From a4c0f4fd7260caf57732a963a3dacc55bf35c525 Mon Sep 17 00:00:00 2001
From: Christian Kasim Loan <christian.kasim.loan@gmail.com>
Date: Sun, 1 Oct 2023 18:22:56 +0100
Subject: [PATCH] Release/511 (#676)

* optimized EMR imports for less chance of import errors

* bump versions

* update release notes

* drop deprecated setup.py

* Endpoint tests (#637)

* example databricks serve notebooks and cluster creation

* updated healthcecks

* databricks serve + john snow labs endpoints module

* `clean_cluster` and `write_db_credentials` parameters for db cluster creation

* databricks serve + john snow labs endpoints docs + typo fixes

* databricks serve + john snow labs endpoints docs + typo fixes

* updated tests

* updated tests

* fix block_until_deployed bug

* fix block_until_deployed bug

* support for GPU and nlu.predict params in endpoints

* bump versions

* Docs update

* update notebooks

* endpoint test job generator

* improved list_db_runtime_versions

* multi cluster testing endpoints

* Support for submitting notebook to databricks and various utils & refactor to databricks utils

* db test refactor into  db_info/endpoint/hdfs/submit tests

* get_or_create_test_cluster() and varous more db testing utils, pytest.ini and non-verbose parameterization of tests

* add docs for notebook execution
---
 .../databricks_utils/submit_notebook.png      | Bin 0 -> 20549 bytes
 .../submit_notebook_result.png                | Bin 0 -> 111216 bytes
 docs/en/jsl/databricks_utils.md               |  32 ++
 docs/en/jsl/jsl_release_notes.md              |  62 ++++
 .../auto_install/databricks/install_utils.py  | 119 ++++++-
 .../auto_install/databricks/work_utils.py     | 159 +++++++--
 johnsnowlabs/auto_install/emr/work_utils.py   |  12 +-
 .../health_checks/benchmark_test.py           |   1 +
 .../health_checks/endpoint_test.py            |  88 +++--
 .../health_checks/generate_endpoint_test.py   |  12 +
 johnsnowlabs/settings.py                      |   4 +-
 setup.py                                      |  54 ---
 tests/databricks/__init__.py                  |   0
 tests/databricks/db_infos_test.py             |  47 +++
 tests/databricks/db_test_utils.py             | 243 +++++++++++++
 tests/databricks/endpoint_tests.py            |  60 ++++
 tests/databricks/hdfs_tests.py                |  18 +
 tests/databricks/sample.ipynb                 |  58 ++++
 tests/databricks/submit_tests.py              | 103 ++++++
 tests/databricks_tests.py                     | 328 ------------------
 tests/pytest.ini                              |   4 +
 21 files changed, 954 insertions(+), 450 deletions(-)
 create mode 100644 docs/assets/images/jsl_lib/databricks_utils/submit_notebook.png
 create mode 100644 docs/assets/images/jsl_lib/databricks_utils/submit_notebook_result.png
 create mode 100644 johnsnowlabs/auto_install/health_checks/benchmark_test.py
 create mode 100644 johnsnowlabs/auto_install/health_checks/generate_endpoint_test.py
 delete mode 100644 setup.py
 create mode 100644 tests/databricks/__init__.py
 create mode 100644 tests/databricks/db_infos_test.py
 create mode 100644 tests/databricks/db_test_utils.py
 create mode 100644 tests/databricks/endpoint_tests.py
 create mode 100644 tests/databricks/hdfs_tests.py
 create mode 100644 tests/databricks/sample.ipynb
 create mode 100644 tests/databricks/submit_tests.py
 delete mode 100644 tests/databricks_tests.py
 create mode 100644 tests/pytest.ini

diff --git a/docs/assets/images/jsl_lib/databricks_utils/submit_notebook.png b/docs/assets/images/jsl_lib/databricks_utils/submit_notebook.png
new file mode 100644
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diff --git a/docs/en/jsl/databricks_utils.md b/docs/en/jsl/databricks_utils.md
index f88f4a00a5..71cd998f77 100644
--- a/docs/en/jsl/databricks_utils.md
+++ b/docs/en/jsl/databricks_utils.md
@@ -100,6 +100,38 @@ And after a while you can see the results
 ![databricks_cluster_submit_raw.png](/assets/images/jsl_lib/databricks_utils/submit_raw_str_result.png)
 
 
+### Run a local Python Notebook in Databricks
+
+Provide the path to a notebook on your localhost, it will be copied to HDFS and executed by the Databricks cluster.
+You need to provide a destination path to your Workspace, where the notebook will be copied to and you have write access to.
+A common pattern that should work is`/Users/<your_databricks_email@somewhere.com>/test.ipynb`
+
+```python
+local_nb_path = "path/to/my/notebook.ipynb"
+remote_dst_path = "/Users/christian@johnsnowlabs.com/test.ipynb"
+
+# notebook.ipynb will run on databricks, url will be printed
+nlp.run_in_databricks(
+  local_nb_path,
+  databricks_host=host,
+  databricks_token=token,
+  run_name="Notebook Test",
+  dst_path=remote_dst_path,
+)
+```
+
+This could be your input notebook
+
+![databricks_cluster_submit_notebook.png](/assets/images/jsl_lib/databricks_utils/submit_notebook.png)
+
+A URL where you can monitor the run will be printed, which will look like this
+
+![databricks_cluster_submit_notebook_result.png](/assets/images/jsl_lib/databricks_utils/submit_notebook_result.png)
+
+
+
+
+
 ### Run a Python Function in Databricks
 
 Define a function, which will be written to a local file, copied to HDFS and executed by the Databricks cluster.
diff --git a/docs/en/jsl/jsl_release_notes.md b/docs/en/jsl/jsl_release_notes.md
index d9208bf003..a68db83d3b 100644
--- a/docs/en/jsl/jsl_release_notes.md
+++ b/docs/en/jsl/jsl_release_notes.md
@@ -15,6 +15,27 @@ sidebar:
 
 See [Github Releases](https://github.com/JohnSnowLabs/johnsnowlabs/releases) for detailed information on Release History and Features.
 
+
+
+## 5.1.1
+Release date: 1-10-2023
+
+The John Snow Labs 5.1.0 Library released with the following pre-installed and recommended dependencies
+
+
+| Library                                                                         | Version |
+|---------------------------------------------------------------------------------|---------|
+| [Visual NLP](https://nlp.johnsnowlabs.com/docs/en/spark_ocr_versions/ocr_release_notes)        | `5.0.1` |
+| [Enterprise NLP](https://nlp.johnsnowlabs.com/docs/en/licensed_annotators)  | `5.1.1` |
+| [Finance NLP](https://nlp.johnsnowlabs.com/docs/en/financial_release_notes) | `1.X.X` |
+| [Legal NLP](https://nlp.johnsnowlabs.com/docs/en/legal_release_notes)       | `1.X.X` |
+| [NLU](https://github.com/JohnSnowLabs/nlu/releases)                             | `5.0.1` |
+| [Spark-NLP-Display](https://sparknlp.org/docs/en/display)           | `4.4`   |
+| [Spark-NLP](https://github.com/JohnSnowLabs/spark-nlp/releases/)                | `5.1.1` |
+| [Pyspark](https://spark.apache.org/docs/latest/api/python/)                     | `3.1.2` |
+
+
+
 ## 5.1.0
 Release date: 25-09-2023
 
