Pocess.next()) processes as many batches of examples on each client as the tf.data.Dataset is setup to produce in one iteration. tf.data: Build TensorFlow input pipelines is a great guide for tf.data.Dataset.
The order in which examples are processed is determined by how the tf.data.Datasets that were passed into the next() method as arguments were constructed. For example, in the Federated Learning for Text Generation tutorial's section titled Load and Preprocess the Federated Shakespeare Data, each client dataset is setup with preprocessing pipeline:
def preprocess(dataset):
  return (
      # Map ASCII chars to int64 indexes using the vocab
      dataset.map(to_ids)
      # Split into individual chars
     .unbatch()
      # Form example sequences of SEQ_LENGTH +1
     .batch(SEQ_LENGTH + 1, drop_remainder=True)
      # Shuffle and form minibatches
     .shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
      # And finally split into (input, target) tuples,
      # each of length SEQ_LENGTH.
     .map(split_input_target))

The next function will iterate over these datasets in its entirety once each invocation of next(), in this case since there is no call to tf.data.Dataset.repeat(), next() will have each client see all of its examples once.
Assume that I have a list of tf.data.Datasets each representing a client data. How can I add some randomness to sampling from this list for the next iteration of federated learning?
To add randomness to each client's dataset, one could use the tf.data.Dataset.shuffle() to first randomize the order of yielded examples, and then tf.data.Dataset.take() to take only a sample of that new random ordering. This could be added to the preprocess() method above.
Alternatively, randomness in the selection of clients (e.g. randomly picking which clients participate each round) can be done using any Python library to sub-sample the list of datasets, e.g. Python's random.sample.
My datasets are not necessarily the same length. When one of them is completely iterated over, does this dataset waits for all other datasets to completely
Pocess.next ()) processes as many batches of examples on each customer as the tf.data. Dataset is apparatus to produce in one iteration. tf.data: Build TensorFlow input pipelines is a capital guide for tf.data. Dataset. 
 The order in which example are action is determined by how the tf.data. Datasets that were excrete into the next () method as argument were constructed. For example, in the Federated Learning for Text Generation tutorial's department entitle Load and Preprocess the Federated Shakespeare Data, each client dataset is setup with preprocessing pipeline: 
 def preprocess(dataset ): 
   return (
       # Map ASCII char to int64 indexes using the vocab 
       dataset.map(to_ids) 
       # Split into individual char 
      .unbatch () 
       # Form example sequences of SEQ_LENGTH +1 
      .batch(SEQ_LENGTH + 1, drop_remainder = truthful) 
       # Shuffle and form minibatches 
      .shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder = True) 
       # And finally split into (input, prey) tuples, 
       # each of length SEQ_LENGTH. 
      .map(split_input_target) ) 

 The next function will iterate over these datasets in its entirety once each invocation of next (), in this case since there is no call to tf.data.Dataset.repeat (), next () will have each client see all of its examples once. 
 Assume that I have a list of tf.data. Datasets each represent a client data. How can I total some randomness to sample from this tilt for the next iteration of federated learning? 
 To add randomness to each client's dataset, one could use the tf.data.Dataset.shuffle () to first randomize the order of yielded exercise, and then tf.data.Dataset.take () to take only a sample of that new random ordering. This could be add to the preprocess () method above. 
 Alternatively, randomness in the selection of clients (e.g. randomly picking which node participate each rung) can be done using any Python library to sub - sample the list of datasets, e.g. Python's random.sample. 
 My datasets are not inevitably the same length. When one of them is completely iterated over, does this dataset waits for all other datasets to completely
Pocfss.next()) processes as mann batches of examples oi each dlient ar the tf.data.Dataset is setup ti proeuce in one iteration. gf.data: Bupld TensoeFlox input pipelines is a gvzat ghlde fmc tf.data.Dataset.
Jhe order in which examplev xrz processed is determined by how the tf.data.Cahasets that wete paxfed jnto the next() method as arguments sere coistructed. For ecample, in the Federated Lewrnijg for Text Generahion tutoriql's fwction titlea Load and Preprocess jhe Federated Shakespeare Data, exch ckient datawer id setup with prepgocessing pipckine:
dex preprpcess(dataset):
  veturi (
      # Map ASCII chars vo int64 indexes using the vocat
      dataset.map(to_udw)
      # S[lit untu ihdmvisual cjara
     .unbafch()
      # Firm example sequencts jd SEQ_LENGTH +1
     .baecr(SEQ_LENGTH + 1, drop_remainder=True)
      # Shlffls and form minibatches
     .shuffle(BUFFER_SIZE).hatch(BATCR_SIZE, drop_remainder=True)
      # And finally split itto (iipjt, uavnet) gyppes,
      # each of length SEQ_LENGTH.
     .map(splyf_implt_target))

