This can be downloaded as part of the Wallaroo Tutorials repository.
This tutorial provides a tutorial for the Run Continuously Task for ML Orchestrations.
Tasks are created from an orchestration through the following methods.
Task Type | Orchestration Method | Description |
---|---|---|
Run Once | run_once | Create one Task Run and end when the Task Run is finished executing or until the timeout for the task is reached . |
Run Scheduled | run_scheduled | Based on a schedule, create a new Task Run until the Scheduled Task is terminated. Repeat every time the schedule pattern is fulfilled (every hour, every Tuesday at 2 PM, etc). Continue generating new Task Runs until the Run Scheduled Task is terminated. |
Run Continuously | run_continuously | Generate a Task Run and continue running that task. These tasks typically have a repeating loop that continues to run until the Run Continuously Task is issued the kill command. |
The tutorial will demonstrate the following:
For this tutorial, we’ll create a workspace, upload our sample model and deploy a pipeline. We’ll perform some quick sample inferences to verify that everything it working.
Here we’ll import the various libraries we’ll use for the tutorial.
import wallaroo
from wallaroo.object import EntityNotFoundError, RequiredAttributeMissing
# to display dataframe tables
from IPython.display import display
# used to display dataframe information without truncating
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
import pyarrow as pa
import time
The first step is to connect to Wallaroo through the Wallaroo client. The Python library is included in the Wallaroo install and available through the Jupyter Hub interface provided with your Wallaroo environment.
This is accomplished using the wallaroo.Client()
command, which provides a URL to grant the SDK permission to your specific Wallaroo environment. When displayed, enter the URL into a browser and confirm permissions. Store the connection into a variable that can be referenced later.
If logging into the Wallaroo instance through the internal JupyterHub service, use wl = wallaroo.Client()
. For more information on Wallaroo Client settings, see the Client Connection guide.
# Login through local Wallaroo instance
wl = wallaroo.Client()
# Setting variables for later steps
workspace_name = f'continuoustutorial'
pipeline_name = f'continuouspipeline'
model_name = f'houseprice'
model_file_name = './models/rf_model.onnx'
connection_name = f'continuous_data_sample'
connection_type = "HTTP"
connection_argument = {'host':'https://github.com/WallarooLabs/Wallaroo_Tutorials/raw/refs/heads/wallaroo2024.3_tutorials/wallaroo-automate/orchestration_sdk_simple_tutorial/data/xtest-1k.arrow'}
We’ll now create our workspace and pipeline for the tutorial. If this tutorial has been run previously, then this will retrieve the existing ones with the assumption they’re for us with this tutorial.
We’ll set the retrieved workspace as the current workspace in the SDK, so all commands will default to that workspace.
workspace = wl.get_workspace(name=workspace_name, create_if_not_exist=True)
wl.set_current_workspace(workspace)
pipeline = wl.build_pipeline(pipeline_name)
We’ll upload our model into our sample workspace, then add it as a pipeline step before deploying the pipeline to it’s ready to accept inference requests.
# Upload the model
housing_model_control = (wl.upload_model(model_name,
model_file_name,
framework=wallaroo.framework.Framework.ONNX)
.configure(tensor_fields=["tensor"])
)
# Add the model as a pipeline step
pipeline.add_model_step(housing_model_control)
name | continuouspipeline |
---|---|
created | 2024-09-24 17:11:25.717188+00:00 |
last_updated | 2024-09-24 17:11:25.717188+00:00 |
deployed | (none) |
workspace_id | 9 |
workspace_name | continuoustutorial |
arch | None |
accel | None |
tags | |
versions | 8ae1c528-d714-4060-af99-98dbf0a93dd0 |
steps | |
published | False |
#deploy the pipeline
deploy_config = wallaroo.DeploymentConfigBuilder().replica_count(1).cpus(0.5).memory("1Gi").build()
pipeline.deploy(deployment_config=deploy_config, wait_for_status=False)
Deployment initiated for continuouspipeline. Please check pipeline status.
name | continuouspipeline |
---|---|
created | 2024-09-24 17:11:25.717188+00:00 |
last_updated | 2024-09-24 17:15:12.541464+00:00 |
deployed | True |
workspace_id | 9 |
workspace_name | continuoustutorial |
arch | x86 |
accel | none |
tags | |
versions | 759bd273-6b20-4cc2-87c1-7c48b81686f4, 085f7dd4-72fb-4a16-928b-bf3fb71b0baa, 8ae1c528-d714-4060-af99-98dbf0a93dd0 |
steps | houseprice |
published | False |
import time
while pipeline.status()['status'] != 'Running':
time.sleep(15)
print("Waiting for deployment.")
pipeline.status()
Waiting for deployment.