@@ -91,6 +112,47 @@ The John Snow Labs 5.0.7 Library released with the following pre-installed and r
 
 
 
+## 5.0.8
+Release date: 11-09-2023
+
+The John Snow Labs 5.0.5 Library released with the following pre-installed and recommended dependencies
+
+
+| Library                                                                         | Version |
+|---------------------------------------------------------------------------------|---------|
+| [Visual NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/ocr_release_notes)        | `5.0.0` |
+| [Enterprise NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/licensed_annotators)  | `5.0.2` |
+| [Finance NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/financial_release_notes) | `1.X.X` |
+| [Legal NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/legal_release_notes)       | `1.X.X` |
+| [NLU](https://github.com/JohnSnowLabs/nlu/releases)                             | `5.0.1` |
+| [Spark-NLP-Display](https://nlp.johnsnowlabs.com/docs/en/jsl/display)           | `4.4`   |
+| [Spark-NLP](https://github.com/JohnSnowLabs/spark-nlp/releases/)                | `5.0.2` |
+| [Pyspark](https://spark.apache.org/docs/latest/api/python/)                     | `3.1.2` |
+
+
+
+
+
+## 5.0.7
+Release date: 3-09-2023
+
+The John Snow Labs 5.0.5 Library released with the following pre-installed and recommended dependencies
+
+
+| Library                                                                         | Version |
+|---------------------------------------------------------------------------------|---------|
+| [Visual NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/ocr_release_notes)        | `5.0.0` |
+| [Enterprise NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/licensed_annotators)  | `5.0.2` |
+| [Finance NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/financial_release_notes) | `1.X.X` |
+| [Legal NLP](https://nlp.johnsnowlabs.com/docs/en/jsl/legal_release_notes)       | `1.X.X` |
+| [NLU](https://github.com/JohnSnowLabs/nlu/releases)                             | `5.0.0` |
+| [Spark-NLP-Display](https://nlp.johnsnowlabs.com/docs/en/jsl/display)           | `4.4`   |
+| [Spark-NLP](https://github.com/JohnSnowLabs/spark-nlp/releases/)                | `5.0.2` |
+| [Pyspark](https://spark.apache.org/docs/latest/api/python/)                     | `3.1.2` |
+
+
+
+
 ## 5.0.6
 Release date: 03-09-2023
 
diff --git a/johnsnowlabs/auto_install/databricks/install_utils.py b/johnsnowlabs/auto_install/databricks/install_utils.py
index 50f5cb8bcb..c330fc8e7c 100644
--- a/johnsnowlabs/auto_install/databricks/install_utils.py
+++ b/johnsnowlabs/auto_install/databricks/install_utils.py
@@ -154,7 +154,7 @@ def list_db_runtime_versions(db: DatabricksAPI):
     # pprint(versions)
     for version in versions["versions"]:
         print(version["key"])
-        print(version["name"])
+        name = version["name"]
         # version_regex = r'[0-9].[0-9].[0-9]'
 
         spark_version = re.findall(r"Apache Spark [0-9].[0-9]", version["name"])
@@ -166,14 +166,15 @@ def list_db_runtime_versions(db: DatabricksAPI):
         has_gpu = len(re.findall("GPU", version["name"])) > 0
         if spark_version:
             spark_version = spark_version + ".x"
-        print(LibVersion(spark_version).as_str(), has_gpu, scala_version)
-
-
-def list_clusters(db: DatabricksAPI):
-    clusters = db.cluster.list_clusters(headers=None)
-    pprint(clusters)
-    print(clusters)
-    return clusters
+        version = LibVersion(spark_version).as_str()
+        print(
+            f"name={name}\n"
+            f"version={version}\n"
+            f"has_gpu={has_gpu}\n"
+            f"scala_version={scala_version}\n"
+            f"spark_version={spark_version}\n"
+            f"{'=' * 25}"
+        )
 
 
 def list_cluster_lib_status(db: DatabricksAPI, cluster_id: str):
@@ -388,13 +389,23 @@ def copy_lib_to_dbfs_cluster(
     return copy_from_local_to_hdfs(db, local_path=local_path, dbfs_path=dbfs_path)
 
 
-def wait_till_cluster_running(db: DatabricksAPI, cluster_id: str):
+def wait_till_cluster_running(db: DatabricksAPI, cluster_id: str, timeout=900):
     # https://docs.databricks.com/dev-tools/api/latest/clusters.html#clusterclusterstate
     import time
 
-    while 1:
+    start_time = time.time()
+
+    while True:
+        elapsed_time = time.time() - start_time
+        if elapsed_time > timeout:
+            print(
+                "Timeout reached while waiting for the cluster to be running. Check cluster UI."
+            )
+            return False
+
         time.sleep(5)
         status = DatabricksClusterStates(db.cluster.get_cluster(cluster_id)["state"])
+
         if status == DatabricksClusterStates.RUNNING:
             return True
         elif status in [
@@ -414,3 +425,89 @@ def wait_till_cluster_running(db: DatabricksAPI, cluster_id: str):
 
 def restart_cluster(db: DatabricksAPI, cluster_id: str):
     db.cluster.restart_cluster(cluster_id=cluster_id)
+
+
+####
+def list_clusters(db: DatabricksAPI):
+    """Lists all clusters."""
+    clusters = db.cluster.list_clusters()
+    return clusters
+
+
+def cluster_exist_with_name_and_runtime(
+    db: DatabricksAPI, name: str, runtime: str
+) -> bool:
+    """Checks if a cluster with a specific name and runtime exists."""
+    clusters = list_clusters(db)
+    for cluster in clusters.get("clusters", []):
+        if cluster["cluster_name"] == name and runtime in cluster["spark_version"]:
+            return True
+    return False
+
+
+def does_cluster_exist_with_id(db: DatabricksAPI, cluster_id: str) -> bool:
+    """Checks if a cluster with a specific ID exists."""
+    try:
+        cluster_info = db.cluster.get_cluster(cluster_id=cluster_id)
+        if cluster_info and "cluster_id" in cluster_info:
+            return True
+    except Exception as e:
+        # Handle specific exceptions based on the Databricks API error responses
+        pass
+    return False
+
+
+def get_cluster_id(db: DatabricksAPI, cluster_name: str, runtime: str) -> str:
+    """
+    Retrieves the cluster ID based on the cluster name and runtime.
+
+    If there are multiple candidates:
+    - Returns any that's in the 'RUNNING' state if there is one.
+    - If none are in the 'RUNNING' state, returns any.
+    """
+    clusters = list_clusters(db)
+    running_clusters = []
+    other_clusters = []
+
+    for cluster in clusters.get("clusters", []):
+        if (
+            cluster["cluster_name"] == cluster_name
+            and runtime in cluster["spark_version"]
+        ):
+            if cluster["state"] == "RUNNING":
+                running_clusters.append(cluster["cluster_id"])
+            else:
+                other_clusters.append(cluster["cluster_id"])
+
+    if running_clusters:
+        return running_clusters[0]
+    elif other_clusters:
+        return other_clusters[0]
+    else:
+        raise Exception(
+            f"No cluster found with name {cluster_name} and runtime {runtime}"
+        )
+
+
+def _get_cluster_id(db: DatabricksAPI, cluster_name: str) -> str:
+    """
+    Retrieves the cluster ID based on the cluster name.
+    """
+    clusters = list_clusters(db)
+    running_clusters = []
+    other_clusters = []
+
+    for cluster in clusters.get("clusters", []):
+        if cluster["cluster_name"] == cluster_name:
+            # if cluster["state"] != "TERMINATED":
+            other_clusters.append(cluster["cluster_id"])
+
+    if running_clusters:
+        return running_clusters[0]
+    elif other_clusters:
+        return other_clusters[0]
+    else:
+        return None  # Return None if no cluster found with the given name
+
+
+#############
diff --git a/johnsnowlabs/auto_install/databricks/work_utils.py b/johnsnowlabs/auto_install/databricks/work_utils.py
index b482323193..e6461bed1e 100644
--- a/johnsnowlabs/auto_install/databricks/work_utils.py
+++ b/johnsnowlabs/auto_install/databricks/work_utils.py
@@ -1,3 +1,4 @@
+import base64
 import inspect
 import os.path
 import time
@@ -125,6 +126,7 @@ def run_local_py_script_as_task(
     cluster_id: str = None,
     run_name: str = None,
     parameters: List[Any] = None,
+    dst_path=None,
 ):
     """
     # https://docs.databricks.com/dev-tools/api/latest/examples.html#jobs-api-examples
@@ -135,47 +137,68 @@ def run_local_py_script_as_task(
     :param run_name:
     :return:
     """
-
-    task_definition = executable_as_script(task_definition)
     if not run_name:
         run_name = settings.db_run_name
+
     if not cluster_id:
         cluster_id = create_cluster(db)
-    copy_from_local_to_hdfs(
-        db=db,
-        local_path=task_definition,
-        dbfs_path=get_db_path(task_definition),
-    )
-    py_task = dict(
-        python_file=get_db_path(task_definition),
-    )
-    if parameters:
-        py_task["parameters"] = parameters
 
