Ultralytics
integrations/mlflow/ #13161
giscus[bot]
bot
Replies: 4 comments 24 replies
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how to set mlflow to true using CLI? |
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even tho --backend-store-uri runs the logged experiments correctly, the "runs/mlflow.db" database folder has empty experiments and runs, this means it an Ultralytics logging issue not an mlflow issue, please advise |
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@pderranger, how do I get yolov8 to automatically log to sqlite database? |
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Hello @MoAbbasid, For logging validation runs in MLflow, you can manually log the metrics after each validation epoch. You can modify the validation loop to include MLflow logging statements similar to how you might have done it for training. Here's a basic example: import mlflow
from ultralytics import YOLO
model = YOLO('yolov8n-seg.pt')
results = model.val(data='coco128.yaml')
# Assuming 'results' contains metrics like validation loss
mlflow.log_metrics({'val_loss': results.metrics['val_loss']})Regarding your issue with setting up a SQLite database for MLflow, ensure that your MLflow server is correctly configured to use the SQLite backend. You might need to specify the SQLite URI when starting the MLflow server: mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifactsMake sure the path to the SQLite database ( If you need further assistance, feel free to ask! |
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@pderranger, I initiated the server using your code: "mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts", and I have set mlflow=True in yolo settings but after I train the results are still logged to directory runs/mlflow and not the sqlite database, |
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Hello @MoAbbasid, It seems like there might be a misunderstanding in setting the MLflow tracking URI. The
If you continue to face issues, please ensure that the path to the SQLite database is correct and accessible, and that there are no permission issues. Also, verify that the MLflow version you are using supports the features and configurations you are trying to use. Let me know if this helps or if you need further assistance! |
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@pderrenger Ive set the tracking uri correctly to point to the sqlite database but the db itself created after the Yolo training run is empty and has experiments or run, this is automatically generated by Ultralytics right? so this is an issue on Ultralytics side? |
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Hi @MoAbbasid, Thanks for reaching out! The creation and management of the SQLite database for MLflow is generally handled by MLflow itself rather than directly by Ultralytics. If the database is empty after a training run, it's possible that the MLflow tracking URI might not be set correctly, or the logging calls are not being executed as expected. Here are a few things you can check:
If these steps don't resolve the issue, it might be helpful to check the MLflow logs for any errors or warnings that could provide more insight. Let us know how it goes or if you need further assistance! |
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Hey I'm facing a strange situation between yolov8 and mlflow. import pandas as pd
from ultralytics import YOLO, settings
import os
import json
import mlflow
settings.reset()
settings.update({
'mlflow': True,
'clearml': False,
'comet': False,
'dvc': False,
'hub': False,
'neptune': False,
'raytune': False,
'tensorboard': False,
'wandb': False
})
mlflow.set_experiment("Teste 01")
mlflow.set_tracking_uri("postgresql://user:senha@localhost:5432/postgres")
model_name = "yolov8n-cls.pt"
model = YOLO(model_name)
epochs = 3
image_size = 128
with mlflow.start_run(run_name=f'{model_name}-{epochs}-{image_size}'):
model.train(name=f'{model_name}-{epochs}-{image_size}', data='dataset', epochs=epochs, imgsz=image_size, hsv_h=1, hsv_s=1, hsv_v=1)The reason I maintained some mlflow configs out of with statement is because I would like to preserve all parameters, metrics and artifacts generated automatically by yolo-mlflow settings. And the error I found was during the db creation: the experiment_id generated by yolo is bigger than serial4 type, which is the type used as postgres-mlflow default (a bigint type would solve I suppose). So, due to that, I'm facing the following error: I was wondering if I could just define the experiment_id as a serial4, maintaining it in MLFlow patterns (numerical order e.g 1, 2, 3, 4...). I thought about fix it manually by changing the type directly on my database, but I believe that there's a smarter way to fix it. Does anyone know how to solve it? |
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@vini-c-costa it seems the issue is related to the experiment_id exceeding the integer range in PostgreSQL. You might consider altering the database schema to use a |
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For multiple GPU training a YOLO model, with autolog enabled and also I manually logged a few items by using mlflow.log_xxx() and I also logged the customized PyFunc model, it turns out to be multiple runs are generated per training. I want to only have 1 run and it better have all the logged artifacts.
Thanks |
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I have tested, in multi-GPU settings,
I have included in the In a single GPU setting, these all work. I suspect the credential env vars are not passed correctly to worker GPUs or threads. Could you verify? |
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2nd Issue: What is the best practice if I have two nodes and each node has two GPUs? for each node, it has GPU 0 and GPU 1 as local rank.
What is the right way to do that? |
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@BlitzBricksterYY-db to avoid overwriting issues during multi-GPU and multi-node training with MLflow autologging, the best practice is usually to log system metrics from only the primary GPU (rank 0) on each node. This ensures unique entries and avoids duplication or conflicts. Configure logging in your script such that only rank 0 GPUs per node execute the logging logic. Let us know if further clarification is needed! |
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For system metrics like memory and network bandwidth usage, it is automatically done in the .train() function. I have the need to monitor each GPU on each node, i.e., [[0,1],[0,1]], what is the best way to achieve that? Automatically logging system metrics will overwritten each other cause only record GPU_0 and GPU_1, no matter it is node_0 or node_1. I know for now single node with multiGPU is a clear solution as it is [0,1,2,3], logged perfectly by your auto logging within .train() |
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@BlitzBricksterYY-db to monitor system metrics for each GPU on each node in a multi-node, multi-GPU setup, the best approach is to disable automatic MLflow autologging for system metrics and implement custom logging. You can use GPU-specific identifiers (e.g., node rank and GPU local rank) to avoid overwriting and ensure distinct logs for each GPU. For assistance with custom implementations, refer to the MLflow documentation. Let us know if further clarification is needed! |
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integrations/mlflow/
Uncover the utility of MLflow for effective experiment logging in your Ultralytics YOLO projects.
https://docs.ultralytics.com/integrations/mlflow/
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