{'status': 'Running',
'details': [],
'engines': [{'ip': '10.28.2.54',
'name': 'engine-df98f4788-q9jjw',
'status': 'Running',
'reason': None,
'details': [],
'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline',
'status': 'Running',
'version': '759bd273-6b20-4cc2-87c1-7c48b81686f4'}]},
'model_statuses': {'models': [{'model_version_id': 7,
'name': 'houseprice',
'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6',
'status': 'Running',
'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}],
'engine_lbs': [{'ip': '10.28.2.53',
'name': 'engine-lb-6b59985857-j7475',
'status': 'Running',
'reason': None,
'details': []}],
'sidekicks': []}
We will create the data source connection via the Wallaroo client command create_connection
.
Connections are created with the Wallaroo client command create_connection
with the following parameters.
Parameter | Type | Description |
---|---|---|
name | string (Required) | The name of the connection. This must be unique - if submitting the name of an existing connection it will return an error. |
type | string (Required) | The user defined type of connection. |
details | Dict (Required) | User defined configuration details for the data connection. These can be {'username':'dataperson', 'password':'datapassword', 'port': 3339} , or {'token':'abcde123==', 'host':'example.com', 'port:1234'} , or other user defined combinations. |
name
as an existing data connection will result in an error.We’ll also create a data connection named inference_results_connection
with our helper function get_connection
that will either create or retrieve a connection if it already exists. From there we’ll create out connections:
houseprice_arrow_table
: An Apache Arrow file stored on GitHub that will be used for our inference input.wl.create_connection(connection_name, connection_type, connection_argument)
Field | Value |
---|---|
Name | continuous_data_sample |
Connection Type | HTTP |
Details | ***** |
Created At | 2024-09-24T17:29:30.959757+00:00 |
Linked Workspaces | [] |
To verify the orchestration will work, we’ll test the relevant code here. The complete code is in ./remote_inference/main.py
.
The run below will run through the inference loop twice to verify the loop code works.
print("Deploy the pipeline.")
deploy_config = wallaroo.DeploymentConfigBuilder().replica_count(1).cpus(0.5).memory("1Gi").build()
pipeline.deploy(deployment_config=deploy_config, wait_for_status=False)
# verify the pipeline is running
while pipeline.status()['status'] != 'Running':
time.sleep(15)
print("Waiting for deployment.")
print(pipeline.status())
# Get the connection - assuming it will be the only one
inference_source_connection = wl.get_connection(name=connection_name)
# our continuous loop - check every minute for the file.
# in a real example, this would be a database with a search filter to inference on the latest
# updates, then store the inference results in another data store
# for this test, we'll just run through twice to verify it works
for x in range (2):
time.sleep(60)
print(f"Getting arrow table file")
# Retrieve the file
# set accept as apache arrow table
headers = {
'Accept': 'application/vnd.apache.arrow.file'
}
response = requests.get(
inference_source_connection.details()['host'],
headers=headers
)
# Arrow table is retrieved
with pa.ipc.open_file(response.content) as reader:
arrow_table = reader.read_all()
print("Inference time. Displaying results after.")
# Perform the inference
result = pipeline.infer(arrow_table)
result_dataframe = result.to_pandas()
print(result_dataframe.head(5))
Deploy the pipeline.
Deployment initiated for continuouspipeline. Please check pipeline status.
{'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.56', 'name': 'engine-8595d79f94-8zvrs', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': 'fd88bf40-1305-491f-9c93-7c27fc49e754'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.55', 'name': 'engine-lb-6b59985857-jwwtb', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
Getting arrow table file
Inference time. Displaying results after.
time \
0 2024-09-24 17:58:40.884
1 2024-09-24 17:58:40.884
2 2024-09-24 17:58:40.884
3 2024-09-24 17:58:40.884
4 2024-09-24 17:58:40.884
in.tensor \
0 [4.0, 2.5, 2900.0, 5505.0, 2.0, 0.0, 0.0, 3.0, 8.0, 2900.0, 0.0, 47.6063, -122.02, 2970.0, 5251.0, 12.0, 0.0, 0.0]
1 [2.0, 2.5, 2170.0, 6361.0, 1.0, 0.0, 2.0, 3.0, 8.0, 2170.0, 0.0, 47.7109, -122.017, 2310.0, 7419.0, 6.0, 0.0, 0.0]
2 [3.0, 2.5, 1300.0, 812.0, 2.0, 0.0, 0.0, 3.0, 8.0, 880.0, 420.0, 47.5893, -122.317, 1300.0, 824.0, 6.0, 0.0, 0.0]
3 [4.0, 2.5, 2500.0, 8540.0, 2.0, 0.0, 0.0, 3.0, 9.0, 2500.0, 0.0, 47.5759, -121.994, 2560.0, 8475.0, 24.0, 0.0, 0.0]
4 [3.0, 1.75, 2200.0, 11520.0, 1.0, 0.0, 0.0, 4.0, 7.0, 2200.0, 0.0, 47.7659, -122.341, 1690.0, 8038.0, 62.0, 0.0, 0.0]
out.variable anomaly.count
0 [718013.7] 0
1 [615094.6] 0
2 [448627.8] 0
3 [758714.3] 0
4 [513264.66] 0
Getting arrow table file
Inference time. Displaying results after.
