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