Client side changes to support tasktype being a property of projects now

Author: Parthib

CHANGELOG.md

@@ -11,6 +11,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 
 * Models and datasets must be added to projects. Added a `Project` helper class.
 * Deprecates `categorical_features_map` in favor of `categorical_feature_names` for model and dataset uploads.
+* Added a convenience method `create_or_load_project` which loads in a project in if it is already created.
+* Moved `TaskType` attribute from the `Model` level to the `Project` level. Creating a `Project` now requires specifying the `TaskType`.
 
 ## [0.2.0a1]
 

examples/tabular-classification/churn-classifier/churn-classifier-sklearn.ipynb

@@ -233,20 +233,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "from unboxapi.tasks import TaskType\n",
     "\n",
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Banking Churn Project\",\n",
-    "    description=\"Project for predicting Banking Churn\"\n",
+    "    description=\"Project for Predicting Banking Churn\",\n",
+    "    task_type=TaskType.TabularClassification,\n",
     ")\n",
     "'''\n",
     "# Use this for loading the project on subsequent runs\n",
     "project = client.load_project(\n",
     "    name=\"Banking Churn Project\"\n",
     ")\n",
     "'''\n",
-    "\n",
     "dataset = project.add_dataframe(\n",
     "    df=x_val,\n",
     "    class_names=class_names,\n",
@@ -270,7 +271,6 @@
     "    function=predict_proba, \n",
     "    model=sklearn_model,\n",
     "    model_type=ModelType.sklearn,\n",
-    "    task_type=TaskType.TabularClassification,\n",
     "    class_names=class_names,\n",
     "    name='Churn Classifier',\n",
     "    description='this is my churn classification model',\n",
@@ -292,6 +292,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4ecb0cf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

examples/tabular-classification/fetal-health/fetal-health-sklearn.ipynb

@@ -182,7 +182,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Sklearn Fetal Health Project\",\n",
-    "    description=\"Project for predicting fetal health\"\n",
+    "    description=\"Project for predicting fetal health\",\n",
+    "    task_type=TaskType.TabularClassification\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -214,7 +215,6 @@
     "    function=predict_proba, \n",
     "    model=clf,\n",
     "    model_type=ModelType.sklearn,\n",
-    "    task_type=TaskType.TabularClassification,\n",
     "    class_names=class_names,\n",
     "    name='Fetal Classifier - N3',\n",
     "    description='this is my second tabular classification model',\n",

examples/tabular-classification/fraud-detection/fraud-classifier-sklearn.ipynb

@@ -253,7 +253,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Sklearn Fraud Classifier\",\n",
-    "    description=\"Project for Classifying Fraud\"\n",
+    "    description=\"Project for Classifying Fraud\",\n",
+    "    task_type=TaskType.TabularClassification\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -287,7 +288,6 @@
     "    function=predict_proba, \n",
     "    model=sklearn_model,\n",
     "    model_type=ModelType.sklearn,\n",
-    "    task_type=TaskType.TabularClassification,\n",
     "    class_names=class_names,\n",
     "    name='Fraud detection',\n",
     "    description='this is my fraud classification model',\n",

examples/tabular-classification/iris-classifier/iris-tabular-sklearn.ipynb

@@ -157,7 +157,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Sklearn Iris Tabular\",\n",
-    "    description=\"Project for Iris Tabular data\"\n",
+    "    description=\"Project for Iris Tabular data\",\n",
+    "    task_type=TaskType.TabularClassification,\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -189,7 +190,6 @@
     "    function=predict_proba, \n",
     "    model=classifiers['L1 logistic'],\n",
     "    model_type=ModelType.sklearn,\n",
-    "    task_type=TaskType.TabularClassification,\n",
     "    class_names=class_names,\n",
     "    name='Iris - L1 Logistic',\n",
     "    description='this is my first tabular classification model',\n",

examples/text-classification/fasttext/fasttext.ipynb

@@ -179,7 +179,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Fasttext Demo\",\n",
-    "    description=\"Fasttext Demo Project\"\n",
+    "    description=\"Fasttext Demo Project\",\n",
+    "    task_type=TaskType.TextClassification,\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -193,7 +194,6 @@
     "    function=get_predictions, \n",
     "    model=model,\n",
     "    model_type=ModelType.fasttext,\n",
-    "    task_type=TaskType.TextClassification,\n",
     "    class_names=class_names,\n",
     "    name='Cooking Fast Text',\n",
     "    description='this is my fasttext model',\n",