time \
0 2024-09-24 17:59:41.327
1 2024-09-24 17:59:41.327
2 2024-09-24 17:59:41.327
3 2024-09-24 17:59:41.327
4 2024-09-24 17:59:41.327
in.tensor \
0 [4.0, 2.5, 2900.0, 5505.0, 2.0, 0.0, 0.0, 3.0, 8.0, 2900.0, 0.0, 47.6063, -122.02, 2970.0, 5251.0, 12.0, 0.0, 0.0]
1 [2.0, 2.5, 2170.0, 6361.0, 1.0, 0.0, 2.0, 3.0, 8.0, 2170.0, 0.0, 47.7109, -122.017, 2310.0, 7419.0, 6.0, 0.0, 0.0]
2 [3.0, 2.5, 1300.0, 812.0, 2.0, 0.0, 0.0, 3.0, 8.0, 880.0, 420.0, 47.5893, -122.317, 1300.0, 824.0, 6.0, 0.0, 0.0]
3 [4.0, 2.5, 2500.0, 8540.0, 2.0, 0.0, 0.0, 3.0, 9.0, 2500.0, 0.0, 47.5759, -121.994, 2560.0, 8475.0, 24.0, 0.0, 0.0]
4 [3.0, 1.75, 2200.0, 11520.0, 1.0, 0.0, 0.0, 4.0, 7.0, 2200.0, 0.0, 47.7659, -122.341, 1690.0, 8038.0, 62.0, 0.0, 0.0]
out.variable anomaly.count
0 [718013.7] 0
1 [615094.6] 0
2 [448627.8] 0
3 [758714.3] 0
4 [513264.66] 0
Before we continue to the Run Continuously Task example, we’ll undeploy the pipeline since our Orchestration deploys the pipeline as part of the task cycle.
pipeline.undeploy()
Waiting for undeployment - this will take up to 45s ................................... ok
name | continuouspipeline |
---|---|
created | 2024-09-24 17:11:25.717188+00:00 |
last_updated | 2024-09-24 17:57:40.189304+00:00 |
deployed | False |
workspace_id | 9 |
workspace_name | continuoustutorial |
arch | x86 |
accel | none |
tags | |
versions | ba43237a-94ca-4375-a259-484775e9ed46, fd88bf40-1305-491f-9c93-7c27fc49e754, bf8e8426-d3ae-4f8d-a549-3ebd7bb30ae8, 6be2d1d9-719f-42ff-8cd6-d253ad924b7f, 759bd273-6b20-4cc2-87c1-7c48b81686f4, 085f7dd4-72fb-4a16-928b-bf3fb71b0baa, 8ae1c528-d714-4060-af99-98dbf0a93dd0 |
steps | houseprice |
published | False |
With the pipeline deployed and our connections set, we will now generate our ML Workload Orchestration. See the Wallaroo ML Workload Orchestrations guide for full details.
Orchestrations are uploaded to the Wallaroo instance as a ZIP file with the following requirements:
Parameter | Type | Description |
---|---|---|
User Code | (Required) Python script as .py files | If main.py exists, then that will be used as the task entrypoint. Otherwise, the first main.py found in any subdirectory will be used as the entrypoint. |
Python Library Requirements | (Optional) requirements.txt file in the requirements file format. A standard Python requirements.txt for any dependencies to be provided in the task environment. The Wallaroo SDK will already be present and should not be included in the requirements.txt. Multiple requirements.txt files are not allowed. | |
Other artifacts | Other artifacts such as files, data, or code to support the orchestration. |
For our example, our orchestration will:
This sample script is stored in remote_inference/main.py
with an empty requirements.txt
file, and packaged into the orchestration as ./remote_inference/remote_inference.zip
. We’ll display the steps in uploading the orchestration to the Wallaroo instance.
Orchestrations are uploaded with the Wallaroo client upload_orchestration(path)
method with the following parameters.
