:jARdZddlZddlZddlZddlmZddlmZddlmZm Z ddl m Z m Z m Z mZmZddlmZddlmZdd lmZdd lmZdd lmZmZdd lmZdd lmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&e rddl'm(Z(ejRe*Z+edZ,de-fdZ.GddeZ/y)zzFabric/PyTorch Lightning logger that enables remote experiment tracking, logging, and artifact management on lightning.ai.N) Namespace)Mapping)datetimetimezone) TYPE_CHECKINGAnyOptionalUnioncast)RequirementCache)Tensor)Module)override)Loggerrank_zero_experiment)get_filesystem) _add_prefix)rank_zero_only)_PATH)ModelCheckpoint)_scan_checkpoints) Experimentzlitlogger>=0.1.0returncddlm}|S)zceZdZdZdZ d3deedeedeedeeeefde d e d e d eed dfd Z e e d efdZ e e d eefdZe e d efdZe e d efdZe d efdZe d eefdZe d efdZeded efdZd efdZedeedeed eefdZe ed edfdZd4dZe ed efdZe ed5deeefdeed dfd Z e e d5d!e!eee"fe#fdeeee"fd dfd"Z$e ed5d#e%d$ee&d dfd%Z'e ed6d&Z(e ed5d'eed dfd(Z)ed!e!eee"fe#fd dfd)Z*e d7d#e"d*eed+e deedeeee"fd df d,Z+e d8ded+e deed dfd-Z,ed9ded+e d efd.Z-eded dfd/Z.d0e/d dfd1Z0d0e/d dfd2Z1y): LitLoggerzaLogger that enables remote experiment tracking, logging, and artifact management on lightning.ai.-Nroot_dirname teamspacemetadata store_step log_model save_logscheckpoint_namerc Xtj|xsd|_|xs t|_d|_||_d|_d|_t|j|_ d|_ d|_ |xsi|_ d|_||_||_d|_i|_||_y)aInitialize the LightningLogger. Args: root_dir: Folder where logs and metadata are stored (default: ./lightning_logs). name: Name of your experiment (defaults to a generated name). teamspace: Teamspace name where charts and artifacts will appear. metadata: Extra metadata to associate with the experiment as tags. log_model: If True, automatically log model checkpoints as artifacts. save_logs: If True, capture and upload terminal logs. checkpoint_name: Override the base name for logged checkpoints. Example:: from lightning.pytorch import Trainer from lightning.pytorch.demos.boring_classes import BoringModel, BoringDataModule from lightning.pytorch.loggers.litlogger import LitLogger class LoggingModel(BoringModel): def training_step(self, batch, batch_idx: int): loss = self.step(batch) # logging the computed loss self.log("train_loss", loss) return {"loss": loss} trainer = Trainer( max_epochs=10, enable_model_summary=False, logger=LitLogger("./lightning_logs", name="boring_model") ) model = BoringModel() data_module = BoringDataModule() trainer.fit(model, data_module) trainer.test(model, data_module) z./lightning_logsNF)osfspath _root_dirr_name_version _teamspace_sub_dir_prefixr_fs _experiment_step _metadata _is_ready _log_model _save_logs_checkpoint_callback_logged_model_time_checkpoint_name) selfr$r%r&r'r(r)r*r+s r__init__zLitLogger.__init__:s\8#A/AB646 '+ #  !$..115 !R##?C!46 /r c|jS)z Gets the name of the experiment.)r2rAs rr%zLitLogger.name}szzr c|jS)z2Get the experiment version - its time of creation.)r3rDs rversionzLitLogger.versions}}r c|jS)zBGets the save directory where the litlogger experiments are saved.)r1rDs rr$zLitLogger.root_dirs~~r cntjj|j|j}t |j tr*tjj||j }tjj|}tjj|S)zThe directory for this run's tensorboard checkpoint. By default, it is named ``'version_${self.version}'`` but it can be overridden by passing a string value for the constructor's version parameter instead of ``None`` or an int. ) r/pathjoinr$r% isinstancesub_dirstr expandvars expanduser)rAlog_dirs rrPzLitLogger.log_dirso'',,t}}dii8 dllC (ggll7DLL9G''$$W-ww!!'**r c|jSN)rPrDs rsave_dirzLitLogger.save_dirs ||r c|jS)zCGets the sub directory where the TensorBoard experiments are saved.)r5rDs rrLzLitLogger.sub_dirs}}r ch|j |jS|jd|jS)Nr#)rFr%rDs r_experiment_namezLitLogger._experiment_names/ << 99 ))Adll^,,r rIc tjj|}||j ds|ntjj |}|j ddS#t$rd}YSwxYw)Nz..\/)r/rIrelpath ValueError startswithbasenamereplace)rIrelkeys r_default_artifact_keyzLitLogger._default_artifact_keysh ''//$'C_S^^D-AcrwwGWGWX\G]{{4%% C sA'' A54A5c|jSrR)rVrDs r _model_keyzLitLogger._model_keys$$$r rFstepc0||S||dk\r t|Sy)Nr)rM)rFrds r_model_versionzLitLogger._model_versions&  N   t9 r rc ddl}|j |jS|jsd|_tjdk(sJd|j r'|j j|j d|jZtjtjjd}|jdd jd d |_|j!