log jobs and job defs in mlflow

jupyter_scheduler/executors.py

@@ -139,15 +139,19 @@ def execute(self):
             kernel_name=nb.metadata.kernelspec["name"], store_widget_state=True, cwd=staging_dir
         )
 
-        try:
-            ep.preprocess(nb, {"metadata": {"path": staging_dir}})
-        except CellExecutionError as e:
-            raise e
-        finally:
-            if getattr(job, "mlflow_logging", False):
-                self.log_to_mlflow(job, nb)
-            self.add_side_effects_files(staging_dir)
-            self.create_output_files(job, nb)
+        mlflow.set_tracking_uri(MLFLOW_SERVER_URI)
+        with mlflow.start_run(run_id=job.mlflow_run_id):
+            try:
+                ep.preprocess(nb, {"metadata": {"path": staging_dir}})
+                if job.parameters:
+                    mlflow.log_params(job.parameters)
+            except CellExecutionError as e:
+                raise e
+            finally:
+                if getattr(job, "mlflow_logging", False):
+                    self.log_to_mlflow(job, nb)
+                self.add_side_effects_files(staging_dir)
+                self.create_output_files(job, nb)
 
     def add_side_effects_files(self, staging_dir: str):
         """Scan for side effect files potentially created after input file execution and update the job's packaged_files with these files"""
@@ -175,8 +179,10 @@ def create_output_files(self, job: DescribeJob, notebook_node):
         for output_format in job.output_formats:
             cls = nbconvert.get_exporter(output_format)
             output, _ = cls().from_notebook_node(notebook_node)
+            output_path = self.staging_paths[output_format]
             with fsspec.open(self.staging_paths[output_format], "w", encoding="utf-8") as f:
                 f.write(output)
+            mlflow.log_artifact(output_path)
 
     def log_to_mlflow(self, job, nb):
         mlflow.set_tracking_uri(MLFLOW_SERVER_URI)

jupyter_scheduler/scheduler.py

@@ -2,14 +2,10 @@
 import os
 import random
 import shutil
-import signal
-import subprocess
 from typing import Dict, List, Optional, Type, Union
-import sys
-from uuid import uuid4
+import subprocess
 
 import fsspec
-import mlflow
 import psutil
 from jupyter_core.paths import jupyter_data_dir
 from jupyter_server.transutils import _i18n
@@ -50,10 +46,6 @@
     create_output_filename,
 )
 
-MLFLOW_SERVER_HOST = "127.0.0.1"
-MLFLOW_SERVER_PORT = "5000"
-MLFLOW_SERVER_URI = f"http://{MLFLOW_SERVER_HOST}:{MLFLOW_SERVER_PORT}"
-
 
 class BaseScheduler(LoggingConfigurable):
     """Base class for schedulers. A default implementation
@@ -409,31 +401,20 @@ class Scheduler(BaseScheduler):
     task_runner = Instance(allow_none=True, klass="jupyter_scheduler.task_runner.BaseTaskRunner")
 
     def start_mlflow_server(self):
-        mlflow_process = subprocess.Popen(
+        subprocess.Popen(
             [
                 "mlflow",
                 "server",
+                "--backend-store-uri",
+                "./mlruns",
+                "--default-artifact-root",
+                "./mlartifacts",
                 "--host",
-                MLFLOW_SERVER_HOST,
+                "0.0.0.0",
                 "--port",
-                MLFLOW_SERVER_PORT,
-            ],
-            preexec_fn=os.setsid,
+                "5000",
+            ]
         )
-        mlflow.set_tracking_uri(MLFLOW_SERVER_URI)
-        return mlflow_process
-
-    def stop_mlflow_server(self):
-        if self.mlflow_process is not None:
-            os.killpg(os.getpgid(self.mlflow_process.pid), signal.SIGTERM)
-            self.mlflow_process.wait()
-            self.mlflow_process = None
-            print("MLFlow server stopped")
-
-    def mlflow_signal_handler(self, signum, frame):
-        print("Shutting down MLFlow server")
-        self.stop_mlflow_server()
-        sys.exit(0)
 
     def __init__(
         self,
@@ -450,9 +431,7 @@ def __init__(
         if self.task_runner_class:
             self.task_runner = self.task_runner_class(scheduler=self, config=config)
 