-    run_id = db.jobs.submit_run(
-        existing_cluster_id=cluster_id,
-        spark_python_task=py_task,
-        run_name=run_name,
-        # new_cluster=None,
-        # libraries=None,
-        # notebook_task=None,
-        # spark_jar_task=None,
-        # spark_submit_task=None,
-        # timeout_seconds=None,
-        # tasks=None,
-        # headers=None,
-        # version=None,
-    )["run_id"]
+    if (
+        isinstance(task_definition, str)
+        and os.path.exists(task_definition)
+        and task_definition.endswith(".ipynb")
+    ):
+        # Notebook task, WIP
+        # raise Exception("Notebook task not supported yet")
+        if not dst_path:
+            raise Exception("dst_path must be provided for notebook tasks")
+        file_name = os.path.split(task_definition)[-1]
+        run_id = submit_notebook_to_databricks(
+            db, task_definition, cluster_id, dst_path
+        )
+
+    else:
+        task_definition = executable_as_script(task_definition)
+
+        copy_from_local_to_hdfs(
+            db=db,
+            local_path=task_definition,
+            dbfs_path=get_db_path(task_definition) if not dst_path else dst_path,
+        )
+        py_task = dict(
+            python_file=get_db_path(task_definition),
+        )
+        if parameters:
+            py_task["parameters"] = parameters
+
+        run_id = db.jobs.submit_run(
+            existing_cluster_id=cluster_id,
+            spark_python_task=py_task,
+            run_name=run_name,
+        )["run_id"]
     print(f"Stated task with run_id={run_id}")
     return run_id
 
 
 def executable_as_script(py_executable: Union[str, ModuleType, Callable]):
-    if isinstance(py_executable, str) and os.path.exists(py_executable):
+    if (
+        isinstance(py_executable, str)
+        and os.path.exists(py_executable)
+        and py_executable.endswith(".py")
+    ):
         print(f"Detected Python Script for Databricks Submission")
         # Py file, we can just run this
         return py_executable
+    if (
+        isinstance(py_executable, str)
+        and os.path.exists(py_executable)
+        and py_executable.endswith(".ipynb")
+    ):
+        print(f"Detected Python Notebook for Databricks Submission")
+        # Py file, we can just run this
+        return py_executable
     if isinstance(py_executable, (str, ModuleType, Callable)):
+        print(f"Module/Callable for Databricks Submission")
         # Convert Module/Callable into a script
         return executable_to_str(py_executable)
     raise TypeError(f"Invalid Executable Python  Task {py_executable}")
@@ -225,6 +248,28 @@ def executable_to_str(executable_to_convert: Union[str, ModuleType, Callable]):
         return str_to_file(inspect.getsource(executable_to_convert), out_path)
 
 
+def encode_nb(nb_path):
+    import base64
+
+    with open(nb_path, "rb") as f:
+        encoded_nb = base64.b64encode(f.read()).decode("utf-8")
+    return encoded_nb
+
+
+def import_notebook_to_workspace(
+    db: DatabricksAPI,
+    dst_path: str,
+    local_nb_path: str,
+):
+    db.workspace.import_workspace(
+        path=dst_path,
+        language="PYTHON",
+        format="JUPYTER",
+        content=encode_nb(local_nb_path),
+        overwrite=True,
+    )
+
+
 def checkon_db_task(
     db: DatabricksAPI,
     run_id: str = None,
@@ -249,14 +294,37 @@ def run_in_databricks(
     databricks_host: Optional[str] = None,
     run_name: str = None,
     block_till_complete=True,
+    dst_path: str = None,
 ):
+    """
+
+    :param py_script_path: Either
+        - Path to local python script i.e "path/to/my_script.py"
+        - Path to local python notebook i.e "path/to/my_notebook.ipynb"
+        - string with python code i.e. "1+1"
+        - python module i.e. import my_module
+        - python function, i.e. def my_func(): return 1+1
+
+    :param databricks_cluster_id: cluster ID to run your task on. If none provided, a cluster will be created first.
+    :param databricks_token: databricks access token
+    :param databricks_host: databricks host
+    :param run_name: name of the run to generate, if None, a random name will be generated
+    :param block_till_complete: if True, this function will block until the task is complete
+    :param dst_path: path to store the python script/notebook. in databricks, mandatory for notebooks.
+        I.e. /Users/<your@databricks.email.com>/test.ipynb
+    :return: job_id
+    """
     from johnsnowlabs.auto_install.databricks.install_utils import (
         get_db_client_for_token,
     )
 
     db_client = get_db_client_for_token(databricks_host, databricks_token)
     job_id = run_local_py_script_as_task(
-        db_client, py_script_path, cluster_id=databricks_cluster_id, run_name=run_name
+        db_client,
+        py_script_path,
+        cluster_id=databricks_cluster_id,
+        run_name=run_name,
+        dst_path=dst_path,
     )
 
