o -wi@s6dZddlZddlZddlZddlZddlZddlmZmZm Z m Z m Z m Z m Z mZddlmZddlZddlmZmZddlmZddlmZddlmZdd lmZdd lmZdd lm Z dd l!m"Z"m#Z#m$Z$dd l%m&Z&erxddl'm(Z(ddhZ)dZ*ede+fddZ,GdddedZ-GdddeZ.dS)zPublic API: runs.N) TYPE_CHECKINGAny CollectionDictListLiteralMappingOptional)gql)envutil)public)Attrs)Api)normalize_exceptions)SizedPaginator)RETRY_TIMEDELTA)ipython json_utilrunid) LogicalPath)RetryingClient_wandb wandb_versionaJfragment RunFragment on Run { id tags name displayName sweepName state config group jobType commit readOnly createdAt heartbeatAt description notes systemMetrics summaryMetrics historyLineCount user { name username } historyKeys }returncCs:d}t|}||}ddd|didigDvS)zReturns True if the server allows us to query the internalId field for a project. 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N2Tr&rentityprojectfiltersorderper_pageinclude_sweepsc stdt|rdnddtd|_||_||_d|_|pi|_||_i|_ ||_ |j|j|jt |jd}t |||dS)Na#graphql query Runs($project: String!, $entity: String!, $cursor: String, $perPage: Int = 50, $order: String, $filters: JSONString) { project(name: $project, entityName: $entity) { internalId runCount(filters: $filters) readOnly runs(filters: $filters, after: $cursor, first: $perPage, order: $order) { edges { node { raX ...RunFragment } cursor } pageInfo { endCursor hasNextPage } } } } )r.r-r0r/)r r* RUN_FRAGMENTQUERYr-r._project_internal_idr/r0_sweeps_include_sweepsjsondumpssuper__init__) selfr&r-r.r/r0r1r2 variables __class__rr r=^s(    z Runs.__init__cCs|js||jddS)Nr.runCount) last_response _load_pager>rrr _lengthsz Runs._lengthcCs |jr|jddddSdS)Nr.runspageInfo hasNextPageTrCrErrr moresz Runs.morecCs$|jr|jdddddSdS)Nr.rGedgescursorrJrErrr rNsz Runs.cursorc Csg}|jdus|jddurtd|j|jdddD]K}t|j|j|j|dd|d|jd}| ||jrk|j rk|j |j vrN|j |j }nt j j|j|j|j|j dd }||j |j <|durhq ||_q |S) Nr.zCould not find project {}rGrLnoder)r2FwithRuns)rCr% ValueErrorformatr.Runr&r-r9append sweep_namer8r Sweepsweep)r>objs run_responserunrXrrr convert_objectss:     zRuns.convert_objects_stepdefaultsampleskeysx_axisrSr_pandaspolarsstream)r_systemcCs|dvr td|dg}|dkr5|D]}|j|||d|d}|s#q|D]} |j| d<q%||q|S|dkr}tjdd d } |D]}|j|||d|d}|sQqB| j|} |j| d<|| qB|sh| S| |} | j d d d | t | j } | S|d krtjd dd } |D]%}|j|||d|d}|sq| |} | | |jd} || q|s| S| j |dd} | t | j } | SdS)alReturn sampled history metrics for all runs that fit the filters conditions. Args: samples : (int, optional) The number of samples to return per run keys : (list[str], optional) Only return metrics for specific keys x_axis : (str, optional) Use this metric as the xAxis defaults to _step format : (Literal, optional) Format to return data in, options are "default", "pandas", "polars" stream : (Literal, optional) "default" for metrics, "system" for machine metrics Returns: pandas.DataFrame: If format="pandas", returns a `pandas.DataFrame` of history metrics. polars.DataFrame: If format="polars", returns a `polars.DataFrame` of history metrics. list of dicts: If format="default", returns a list of dicts containing history metrics with a run_id key. rczInvalid format: z.. 