vitor-aignosi
dac4f1718f
QTZPOC-20: Implement model import workflow for .sientia bundles Introduces the import_model workflow and its associated activities to enable importing .sientia bundles into MLflow and MongoDB. This includes: * New core workflow and activity logic for handling bundle download, decryption, validation, MLflow provisioning (experiments, runs, registered versions), and MongoDB model document creation. * Comprehensive E2E and unit test suites, including a dedicated test harness and a manual run script for development. * Updated .env.example and README.md with new configurations and usage instructions. * Ensures correct MLflow model source resolution for legacy loader compatibility and robust MongoDB document management. * Refactors existing utility scripts into a scripts/ directory.
28 lines
496 B
Python
28 lines
496 B
Python
# %%
|
|
|
|
# Load logs.txt
|
|
with open('logs.txt') as file:
|
|
lines = file.readlines()
|
|
|
|
# %%
|
|
import re
|
|
|
|
# Grep "inter-arrival_s=number" with regex
|
|
intervals = []
|
|
for line in lines:
|
|
match = re.search(r'inter-arrival_s=([0-9.]+)', line)
|
|
if match:
|
|
intervals.append(float(match.group(1)))
|
|
# %%
|
|
|
|
print(intervals)
|
|
# %%
|
|
import matplotlib.pyplot as plt
|
|
|
|
plt.plot(intervals)
|
|
plt.ylabel('Inter-arrival time (s)')
|
|
plt.xlabel('Sample')
|
|
plt.title('Inter-arrival time distribution')
|
|
plt.show()
|
|
# %%
|