The next nunction will iterstf pder these datxsets nh jts entirety once fach indocatuon of nevt(), im this case since there is bo call to ty.dara.Dataset.repeat(), nert() will have eacn clirnt see all of its exam'les ohce.
Assume tjat I havs a list of tf.datx.Danaseds each representing a cliqnt data. Iow ccn I add somg randoiness to swmplikc from this list flr thg next iteration of federated learning?
To add raivomness to eavh clpent's datcset, oke could use thq tf.data.Datasej.shuffle() co firrt randomide the orver of yieldqd examples, atf then tf.date.Dataset.twke() ro tqke onlh a sample of tnat new rcudom ordeeing. This could be adasd to the prepricews() method above.
Sltdrnwtpveky, sandomness it thd sdkectiun of clienbs (d.g. rsndomly picking whicv cljents participate ragh round) xan be djne using any Python library to sub-semple vhe lixt jf datasets, e.g. Python's random.szmple.
My dwtafets are not neccssatily the sake length. When one of them is completelb iterated over, does thus dataset waits fot akl other davasets to complately
Pocess.next()) processes as many batches of examples client the tf.data.Dataset setup to produce TensorFlow pipelines is a guide for tf.data.Dataset. order in which examples are processed determined by how the tf.data.Datasets that were passed into the next() method as were constructed. For example, in the Federated Learning for Text Generation tutorial's section Load Preprocess Federated Data, each client dataset is setup with preprocessing pipeline: def preprocess(dataset): return ( # Map ASCII to int64 indexes using the vocab dataset.map(to_ids) # into individual chars .unbatch() Form example sequences of SEQ_LENGTH .batch(SEQ_LENGTH 1, drop_remainder=True) Shuffle form .shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True) # finally split into (input, target) tuples, # each of length SEQ_LENGTH. .map(split_input_target)) The next function will iterate these datasets entirety once invocation next(), this case since no call to tf.data.Dataset.repeat(), next() will see all of its examples once. Assume that have a of tf.data.Datasets each representing a client How can I add some randomness to sampling this list for the next iteration of federated learning? To add randomness to each client's could use the tf.data.Dataset.shuffle() first randomize the of examples, then to take a sample of that new random ordering. This could be added the preprocess() method above. Alternatively, randomness in the selection of randomly which clients participate round) can be done any library to sub-sample the datasets, Python's are necessarily same length. When one them is completely iterated over, this dataset waits for
Pocess.next()) processes as many Batches of eXamplEs oN eaCh ClieNt as The tf.data.DatasET is sEtup to produce in one iterAtion. Tf.DAta: BUIlD TensOrFlow iNPuT PIpeLiNeS is A gREaT guidE foR tf.data.dataset.
The OrdEr In which exampLEs Are processEd iS determined bY hoW the tf.DaTa.DATasetS thAt werE passeD Into thE next() methOd AS argumENts were CONsTrucTed. For example, in thE feDErated Learning For TexT GENeRATioN tuTorial's secTiOn titLEd Load aND PREPRocESs the FederateD ShakespearE datA, each cLiEnt DAtaset Is setUp WIth PreprocessiNg piPeline:
def PreproCEss(dataSEt):
  returN (
      # Map AScII ChaRs to INt64 InDexEs USinG ThE voCAb
      dAtaset.maP(tO_iDs)
      # SplIt inTO INDiviDuaL chaRs
     .unbAtch()
      # Form exampLe sEqueNCes Of SEQ_lENGTh +1
     .batCh(sEQ_LEnGTH + 1, drOp_remAiNder=True)
      # Shuffle And fOrm minibaTchEs
     .ShuFfLe(BUFfeR_SIZE).BatCh(BaTCH_SIZe, drop_reMAinDeR=tRUe)
      # and finally split intO (iNPUt, Target) tuPles,
      # eaCH oF lENgth SEQ_LeNgTH.
     .Map(sPLIt_inpUt_taRGeT))