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Attributes: tags ([str]): a list of tags associated with the run url (str): the url of this run id (str): unique identifier for the run (defaults to eight characters) name (str): the name of the run state (str): one of: running, finished, crashed, killed, preempting, preempted config (dict): a dict of hyperparameters associated with the run created_at (str): ISO timestamp when the run was started system_metrics (dict): the latest system metrics recorded for the run summary (dict): A mutable dict-like property that holds the current summary. Calling update will persist any changes. project (str): the project associated with the run entity (str): the name of the entity associated with the run project_internal_id (int): the internal id of the project user (str): the name of the user who created the run path (str): Unique identifier [entity]/[project]/[run_id] notes (str): Notes about the run read_only (boolean): Whether the run is editable history_keys (str): Keys of the history metrics that have been logged with `wandb.log({key: value})` metadata (str): Metadata about the run from wandb-metadata.json NTr&rr-r.rhattrsr2cs|pi}tt|||_||_||_i|_tt |_ ||_ d|_ ||_tjj|j g|jR|_zt|jWn tyGYnwd|_d|_|dd|_d|_|j| ddS)zInitialize a Run object. Run is always initialized by calling api.runs() where api is an instance of wandb.Api. Nstatez not found)force)r<r=dictr&_entityr._filesr get_dirtempfile gettempdir _base_dirrorXr9ospathjoindirmakedirsOSError_summary _metadatar%_state!server_provides_internal_id_fieldload)r>r&r-r.rhrr2_attrsr@rr r=Fs* z Run.__init__cC|jSN)rrErrr rjz Run.statecCrr)rrErrr r-nrz Run.entitycCstd|jS)Nz:Run.username is deprecated. Please use Run.entity instead.)wandbtermwarnrrErrr usernamers z Run.usernamecC |jdS)Nrorr%rErrr storage_idws zRun.storage_idcCrNrrrErrr ro~ zRun.idcCs|j}||d<|Srr)r>new_idrrrr roscCrN displayNamerrErrr rrzRun.namecCs||jd<|Srr)r>new_namerrr rs runningr)rpendingc Cs|pt}|p|jdpd}td}||||d}|jj||d}|dd}t|j|ddd |dd |d |d d d d gd d |d S)z#Create a run for the given project.r. uncategorizeda mutation UpsertBucket($project: String, $entity: String, $name: String!, $state: String) { upsertBucket(input: {modelName: $project, entityName: $entity, name: $name, state: $state}) { bucket { project { name entity { name } } id name } inserted } } )r-r.rrvariable_values upsertBucketbucketr-rro{}N)roconfig systemMetricssummaryMetricstags descriptionnotesr)r generate_idsettingsr%r r&r$rT) clsapirhr.r-rmutationr?r)rrr creates6   z Run.createFc Cs |s|js]tdt|jrdnddtd}||}|dus0|ddus0|dddur7td||dd|_|jd |_ |j r]|j r]|j s]t jj|j|j|j|j d d |_ d|jvrkt|jd|_nd|_z|jd r}t|jd ni|jd <Wntjjytj|jd d d |jd <Ynw|jdrt|jdni|jd<|jdrt |j|jd|_ii}}t|jdpdD] \}}|tvr|n|}t|trd|vr|d||<q|||<q||||jd<||jd<|jS)Nz#graphql query Run($project: String!, $entity: String!, $name: String!) { project(name: $project, entityName: $entity) { run(name: $name) { rr3zj ...RunFragment } } } r4r.r[zCould not find run {}rFrPr)strictruserrrvalue rawconfig)rr r*r&r5_execr%rRrSrr9rVrXr rWr-r.rr7r:loadsdecoderJSONDecodeErrorUserritemsWANDB_INTERNAL_KEYS isinstancerupdate) r>rr(response config_user config_rawkeyrrrrr rsf         "    zRun.loadcCsLtd} ||}|ddd}|dvr ||jd<||_dStdq)Na query RunState($project: String!, $entity: String!, $name: String!) { project(name: $project, entityName: $entity) { run(name: $name) { state } } } Tr.r[r)finishedcrashedfailed)r rrrtimesleep)r>r(r)rrrr wait_until_finisheds   zRun.wait_until_finishedc CsHtdt}|j||j|j|j|j|j|j |j |j d }|j dS)z7Persist changes to the run object to the wandb backend.a mutation UpsertBucket($id: String!, $description: String, $display_name: String, $notes: String, $tags: [String!], $config: JSONString!, $groupName: String, $jobType: String) {{ upsertBucket(input: {{id: $id, description: $description, displayName: $display_name, notes: $notes, tags: $tags, config: $config, groupName: $groupName, jobType: $jobType}}) {{ bucket {{ ...RunFragment }} }} }} {} )rorrr display_namer groupNamejobTypeN)r rSr5rrrrrr json_configgroupjob_typesummaryr)r>r_rrr r$s    z Run.updatecCs<td|rdnd|r dnd}|jj||j|dddS)z,Delete the given run from the wandb backend.a mutation DeleteRun( $id: ID!, {} ) {{ deleteRun(input: {{ id: $id, {} }}) {{ clientMutationId }} }} z$deleteArtifacts: Booleanr3z!deleteArtifacts: $deleteArtifacts)rodeleteArtifactsrN)r rSr&r$r)r>delete_artifactsrrrr delete@s   z Run.deletecCs |dSr)rrErrr save^s zRun.savecCsNi}d|jvr|jddd|d<|jD] \}}|dd||<qt|S)Nr)rdesc)rrrr:r;)r>rkvrrr ras   zRun.json_configcKs,|j|j|jd}|||jj||dS)z*Execute a query against the cloud backend.)r-r.rr)r-r.rorr&r$)r>r(kwargsr?rrr rjs z Run._execr^r]cCsB|g||d}td}|j|t|gd}|ddddS)N)rar`a query RunSampledHistory($project: String!, $entity: String!, $name: String!, $specs: [JSONString!]!) { project(name: $project, entityName: $entity) { run(name: $name) { sampledHistory(specs: $specs) } } } )specsr.r[sampledHistoryr)r rr:r;)r>rarbr`specr(rrrr _sampled_historyps  zRun._sampled_historyr_cCsF|dkrdnd}td|}|j||d}dd|dd |DS) Nr_rneventsa query RunFullHistory($project: String!, $entity: String!, $name: String!, $samples: Int) {{ project(name: $project, entityName: $entity) {{ run(name: $name) {{ {}(samples: $samples) }} }} }} )r`cSsg|]}t|qSr)r:r)rlinerrr r!sz%Run._full_history..r.r[)r rSr)r>r`rfrOr(rrrr _full_historys zRun._full_historyr,cCst|j||pg|S)a$Return a file path for each file named. Args: names (list): names of the requested files, if empty returns all files per_page (int): number of results per page. Returns: A `Files` object, which is an iterator over `File` objects. r Filesr&)r>namesr1rrr filess z Run.filescCst|j||gdS)zReturn the path of a file with a given name in the artifact. Args: name (str): name of requested file. Returns: A `File` matching the name argument. rr)r>rrrr files zRun.file.cCst|j|jdtd}||jtj|}tj ||}t tj ||d}| t ||iWdn1srrootrrfrrr upload_files   zRun.upload_filecCs|durt|tstdgS|dur(t|dkr(t|dts(tdgS|r5|dkr5tdgS|r@|j|||d}n|j||d}|r]t d }|rX|j |}|St d |S) aReturn sampled history metrics for a run. This is simpler and faster if you are ok with the history records being sampled. Args: samples : (int, optional) The number of samples to return pandas : (bool, optional) Return a pandas dataframe keys : (list, optional) Only return metrics for specific keys x_axis : (str, optional) Use this metric as the xAxis defaults to _step stream : (str, optional) "default" for metrics, "system" for machine metrics Returns: pandas.DataFrame: If pandas=True returns a `pandas.DataFrame` of history metrics. list of dicts: If pandas=False returns a list of dicts of history metrics. N keys must be specified in a listr'keys argument must be a list of stringsr_z+stream must be default when specifying keys)rarbr`)r`rfrdz5Unable to load pandas, call history with pandas=False) rlistr termerrorlenrrrr rqrrrsr)r>r`rarbrdrflinesrrrr rns& "      z Run.historycCs|durt|tstdgS|dur(t|dkr(t|dts(tdgS|j}|dur1d}|dur9|d}||krA|d}|durPtj||j |||dStj ||j ||||dS)a.Returns an iterable collection of all history records for a run. Example: Export all the loss values for an example run ```python run = api.run("l2k2/examples-numpy-boston/i0wt6xua") history = run.scan_history(keys=["Loss"]) losses = [row["Loss"] for row in history] ``` Args: keys ([str], optional): only