Package: pyraisdk
pyraisdkOverviewAML models are meant to be deployed to GPU instances to provide inference service. If the code that operates the model uses the GPU for inferencing in each request separately,
the overall performance of the model will be quite inefficent. This SDK has APIs that can allocate batches of inference requests to run on the GPU in a separate thread, thereby considerably
improving the usage efficiency of GPU and making the model more performant.The SDK also collects telemetry data for each so the performance of the model can be evaluated and tracked and provides logging primitives that can be used
to produce additional troubleshooting information.How to find Logs and MetricsFor AML models deployed with CD pipeline, Logs generated by pyraisdk will be found inAuxUnstructuredEventTableand Metrics inAuxStructuredEventTable. More details please refer toraidri.From remote model dashboard, we can see below data from pyraisdk and debug model side metrics.From there you can explore Queries and either play with the matviews more or else look at the definition of the matviews and thus see how those queries are done.Using pyraisdkIntegrating with ModelFollow the steps below to integrate pyraisdk.In environment configuration, list latestpyraisdk versionas a dependency.Import resources from pyraisdkfrom pyraisdk import rlog
from pyraisdk.dynbatch import BaseModel, DynamicBatchModelImplementBaseModelclass, which includes methodspreprocess()andpredict(), to define how data must be preprocessed and how inferences are made.At process start (e.g. ininit()function if deploying with scoring script), initialize logging, instantiate object of class that implementsBaseModel, and define global variable for batched model as shown below.rlog.initialize()

global batched_model
malware_model = MalwareModel()
batched_model = DynamicBatchModel(malware_model)In request handler (e.g. inrun()function if deploying with scoring script), pass list of requests to the batched model'spredict()method and return results. Under-the-hood, pyraisdk creates batches and uses your model'spreprocess()andpredict()methods to generate results.return batched_model.predict(json.loads(request_data)['data'])Optionally log additional structured and unstructured events usingEventLoggermethods. It is not necessary to log latency information, as pyraisdk does so automatically.Configuring CD PipelineIn addition to following the CD pipeline'sdeveloper guidelinesto onboard your model to RAI's deployment infrastructure, you must configureBatchingConfigfor each deployment target to which your model will be deployed. To do so,Open the appropriate configuration file underdeployment-target-configs/../*.jsonConfigure the model'sVersion,InstanceType/ SKU, andBatchingConfigas shown below{"Name":"MalwareNeural","Version":19,"InstanceType":"Standard_NC6s_v3","BatchingConfig":{"MaxBatchSize":12,"IdleBatchSize":5,"MaxBatchInterval":0.002}}When the CD pipeline deploys the model to an AML online deployment, it exports environment variables for eachBatchingConfigfield, which pyraisdk utilizes at process start to configure batching parameters. The CD pipeline also exports environment variables to enable pyraisdk to log to RAI's Azure Data Explorer clusters.Test or Deploy without RAI CD PipelineTest or Deploy independetly outside of RAI CD Pipeline, it's required to set environment variables forBatchingConfigmanually before object ofDynamicBatchModelis created. Refer toBatching Parameter.The recommended way to set these variables is callingos.environ.setdefaultininit(). Like below:definit():os.environ.setdefault('PYRAISDK_MAX_BATCH_SIZE','12')os.environ.setdefault('PYRAISDK_IDLE_BATCH_SIZE','5')os.environ.setdefault('PYRAISDK_MAX_BATCH_INTERVAL','0.002')...batch_model=DynamicBatchModel(malware_model)This should work well independent of RAI CD Pipeline. And you don't need to specifically remove these 3 lines when switching to deploy in CD Pipeline.But please note that this kind of code should beAvoided:os.environ['PYRAISDK_MAX_BATCH_SIZE'] = '12'. It will override the configuration from CD pipeline, which is not desirable in most cases.To enable log publishing (to eventhub), there are another several environment variables need to be set, refer to following logging part. It's optional.Dynamic Batching SupportThere are APIs youmustimplement in your model to support batching of inference requests for best model performance. Those APIs allow the SDK
to distribute load efficiently to the GPU instances. The APIs are:preprocessModifies the input to the model, if necessary. For example, if your model needs the input in a special JSON format instead of as a list
