_cmnd[1:])
+
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "regression",
+                    "accuracy.sh",
+                ),
+                "r",
+            ) as f:
+                accuracy_cmnd = clean_args(f, tempdir)
+            await CLI.cli(*accuracy_cmnd[1:])
+
+            with open(
+                os.path.join(
+                    os.path.dirname(os.path.dirname(__file__)),
+                    "examples",
+                    "regression",
+                    "predict.sh",
+                ),
+                "r",
+            ) as f:
+                predict_cmnd = clean_args(f, tempdir)
+                results = await CLI._main(*predict_cmnd[1:])
+                self.assertTrue(isinstance(results, list))
+                self.assertTrue(results)
+                results = results[0].export()
+                self.assertIn("prediction", results)
+                results = results["prediction"]
+                self.assertIn("TARGET", results)
+                results = results["TARGET"]
+                self.assertIn("value", results)
+                self.assertAlmostEqual(results["value"], 3.639936099771113, 2)
+