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SIENTIAPDE-1182

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Update tests.ipynb and values.yaml for improved functionality and versioning

- Updated execution count in tests.ipynb for reproducibility.
- Modified DataFrame creation in tests.ipynb to include a timestamp column and save to CSV.
- Changed image tag in values.yaml from "0.4.4" to "0.4.5" for versioning.
- Updated GITHUB_BRANCH in values.yaml to reflect the latest branch adjustments.
This commit is contained in:
vitor-aignosi
2025-09-04 09:36:23 -03:00
parent 460bfe4073
commit da21c128f8
4 changed files with 328 additions and 47 deletions
Download Patch File Download Diff File Expand all files Collapse all files

43
laborious/activities/gates.py
View File

@@ -403,6 +403,10 @@ class Gates(BaseActivity):
data = DataFrame(input_data['data']) data = DataFrame(input_data['data'])
# Create timestamp column from index and reset index
data['timestamp'] = data.index
data = data.reset_index(drop=True)
self.debug( self.debug(
f"Prediction store policy: {prediction_store_policy}", metadata) f"Prediction store policy: {prediction_store_policy}", metadata)
@@ -410,29 +414,24 @@ class Gates(BaseActivity):
prediction_store_policy, metadata) prediction_store_policy, metadata)
# If data has no timestamp, we use the default timestamp and not sort the data # If data has no timestamp, we use the default timestamp and not sort the data
if 'timestamp' not in data.columns: self.info(
self.warning( f"Sorting data by timestamp and applying policy: {policy_type}:{policy_value}", metadata)
"Data has no timestamp, using default timestamp", metadata)
data['timestamp'] = input_data['timestamp']
else:
self.debug(
"Data has timestamp, sorting data by timestamp", metadata)
# If policy_type is lts, we need to sort the data by timestamp descending and take the first policy_value rows # If policy_type is lts, we need to sort the data by timestamp descending and take the first policy_value rows
if policy_type == 'lts': if policy_type == 'lts':
self.debug( self.debug(
"Sorting data by timestamp descending", metadata) "Sorting data by timestamp descending", metadata)
data = data.sort_values(by='timestamp', ascending=False) data = data.sort_values(by='timestamp', ascending=False)
# If policy_type is erl, we need to sort the data by timestamp ascending and take the first policy_value rows # If policy_type is erl, we need to sort the data by timestamp ascending and take the first policy_value rows
elif policy_type == 'erl': elif policy_type == 'erl':
self.debug( self.debug(
"Sorting data by timestamp ascending", metadata) "Sorting data by timestamp ascending", metadata)
data = data.sort_values(by='timestamp', ascending=True) data = data.sort_values(by='timestamp', ascending=True)
else: else:
self.error( self.error(
f"Invalid policy type: {policy_type}, using default policy", metadata) f"Invalid policy type: {policy_type}, using default policy", metadata)
raise ValueError( raise ValueError(
f"Invalid policy type: {policy_type}") f"Invalid policy type: {policy_type}")
data = data.head(int(policy_value)) data = data.head(int(policy_value))

4
laborious/utils/repository/model_repository.py
View File

@@ -39,6 +39,7 @@ class MLFlowRepository():
""" """
try: try:
return { return {
'success': True, 'success': True,
'content': self.model_serving.get_cached_transform( 'content': self.model_serving.get_cached_transform(
@@ -67,12 +68,15 @@ class MLFlowRepository():
- dict: A dictionary containing the predicted data. - dict: A dictionary containing the predicted data.
""" """
try: try:
input_index = data.index
start_time = datetime.now() start_time = datetime.now()
data = self.model_serving.get_cached_predict( data = self.model_serving.get_cached_predict(
model_name, data, model_retention) model_name, data, model_retention)
end_time = datetime.now() end_time = datetime.now()
data = pd.DataFrame(data, columns=['prediction']) data = pd.DataFrame(data, columns=['prediction'])
data.index = input_index
data['response_time'] = (end_time - start_time).total_seconds() data['response_time'] = (end_time - start_time).total_seconds()
return { return {

