o -wip- @sdZddlZddlZddlmZddlmZddlmZddl Z ddl m Z zddl mZWney?ZzededZ[wwzdd lmZWneyYZzed edZ[wwz&ddlZe  d(d ed ejfddZe  d(d ed ejfddZWneye dYnwz,ddlZddlmZe  d(d ed ejfddZe  d(d ed ejfddZWneye dYnwz&ddlm Z e  d(d ed e fddZe  d(d ed e fddZWneye dYnwGdddZ!e  d)d ed ee"e#e$ee%e&e'ffddZe d(d ed efddZe d(d efddZed efdd Z(ed ed ee"e#e$ee%e&e'ffd!d Z(e d(d ed efd"dZed(d efd#dZd$d%Z)  d*d&d'Z*dS)+aW&B Integration for Metaflow. This integration lets users apply decorators to Metaflow flows and steps to automatically log parameters and artifacts to W&B by type dispatch. - Decorating a step will enable or disable logging for certain types within that step - Decorating the flow is equivalent to decorating all steps with a default - Decorating a step after decorating the flow will overwrite the flow decoration Examples can be found at wandb/wandb/functional_tests/metaflow Nwraps)Path)Union) telemetry)currentzrError: `metaflow` not installed >> This integration requires metaflow! To fix, please `pip install -Uqq metaflow`)dispatchzError: `plum-dispatch` not installed >> This integration requires plum-dispatch! To fix, please `pip install -Uqq plum-dispatch`FnamedatacOH|r|rdSdS|r"||dtd|dt|ddSdSNdatasets:latestUsing artifact:  () use_artifactwandbtermlogtyper r r runtestingargskwargsr`/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/wandb/integration/metaflow/metaflow.py _wandb_use(   rc Os|r|rdSdS|rFtj|dd}||dd}|j|ddWdn1s,wY||td|d t|d dSdS) Nz pd.DataFramedatasetrz.parquetwbpyarrow)engineLogging artifact: rr)rArtifactnew_file to_parquet log_artifactrr) r r r rrrrartifactfrrr wandb_track9s   r,zR`pandas` not installed >> @wandb_log(datasets=True) may not auto log your dataset!cOr Nmodelsrrrrrr r r.rrrrrrrrVrc O|r|rdSdS|rEtj|dd}||dd}t||Wdn1s+wY||td|dt|ddSdS) Nz nn.Modulemodelr!.pklr"r%rr)rr&r'torchsaver)rr r r r.rrrrr*r+rrrr,g   zO`pytorch` not installed >> @wandb_log(models=True) may not auto log your model!) BaseEstimatorcOr r-rr/rrrrrc Or0) Nr7r1r!r2r"r%rrrr&r'pickledumpr)rrr5rrrr,r6zO`sklearn` not installed >> @wandb_log(models=True) may not auto log your model!c@s$eZdZddZddZddZdS) ArtifactProxyc s4|jiittfddtjDddS)Ncsi|]}|t|qSr)getattr).0pflowrr sz*ArtifactProxy.__init__..)r@inputsoutputsbaseparams)__dict__updatesetdirrparameter_names)selfr@rr?r__init__s zArtifactProxy.__init__cCs||j|<t|j||SN)rCsetattrr@)rKkeyvalrrr __setattr__s zArtifactProxy.__setattr__cCs2||jvr||jvrt|j||j|<t|j|SrM)rDrCr<r@rB)rKrOrrr __getattr__s zArtifactProxy.__getattr__N)__name__ __module__ __qualname__rLrQrRrrrrr;s r;cOs|rdS|||idS)Nscalar)log)r r rrrrrrrr,s cOsv|r|rdSdS|r9tj|dd}|r||n |r$||||td|dt|ddSdS)Nrr r!r%rr) rr&is_diradd_diris_fileadd_filer)rr)r r r rrrrr*rrrr,s     c Or0) Ngenericotherr!r2r"r%rrr8) r r othersrrrrr*r+rrrr,s   c OsNz t||g|Ri|WStjy&td|dt|dYdSw)NzThis artifact (z, z) does not exist in the wandb datastore!If you created an instance inline (e.g. sklearn.ensemble.RandomForestClassifier), then you can safely ignore thisOtherwise you may want to check your internet connection!)rr CommErrortermwarnrr r rrrrr wandb_uses rbcOsdSrMrrarrrrbscOr r rrrrrrs   cOr )Nr^rrrrr)r r r^rrrrrrrrs   cGstdd|DdS)Ncss|] }|dur|VqdSrMr)r=arrr szcoalesce..)next)argrrrcoalescesrgcs0t|fdd|durS|S)aAutomatically log parameters and artifacts to W&B by type dispatch. This decorator can be applied to a flow, step, or both. - Decorating a step will enable or disable logging for certain types within that step - Decorating the flow is equivalent to decorating all steps with a default - Decorating a step after decorating the flow will overwrite the flow decoration Args: func: (`Callable`). The method or class being decorated (if decorating a step or flow respectively). datasets: (`bool`). If `True`, log datasets. Datasets can be a `pd.DataFrame` or `pathlib.Path`. The default value is `False`, so datasets are not logged. models: (`bool`). If `True`, log models. Models can be a `nn.Module` or `sklearn.base.BaseEstimator`. The default value is `False`, so models are not logged. others: (`bool`). If `True`, log anything pickle-able. The default value is `False`, so files are not logged. settings: (`wandb.sdk.wandb_settings.Settings`). Custom settings passed to `wandb.init`. The default value is `None`, and is the same as passing `wandb.Settings()`. If `settings.run_group` is `None`, it will be set to `{flow_name}/{run_id}. If `settings.run_job_type` is `None`, it will be set to `{run_job_type}/{step_name}` cstr&}|jD]}tt||r#t|ds#t||t||q |Stdr-Stdfdd }|_d|_ |S)N _base_funcsettingsc s*t|tjjjs t}|t|jtj dtj t|j tj dtj |da}tj|d }d|j_Wdn1s@wYt|}|j|j|g|Ri||jD]\}}t|||dq`|jD]\}}t|||dqtWddS1swYdS)N/) run_group run_job_typeri)rT)r r.r^r) isinstancersdkwandb_settingsSettingsupdate_from_dictrgrlr flow_namerun_idrm step_nameinit wb_telemetrycontextfeaturemetaflowr;configrGrErBitemsrbrCr,) rKrjrrrtelproxyr r )r funcr.r^rrwrapperHsH    "z-wandb_log..decorator..wrapper)r r.r^rj) inspectisclassrFcallabler<hasattrrNrrh_kwargs)rclsattrrr decoratorr.r^rj)rrr9s&    (zwandb_log..decoratorNr)rr r.r^rjrrr wandb_log"s Br)FNF)NF)NFFFN)+__doc__rr9 functoolsrpathlibrtypingrr wandb.sdk.librrwrzr ImportErrore Exceptionplumrpandaspdstr DataFramerr,r`r3torch.nnnnModule sklearn.baser7r;dictlistrHintfloatboolrbrgrrrrrs*