Fix errors in notebooks when running from E2E tests

examples/README.md

@@ -138,7 +138,7 @@ This code sample shows how to get a deployment from the server.
 > geti = Geti(server_config=server_config)
 >
 > # Create deployment for the project, and prepare it for running inference
-> deployment = geti.deploy_project(PROJECT_NAME)
+> deployment = geti.deploy_project(project_name=PROJECT_NAME)
 >
 > # Save deployment on local
 > deployment.save(PATH_TO_DEPLOYMENT)

geti_sdk/demos/demo_projects/coco_demos.py

@@ -13,6 +13,7 @@
 # and limitations under the License.
 
 import logging
+import time
 from typing import Optional
 
 from geti_sdk import Geti
@@ -382,6 +383,12 @@ def ensure_trained_example_project(
                 number_of_images_to_annotate=45,
                 enable_auto_train=True,
             )
+            # Should wait for some time for the job to appear as scheduled before checking if
+            # the project is trained. Auto training is triggered after around 5 seconds.
+            print(
+                "Project created. Waiting for training job to be scheduled. This may take a few seconds."
+            )
+            time.sleep(5)
         else:
             raise ValueError(
                 f"The project named `{project_name}` does not exist on the server at "

geti_sdk/demos/demo_projects/utils.py

@@ -51,7 +51,9 @@ def ensure_project_is_trained(geti: Geti, project: Project) -> bool:
     )
     # If there are no jobs running for the project, we launch them
     jobs = training_client.get_jobs(project_only=True)
-    running_jobs = [job for job in jobs if job.state == JobState.RUNNING]
+    running_jobs = [
+        job for job in jobs if job.state in [JobState.RUNNING, JobState.SCHEDULED]
+    ]
     tasks = project.get_trainable_tasks()
 
     new_jobs = []

geti_sdk/geti.py

@@ -371,7 +371,7 @@ def download_project_data(
         # Download deployment
         if include_deployment:
             logging.info("Creating deployment for project...")
-            self.deploy_project(project, output_folder=target_folder)
+            self.deploy_project(project=project, output_folder=target_folder)
 
         logging.info(f"Project '{project.name}' was downloaded successfully.")
         return project
@@ -1132,7 +1132,8 @@ def upload_and_predict_video(
 
     def deploy_project(
         self,
-        project: Project,
+        project: Optional[Project] = None,
+        project_name: Optional[str] = None,
         output_folder: Optional[Union[str, os.PathLike]] = None,
         models: Optional[Sequence[BaseModel]] = None,
         enable_explainable_ai: bool = False,
@@ -1147,7 +1148,10 @@ def deploy_project(
         for each task in the project. However, it is possible to specify a particular
         model to use, by passing it in the list of `models` as input to this method.
 
-        :param project: Project object to deploy
+        :param project: Project object to deploy. Either `project` or `project_name`
+            must be specified.
+        :param project_name: Name of the project to deploy. Either `project` or
+            `project_name` must be specified.
         :param output_folder: Path to a folder on local disk to which the Deployment
             should be downloaded. If no path is specified, the deployment will not be
             saved.
@@ -1165,6 +1169,11 @@ def deploy_project(
             launch an OVMS container serving the models.
         :return: Deployment for the project
         """
+        if project is None and project_name is None:
+            raise ValueError("Either `project` or `project_name` must be specified.")
+        if project is None:
+            project = self.project_client.get_project_by_name(project_name=project_name)
+
         deployment_client = self._deployment_clients.get(project.id, None)
         if deployment_client is None:
             # Create deployment client and add to cache.