324
tests.ipynb
View File

@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"id": "b10e5c25", "id": "b10e5c25",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -110,12 +110,12 @@
" 0.06282208 -0.79004057 -0.20289499 0.83467535 0.26166448 -0.64498684\n", " 0.06282208 -0.79004057 -0.20289499 0.83467535 0.26166448 -0.64498684\n",
" -0.32228873 -0.61679398 -0.95035374 0.85492092 -0.10358534 -0.38492986\n", " -0.32228873 -0.61679398 -0.95035374 0.85492092 -0.10358534 -0.38492986\n",
" 0.19695438 -0.98537109 -0.44395579 0.40606693 0.26753955]\n", " 0.19695438 -0.98537109 -0.44395579 0.40606693 0.26753955]\n",
" Counter Rollout CounterPlusRollout Timestamp\n", " Counter Rollout CounterPlusRollout Timestamp\n",
"0 0.000000 0.000000 0.000000 2025-01-01 00:00:00\n", "0 37.270658 -59.474345 -22.203688 2025-01-01 00:00:00\n",
"1 0.547912 0.284967 0.832879 2025-01-01 00:00:01\n", "1 43.561734 -54.184658 -10.622924 2025-01-01 00:00:01\n",
"2 0.425669 0.842959 1.268628 2025-01-01 00:00:02\n", "2 42.158150 -43.826926 -1.668776 2025-01-01 00:00:02\n",
"3 1.142865 0.112064 1.254928 2025-01-01 00:00:03\n", "3 50.392926 -57.394165 -7.001239 2025-01-01 00:00:03\n",
"4 1.537601 0.184200 1.721801 2025-01-01 00:00:04\n" "4 54.925249 -56.055140 -1.129892 2025-01-01 00:00:04\n"
] ]
} }
], ],
@@ -166,31 +166,309 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"id": "e7c8eeb1", "id": "c61be7ab",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "TypeError", "name": "stdout",
"evalue": "MLFlowRepository.__init__() missing 1 required positional argument: 'logger'", "output_type": "stream",
"output_type": "error", "text": [
"traceback": [ "[ 0.5479121 -0.12224312 0.71719584 0.39473606 -0.8116453 0.9512447\n",
"\u001b[31m---------------------------------------------------------------------------\u001b[39m", " 0.5222794 0.57212861 -0.74377273 -0.09922812 -0.25840395 0.85352998\n",
"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", " 0.28773024 0.64552323 -0.1131716 -0.54552256 0.10916957 -0.87236549\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mlaborious\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mrepository\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mmodel_repository\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m MLFlowRepository\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m mlflow_repository = \u001b[43mMLFlowRepository\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mhost\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mhttp://localhost:5080/\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43musername\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43maignosi\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mpassword\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43maignosi\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 7\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m mlflow_repository.get_experiment_by_run_id(\u001b[33m\"\u001b[39m\u001b[33m1\u001b[39m\u001b[33m\"\u001b[39m)\n", " 0.65526234 0.2633288 0.51617548 -0.29094806 0.94139605 0.78624224\n",
"\u001b[31mTypeError\u001b[39m: MLFlowRepository.__init__() missing 1 required positional argument: 'logger'" " 0.55676699 -0.61072258 -0.06655799 -0.91239247 -0.69142102 0.36609791\n",
" 0.48952431 0.93501946 -0.34834928 -0.25908059 -0.06088838 -0.62105728\n",
" -0.74015699 -0.04859015 -0.5461813 0.33962799 -0.12569616 0.66535639\n",
" 0.4005302 -0.37526672 0.6645196 0.60952871 -0.22504324 -0.42334379\n",
" 0.36499101 -0.72049503 -0.6001836 -0.98527546 0.57384876 0.32970171\n",
" 0.41033076 0.56145806 -0.08216845 0.13748239 -0.720406 -0.77093985\n",
