Patryk Oleniuk patryk-oleniuk

## correct_run.py
# to correct a parameter, metric or artifact of an existing run, just pass run_id
# instead of experiment_id to mlflow.start_run function
with mlflow.start_run(run_id="your_run_id") as run:
    mlflow.log_param("p1","your_corrected_value")
    mlflow.log_metric("m1",42.0) # your corrected metrics
    mlflow.log_artifact("data_sample.html") # your corrected artifact file

## mlflow_nested_runs.py
with mlflow.start_run(experiment_id=1, run_name="top_lever_run") as run:
    with mlflow.start_run(experiment_id=1, run_name="subrun1",nested=True) as subrun1:
        mlflow.log_param("p1","red")
        mlflow.log_metric("m1", 5.1)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun1",nested=True) as subsubrun1:
            mlflow.log_param("p3","green")
            mlflow.log_metric("m3", 5.24)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun2", nested=True) as subsubrun2:
            mlflow.log_param("p4","blue")
            mlflow.log_metric("m5", 3.25)

## artifact_viewer_folders.py
"""
the output directory is prepared before logging:
├── output
│   ├── data
│   │   ├── data_sample.csv
│   │   └── data_sample.html
│   ├── images
│   │   ├── gif_sample.gif
│   │   └── image_sample.png
│   ├── maps

## artifact_viewer_web_embed.py
website_embed = '''<!DOCTYPE html>
<html>
<iframe src="https://en.wikipedia.org/wiki/Machine_learning" style='width: 700px; height: 450px' sandbox='allow-same-origin allow-scripts'>
</iframe>
</html>'''

with mlflow.start_run(experiment_id=1, run_name="website_embedding") as run:
    with open("output.html", "w") as f:
        f.write(website_embed)
    mlflow.log_artifact("output.html")

## artifact_viewer_filetypes.py
import json
import plotly.express as px
import mlflow
import requests

### prepare sample files to log
# test data
df = px.data.iris()

# sample CSV file
	# to correct a parameter, metric or artifact of an existing run, just pass run_id
	# instead of experiment_id to mlflow.start_run function
	with mlflow.start_run(run_id="your_run_id") as run:
	mlflow.log_param("p1","your_corrected_value")
	mlflow.log_metric("m1",42.0) # your corrected metrics
	mlflow.log_artifact("data_sample.html") # your corrected artifact file
	with mlflow.start_run(experiment_id=1, run_name="top_lever_run") as run:
	with mlflow.start_run(experiment_id=1, run_name="subrun1",nested=True) as subrun1:
	mlflow.log_param("p1","red")
	mlflow.log_metric("m1", 5.1)
	with mlflow.start_run(experiment_id=1, run_name="subsubrun1",nested=True) as subsubrun1:
	mlflow.log_param("p3","green")
	mlflow.log_metric("m3", 5.24)
	with mlflow.start_run(experiment_id=1, run_name="subsubrun2", nested=True) as subsubrun2:
	mlflow.log_param("p4","blue")
	mlflow.log_metric("m5", 3.25)
	"""
	the output directory is prepared before logging:
	├── output
	│ ├── data
	│ │ ├── data_sample.csv
	│ │ └── data_sample.html
	│ ├── images
	│ │ ├── gif_sample.gif
	│ │ └── image_sample.png
	│ ├── maps
	website_embed = '''<!DOCTYPE html>
	<html>
	<iframe src="https://en.wikipedia.org/wiki/Machine_learning" style='width: 700px; height: 450px' sandbox='allow-same-origin allow-scripts'>
	</iframe>
	</html>'''

	with mlflow.start_run(experiment_id=1, run_name="website_embedding") as run:
	with open("output.html", "w") as f:
	f.write(website_embed)
	mlflow.log_artifact("output.html")
	import json
	import plotly.express as px
	import mlflow
	import requests

	### prepare sample files to log
	# test data
	df = px.data.iris()

	# sample CSV file