|j"|j$|j&j)Dcic]\}}|t+|c}}dd|j,|j. |_|jj1|jScc}}w) z3Returns the underlying litlogger Experiment object.rNTz+tried to init log dirs in non global_rank=0)exist_ok milliseconds)timespec:r#z+00:00Z)r%r&r'r(store_created_atrPr*) litloggerr8r;rrankr$r7makedirsrFrnowrutc isoformatr^r3rrVr4r:itemsrMrPr= print_url)rArn timestampkvs r experimentzLitLogger.experiments=     '## #~~!DN""a'V)VV' == HH  dmmd  ; <<  X\\2<? ?r c6|jjSrR)r|urlrDs rr~z LitLogger.urls'')---r metricsc tjdk(sJd|j}||jdzn||_t ||j |j }|jDcic](\}}|t|tr|jn|*}}}|jD]%\}}||j||j'ycc}}w)Nrz-experiment tried to log from global_rank != 0)rd) rror|r9rr6LOGGER_JOIN_CHARrtrKr itemappend)rArrdryrwrxr`values r log_metricszLitLogger.log_metricss""a'X)XX'--/ '+|TZZ!^ gt||T5J5JKKR==?[41a1*Q"7affhQ>[[!--/ ;JC sO " "5tzz " : ;\s7-C paramsct|tr |j}|j}|j D]\}}t |||<yzLog hyperparams.NrKr__dict__r|rtrM)rArrryr`rs rlog_hyperparamszLitLogger.log_hyperparamssK fi (__F--/  ,,. )JC!%jJsO )r model input_arrayc<tjdtdy)Nz&LitLogger does not support `log_graph`) stacklevel)warningswarn UserWarning)rArrs r log_graphzLitLogger.log_graphs  > XYZr cyrRrDs rsavezLitLogger.saves r statusc|jC|jr|j|j|jj|yyrR)r8r>_scan_and_log_checkpointsfinalize)rArs rrzLitLogger.finalizesF    '((..t/H/HI    % %f - (r ct|tr |j}|j}|j D]\}}t |||<yrr)rArryr`rs r log_metadatazLitLogger.log_metadata&sK fi (__F--/  ,,. )JC!%jJsO )r staging_dirverbosec ddlm}|||j||jt t t ttf|||j|j<y)aSave and upload a model object to cloud storage. Args: model: The model object to save and upload (e.g., torch.nn.Module). staging_dir: Optional local directory for staging the model before upload. verbose: Whether to show progress bar during upload. version: Optional version string for the model. metadata: Optional metadata dictionary to store with the model. rModel)rFr'rN) rnrrfr9r r dictrMr|rc)rArrrrFr'rs rr)zLitLogger.log_model2sW& $8= ''<(4S>2H=# 9   "4??#45r cddlm}|||j||j|j |j <y)aVUpload a model file or directory to cloud storage using litmodels. Args: path: Path to the local model file or directory to upload. verbose: Whether to show progress bar during upload. Defaults to False. version: Optional version string for the model. Defaults to the experiment version. rrrFN)rnrrfr9r|rc)rArIrrFrs rlog_model_artifactzLitLogger.log_model_artifactNs= $8=dDL_L_`gimisisLt8u  "4??#45r ctt|j|j|}|j |S)a?Download a file artifact from the cloud for this experiment. Args: path: Path where the file should be saved locally. verbose: Whether to print a confirmation message after download. Defaults to True. Returns: str: The local path where the file was saved. )r rr|rar)rArIrfiles rget_filezLitLogger.get_fileas7C113D4N4Nt4TUVyyr c`ddlm}|||j|j|<y)aMLog a file as an artifact to the Lightning platform. The file will be logged in the Teamspace drive, under a folder identified by the experiment name. Args: path: Path to the file to log. Example:: logger = LitLogger(...) logger.log_file('config.yaml') r)FileN)rnrr|ra)rArIrs rlog_filezLitLogger.log_fileps) #GKDz  "4#=#=d#CDr checkpoint_callbackcr|jdury|jdk(r|j|y||_y)z`Called after a checkpoint is saved. Logs checkpoints as artifacts if enabled. FNr.)r< save_top_krr>)rArs rafter_save_checkpointzLitLogger.after_save_checkpoints8 ??e #   ) )R /  * *+> ?(;D %r ct||j}|D]p\}}}}|j}|jxs |j}t |dd} ddlm} | ||jd| ||<||j|<ry)zGFind new checkpoints from the callback and log them as model artifacts._last_global_step_savedNrrr) rr?r|r@r%getattrrnrrf) rAr checkpointsrv path_ckpt_score_tagrycheckpoint_keycheckpoint_steprs rrz#LitLogger._scan_and_log_checkpointss'(;T=T=TU 2= ; .Iy&$113J!22EjooN%&9;TVZ[O ').y$BUBUVZ\kBl)mJ~ &1:D # #I . ;r )NNNNTFTN)rrrR)rN)NFNN)FN)T)2__name__ __module__ __qualname____doc__rr rrMrboolrBpropertyrr%rFr$rPrSrLrV staticmethodrarcintrfrryr|rr~rfloatrr rrrrr rrrrr)rrrrrrrr rr"r"5sk%)"#'-1)-=05/=0sm=0C= =0 4S>* =0  =0=0=0"#=0 =0F c # #  + + +##-#-- &C&C&&%C% Xc]xPS} H\2  @ .S.. ;73:#6 ;hsm ;W[ ; ;-1 )d38ni/0 )$sCx.) )  ) )[v[HV4D[PT[[  .x}... )d38ni/0 )  ) )&*!%-1   c]   #  4S>*     6!% vvv# v  vv$ S 4 3  RSRTRR, < rs #'<<=&H>9?27A$g!'(:;o;o;r