" 0.33680592 -0.05780759 0.13047221 0.52999771 0.26943664 0.1071588\n",
" 0.11841432 -0.3920998 -0.93836433 -0.12656522 -0.57083065 -0.18294271\n",
" 0.70680615 -0.53212103 -0.88339452 -0.43723222 -0.41281248 0.32383303\n",
" 0.1140643 0.56779642 0.32862708 -0.18722628 0.62804077 -0.66605416\n",
" -0.95457585 -0.81990428 0.4447187 -0.07624554 -0.67745644 0.00208955\n",
" -0.69537579 0.39264075 -0.10768745 -0.23795755 -0.39697582 0.26056519\n",
" -0.27637478 -0.82470016 -0.7639882 0.92379533 0.81716138 0.39941427\n",
" -0.46826008 0.93835275 0.55750181 0.43378038 -0.101277 -0.45551688\n",
" -0.80721808 0.80520479 -0.08844742 -0.59527327 -0.38808675 0.15843914\n",
" -0.64645443 0.71322857 0.51703906 0.43892591 -0.13581392 0.25461768\n",
" 0.16819594 0.2996932 -0.83111136 -0.1683852 -0.91677165 -0.01201836\n",
" -0.34027758 -0.71095162 -0.79319406 0.17528914 -0.65881406 0.85024024\n",
" 0.16212228 -0.30626039 0.18183098 -0.95439226 0.91711843 -0.03539313\n",
" 0.56547045 -0.83454 -0.02668334 -0.01858601 0.87565291 0.1434561\n",
" -0.0530212 -0.46604867 -0.33686201 0.0413448 -0.12217708 -0.95677584\n",
" 0.65258385 0.79232154 -0.71950182 0.10807229 -0.78284852 0.34448019\n",
" -0.43753243 0.31884527 0.45398923 0.53729498 -0.78451811 0.83202369\n",
" -0.53957202 -0.92517489 0.10970494 -0.25815543 0.65957949 0.61650294\n",
" -0.36572221 0.90579879 -0.41816432 0.03011426 -0.48806982 0.87208714\n",
" -0.67078436 -0.91017876 -0.12980588 0.98475113 0.78335453 0.49721604\n",
" 0.78158498 0.78689328 0.03771672 -0.3681419 0.54402486 0.32332253\n",
" -0.25268454 -0.81106666 0.49357922 -0.47507897 0.8736263 -0.51805885\n",
" -0.75448414 0.66222534 -0.69343137 -0.64146338 0.19876558 0.74912408\n",
" -0.60713067 -0.37935265 0.55480968 0.94365285 0.00148237 -0.71220499\n",
" -0.97212742 -0.54068794 -0.73635556 0.35531735 -0.75633499 0.01265986\n",
" 0.38852487 0.16223322 -0.6004487 0.60824905 0.43081426 0.47796801\n",
" -0.7378845 -0.75249239 0.8551251 -0.20484361 -0.39810262 -0.02283191\n",
" 0.32572843 0.91124651 -0.42710755 0.84961686 -0.95028102 0.11039608\n",
" 0.26795022 -0.78820519 -0.71932081 -0.16177136 0.93246382 0.19208511\n",
" 0.86604644 0.60872183 -0.0652368 0.5695269 -0.96432643 -0.78171201\n",
" 0.65885723 0.59363418 -0.53471852 0.06153918 0.21203164 0.73547791\n",
" 0.20621431 -0.17485686 -0.25163191 -0.14823583 0.30386205 0.73498126\n",
" -0.09220624 -0.50432087 -0.52667527 0.49202856 0.63313753 -0.78944384\n",
" -0.86688229 0.18886733 -0.70765351 0.64932838 -0.37933065 -0.71225613\n",
" 0.84194094 -0.66893655 -0.43055984 -0.69277321 -0.76901987 -0.95770397\n",
" -0.88920918 -0.65071706 -0.89323613 0.18228763 0.36142905 -0.21273909\n",
" -0.36401781 0.00905247 0.75000988 0.70226325 -0.91304988 -0.63700318\n",
" -0.52651026 -0.50122485 0.1424653 -0.16747515 -0.90149176 -0.25277172\n",
" 0.0475059 -0.79665619 0.66691711 -0.89607627 0.84968374 -0.80177372\n",
" 0.6871499 0.80530629 0.95914136 0.60405176 0.55895508]\n",
"[ 0.28496655 0.55799271 -0.73089558 0.07213607 0.02844574 0.71514429\n",
" -0.07440127 -0.22982101 0.27912654 -0.46707336 -0.72046318 -0.04424545\n",
" -0.16622126 -0.53486012 -0.26497638 -0.2672151 -0.34500887 -0.24107184\n",
" 0.37148669 -0.40624705 0.89771585 0.83269604 -0.03817914 -0.34327759\n",
" 0.07086958 0.69712098 0.30517468 0.60878366 0.06544455 0.26583526\n",
" -0.42368877 0.46978632 -0.59519081 0.38959626 0.72143814 -0.73579433\n",
" 0.22875948 -0.8098085 0.45143126 -0.83101356 0.87187965 -0.72518414\n",
" 0.91776049 0.60176835 0.18736401 0.56524821 0.59022968 0.89205413\n",