     if not block_till_complete:
@@ -277,3 +345,38 @@ def run_in_databricks(
                 f"Waiting 30 seconds, job {job_id}  is still running, its {job_status['state']}"
             )
         time.sleep(30)
+
+    return job_id
+
+
+def submit_notebook_to_databricks(db, local_nb_path, cluster_id, remote_path):
+    # Instantiate the DatabricksAPI client
+    # Read the local notebook content
+    with open(local_nb_path, "rb") as file:
+        content = file.read()
+
+    # Base64 encode the content
+    encoded_content = base64.b64encode(content).decode("utf-8")
+
+    # Define the path on Databricks workspace where the notebook will be uploaded
+
+    # Import the notebook to Databricks workspace
+    db.workspace.import_workspace(
+        path=remote_path,
+        format="JUPYTER",
+        language="PYTHON",
+        content=encoded_content,
+        overwrite=True,
+    )
+
+    # Define the notebook task
+    notebook_task = {"notebook_path": remote_path}
+
+    # Submit the job to run the notebook
+    response = db.jobs.submit_run(
+        run_name="Notebook_Run",
+        existing_cluster_id=cluster_id,
+        notebook_task=notebook_task,
+    )
+
+    return response["run_id"]
diff --git a/johnsnowlabs/auto_install/emr/work_utils.py b/johnsnowlabs/auto_install/emr/work_utils.py
index 3fa6c34936..9259b0515e 100644
--- a/johnsnowlabs/auto_install/emr/work_utils.py
+++ b/johnsnowlabs/auto_install/emr/work_utils.py
@@ -2,9 +2,6 @@
 from random import randrange
 from typing import Optional
 
-import boto3
-import botocore
-
 from johnsnowlabs.utils.boto_utils import BotoException
 from johnsnowlabs.utils.file_utils import path_tail
 from johnsnowlabs.utils.s3_utils import (
@@ -15,11 +12,13 @@
 )
 
 
-def create_emr_bucket(boto_session: boto3.Session, bucket=None):
+def create_emr_bucket(boto_session: "boto3.Session", bucket=None):
     """Create a bucket for EMR cluster logs
     :param boto_session: Boto3 session
     :param bucket: Bucket name
     """
+    import botocore
+
     try:
         sts_client = boto_session.client("sts")
         account_id = sts_client.get_caller_identity()["Account"]
@@ -40,7 +39,7 @@ def create_emr_bucket(boto_session: boto3.Session, bucket=None):
 def run_in_emr(
     py_script_path: str,
     cluster_id: str,
-    boto_session: Optional[boto3.Session] = None,
+    boto_session: Optional["boto3.Session"] = None,
     script_bucket: Optional[str] = None,
     bucket_folder_path: Optional[str] = None,
     run_name: Optional[str] = None,
@@ -56,6 +55,9 @@ def run_in_emr(
     :param execution_role_arn: IAM role to use for the step
     :return: Step id
     """
+    import boto3
+    import botocore
+
     if not boto_session:
         boto_session = boto3.Session()
     if check_if_file_exists_in_s3(py_script_path):
diff --git a/johnsnowlabs/auto_install/health_checks/benchmark_test.py b/johnsnowlabs/auto_install/health_checks/benchmark_test.py
new file mode 100644
index 0000000000..8b13789179
--- /dev/null
+++ b/johnsnowlabs/auto_install/health_checks/benchmark_test.py
@@ -0,0 +1 @@
+
diff --git a/johnsnowlabs/auto_install/health_checks/endpoint_test.py b/johnsnowlabs/auto_install/health_checks/endpoint_test.py
index 57b3655ce5..7a2f269a57 100644
--- a/johnsnowlabs/auto_install/health_checks/endpoint_test.py
+++ b/johnsnowlabs/auto_install/health_checks/endpoint_test.py
@@ -1,38 +1,82 @@
-lic = """
-ENDPOINT LICENSE
-"""
-import mlflow
+lic = """ENDPOINT LICENSE"""
+model = """MODEL TO TEST"""
 
-from johnsnowlabs.auto_install.databricks.endpoints import *
 
+# def new_req():
+#     from mlflow.utils.requirements_utils import _get_pinned_requirement
+#     from johnsnowlabs import settings
+#
+#     _SPARK_NLP_JSL_WHEEL_URI = (
+#         "https://pypi.johnsnowlabs.com/{secret}/spark-nlp-jsl/spark_nlp_jsl-"
+#         + f"{settings.raw_version_medical}-py3-none-any.whl"
+#     )
+#
+#     return [
+#         f"johnsnowlabs_for_databricks_by_ckl=={settings.raw_version_jsl_lib}",
+#         _get_pinned_requirement("pyspark"),
+#         _SPARK_NLP_JSL_WHEEL_URI.format(secret=os.environ["SECRET"]),
+#         "pandas<=1.5.3",
+#     ]
+#
+# mlflow.johnsnowlabs.get_default_pip_requirements = new_req
 
-def new_req():
-    from mlflow.utils.requirements_utils import _get_pinned_requirement
-    from johnsnowlabs import settings
 
-    _SPARK_NLP_JSL_WHEEL_URI = (
-        "https://pypi.johnsnowlabs.com/{secret}/spark-nlp-jsl/spark_nlp_jsl-"
-        + f"{settings.raw_version_medical}-py3-none-any.whl"
-    )
+def print_query_df(query_df):
+    print(query_df)
+    for c in query_df.columns:
+        print(query_df[c])
+
+
+def find_writable_directory():
+    import os
 
-    return [
-        f"johnsnowlabs_for_databricks_by_ckl=={settings.raw_version_jsl_lib}",
-        _get_pinned_requirement("pyspark"),
-        _SPARK_NLP_JSL_WHEEL_URI.format(secret=os.environ["SECRET"]),
-        "pandas<=1.5.3",
-    ]
+    # TODO this does not work
+    # List of directories to check
+    candidate_dirs = [os.getcwd()]
 
+    for dir_path in candidate_dirs:
+        if os.access(dir_path, os.W_OK):
+            print(f"Found writable directory: {dir_path}")
+            return dir_path
+    print("Could not find a writable directory")
+    return None
 
-mlflow.johnsnowlabs.get_default_pip_requirements = new_req
+
+# mlflow.set_experiment(find_writable_directory())
 
 
 def run_test():
-    mlflow.set_experiment("/Users/christian@johnsnowlabs.com/my-experiment")
+    import mlflow
+    import os
+    from johnsnowlabs.auto_install.databricks.endpoints import (
+        query_and_deploy_if_missing,
+        delete_endpoint,
+        nlu_name_to_endpoint,
+    )
+
+    # mlflow.set_experiment(find_writable_directory())
+    mlflow.set_experiment("/Users/christian@johnsnowlabs.com/my-experiment123")
     os.environ["JOHNSNOWLABS_LICENSE_JSON_FOR_CONTAINER"] = lic
     os.environ["JOHNSNOWLABS_LICENSE_JSON"] = lic
-    res = query_and_deploy_if_missing("tokenize", "Hello World", True, True)
-    print(res)
+    # 1) one query to construct endpoint and run with default output level
+    print(query_and_deploy_if_missing(model, "Hello World", True, True))
+
+    # # 2) One query for every output level
+    # for o in ["token", "sentence", "document"]:
+    #     print_query_df(
+    #         query_and_deploy_if_missing(model, "Hello World", output_level=o)
+    #     )
+
+    # 3) cleanup
+    print(f"Deleting: {nlu_name_to_endpoint(model)}")
+    delete_endpoint(
+        nlu_name_to_endpoint(model),
+        host=os.environ["DATABRICKS_HOST"],
+        token=os.environ["DATABRICKS_TOKEN"],
+    )
 