notebooks/001_create_project.ipynb

@@ -14,15 +14,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "3cb49dd2-7032-40e0-a4c2-8203ba1072bf",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from geti_sdk.utils import get_server_details_from_env\n",
     "\n",
     "geti_server_configuration = get_server_details_from_env()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -34,15 +34,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "ba8318f4-3d97-4949-abf0-6cdafe46572e",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from geti_sdk import Geti\n",
     "\n",
     "geti = Geti(server_config=geti_server_configuration)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -55,17 +55,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "081e700d-9e2e-4022-b4d6-dac3adf1d4f8",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from geti_sdk.rest_clients import ProjectClient\n",
     "\n",
     "project_client = ProjectClient(session=geti.session, workspace_id=geti.workspace_id)\n",
     "\n",
     "projects = project_client.list_projects()"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -89,10 +89,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "30e46136-5283-4e3d-8dcf-7a89bee4de6b",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from geti_sdk.data_models.enums import TaskType\n",
     "\n",
@@ -101,7 +99,9 @@
     "for task_type in TaskType:\n",
     "    if task_type.is_trainable:\n",
     "        print(\"  \" + str(task_type))"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -138,29 +138,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "b18c25d1-f53b-495e-a48e-ee89b7f95d4e",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# First set the project parameters. Feel free to experiment here!\n",
     "PROJECT_NAME = \"Segmentation demo\"\n",
     "PROJECT_TYPE = \"segmentation\"\n",
     "LABELS = [[\"dog\", \"cat\", \"horse\"]]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "4941f712-f97b-48ff-a11b-74687d0bb49f",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Now, use the project client to create the project\n",
     "project = project_client.create_project(\n",
     "    project_name=PROJECT_NAME, project_type=PROJECT_TYPE, labels=LABELS\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -173,13 +173,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "c9d3496b-663d-41d3-b381-5e05b4c8e5b0",
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(project.summary)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -191,13 +191,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "8786e8df-3ecb-4947-ad71-c8512e0b22ac",
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(project.overview)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -209,16 +209,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "01cb2ee6-9d45-4d17-aa6f-2ac0330ce369",
    "metadata": {},
-   "outputs": [],
    "source": [
     "task_list = project.get_trainable_tasks()\n",
     "print(f\"Project '{project.name}' contains {len(task_list)} trainable tasks.\")\n",
     "for task in task_list:\n",
     "    print(task.summary)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -230,14 +230,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "71dbcca8-b79e-441d-9143-091c2b08ba35",
    "metadata": {},
-   "outputs": [],
    "source": [
     "project = project_client.get_project(project_name=PROJECT_NAME)\n",
     "print(project.summary)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -250,10 +250,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "9c20a99c-07b6-4cc2-87fa-abf0ba210031",
    "metadata": {},
-   "outputs": [],
    "source": [
     "PIPELINE_PROJECT_NAME = \"Detection to hierarchical classification demo\"\n",
     "PIPELINE_PROJECT_TYPE = \"detection_to_classification\"\n",
@@ -270,21 +268,23 @@
     "        {\"name\": \"school bus\", \"parent_id\": \"bus\", \"group\": \"bus\"},\n",
     "    ],\n",
     "]"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "01d01757-035c-4690-86f0-2f40487a6c52",
    "metadata": {},
-   "outputs": [],
    "source": [
     "pipeline_project = project_client.create_project(\n",
     "    project_name=PIPELINE_PROJECT_NAME,\n",
     "    project_type=PIPELINE_PROJECT_TYPE,\n",
     "    labels=PIPELINE_LABELS,\n",
     ")"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -296,13 +296,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "71a5b21f-edf6-4be4-8e86-08db768040f2",
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(pipeline_project.summary)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -314,13 +314,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "d23f29b8-056d-476a-a244-5f7355e00743",
    "metadata": {},
-   "outputs": [],
    "source": [
     "print(pipeline_project.overview)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -339,15 +339,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "e2a2548a-8f84-4395-bdf7-8a4ebf3447a3",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Delete the simple project\n",
     "\n",
     "# project_client.delete_project(project)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -361,22 +361,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "ab209bce-5195-4f40-9d40-032af0bb5031",
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Delete the pipeline project\n",
-    "project_client.delete_project(pipeline_project, requires_confirmation=False)"
-   ]
+    "\n",
+    "# project_client.delete_project(pipeline_project, requires_confirmation=False)"
+   ],
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "fa885dd0-b669-4c83-b6b2-bbde010144cd",
    "metadata": {},
+   "source": [],
    "outputs": [],
-   "source": []
+   "execution_count": null
   }
  ],
  "metadata": {