" -0.49323329 0.18015179 -0.8099016 0.2323314 -0.65741739 0.12990122\n",
" 0.14486103 -0.06802969 0.04526355 0.52784678 0.59848943 -0.01569357\n",
" 0.19918688 0.86247247 -0.76053282 -0.76579287 -0.82458198 0.31572657\n",
" -0.1627834 0.54864283 0.34246283 -0.33272448 0.79673309 0.52506429\n",
" -0.45893012 -0.27161596 -0.37112004 -0.6847767 -0.70443325 0.87225493\n",
" -0.12419193 -0.23336035 0.45937142 0.10598613 0.87227997 0.56060299\n",
" -0.04126087 -0.24728105 0.97326309 0.43552047 0.90238932 -0.76304285\n",
" 0.70106736 0.27414777 -0.75615664 0.176516 0.37219273 -0.97539463\n",
" -0.09136408 0.65079902 -0.40928195 -0.08290384 -0.11537175 -0.39614522\n",
" 0.83688379 0.56258807 -0.77882318 0.99406932 0.75840005 -0.43218312\n",
" 0.67379316 -0.78716094 0.99820946 0.33136947 0.30025003 -0.81911855\n",
" 0.7940668 -0.94200099 -0.51834388 -0.71395625 0.55353588 -0.60359155\n",
" 0.82127645 0.31253808 -0.92767458 -0.98914033 -0.89668417 0.21185036\n",
" 0.60296362 -0.52289436 0.69881769 -0.88553612 0.60192771 0.85559086\n",
" 0.5442168 0.39624157 0.67596044 -0.9196974 -0.59643578 -0.75015264\n",
" 0.00906198 0.49037626 0.26002369 0.7022622 -0.68957402 0.46924218\n",
" -0.61391702 -0.4584825 0.41980939 0.96040957 0.22308721 -0.89099937\n",
" 0.23261794 -0.9152989 0.76829142 0.41915657 -0.65374431 -0.81655799\n",
" -0.63293354 0.96005436 -0.08287872 0.5681619 0.27281668 0.1448263\n",
" -0.70973949 0.89204891 -0.39731473 0.15603443 0.39955189 0.29846631\n",
" 0.88118882 -0.70312202 0.01670548 -0.19193122 -0.05166254 -0.76156495\n",
" -0.73181078 -0.44384891 -0.39059079 -0.14419357 0.22197509 0.26925823\n",
" -0.17637821 -0.18243378 -0.56474295 0.1766125 -0.36591818 -0.92788033\n",
" -0.16319991 -0.05173465 -0.54881426 0.14491587 0.1315438 0.40400436\n",
" 0.29589696 0.30486611 -0.3675717 0.57486444 0.09828877 -0.13716361\n",
" 0.25202496 -0.27868533 0.02547849 0.47341138 0.77280577 0.84211439\n",
" 0.00726585 0.04055023 0.59974082 -0.37109862 0.67476472 -0.01171671\n",
" -0.76828655 -0.85588171 0.68398642 -0.88886417 -0.43877713 -0.33173992\n",
" -0.65401111 -0.37221326 0.48538513 -0.97063431 0.65434685 0.71309605\n",
" -0.25547685 -0.6927742 0.20168082 -0.76065489 -0.27016128 0.91685836\n",
" 0.99092895 0.54420978 -0.37807698 0.3753301 0.41081273 -0.22431661\n",
" 0.28177727 -0.97854471 -0.58188468 0.05017661 -0.67249739 -0.66818626\n",
" 0.67260858 0.97826601 0.11193886 0.67813946 0.98064333 -0.71680822\n",
" -0.10350877 -0.21485457 -0.83990143 0.51066035 -0.13244195 -0.06134613\n",
" -0.69865405 -0.6381467 0.81420724 -0.91070182 -0.53429543 -0.41588134\n",
" -0.01960492 0.17289035 -0.01342005 -0.83176933 -0.51266509 0.68717677\n",
" 0.2751774 0.2982981 0.34040651 0.52580604 -0.88378304 -0.26678323\n",
" 0.07905487 -0.32308703 0.68895775 -0.03485498 0.53725518 0.70403103\n",
" 0.00958297 0.81910449 0.17424788 0.7005486 -0.31881841 -0.00236608\n",
" 0.06282208 -0.79004057 -0.20289499 0.83467535 0.26166448 -0.64498684\n",
" -0.32228873 -0.61679398 -0.95035374 0.85492092 -0.10358534 -0.38492986\n",
" 0.19695438 -0.98537109 -0.44395579 0.40606693 0.26753955]\n",
" Counter Rollout Square Timestamp\n",
"0 37.270658 -59.474345 -47.219627 2025-01-01 00:00:00\n",
"1 43.561734 -54.184658 -36.038896 2025-01-01 00:00:01\n",
"2 42.158150 -43.826926 -33.245890 2025-01-01 00:00:02\n",
"3 50.392926 -57.394165 -33.767886 2025-01-01 00:00:03\n",
"4 54.925249 -56.055140 -27.701114 2025-01-01 00:00:04\n"
] ]
} }
], ],
"source": [ "source": [
"from laborious.utils.repository.model_repository import MLFlowRepository\n", "import pandas as pd\n",
"import numpy as np\n",
"\n", "\n",