examples/text-classification/sklearn/banking/demo-banking.ipynb

@@ -197,7 +197,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Sklearn Banking Project\",\n",
-    "    description=\"Project to Demo Sklearn with Unbox\"\n",
+    "    description=\"Project to Demo Sklearn with Unbox\",\n",
+    "    task_type=TaskType.TextClassification,\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -211,7 +212,6 @@
     "    function=predict_function, \n",
     "    model=model,\n",
     "    model_type=ModelType.sklearn,\n",
-    "    task_type=TaskType.TextClassification,\n",
     "    class_names=label_list,\n",
     "    name='Banking Model',\n",
     "    description='this is my sklearn banking model'\n",

examples/text-classification/sklearn/stacked-models/fastttext-stacked.ipynb

@@ -225,7 +225,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Fasttext Stacked Model\",\n",
-    "    description=\"Project to Demo Fasttext Stacked\"\n",
+    "    description=\"Project to Demo Fasttext Stacked\",\n",
+    "    task_type=TaskType.TextClassification,\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -257,7 +258,6 @@
     "    dependent_dir=folder_name,\n",
     "    custom_model_code=custom_model_code,\n",
     "    model_type=ModelType.custom,\n",
-    "    task_type=TaskType.TextClassification,\n",
     "    class_names=all_class_names,\n",
     "    requirements_txt_file=\"./requirements.txt\",\n",
     "    name='Stacked model',\n",

examples/text-classification/transformers/transformers.ipynb

@@ -126,7 +126,8 @@
     "# Comment this out and uncomment the next section to load the project\n",
     "project = client.create_project(\n",
     "    name=\"Transformer Demo Project\",\n",
-    "    description=\"Project to Demo Transformers with Unbox\"\n",
+    "    description=\"Project to Demo Transformers with Unbox\",\n",
+    "    task_type=TaskType.TextClassification,\n",
     ")\n",
     "\n",
     "# Use this for loading the project on subsequent runs\n",
@@ -140,7 +141,6 @@
     "    function=predict_proba, \n",
     "    model=model,\n",
     "    model_type=ModelType.transformers,\n",
-    "    task_type=TaskType.TextClassification,\n",
     "    class_names=['negative', 'positive'],\n",
     "    name='transformers.sentiment_analyzer',\n",
     "    description='this is my transformers sentiment model',\n",

unboxapi/__init__.py

@@ -11,7 +11,7 @@
 from .api import Api
 from bentoml.saved_bundle.bundler import _write_bento_content_to_dir
 from bentoml.utils.tempdir import TempDirectory
-from .exceptions import UnboxException, UnboxInvalidRequest
+from .exceptions import UnboxDuplicateTask, UnboxException, UnboxInvalidRequest
 from .models import Model, ModelType, create_template_model
 from .datasets import Dataset
 from .tasks import TaskType
@@ -62,17 +62,29 @@ def __init__(self, api_key: str):
         else:
             self.upload = self.api.transfer_blob
 
-    def create_project(self, name: str, description: str):
+    def create_project(self, name: str, description: str, task_type: TaskType):
         endpoint = "projects"
-        payload = dict(name=name, description=description)
+        payload = dict(
+            name=name, 
+            description=description,
+            taskType=task_type.value,
+        )
         project_data = self.api.post_request(endpoint, body=payload)
+        print(project_data)
         return Project(project_data, self.upload, self.subscription_plan, self)
 
     def load_project(self, name: str):
         endpoint = f"me/projects/{name}"
         project_data = self.api.get_request(endpoint)
+        print(project_data)
         return Project(project_data, self.upload, self.subscription_plan, self)
 
+    def create_or_load_project(self, name: str, description: str, task_type: TaskType):
+        try:
+            return self.create_project(name, description, task_type)
+        except UnboxDuplicateTask:
+            return self.load_project(name)
+
     def add_model(
         self,
         name: str,
@@ -440,7 +452,6 @@ def add_model(
                         project_id=project_id,
                         description=description,
                         classNames=class_names,
-                        taskType=task_type.value,
                         architectureType=model_type.name,
                         kwargs=list(kwargs.keys()),
                         featureNames=feature_names,