Fixing quick start

dockerfiles/jupyter/Dockerfile

@@ -29,6 +29,7 @@ ENV MLRUN_DBPATH=http://localhost:8080 \
     JUPYTER_ENABLE_LAB=yes \
     MLRUN_HTTPDB__DATA_VOLUME=$HOME/data \
     MLRUN_HTTPDB__DSN='sqlite:////home/jovyan/data/mlrun.db?check_same_thread=false' \
+    MLRUN_HTTPDB__LOGS_PATH=$HOME/data/logs \
     MLRUN_PVC_MOUNT=nfsvol:/home/jovyan/data
 
 # run the mlrun db (api) and the notebook in parallel

docs/quick-start.md

@@ -21,15 +21,14 @@ MLRun requires separate containers for the API and the dashboard (UI).
 
 To install and run MLRun locally using Docker:
 ``` sh
-MLRUN_IP=localhost
-SHARED_DIR=/home/me/data
-# On Windows, use host.docker.internal for MLRUN_IP
+SHARED_DIR=~/mlrun-data
 
 docker pull mlrun/mlrun-ui:0.5.2
 docker pull mlrun/jupyter:0.5.2
 
-docker run -it -p 4000:80 --rm -d --name mlrun-ui -e MLRUN_API_PROXY_URL=http://${MLRUN_IP}:8080 mlrun/mlrun-ui:0.5.2
-docker run -it -p 8080:8080 -p 8888:8888 --rm -d --name jupy -v $(SHARED_DIR}:/home/jovyan/data mlrun/jupyter:0.5.2
+docker network create mlrun-network
+docker run -it -p 8080:8080 -p 8888:8888 --rm -d --network mlrun-network --name jupyter -v ${SHARED_DIR}:/home/jovyan/data mlrun/jupyter:0.5.2
+docker run -it -p 4000:80 --rm -d --network mlrun-network --name mlrun-ui -e MLRUN_API_PROXY_URL=http://jupyter:8080 mlrun/mlrun-ui:0.5.2
 ```
 
 When using Docker MLRun can only use local runs.

mlrun/api/api/endpoints/workflows.py

@@ -1,6 +1,7 @@
 from fastapi import APIRouter
 
-from mlrun.run import list_piplines
+from mlrun.k8s_utils import get_k8s_helper
+from mlrun.run import list_pipelines
 
 router = APIRouter()
 
@@ -15,16 +16,18 @@ def list_workflows(
     full: bool = False,
     page_size: int = 10,
 ):
-    total_size, next_page_token, runs = list_piplines(
-        full=full,
-        page_token=page_token,
-        page_size=page_size,
-        sort_by=sort_by,
-        experiment_id=experiment_id,
-        namespace=namespace,
-    )
+    total_size, next_page_token, runs = None, None, None
+    if get_k8s_helper(silent=True).is_running_inside_kubernetes_cluster():
+        total_size, next_page_token, runs = list_pipelines(
+            full=full,
+            page_token=page_token,
+            page_size=page_size,
+            sort_by=sort_by,
+            experiment_id=experiment_id,
+            namespace=namespace,
+        )
     return {
-        "runs": runs,
-        "total_size": total_size,
-        "next_page_token": next_page_token,
+        "runs": runs or [],
+        "total_size": total_size or 0,
+        "next_page_token": next_page_token or None,
     }

mlrun/run.py

@@ -888,7 +888,7 @@ def get_pipeline(run_id, namespace=None):
     return resp
 
 
-def list_piplines(
+def list_pipelines(
     full=False,
     page_token="",
     page_size=10,

tests/api/api/test_workflows.py

@@ -0,0 +1,18 @@
+import deepdiff
+from http import HTTPStatus
+
+from fastapi.testclient import TestClient
+from sqlalchemy.orm import Session
+
+
+def test_list_workflows(db: Session, client: TestClient) -> None:
+    response = client.get("/api/workflows")
+    assert response.status_code == HTTPStatus.OK.value
+    expected_response = {
+        "runs": [],
+        "total_size": 0,
+        "next_page_token": None,
+    }
+    assert (
+        deepdiff.DeepDiff(expected_response, response.json(), ignore_order=True,) == {}
+    )