Patryk Oleniuk patryk-oleniuk

Versatile Software Engineer with ML Experience.  Excited about: ML / AI / Data Science apps, interacting with hardware, time series data.

patryk-oleniuk / artifact_viewer_filetypes.py

Last active May 11, 2020 03:47

Creating a bunch of test files, and then logging then into mlflow experiment

patryk-oleniuk / artifact_viewer_web_embed.py

Created May 10, 2020 06:14

How to embed a website in mlflow artifact viewer

	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")

patryk-oleniuk / artifact_viewer_folders.py

Created May 10, 2020 09:44

showing how to log folder structure in mlflow

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

patryk-oleniuk / mlflow_nested_runs.py

Created May 10, 2020 10:01

	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)

patryk-oleniuk / correct_run.py

Last active May 10, 2020 10:47

correct mlflow run

	# 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