 
 if __name__ == "__main__":
+    dbutils.library.installPyPI("mlflow_by_johnsnowlabs")  # , version="2.10.0")
+    dbutils.library.installPyPI("pandas", version="1.5.0")
     run_test()
diff --git a/johnsnowlabs/auto_install/health_checks/generate_endpoint_test.py b/johnsnowlabs/auto_install/health_checks/generate_endpoint_test.py
new file mode 100644
index 0000000000..7557122848
--- /dev/null
+++ b/johnsnowlabs/auto_install/health_checks/generate_endpoint_test.py
@@ -0,0 +1,12 @@
+import inspect
+
+import johnsnowlabs.auto_install.health_checks.endpoint_test as endp_test
+
+
+def generate_endpoint_test(model, lic):
+    # read source of endpoint_test.py and replace placeholders with actual values and return new source code
+    return (
+        inspect.getsource(endp_test)
+        .replace("ENDPOINT LICENSE", lic)
+        .replace("MODEL TO TEST", model)
+    )
diff --git a/johnsnowlabs/settings.py b/johnsnowlabs/settings.py
index 5f831a5801..8a38bbe04e 100644
--- a/johnsnowlabs/settings.py
+++ b/johnsnowlabs/settings.py
@@ -9,8 +9,8 @@
 )
 
 # These versions are used for auto-installs and version  checks
-raw_version_jsl_lib = "5.1.0"
-raw_version_nlp = "5.1.0"
+raw_version_jsl_lib = "5.1.1"
+raw_version_nlp = "5.1.1"
 
 raw_version_nlu = "5.0.1"
 
