o W h6 @sdZddlmZmZmZmZmZz0ddlZer"dej ddvs"Jeddus*Jddl a e t d s5Jdd l m Z ed ZWn eefyMda Ynwd ed efddZddZddZddZddZt rreeeedZdSiZdS)az MLflow Logging for Ultralytics YOLO. This module enables MLflow logging for Ultralytics YOLO. It logs metrics, parameters, and model artifacts. For setting up, a tracking URI should be specified. The logging can be customized using environment variables. Commands: 1. To set a project name: `export MLFLOW_EXPERIMENT_NAME=` or use the project= argument 2. To set a run name: `export MLFLOW_RUN=` or use the name= argument 3. To start a local MLflow server: mlflow server --backend-store-uri runs/mlflow It will by default start a local server at http://127.0.0.1:5000. To specify a different URI, set the MLFLOW_TRACKING_URI environment variable. 4. To kill all running MLflow server instances: ps aux | grep 'mlflow' | grep -v 'grep' | awk '{print $2}' | xargs kill -9 )LOGGERRUNS_DIRSETTINGS TESTS_RUNNINGcolorstrN test_mlflowPYTEST_CURRENT_TESTmlflowT __version__)PathzMLflow: xreturncCsdd|DS)zQSanitize dictionary keys by removing parentheses and converting values to floats.cSs*i|]\}}|ddddt|qS)(r ))replacefloat).0kvrV/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/utils/callbacks/mlflow.py ,s*z!sanitize_dict..)items)r rrr sanitize_dict*src Cs:tjdp ttd}ttd|t |tjdp&|j j p&d}tjdp0|j j }t |tz=tpDtj|d}ttd|jjd |t|rettd |d ttd tt|j WdSty}zttd |ttdWYd}~dSd}~ww)a Log training parameters to MLflow at the end of the pretraining routine. This function sets up MLflow logging based on environment variables and trainer arguments. It sets the tracking URI, experiment name, and run name, then starts the MLflow run if not already active. It finally logs the parameters from the trainer. Args: trainer (ultralytics.engine.trainer.BaseTrainer): The training object with arguments and parameters to log. Global: mlflow: The imported mlflow module to use for logging. Environment Variables: MLFLOW_TRACKING_URI: The URI for MLflow tracking. If not set, defaults to 'runs/mlflow'. MLFLOW_EXPERIMENT_NAME: The name of the MLflow experiment. If not set, defaults to trainer.args.project. MLFLOW_RUN: The name of the MLflow run. If not set, defaults to trainer.args.name. MLFLOW_KEEP_RUN_ACTIVE: Boolean indicating whether to keep the MLflow run active after the end of training. MLFLOW_TRACKING_URIr z tracking uri: MLFLOW_EXPERIMENT_NAMEz/Shared/Ultralytics MLFLOW_RUN)run_namezlogging run_id(z) to zFview at http://127.0.0.1:5000 with 'mlflow server --backend-store-uri ')disable with 'yolo settings mlflow=False'zFailed to initialize: zNot tracking this runN)osenvirongetstrrrdebugPREFIXr set_tracking_uriargsprojectnameset_experimentautolog active_run start_runinforun_idr is_dir log_paramsdict Exceptionwarning)traineruriexperiment_namerr-errron_pretrain_routine_end/s&   r:cCs:trtjit|jt|j|jdd|jddSdS)z>Log training metrics at the end of each train epoch to MLflow.train)prefixmetricsstepN)r log_metricsrlrlabel_loss_itemstlossepochr6rrron_train_epoch_end[s rFcCs"trtjt|j|jddSdS)zrDrErrron_fit_epoch_endgsrGcCstsdStt|jj|jdD]}|jdvr!tt|qtj dd dk}|r8t tdn tt tdt td td td dS) z/Log model artifacts at the end of the training.N*>.pt.csv.jpg.png.yamlMLFLOW_KEEP_RUN_ACTIVEFalsetruezCmlflow run still alive, remember to close it using mlflow.end_run()zmlflow run endedzresults logged to  r )r log_artifactr$bestparentsave_dirglobsuffixr!r"r#lowerrr/r&end_runr%get_tracking_uri)r6fkeep_run_activerrr on_train_endms r])r:rFrGr])__doc__ultralytics.utilsrrrrrr!r"r#r hasattrpathlibr r& ImportErrorAssertionErrorr3rr:rFrGr] callbacksrrrrs8  ,