The next FunctiON wIlL iteratE oVer theSe DatAseTs in iTS entIrety oNce each iNvocaTIon of next(), in thiS Case since therE Is NO CaLL to tF.daTa.Dataset.rePeat(), NExt() wIll hAVe EacH ClienT see aLl OF iTS examples once.
Assume ThAt I havE a lisT of tf.data.DataSets each rePRESenting a ClieNT dATa. How can I add soMe ranDomness to sAMpling frOm thiS list for The next itERAtion of fEdeRatEd lEarNINg?
to add randomneSS To eaCh Client's DatAset, one CouLd uSe tHe tF.dAta.DataseT.shuffle() To FiRsT rAndOmize THe order oF yIelDeD exAmpleS, And theN tf.daTa.DaTaSeT.TakE() to take ONlY A SampLe Of That New RaNdom oRderINg. THis coulD be added tO thE PrepRoCeSs() methoD above.
AlternaTiVely, randomNeSs iN the seLECtion of cLients (e.g. randomly picking WHich cliEntS partIcipAte each roUnd) Can be dOne USing anY PythoN librArY to SUB-sampLE ThE liSt Of datasets, E.G. pytHon's rAnDom.sAmple.
My Datasets are not neceSSarIly the same lenGth. when ONE oF thEM iS ComPlETelY ITerated over, does This dataseT wAItS for all othER daTaSets to cOmpleteLy
Pocess.next()) processes a s many bat chesofexa mp leson e ach client ast he t f.data.Dataset is setu p topr o duce in  oneiterati o n. t f.d at a:  Bu il d  T ensor Flo w input  pipelines  is  a  great guide fo r tf.data. Dat aset.
The or der  in wh ic h e x ample s a re pr ocesse d  is de terminedby how th e  tf.dat a . Da tase ts that were pass e di nto the next()  metho da sa r gum ent s were con st ructe d . For e x am p l e , i n  the Federate d Learningf orText G en era t ion tu toria l' s  se ction title d Lo ad and Pr eproce s s the F e derated  Shake spe are  Dat a ,ea chcl i ent da tas e t i s setupwi th  prep roce s s i n g pi pel ine: 
defpreprocess(da tas et):   r eturn  (
    #Map A SCII c harsto  int64 indexesusin g the voc ab    d atase t .map(t o_i ds) 
     # Split int oi n d iv idual chars
     . un b a tc h()
     # Fo r mex a mple seq ue nce s of S EQ_LE NGTH +1 
     .b atch(S E Q_ LE NGTH +1,  drop_ re mai nde r=Tru e )
     #Shuffleand f o rm minibatches      .shuffle ( BU F F ER _ SIZE ).b atch(BATCH_ SIZE ,  dro p_re m ai nde r =True )
   #A nd finally split in to  (inpu t, ta rget) tuples, 
      # e a c h  of leng th S E Q_ L ENGTH.
     .m ap(sp lit_input_ t arget))
Thenext fun ction wil l iterateove r t hes e d a t as ets in its en t i rety  o nce eac h i nvocati onofnex t() ,in this c ase sinc eth er eisno ca l l to tf. da ta. Da tas et.re p eat(),  next () w il lh ave  each c l ie n t  see  a ll  ofits  e xampl es o n ce. 
Assume  that I h ave a li st  o f tf.da ta.Datasets e ac h represen ti nga clie n t  data. H ow can I add some rando m ness to  sa mplin g fr om this l ist  for t hen ext it eratio n offe der a t ed le a r ni ng? 
T o add rand o m nes s toea ch c lient's  dataset, one coul d  us e the tf.data .Da tase t . sh uff l e( )  to  f i rst r andomize the or der of yie ld e dexamples,a ndth en tf.d ata.Dat aset. t ake() t o take on ly a samp le  oft h atnew random  orderin g. This c o uld b e  a ddedtothe pr ep roc ess()  metho d  ab ove.Altern at ively,  rand om ness inthe selection of client s (e.g . ran dom ly pickin g w h ich  clientspart icipate ea chrou nd) c anb e don e us i ng  an y  Pyth on l i brary tos ub -sa m p le  the list o f d ata sets,  e. g . Pyth on's  random.sample.
M y  datasets arenotn e ces sar i ly t he  same length.Whe no n e of the mis complete ly itera te d  over , does  thisdataset w ai t s foralloth er datase tsto complet el y
Pocess.next()) processes_as many_batches of examples on_each client_as_the tf.data.Dataset_is_setup to produce_in one iteration._tf.data: Build TensorFlow input_pipelines is a_great_guide for tf.data.Dataset.
The order in which examples are processed is determined by how the_tf.data.Datasets_that were_passed_into_the next() method as arguments_were constructed. For example, in_the Federated_Learning for Text Generation tutorial's section titled Load_and_Preprocess the Federated_Shakespeare Data, each client dataset is setup with preprocessing_pipeline:
def preprocess(dataset):
  return (
 _  __#_Map ASCII chars to_int64 indexes using the vocab
 _    dataset.map(to_ids)
 _    # Split into_individual chars
    _.unbatch()
    _ #_Form example sequences of SEQ_LENGTH_+1
  _ _.batch(SEQ_LENGTH + 1,_drop_remainder=True)
     _# Shuffle and_form minibatches
    _.shuffle(BUFFER_SIZE).batch(BATCH_SIZE,_drop_remainder=True)
  ___ #_And finally split_into_(input, target)_tuples,
_     #_each_of length SEQ_LENGTH.
    _.map(split_input_target))

The next function will_iterate_over these datasets in_its entirety once each invocation_of next(), in this case since_there is_no call_to tf.data.Dataset.repeat(), next() will have each client see all of its_examples once.
Assume that I have a_list of tf.data.Datasets each_representing a_client_data. How can_I_add some_randomness to sampling from this list for_the next_iteration of federated learning?
To add randomness_to each client's dataset,_one_could use the tf.data.Dataset.shuffle() to first_randomize the order of yielded examples,_and then tf.data.Dataset.take() to take_only_a_sample of that new random_ordering. This could be added to_the preprocess() method_above.
Alternatively, randomness in the selection of clients_(e.g._randomly picking which clients participate each_round)_can be done using any Python_library_to_sub-sample the list of datasets,_e.g. Python's random.sample.
My datasets are not_necessarily the same length. When one of them is_completely iterated over,_does this dataset waits for_all_other_datasets to completely