Parameter | Type | Description |
---|---|---|
path | string (Required) | The path to the ZIP file to be uploaded. |
Once uploaded, the deployment will be prepared and any requirements will be downloaded and installed.
For this example, the orchestration ./remote_inference/remote_inference.zip
will be uploaded and saved to the variable orchestration
.
We will loop until the uploaded orchestration’s status
displays ready
.
orchestration = wl.upload_orchestration(path="./remote_inference/remote_inference.zip")
while orchestration.status() != 'ready' and orchestration.status() != 'error':
print(orchestration.status())
time.sleep(5)
pending_packaging
pending_packaging
packaging
packaging
packaging
packaging
packaging
packaging
packaging
packaging
Once an Orchestration has the status ready
, it can be run as a Task.
Tasks are created from an Orchestration through the following methods.
Task Type | Orchestration Method | Description |
---|---|---|
Run Once | run_once | Create one Task Run and end when the Task Run is finished executing or until the timeout for the task is reached . |
Run Scheduled | run_scheduled | Based on a schedule, create a new Task Run until the Scheduled Task is terminated. Repeat every time the schedule pattern is fulfilled (every hour, every Tuesday at 2 PM, etc). Continue generating new Task Runs until the Run Scheduled Task is terminated. |
Run Continuously | run_continuously | Generate a Task Run and continue running that task. These tasks typically have a repeating loop that continues to run until the Run Continuously Task is issued the kill command. |
We’ll create a Run Continuously Task from our Orchestration. This Task has an infinite while
loop, so it will continue to loop until the Task is issued the kill
command.
Run Continuously Tasks are generated Orchestration run_continuously(name, json_args)
method. Any arguments for the orchestration are passed in through the json_args
parameter as a Dict
. If there are no arguments, then an empty set {}
is passed.
# Example: run continuously
import datetime
task_start = datetime.datetime.now()
task_continuously = orchestration.run_continuously(name="run continuous sample",
json_args={"workspace_name": workspace_name,
"pipeline_name": pipeline_name,
"connection_name": connection_name
})
The list of tasks in the Wallaroo instance is retrieves through the Wallaroo Client list_tasks()
method. This returns an array list of the following.
Parameter | Type | Description |
---|---|---|
id | string | The UUID identifier for the task. |
last run status | string | The last reported status the task. Values are:
|
type | string | The type of the task. Values are:
|
created at | DateTime | The date and time the task was started. |
updated at | DateTime | The date and time the task was updated. |
For this example, the status of the previously created task will be generated, then looped until it has reached status started
.
while task_continuously.status() != "started":
display(task_continuously.status())
time.sleep(5)
We can view the inferences from our logs and verify that new entries were added from our task. We can do that with the task last_runs()
method to see the list of task runs executed, then show the log from the last completed task run.
# give time for the first task run to generate
time.sleep(30)
task_continuously.last_runs()
task id | pod id | status | created at | updated at |
---|---|---|---|---|
51ade31c-d69f-4f95-9b7a-b48096ef3d3b | 0f8c2940-f025-4abc-9daa-b608e4dbde05 | running | 2024-24-Sep 20:17:03 | 2024-24-Sep 20:17:03 |
For this example, we’ll update the Task Run logs ever 20 seconds to show the loop is executing.
for x in range (10):
time.sleep(20)
display(task_continuously.last_runs()[0].logs())
(no logs yet)
(no logs yet)
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:19:12 Getting arrow table file
2024-24-Sep 20:19:12 Inference time. Displaying results after.
2024-24-Sep 20:19:12 time ... anomaly.count
2024-24-Sep 20:19:12 0 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 1 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 2 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 3 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 4 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12
2024-24-Sep 20:19:12 [5 rows x 4 columns]
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:19:12 Getting arrow table file
2024-24-Sep 20:19:12 Inference time. Displaying results after.
2024-24-Sep 20:19:12 time ... anomaly.count
2024-24-Sep 20:19:12 0 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 1 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 2 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 3 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 4 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12
2024-24-Sep 20:19:12 [5 rows x 4 columns]
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:18:12
2024-24-Sep 20:19:12 Getting arrow table file
2024-24-Sep 20:19:12 Inference time. Displaying results after.
2024-24-Sep 20:19:12 time ... anomaly.count
2024-24-Sep 20:19:12 0 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 1 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 2 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 3 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 4 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12
2024-24-Sep 20:19:12 [5 rows x 4 columns]
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:19:12 Getting arrow table file
2024-24-Sep 20:19:12 Inference time. Displaying results after.