of strings, you can do that modification in thepreprocessmethod.predictExecutes the model inference for a list of input stringsBatching Parameter (Attention)Batching parameters are mandatory and should be ready before DynamicBatchModel is created. They are set through environment variables:PYRAISDK_MAX_BATCH_SIZE(int): Max size of each processing batch.PYRAISDK_IDLE_BATCH_SIZE(int): If there's no more data in queue, a new batch will be launched when size reaches this value.PYRAISDK_MAX_BATCH_INTERVAL(float): Max interval in seconds to wait for items. When waiting time exceeds, will launch a batch immediately.Usage ExamplesBuildYourModelclass inherited frompyraisdk.dynbatch.BaseModel.fromtypingimportListfrompyraisdk.dynbatchimportBaseModelclassYourModel(BaseModel):defpredict(self,items:List[str])->List[int]:rs=[]foriteminitems:rs.append(len(item))returnrsdefpreprocess(self,items:List[str])->List[str]:rs=[]foriteminitems:rs.append(f'[{item}]')returnrsInitialize apyraisdk.dynbatch.DynamicBatchModelwithYourModelinstance, and callpredict / predict_onefor inferencing.frompyraisdk.dynbatchimportDynamicBatchModel# prepare modelsimple_model=YourModel()batch_model=DynamicBatchModel(simple_model)# predictitems=['abc','123456','xyzcccffaffaaa']predictions=batch_model.predict(items)assertpredictions==[5,8,16]# predict_oneitem='abc'prediction=batch_model.predict_one(item)assertprediction==5Concurrent requests topredict / predict_one, in different threads.fromthreadingimportThreadfrompyraisdk.dynbatchimportDynamicBatchModel# prepare modelsimple_model=YourModel()batch_model=DynamicBatchModel(simple_model)# thread run functiondefrun(name,num):forstepinrange(num):item=f'{name}-{step}'prediction=batch_model.predict_one(item)assertprediction==len(item)+2# start concurrent inferencethreads=[Thread(target=run,args=(f'{tid}',100))fortidinrange(20)]fortinthreads:t.start()fortinthreads:t.join()Loging & EventsDescriptionThis module is for logging and event tracing.interfacedefinitialize(eh_hostname:Optional[str]=None,client_id:Optional[str]=None,eh_conn_str:Optional[str]=None,eh_structured:Optional[str]=None,eh_unstructured:Optional[str]=None,role:Optional[str]=None,instance:Optional[str]=None,sys_metrics_enable:bool=True,)Parameter description forinitialize:eh_hostname: Fully Qualified Namespace aka EH Endpoint URL (*.servicebus.windows.net). Default, read${EVENTHUB_NAMESPACE}.servicebus.windows.netclient_id: client_id of service principal. Default, read $UAI_CLIENT_IDeh_conn_str: connection string of eventhub namespace. Default, read $EVENTHUB_CONN_STRINGeh_structured: structured eventhub name. Default, read $EVENTHUB_AUX_STRUCTUREDeh_unstructured: unstructured eventhub name. Default, read $EVENTHUB_AUX_UNSTRUCTUREDrole: role, Default:RemoteModelinstance: instance, Default:${MODEL_NAME}|${ENDPOINT_VERSION}|{hostname}or${MODEL_NAME}|${ENDPOINT_VERSION}|{_probably_unique_id()}sys_metrics_enable: Whether to enable auto metrics reporting periodically for system info like gpu, memory and gpu. Default: Truedefevent(self,key:str,code:str,numeric:float,detail:str='',corr_id:str='',elem:int=-1)definfof(self,format:str,*args:Any)definfocf(self,corr_id:str,elem:int,format:str,*args:Any)defwarnf(self,format:str,*args:Any)defwarncf(self,corr_id:str,elem:int,format:str,*args:Any)deferrorf(self,format:str,*args:Any)deferrorcf(self,corr_id:str,elem:int,ex:Optional[Exception],format:str,*args:Any)deffatalf(self,format:str,*args:Any)deffatalcf(self,corr_id:str,elem:int,ex:Optional[Exception],format:str,*args:Any)examples# export EVENTHUB_AUX_UNSTRUCTURED='ehunstruct'# export EVENTHUB_AUX_STRUCTURED='ehstruct'# export UAI_CLIENT_ID='xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx'# export EVENTHUB_NAMESPACE='raieusdev-eh-namespace'frompyraisdkimportrlogrlog.initialize()rlog.infof('this is a info message%s',123)rlog.event('LifetimeEvent','STOP_GRACEFUL_SIGNAL',0,'detail info')# export EVENTHUB_AUX_UNSTRUCTURED='ehunstruct'# export EVENTHUB_AUX_STRUCTURED='ehstruct'# export EVENTHUB_CONN_STRING='<connection string>'frompyraisdkimportrlogrlog.initialize()rlog.infocf('corrid',-1,'this is a info message:%s',123)rlog.event('RequestDuration','200',0.01,'this is duration in seconds')frompyraisdkimportrlogrlog.initialize(eh_structured='ehstruct',eh_unstructured='ehunstruct',eh_conn_str='<eventhub-conn-str>')rlog.errorcf('corrid',-1,Exception('error msg'),'error message:%s%s',1,2)rlog.event('CpuUsage','',0.314,detail='cpu usage',corr_id='corrid',elem=-1)