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 41084ab34a..0000000000
--- a/setup.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from codecs import open
-from os import path
-
-from setuptools import find_packages, setup
-
-import johnsnowlabs.settings
-
-here = path.abspath(path.dirname(__file__))
-
-with open(path.join(here, "README.md"), encoding="utf-8") as f:
-    long_description = f.read()
-
-REQUIRED_PKGS = [
-    f"pyspark=={johnsnowlabs.settings.raw_version_pyspark}",
-    f"spark-nlp=={johnsnowlabs.settings.raw_version_nlp}",
-    f"nlu=={johnsnowlabs.settings.raw_version_nlu}",
-    f"spark-nlp-display=={johnsnowlabs.settings.raw_version_nlp_display}",
-    "numpy",
-    "dataclasses",
-    "requests",
-    "databricks-api",
-    f"pydantic=={johnsnowlabs.settings.raw_version_pydantic}",
-    "colorama",
-    "boto3"
-]
-
-setup(
-    version=johnsnowlabs.settings.raw_version_jsl_lib,
-    # name="johnsnowlabs_for_databricks",
-    name="johnsnowlabs",
-    description="The John Snow Labs Library gives you access to all of John Snow Labs Enterprise And Open Source products in an easy and simple manner. Access 10000+ state-of-the-art NLP and OCR models for "
-    "Finance, Legal and Medical domains. Easily scalable to Spark Cluster ",
-    long_description=long_description,
-    install_requires=REQUIRED_PKGS,
-    long_description_content_type="text/markdown",
-    url="https://www.johnsnowlabs.com/",
-    author="John Snow Labs",
-    author_email="christian@johnsnowlabs.com",
-    classifiers=[
-        "Development Status :: 5 - Production/Stable",
-        "Intended Audience :: Developers",
-        "Topic :: Software Development :: Build Tools",
-        "License :: OSI Approved :: Apache Software License",
-        # Specify the Python versions you support here. In particular, ensure
-        # that you indicate whether you support Python 2, Python 3 or both.
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.4",
-        "Programming Language :: Python :: 3.5",
-        "Programming Language :: Python :: 3.6",
-    ],
-    keywords="Spark NLP OCR Finance Legal Medical John Snow Labs  ",
-    packages=find_packages(exclude=["test*", "tmp*"]),  # exclude=['test']
-    include_package_data=True,
-)
diff --git a/tests/databricks/__init__.py b/tests/databricks/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/databricks/db_infos_test.py b/tests/databricks/db_infos_test.py
new file mode 100644
index 0000000000..ebddc673dd
--- /dev/null
+++ b/tests/databricks/db_infos_test.py
@@ -0,0 +1,47 @@
+from johnsnowlabs.auto_install.databricks.install_utils import *
+from johnsnowlabs.auto_install.databricks.install_utils import _get_cluster_id
+from tests.databricks.db_test_utils import *
+from tests.databricks.db_test_utils import get_or_create_test_cluster
+
+
+@db_cloud_params
+def test_list_workspace_infos(creds):
+    # List infos about the workspace
+    lic, host, token = creds
+    db_client = get_db_client_for_token(host, token)
+    list_db_runtime_versions(db_client)
+    list_node_types(db_client)
+    list_clusters(db_client)
+
+
+# Parameterizing over both creds and node_type
+@db_cloud_node_params
+def test_get_db_cluster_infos(creds, node_type):
+    # list infos specific to a cluster
+    lic, host, token = creds
+    db_client = get_db_client_for_token(host, token)
+
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    list_cluster_lib_status(db_client, cluster_id)
+    pprint(db_client.cluster.get_cluster(cluster_id))
+
+    # Check cluster name+runtime detected
+    assert (
+        cluster_exist_with_name_and_runtime(
+            db_client, "TEST_CLUSTER_0", settings.db_spark_version
+        )
+        == True
+    )
+
+    # check cluster id exists
+    assert does_cluster_exist_with_id(db_client, cluster_id) == True
+    # cluster_id = get_or_create_test_cluster(aws_creds, aws_cpu_node, 0)
+
+
+@db_cloud_params
+def test_delete_all_test_clusters(creds):
+    lic, host, token = creds
+    db_client = get_db_client_for_token(host, token)
+    delete_all_test_clusters(db_client)
+    for i in range(10):
+        assert _get_cluster_id(db_client, f"TEST_CLUSTER_{i}") is None
diff --git a/tests/databricks/db_test_utils.py b/tests/databricks/db_test_utils.py
new file mode 100644
index 0000000000..e4967e27e3
--- /dev/null
+++ b/tests/databricks/db_test_utils.py
@@ -0,0 +1,243 @@
+import queue
+import time
+
+import pytest
+
+from johnsnowlabs import nlp, settings
+from johnsnowlabs.auto_install.databricks.install_utils import (
+    cluster_exist_with_name_and_runtime,
+    get_db_client_for_token,
+    get_cluster_id,
+    wait_till_cluster_running,
+    _get_cluster_id,
+)
+from johnsnowlabs.auto_install.health_checks.generate_endpoint_test import (
+    generate_endpoint_test,
+)
+from johnsnowlabs.utils.enums import DatabricksClusterStates
+from tests.utilsz import secrets as sct
+
+
+@pytest.fixture()
+def azure_gpu_node_type():
+    return "Standard_NC4as_T4_v3"
+
+
+@pytest.fixture()
+def azure_cpu_node():
+    return "Standard_DS3_v2"
+
+
+@pytest.fixture()
+def aws_cpu_node():
+    return "i3.xlarge"
+
+
+@pytest.fixture()
+def azure_creds():
+    import tests.utilsz.secrets as sct
+
+    lic = sct.db_lic_azure
+    host = sct.ckl_host_azure
+    token = sct.ckl_token_azure
+    return lic, host, token
+
+
+@pytest.fixture()
+def azure_trial_creds():
+    import tests.utilsz.secrets as sct
+
+    lic = sct.azure_trail_lic2
+    host = sct.azure_trail_host2
+    token = sct.azure_trail_token2
+    return lic, host, token
+
+
+@pytest.fixture()
+def aws_creds():
+    import tests.utilsz.secrets as sct
+
+    lic = sct.ckl_lic_aws
+    host = sct.ckl_host_aws
+    token = sct.ckl_token_aws
+    return lic, host, token
+
+
+@pytest.fixture
+def creds(request):
+    return request.getfixturevalue(request.param)
+
+
+@pytest.fixture
+def node_type(request):
+    return request.getfixturevalue(request.param)
+
+
+def host_creds(host):
+    if host == "aws":
+        return aws_creds()
+    elif host == "azure":
+        return azure_creds()
+    else:
+        raise Exception("Unknown host")
+
+
+def assert_job_suc(state):
+    assert state["state"]["result_state"] == "SUCCESS"
+
+
+def run_endpoint_tests(test_cluster_id, host, token, model):
+    # generate job.py script and submit it
+    assert_job_suc(
+        nlp.run_in_databricks(
+            generate_endpoint_test(model, sct.container_lic_json),
+            databricks_cluster_id=test_cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name=f"endpoint_creation_test_run_{model}",
+        )
+    )
+
+
+def run_cluster_test_suite(test_cluster_id, host, token):
+    # run test suite again a existing cluster
+
+    # Test modules
+    import johnsnowlabs.auto_install.health_checks.hc_test as hc_test
+    import johnsnowlabs.auto_install.health_checks.ocr_test as ocr_test
+    import johnsnowlabs.auto_install.health_checks.nlp_test as nlp_test
+
+    assert_job_suc(
+        nlp.run_in_databricks(
+            nlp_test,
+            databricks_cluster_id=test_cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="nlp_test",
+        )
+    )
+
+    assert_job_suc(
+        nlp.run_in_databricks(
+            hc_test,
+            databricks_cluster_id=test_cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="hc_test",
+        )
+    )
+
+    assert_job_suc(
+        nlp.run_in_databricks(
+            ocr_test,
+            databricks_cluster_id=test_cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="ocr_test",
+        )
+    )
+
+
+def get_one_model_per_class():
+    # todo actually generate from spellbook
+    return ["sentiment", "ner", "spell", "bert", "elmo", "albert", "roberta"]
+
+
+def delete_all_test_clusters(db_client):
+    for i in range(10):
+        while _get_cluster_id(db_client, f"TEST_CLUSTER_{i}"):
+            cluster_id = _get_cluster_id(db_client, f"TEST_CLUSTER_{i}")
+            db_client.cluster.permanent_delete_cluster(cluster_id)
+            print(f"Deleted cluster {cluster_id}")
+            time.sleep(5)
+
+
+def get_or_create_test_cluster(
+    creds, node_type, n=0, runtime=None, clean_workspace=False
+):
+    """
+    Creates a cluster with name TEST_CLUSTER_{n} and runtime runtime if it doesn't exist.
+    If it exists, it checks if it's running and if not, it starts it.
+    "exists" means another cluster with same name and runtime exists.
+    """
+    lic, host, token = creds
+    # runtime = "9.1.x-scala2.12"
+    if runtime is None:
+        runtime = settings.db_spark_version
+    cluster_name = f"TEST_CLUSTER_{n}"
+    db_client = get_db_client_for_token(host, token)
+
+    # 1) Check if cluster with name and correct runtime exists and it's running state If not, create it.
+    if cluster_exist_with_name_and_runtime(db_client, cluster_name, runtime):
+        cluster_id = get_cluster_id(db_client, cluster_name, runtime)
+
+        status = DatabricksClusterStates(
+            db_client.cluster.get_cluster(
+                cluster_id,
