Protocol Documentation

The number of heads Default: 1

The dimension of heads 

The number of interacting layers Default: 1

 Default: 0.0001

a few sub DAGs 

a few blocks generating a DAG 

the names of output blocks 

optional top mlp layer 

the input names of feature groups or other blocks 

 Default: -1

sequential layers 

package name 

a few blocks generating a DAG 

the names of output blocks 

 Default: false

 Default: true

 Default: 1

 Default: 1

default output the list of multiple outputs 

The number of heads of cross modal fusion layer Default: 1

The number of heads of image feature learning layer Default: 1

The number of heads of text feature learning layer Default: 1

The dimension of text heads 

The dimension of image heads Default: 64

The number of patches of image feature, take effect when there is only one image feature Default: 1

Do dimension reduce to this size for image feature before single modal learning module Default: 0

The number of self attention layers for image features Default: 0

The number of self attention layers for text features Default: 1

The number of cross modal layers Default: 1

The dimension of image cross modal heads 

The dimension of text cross modal heads 

Dropout probability for hidden layers Default: 0

Dropout probability of the attention probabilities Default: 0

Whether to add embeddings for different text sequence features Default: false

Whether to add position embeddings for the position of each token in the text sequence Default: true

Maximum sequence length that might ever be used with this model Default: 0

Dropout probability for text sequence embeddings Default: 0.1

dnn layers for other features 

 Default: 0.0001

 Default: true

support gfile.Glob 

in json format: {"0":{"cursor": ""}, "1":{"cursor":""}} 

offset_time could be two formats:
1: %Y%m%d %H:%M:%S  "20220508 12:00:00"
2: %s               "1651982400" 

in json format: {'0':10, '1':20} 

offset_time could be two formats:
1: %Y%m%d %H:%M:%S  '20220508 12:00:00'
2: %s               '1651982400' 

kafka global config, such as: fetch.max.bytes=1024 

kafka topic config, such as: max.partition.fetch.bytes=1024 

mini batch size to use for training and evaluation. Default: 32

set auto_expand_input_fields to true to
auto_expand field[1-21] to field1, field2, ..., field21 Default: false

label fields, normally only one field is used.
For multiple target models such as MMOE
multiple label_fields will be set. 

label separator 

label dimensions which need to be set when there
are labels have dimension > 1 

whether to shuffle data Default: true

shufffle buffer for better performance, even shuffle buffer is set,
it is suggested to do full data shuffle before training
especially when the performance of models is not good. Default: 32

The number of times a data source is read. If set to zero, the data source
will be reused indefinitely. Default: 0

Number of decoded batches to prefetch. Default: 32

shard dataset to 1/num_workers in distribute mode
this param is not used anymore Default: false

shard by file, not by sample, valid only for CSVInput Default: false

separator of column features, only used for CSVInput*
not used in OdpsInput*
binary separators are supported:
  CTRL+A could be set as '\001'
  CTRL+B could be set as '\002'
  CTRL+C could be set as '\003'
for RTPInput and OdpsRTPInput it is usually set
to '\002' Default: ,

parallel preproces of raw data, avoid using too small
or too large numbers(suggested be to small than
number of the cores) Default: 8

only used for OdpsInput/OdpsInputV2/OdpsRTPInput, comma separated
for RTPInput, selected_cols use indices as column names
 such as '1,2,4', where 1,2 are label columns, and
 4 is the feature column, column 0,3 are not used, 

selected col types, only used for OdpsInput/OdpsInputV2
to avoid error setting of data types 

the input fields must be the same number and in the
same order as data in csv files or odps tables 

for RTPInput only Default: ;

ignore some data errors
it is not suggested to set this parameter Default: false

whether to use pai global shuffle queue, only for OdpsInput,
OdpsInputV2, OdpsRTPInputV2 Default: false

 Default: 100

if true, one worker will duplicate the data of the chief node
and undertake the gradient computation of the chief node Default: false

input field for sample weight 

the compression type of tfrecord 

n data for one feature in tfrecord 

for csv files, may optionally with an header
in that case, input_name must match header name,
and the number and the order of input_fields
may not be the same as that in csv files. Default: false