2024-24-Sep 20:19:12 time ... anomaly.count
2024-24-Sep 20:19:12 0 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 1 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 2 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 3 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 4 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12
2024-24-Sep 20:19:12 [5 rows x 4 columns]
2024-24-Sep 20:20:12 Getting arrow table file
2024-24-Sep 20:20:13 Inference time. Displaying results after.
2024-24-Sep 20:20:13 time ... anomaly.count
2024-24-Sep 20:20:13 0 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 1 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 2 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 3 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 4 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13
2024-24-Sep 20:20:13 [5 rows x 4 columns]
2024-24-Sep 20:17:10 Getting the workspace continuoustutorial
2024-24-Sep 20:17:10 Getting the pipeline continuouspipeline
2024-24-Sep 20:17:11 Deploy the pipeline.
2024-24-Sep 20:17:11 Deployment initiated for continuouspipeline. Please check pipeline status.
2024-24-Sep 20:17:11 {'status': 'Running', 'details': [], 'engines': [{'ip': '10.28.2.58', 'name': 'engine-77cb97f568-lpgk4', 'status': 'Running', 'reason': None, 'details': [], 'pipeline_statuses': {'pipelines': [{'id': 'continuouspipeline', 'status': 'Running', 'version': '0bb33df8-532b-46f6-990f-5bc3e69dd04e'}]}, 'model_statuses': {'models': [{'model_version_id': 7, 'name': 'houseprice', 'sha': 'e22a0831aafd9917f3cc87a15ed267797f80e2afa12ad7d8810ca58f173b8cc6', 'status': 'Running', 'version': '5b7438ae-04f0-4398-8dae-3d7a0370e746'}]}}], 'engine_lbs': [{'ip': '10.28.2.57', 'name': 'engine-lb-6b59985857-tkx7c', 'status': 'Running', 'reason': None, 'details': []}], 'sidekicks': []}
2024-24-Sep 20:17:11 Inference time. Displaying results after.
2024-24-Sep 20:17:11 Getting arrow table file
2024-24-Sep 20:17:11 time ... anomaly.count
2024-24-Sep 20:17:11 1 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 0 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 2 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 3 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11 4 2024-09-24 20:17:11.906 ... 0
2024-24-Sep 20:17:11
2024-24-Sep 20:17:11 [5 rows x 4 columns]
2024-24-Sep 20:18:11 Getting arrow table file
2024-24-Sep 20:18:12 Inference time. Displaying results after.
2024-24-Sep 20:18:12 time ... anomaly.count
2024-24-Sep 20:18:12 0 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 1 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 2 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 3 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12 4 2024-09-24 20:18:12.353 ... 0
2024-24-Sep 20:18:12
2024-24-Sep 20:18:12 [5 rows x 4 columns]
2024-24-Sep 20:19:12 Getting arrow table file
2024-24-Sep 20:19:12 Inference time. Displaying results after.
2024-24-Sep 20:19:12 time ... anomaly.count
2024-24-Sep 20:19:12 0 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 1 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 2 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 3 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12 4 2024-09-24 20:19:12.748 ... 0
2024-24-Sep 20:19:12
2024-24-Sep 20:19:12 [5 rows x 4 columns]
2024-24-Sep 20:20:12 Getting arrow table file
2024-24-Sep 20:20:13 Inference time. Displaying results after.
2024-24-Sep 20:20:13 time ... anomaly.count
2024-24-Sep 20:20:13 0 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 1 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 2 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 3 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13 4 2024-09-24 20:20:13.119 ... 0
2024-24-Sep 20:20:13
2024-24-Sep 20:20:13 [5 rows x 4 columns]
With our example complete, we will kill the task and undeploy the pipeline.
task_continuously.kill()
<ArbexStatus.PENDING_KILL: ‘pending_kill’>
pipeline.undeploy()
Waiting for undeployment - this will take up to 45s .................................... ok
name | continuouspipeline |
---|---|
created | 2024-09-24 17:11:25.717188+00:00 |
last_updated | 2024-09-24 17:57:40.189304+00:00 |
deployed | False |
workspace_id | 9 |
workspace_name | continuoustutorial |
arch | x86 |
accel | none |
tags | |
versions | ba43237a-94ca-4375-a259-484775e9ed46, fd88bf40-1305-491f-9c93-7c27fc49e754, bf8e8426-d3ae-4f8d-a549-3ebd7bb30ae8, 6be2d1d9-719f-42ff-8cd6-d253ad924b7f, 759bd273-6b20-4cc2-87c1-7c48b81686f4, 085f7dd4-72fb-4a16-928b-bf3fb71b0baa, 8ae1c528-d714-4060-af99-98dbf0a93dd0 |
steps | houseprice |
published | False |