+            )["state"]
+        )
+
+        # if status is terminated, try restart it
+        if status == DatabricksClusterStates.TERMINATED:
+            db_client.cluster.start_cluster(cluster_id=cluster_id)
+            wait_till_cluster_running(db_client, cluster_id)
+            status = DatabricksClusterStates(
+                db_client.cluster.get_cluster(
+                    cluster_id,
+                )["state"]
+            )
+        if status == DatabricksClusterStates.RUNNING:
+            return cluster_id
+        else:
+            print(
+                f"Could not find or start {cluster_name} with runtime {runtime} creating a new one"
+            )
+
+    else:
+        #  no cluster exists, create new one
+        cluster_id = nlp.install(
+            spark_version=runtime,
+            json_license_path=lic,
+            databricks_host=host,
+            databricks_token=token,
+            driver_node_type_id=node_type,
+            node_type_id=node_type,
+            cluster_name=cluster_name,
+            clean_cluster=clean_workspace,
+            block_till_cluster_ready=True,
+        )
+        return cluster_id
+
+
+def subtester_thread(cluster_id, job_que, host, token, results):
+    while not job_que.empty():
+        try:
+            model = job_que.get_nowait()
+        except queue.Empty:
+            return
+        try:
+            run_endpoint_tests(cluster_id, host, token, model)
+            print(f"{model} success!")
+            results[model] = "SUCCESS"
+        except Exception as e:
+            print(f"{model} failed!")
+            results[model] = f"FAILED: {str(e)}"
+
+
+db_cloud_node_params = pytest.mark.parametrize(
+    "creds, node_type",
+    [
+        ("aws_creds", "aws_cpu_node"),
+        ("azure_creds", "azure_cpu_node"),
+        # ("azure_creds", "azure_gpu_node_type"),
+    ],
+    indirect=["creds", "node_type"],
+)
+db_cloud_params = pytest.mark.parametrize(
+    "creds",
+    [
+        "aws_creds",
+        "azure_creds",
+    ],
+    indirect=["creds"],
+)
diff --git a/tests/databricks/endpoint_tests.py b/tests/databricks/endpoint_tests.py
new file mode 100644
index 0000000000..f86a84ebc3
--- /dev/null
+++ b/tests/databricks/endpoint_tests.py
@@ -0,0 +1,60 @@
+from multiprocessing import Queue
+from threading import Thread
+
+from tests.databricks.db_test_utils import *
+from tests.databricks.db_test_utils import (
+    run_endpoint_tests,
+    get_one_model_per_class,
+    get_or_create_test_cluster,
+    subtester_thread,
+)
+
+
+@pytest.mark.skip(reason="WIP")
+@db_cloud_node_params
+def test_endpoints_multi_cluster(creds, node_type):
+    n_clusters = 1
+    # n_parallel_jobs_per_cluster = 4  # todo add
+    # 1) Create clusters
+    cluster_ids = [
+        get_or_create_test_cluster(creds, node_type, i) for i in range(n_clusters)
+    ]
+
+    # 2) Define job-queue
+    job_que = Queue()
+    one_model_per_class = get_one_model_per_class()
+    for model in one_model_per_class:
+        job_que.put(model)
+
+    # Create a semaphore to limit parallelism per cluster
+
+    # 3) For each cluster, start a tester-thread.
+    # Threads take jobs from the queue and run them on the cluster till completion.
+    lic, host, token = aws_creds
+    threads = []
+    results = {}
+    for cluster_id in cluster_ids:
+        t = Thread(
+            target=subtester_thread, args=(cluster_id, job_que, host, token, results)
+        )
+        threads.append(t)
+        t.start()
+
+    # Wait for all threads to finish
+    for t in threads:
+        t.join()
+
+    # 4) Print results
+    for model, result in results.items():
+        print(f"Model {model}: {result}")
+
+    # 5) Delete all clusters
+    # for cluster_id in cluster_ids:
+    #     delete_cluster(cluster_id)
+
+
+@db_cloud_node_params
+def test_endpoint(creds, node_type):
+    lic, host, token = creds
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    run_endpoint_tests(cluster_id, host, token, "tokenize")
diff --git a/tests/databricks/hdfs_tests.py b/tests/databricks/hdfs_tests.py
new file mode 100644
index 0000000000..8f50a61d84
--- /dev/null
+++ b/tests/databricks/hdfs_tests.py
@@ -0,0 +1,18 @@
+import os
+from johnsnowlabs.auto_install.databricks.install_utils import *
+# fmt: off
+from tests.databricks.db_test_utils import aws_creds
+# fmt: on
+
+
+def test_hdfs_basic_methods(aws_creds):
+    lic, host, token = aws_creds
+    db_client = get_db_client_for_token(host, token)
+    src_p = os.path.abspath(__file__)
+    target_p = "/johnsnowlabs/testf"
+    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p)
+    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p + "2")
+    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p + "3")
+    pprint(dbfs_ls(db_client, "/johnsnowlabs"))
+    dbfs_rm(db_client, target_p)
+    pprint(dbfs_ls(db_client, "/johnsnowlabs"))
diff --git a/tests/databricks/sample.ipynb b/tests/databricks/sample.ipynb
new file mode 100644
index 0000000000..e1199c7702
--- /dev/null
+++ b/tests/databricks/sample.ipynb
@@ -0,0 +1,58 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from johnsnowlabs import nlp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "pipe = nlp.load('ner')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "pipe.predict('Peter loves Paris and he lives in London.')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tests/databricks/submit_tests.py b/tests/databricks/submit_tests.py
new file mode 100644
index 0000000000..e3d4e6311c
--- /dev/null
+++ b/tests/databricks/submit_tests.py
@@ -0,0 +1,103 @@
+import os.path
+
+from tests.databricks.db_test_utils import *
+from tests.databricks.db_test_utils import (
+    assert_job_suc,
+    run_cluster_test_suite,
+    get_or_create_test_cluster,
+)
+
+
+def nlu_func():
+    import nlu
+
+    medical_text = """A 28-year-old female with a history of gestational
+            diabetes presented with a one-week history of polyuria ,
+             polydipsia , poor appetite , and vomiting ."""
+    # TODO use licensed models
+    # df = nlu.load("en.med_ner.diseases").predict(medical_text)
+    df = nlu.load("tokenize").predict(medical_text)
+    for c in df.columns:
+        print(df[c])
+
+
+@db_cloud_node_params
+def test_run_integration_tests(creds, node_type):
+    lic, host, token = creds
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    run_cluster_test_suite(cluster_id, host, token)
+
+
+@db_cloud_node_params
+def test_submit_string_task_to_databricks(creds, node_type):
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    lic, host, token = creds
+    script = """
+import nlu
+print(nlu.load('sentiment').predict('That was easy!'))
+        """
+    assert_job_suc(
+        nlp.run_in_databricks(
+            script,
+            databricks_cluster_id=cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="Python Code String Example",
+        )
+    )
+
+
+@db_cloud_node_params
+def test_submit_local_py_file_task_to_databricks(creds, node_type):
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    lic, host, token = creds
+    # Test script
+    import johnsnowlabs.auto_install.health_checks.nlp_test as nlp_test
+
+    py_script_file_path = os.path.abspath(nlp_test.__file__)
+    assert_job_suc(
+        nlp.run_in_databricks(
+            py_script_file_path,
+            databricks_cluster_id=cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="Script test",
+        )
+    )
+
+
+@db_cloud_node_params
+def test_submit_function_task_to_databricks(creds, node_type):
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    lic, host, token = creds
+    # basically reads the function code and submits it to databricks.
+    # It also copies indentation and comments,watch out for that
+    assert_job_suc(
+        nlp.run_in_databricks(
+            nlu_func,
+            databricks_cluster_id=cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="Function test",
+        )
+    )
+
+
+@db_cloud_node_params
+def test_submit_notebook_task_to_databricks(creds, node_type):
+    cluster_id = get_or_create_test_cluster(creds, node_type, 0)
+    lic, host, token = creds
+
+    nb_path = "sample.ipynb"
+    dst_path = "/Users/christian@johnsnowlabs.com/test.ipynb"
+    # nb_path = "/home/ckl/Documents/tmp/databricks_endpoints_tutorial_PUBLIC.ipynb"
+    assert_job_suc(
+        nlp.run_in_databricks(
+            nb_path,
+            databricks_cluster_id=cluster_id,
+            databricks_host=host,
+            databricks_token=token,
+            run_name="Notebook Test",
+            dst_path=dst_path,
+        )
+    )
diff --git a/tests/databricks_tests.py b/tests/databricks_tests.py
deleted file mode 100644
index 81af0dafb1..0000000000
--- a/tests/databricks_tests.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import pytest
-
-import tests.utilsz.secrets as sct
-from johnsnowlabs import *
-from johnsnowlabs.auto_install.databricks.install_utils import *
-
-
-def assert_job_suc(state):
-    assert state["state"]["result_state"] == "SUCCESS"