"mlflow_repository = MLFlowRepository(\n", "# Set random seed for reproducibility\n",
" host=\"http://localhost:5080/\",\n", "rng = np.random.default_rng(42)\n",
" username=\"aignosi\",\n",
" password=\"aignosi\"\n",
")\n",
"\n", "\n",
"mlflow_repository.get_experiment_by_run_id(\"1\")" "# Generate random walks starting at 0\n",
"counter = np.zeros(300)\n",
"rollout = np.zeros(300)\n",
"square = np.zeros(300)\n",
"\n",
"# Generate random steps between -1 and 1\n",
"counter_steps = rng.uniform(-1, 1, 299)\n",
"rollout_steps = rng.uniform(-1, 1, 299)\n",
"square_steps = rng.uniform(-1, 1, 299)\n",
"\n",
"print(counter_steps)\n",
"print(rollout_steps)\n",
"\n",
"# Calculate cumulative sum and scale to -100 to 100 range\n",
"for i in range(1, 300):\n",
" counter[i] = counter[i-1] + counter_steps[i-1]\n",
" rollout[i] = rollout[i-1] + rollout_steps[i-1]\n",
" square[i] = square[i-1] + square_steps[i-1]\n",
"\n",
"# normalize values between -100 and 100, lowest value is -100, highest value is 100\n",
"counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n",
"rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n",
"square = (square - min(square)) / (max(square) - min(square)) * 200 - 100\n",
"\n",
"# Create DataFrame\n",
"df = pd.DataFrame({\n",
" 'Counter': counter,\n",
" 'Rollout': rollout,\n",
" 'Square': square\n",
"})\n",
"\n",
"# add a timestamp column\n",
"df['Timestamp'] = pd.date_range(start='2025-01-01', periods=300, freq='1s')\n",
"\n",
"# Save to CSV\n",
"df.to_csv('random_walks_demo.csv', index=False)\n",
"\n",
"# Display first few rows\n",
"print(df.head())\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e7c8eeb1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2025-01-01</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-02</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2025-01-03</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"timestamp \n",
"2025-01-01 1 4\n",
"2025-01-02 2 5\n",
"2025-01-03 3 6"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2025-01-03</th>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b\n",
"timestamp \n",
"2025-01-03 3 6"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas import DataFrame\n",
"\n",
"a = DataFrame({\n",
" \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
" \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
"})\n",
"\n",
"a.index.name = \"timestamp\"\n",
"\n",
"display(a)\n",
"\n",
"print(\"a\" in a.columns)\n",
"\n",
"display(a.tail(1))"
] ]
} }
], ],

4
values.yaml
View File

@@ -11,7 +11,7 @@ image:
# This sets the pull policy for images. # This sets the pull policy for images.
pullPolicy: Always pullPolicy: Always
# Overrides the image tag whose default is the chart appVersion. # Overrides the image tag whose default is the chart appVersion.
tag: "0.4.4" tag: "0.4.5"
# This is for the secrets for pulling an image from a private repository more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/ # This is for the secrets for pulling an image from a private repository more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
imagePullSecrets: imagePullSecrets:
@@ -151,7 +151,7 @@ env:
- name: GITHUB_REPO_URL - name: GITHUB_REPO_URL
value: "git@github.com:Aignosi/sientia-dataops-laborious_temporal.git" value: "git@github.com:Aignosi/sientia-dataops-laborious_temporal.git"
- name: GITHUB_BRANCH - name: GITHUB_BRANCH
value: "SIENTIAPDE-1205-alterar-opc-para-assincrono" value: "SIENTIAPDE-1182-ajustar-laborious-para-pegar-timestamp-da-resposta-do-mlflow"
- name: PYTHON_APP - name: PYTHON_APP
value: "laborious.worker.worker" value: "laborious.worker.worker"