fetch these keys, and only fetch rows that have all of keys defined. page_size (int, optional): size of pages to fetch from the api. min_step (int, optional): the minimum number of pages to scan at a time. max_step (int, optional): the maximum number of pages to scan at a time. Returns: An iterable collection over history records (dict). Nr rr)r[r& page_sizemin_stepmax_step)r[r&rarrr) rrrrrrlastHistoryStepr HistoryScanr&SampledHistoryScan)r>rarrr last_steprrr scan_historys: " zRun.scan_historydr1rcCtj|j|d|dS)aFetches all artifacts logged by this run. Retrieves all output artifacts that were logged during the run. Returns a paginated result that can be iterated over or collected into a single list. Args: per_page: Number of artifacts to fetch per API request. Returns: An iterable collection of all Artifact objects logged as outputs during this run. Example: >>> import wandb >>> import tempfile >>> with tempfile.NamedTemporaryFile( ... mode="w", delete=False, suffix=".txt" ... ) as tmp: ... tmp.write("This is a test artifact") ... tmp_path = tmp.name >>> run = wandb.init(project="artifact-example") >>> artifact = wandb.Artifact("test_artifact", type="dataset") >>> artifact.add_file(tmp_path) >>> run.log_artifact(artifact) >>> run.finish() >>> api = wandb.Api() >>> finished_run = api.run(f"{run.entity}/{run.project}/{run.id}") >>> for logged_artifact in finished_run.logged_artifacts(): ... print(logged_artifact.name) test_artifact loggedmoder1r RunArtifactsr&r>r1rrr logged_artifacts"s zRun.logged_artifactscCr)aFetches artifacts explicitly used by this run. Retrieves only the input artifacts that were explicitly declared as used during the run, typically via `run.use_artifact()`. Returns a paginated result that can be iterated over or collected into a single list. Args: per_page: Number of artifacts to fetch per API request. Returns: An iterable collection of Artifact objects explicitly used as inputs in this run. Example: >>> import wandb >>> run = wandb.init(project="artifact-example") >>> run.use_artifact("test_artifact:latest") >>> run.finish() >>> api = wandb.Api() >>> finished_run = api.run(f"{run.entity}/{run.project}/{run.id}") >>> for used_artifact in finished_run.used_artifacts(): ... print(used_artifact.name) test_artifact usedr r"r$rrr used_artifactsDszRun.used_artifactscCs|t|j|jdtd}||jt|tjr,| s,|j |j|p#|j |j|jd|St|tjr:| r:t dt d)aDeclare an artifact as an input to a run. Args: artifact (`Artifact`): An artifact returned from `wandb.Api().artifact(name)` use_as (string, optional): A string identifying how the artifact is used in the script. Used to easily differentiate artifacts used in a run, when using the beta wandb launch feature's artifact swapping functionality. Returns: A `Artifact` object. rr)use_asartifact_entity_nameartifact_project_name_Only existing artifacts are accepted by this api. Manually create one with `wandb artifact put`6You must pass a wandb.Api().artifact() to use_artifact) rr-r.rrrorrArtifactis_draft use_artifactrrR)r>artifactr(rrrr r/_s$  zRun.use_artifactr0zwandb.ArtifactaliasesrcCst|j|jdtd}||jt|tjst d| r#t d|j|j ks/|j|j kr3t d|jdd}|j|j||j||d|S) aqDeclare an artifact as output of a run. Args: artifact (`Artifact`): An artifact returned from `wandb.Api().artifact(name)`. aliases (list, optional): Aliases to apply to this artifact. tags: (list, optional) Tags to apply to this artifact, if any. 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