-
-
-def run_endpoint_tests(test_cluster_id, host, token):
-    import johnsnowlabs.auto_install.health_checks.endpoint_test as endp_test
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            endp_test,
-            databricks_cluster_id=test_cluster_id,
-            databricks_host=host,
-            databricks_token=token,
-            run_name="endpoint_creation_test_run",
-        )
-    )
-
-
-def run_cluster_test_suite(test_cluster_id, host, token):
-    # run test suite again a existing cluster
-
-    # Test modules
-    import johnsnowlabs.auto_install.health_checks.hc_test as hc_test
-    import johnsnowlabs.auto_install.health_checks.ocr_test as ocr_test
-    import johnsnowlabs.auto_install.health_checks.nlp_test as nlp_test
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            nlp_test,
-            databricks_cluster_id=test_cluster_id,
-            databricks_host=host,
-            databricks_token=token,
-            run_name="nlp_test",
-        )
-    )
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            hc_test,
-            databricks_cluster_id=test_cluster_id,
-            databricks_host=host,
-            databricks_token=token,
-            run_name="hc_test",
-        )
-    )
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            ocr_test,
-            databricks_cluster_id=test_cluster_id,
-            databricks_host=host,
-            databricks_token=token,
-            run_name="ocr_test",
-        )
-    )
-
-
-@pytest.fixture()
-def host_creds(host):
-    if host == "aws":
-        return aws_creds()
-    elif host == "azure":
-        return azure_creds()
-    else:
-        raise Exception("Unknown host")
-
-
-@pytest.fixture()
-def azure_gpu_node_type():
-    return "Standard_NC4as_T4_v3"
-
-
-@pytest.fixture()
-def azure_cpu_node():
-    return "Standard_DS3_v2"
-
-
-@pytest.fixture()
-def azure_creds():
-    lic = sct.db_lic_azure
-    host = sct.ckl_host_azure
-    token = sct.ckl_token_azure
-    return lic, host, token
-
-
-@pytest.fixture()
-def azure_trial_creds():
-    lic = sct.azure_trail_lic2
-    host = sct.azure_trail_host2
-    token = sct.azure_trail_token2
-    return lic, host, token
-
-
-@pytest.fixture()
-def aws_creds():
-    lic = sct.ckl_lic_aws
-    host = sct.ckl_host_aws
-    token = sct.ckl_token_aws
-    return lic, host, token
-
-
-@pytest.fixture
-def creds(request):
-    return request.getfixturevalue(request.param)
-
-
-@pytest.mark.parametrize("creds", ["aws_creds", "azure_creds"], indirect=True)
-def test_list_db_infos(creds):
-    # List infos about the workspace
-    lic, host, token = creds
-    db_client = get_db_client_for_token(host, token)
-    list_db_runtime_versions(db_client)
-    list_node_types(db_client)
-    list_clusters(db_client)
-
-
-def test_endpoint(azure_trial_creds, azure_cpu_node):
-    cluster_id = "0831-180255-dki33myd"
-    lic, host, token = azure_trial_creds
-    run_endpoint_tests(cluster_id, host, token)
-
-
-def test_get_db_cluster_infos(aws_creds):
-    # list infos specific to a cluster
-    lic, host, token = aws_creds
-    db_client = get_db_client_for_token(host, token)
-    list_cluster_lib_status(db_client, cluster_id)
-    pprint(db_client.cluster.get_cluster(cluster_id))
-
-
-def test_create_fresh_cluster(azure_trial_creds, azure_cpu_node):
-    # Create a fresh cluster
-    lic, host, token = azure_trial_creds
-    #
-    # nlp.install(
-    #     json_license_path=lic,
-    #     databricks_host=host,
-    #     databricks_token=token,
-    #     hardware_platform="gpu",
-    #     node_type_id="Standard_NC12s_v3",
-    #     driver_node_type_id="Standard_NC6s_v3",
-    #     spark_version="13.1.x-gpu-ml-scala2.12",
-    #     visual=True,
-    #     clean_cluster=False,
-    # )
-    #
-    instance_type = azure_cpu_node
-    # lic, host, token = aws_creds
-
-    nlp.install(
-        json_license_path=lic,
-        databricks_host=host,
-        databricks_token=token,
-        driver_node_type_id=instance_type,
-        node_type_id=instance_type,
-    )
-
-
-def test_create_fresh_cluster_and_run_checks(azure_trial_creds, azure_cpu_node):
-    instance_type = azure_cpu_node
-
-    lic, host, token = azure_trial_creds
-    test_cluster_id = nlp.install(
-        json_license_path=lic,
-        databricks_host=host,
-        databricks_token=token,
-        visual=True,
-        driver_node_type_id=instance_type,
-        node_type_id=instance_type,
-        hardware_platform="gpu",
-        clean_cluster=False,
-    )
-    # test_cluster_id = "0829-125925-deug1ja1"
-    run_cluster_test_suite(test_cluster_id, host, token)
-    # TODO delete cluster
-
-
-def test_create_fresh_cluster2(azure_creds, azure_cpu_node):
-    instance_type = azure_cpu_node
-
-    parameters = [{}, {}]
-
-    for param in parameters:
-        lic, host, token = azure_creds
-        test_cluster_id = nlp.install(
-            json_license_path=lic,
-            databricks_host=host,
-            databricks_token=token,
-            visual=True,
-            driver_node_type_id=instance_type,
-            node_type_id=instance_type,
-            spark_conf=param,
-            # hardware_platform="gpu",
-        )
-        # test_cluster_id = "0829-125925-deug1ja1"
-        start_time = time.time()
-
-        run_cluster_test_suite(test_cluster_id, host, token)
-        # TODO delete cluster
-
-        end_time = time.time()
-        print("Time taken: ", end_time - start_time)
-
-
-def test_install_to_databricks():
-    db_client = get_db_client_for_token(sct.ckl_host, sct.ckl_token)
-    cluster_id = nlp.install(
-        databricks_host=sct.ckl_host,
-        databricks_token=sct.ckl_token,
-    )
-
-
-def test_submit_task_api_to_databricks():
-    # Make cluster
-    # cluster_id = nlp.install(json_license_path=sct.db_lic_by_jsl, databricks_host=sct.ckl_host,
-    #                          databricks_token=sct.ckl_token)
-    cluster_id = "1006-050402-4nsqdu8h"
-
-    # Test script
-    py_script = "/home/ckl/Documents/freelance/jsl/johnsnowlabs/johnsnowlabs/auto_install/health_checks/hc_test.py"
-    assert_job_suc(
-        nlp.run_in_databricks(
-            py_script,
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Script test ",
-        )
-    )
-
-    # Test String
-    script = """
-import nlu
-print(nlu.load('sentiment').predict('That was easy!'))
-    """
-    assert_job_suc(
-        nlp.run_in_databricks(
-            script,
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Python Code String Example",
-        )
-    )
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            "print('noice')",
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Code String test 2",
-        )
-    )
-
-    def nlu_func():
-        import nlu
-
-        medical_text = """A 28-year-old female with a history of gestational 
-        diabetes presented with a one-week history of polyuria ,
-         polydipsia , poor appetite , and vomiting ."""
-        df = nlu.load("en.med_ner.diseases").predict(medical_text)
-        for c in df.columns:
-            print(df[c])
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            nlu_func,
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Function test",
-        )
-    )
-
-    # Test String
-    script = """
-import nlu
-print(nlu.load('sentiment').predict('That was easy!'))
-    """
-    assert_job_suc(
-        nlp.run_in_databricks(
-            script,
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Python Code String Example",
-        )
-    )
-
-    # Test Function
-    def sample_submit_func():
-        print("Test Function")
-
-    assert_job_suc(
-        nlp.run_in_databricks(
-            sample_submit_func,
-            databricks_cluster_id=cluster_id,
-            databricks_host=sct.ckl_host,
-            databricks_token=sct.ckl_token,
-            run_name="Function test",
-        )
-    )
-
-    run_cluster_test_suite(cluster_id, sct.ckl_host, sct.ckl_token)
-
-
-def test_hdfs_basic_methods():
-    db_client = get_db_client_for_token(sct.ckl_host, sct.ckl_token)
-    src_p = "/home/ckl/old_home/ckl/Documents/freelance/johnsnowlabs_lib/setup.py"
-    target_p = "/johnsnowlabs/testf"
-    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p)
-    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p + "2")
-    copy_from_local_to_hdfs(db_client, local_path=src_p, dbfs_path=target_p + "3")
-    pprint(dbfs_ls(db_client, "/johnsnowlabs"))
-    dbfs_rm(db_client, target_p)
-    pprint(dbfs_ls(db_client, "/johnsnowlabs"))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/pytest.ini b/tests/pytest.ini
new file mode 100644
index 0000000000..2b8ae9edab
--- /dev/null
+++ b/tests/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+markers =
+    db_cloud_node_params: marker for parameterizing databricks tests with cloud credentials and node types (azure,aws, gcp)
+    db_cloud_params: marker for parameterizing databricks tests over all cloud credentials (azure,aws, gcp)