-        self.mlflow_process = self.start_mlflow_server()
-        signal.signal(signal.SIGINT, self.mlflow_signal_handler)
-        signal.signal(signal.SIGTERM, self.mlflow_signal_handler)
+        self.start_mlflow_server()
 
     @property
     def db_session(self):
@@ -502,21 +481,6 @@ def create_job(self, model: CreateJob) -> str:
             if not model.output_formats:
                 model.output_formats = []
 
-            mlflow_client = mlflow.MlflowClient()
-
-            if model.job_definition_id and model.mlflow_experiment_id:
-                experiment_id = model.mlflow_experiment_id
-            else:
-                experiment_id = mlflow_client.create_experiment(f"{model.input_filename}-{uuid4()}")
-                model.mlflow_experiment_id = experiment_id
-                input_file_path = os.path.join(self.root_dir, model.input_uri)
-                mlflow.log_artifact(input_file_path, "input")
-
-            mlflow_run = mlflow_client.create_run(
-                experiment_id=experiment_id, run_name=f"{model.input_filename}-{uuid4()}"
-            )
-            model.mlflow_run_id = mlflow_run.info.run_id
-
             job = Job(**model.dict(exclude_none=True, exclude={"input_uri"}))
 
             session.add(job)
@@ -664,12 +628,6 @@ def create_job_definition(self, model: CreateJobDefinition) -> str:
             if not self.file_exists(model.input_uri):
                 raise InputUriError(model.input_uri)
 
-            mlflow_client = mlflow.MlflowClient()
-            experiment_id = mlflow_client.create_experiment(f"{model.input_filename}-{uuid4()}")
-            model.mlflow_experiment_id = experiment_id
-            input_file_path = os.path.join(self.root_dir, model.input_uri)
-            mlflow.log_artifact(input_file_path, "input")
-
             job_definition = JobDefinition(**model.dict(exclude_none=True, exclude={"input_uri"}))
             session.add(job_definition)
             session.commit()

src/mainviews/create-job.tsx

@@ -176,7 +176,6 @@ export function CreateJob(props: ICreateJobProps): JSX.Element {
 
   const handleInputChange = (event: ChangeEvent<HTMLInputElement>) => {
     const target = event.target;
-
     const parameterNameIdx = parameterNameMatch(target.name);
     const parameterValueIdx = parameterValueMatch(target.name);
     const newParams = props.model.parameters || [];
@@ -323,7 +322,10 @@ export function CreateJob(props: ICreateJobProps): JSX.Element {
       idempotency_token: props.model.idempotencyToken,
       tags: props.model.tags,
       runtime_environment_parameters: props.model.runtimeEnvironmentParameters,
-      package_input_folder: props.model.packageInputFolder
+      package_input_folder: props.model.packageInputFolder,
+      mlflow_logging: props.model.mlflowLogging,
+      mlflow_experiment_id: props.model.mlflowExperimentId,
+      mlflow_run_id: props.model.mlflowRunId
     };
 
     if (props.model.parameters !== undefined) {
@@ -372,7 +374,9 @@ export function CreateJob(props: ICreateJobProps): JSX.Element {
       runtime_environment_parameters: props.model.runtimeEnvironmentParameters,
       schedule: props.model.schedule,
       timezone: props.model.timezone,
-      package_input_folder: props.model.packageInputFolder
+      package_input_folder: props.model.packageInputFolder,
+      mlflow_logging: props.model.mlflowLogging,
+      mlflow_experiment_id: props.model.mlflowExperimentId
     };
 
     if (props.model.parameters !== undefined) {