 Default: 4096

 Default: false

 Default: STRING

 Default: 1

 Default: 1

user-defined function for label. eg: tf.math.log1p, remap_lbl 

user-defined function path. eg: /samples/demo_script/process_lbl.py 

output field type of user-defined function. 

ignore value 

user data path
userid weight 

item data path
itemid weight attrs 

hard negative edge path
userid itemid weight 

number of negative sample 

max number of hard negative sample 

field names of attrs in train data or eval data 

field name of item_id in train data or eval data 

field name of user_id in train data or eval data 

 Default: :

 Default: 0

only works on DataScience/Local Default: 

user data path
userid weight 

item data path
itemid weight attrs 

positive edge path
userid itemid weight 

hard negative edge path
userid itemid weight 

number of negative sample 

max number of hard negative sample 

field names of attrs in train data or eval data 

field name of item_id in train data or eval data 

field name of user_id in train data or eval data 

 Default: :

 Default: 0

only works on DataScience/Local Default: 

sample data path
itemid weight attrs 

number of negative sample 

field names of attrs in train data or eval data 

field name of item_id in train data or eval data 

 Default: :

 Default: 0

only works on DataScience/Local Default: 

sample data path
itemid weight attrs 

number of negative sample 

field names of attrs in train data or eval data 

field name of item_id in train data or eval data 

 Default: :

 Default: 0

only works on DataScience/Local Default: 

user data path
userid weight 

item data path
itemid weight attrs 

positive edge path
userid itemid weight 

number of negative sample 

field names of attrs in train data or eval data 

field name of item_id in train data or eval data 

field name of user_id in train data or eval data 

 Default: :

 Default: 0

only works on DataScience/Local Default: 

shared bottom cmbf layer 

shared bottom uniter layer 

shared bottom dnn layer 

mmoe expert dnn layer definition 

number of mmoe experts Default: 0

bayes task tower 

l2 regularization Default: 0.0001

The number of cross layers Default: 3

 Default: 0.0001

 Default: 1

deprecated Default: 0.0001

deprecated Default: 0.0001

 Default: 0.0001

options are: dot and cat Default: dot

whether a feature will interact with itself Default: false

whether to include dense features after interaction Default: false

 Default: 1e-05

hidden units for each layer 

ratio of dropout 

activation function Default: tf.nn.relu

use batch normalization Default: true

hidden units for each layer 

ratio of dropout 

activation function Default: relu

use batch normalization Default: true

 Default: true

 Default: relu

 Default: false

kernel_initializer Default: he_uniform

 Default: false

 Default: 0

 Default: 0

 Default: 0.5

 Default: 0.0001

 Default: COSINE

add a layer for scaling the similarity Default: true

 Default: false

just a name for backbone config 

actually input layers, each layer produce a group of feature 

implemented in easy_rec/python/model/easy_rec_estimator
add regularization to all variables with "embedding_weights:"
in name Default: 0

 Default: CLASSIFICATION

 Default: 1

filter variables matching any pattern in restore_filters
common filters are Adam, Momentum, etc. 

 Default: Fixed

default to be logits Default: true

for CROSS_ENTROPY_LOSS, soft_label must be logits instead of probs 

default to be logits Default: true

currently only support CROSS_ENTROPY_LOSS and L2_LOSS 

 Default: 1

only for loss_type == CROSS_ENTROPY_LOSS Default: 1

 Default: 0.0001

 Default: 200

 Default: 5

Number of examples to process of evaluation. Default: 0

How often to run evaluation. Default: 300

Maximum number of times to run evaluation. If set to 0, will run forever. Default: 0

Whether the TensorFlow graph used for evaluation should be saved to disk. Default: false

Type of metrics to use for evaluation.
possible values: 

Evaluation online with batch forward data of training Default: false

uid field name 

reduction method for auc of different users
* "mean": simple mean of different users
* "mean_by_sample_num": weighted mean with sample num of different users
* "mean_by_positive_num": weighted mean with positive sample num of different users Default: mean

 Default: 5

session id field name 

reduction: reduction method for auc of different sessions
* "mean": simple mean of different sessions
* "mean_by_sample_num": weighted mean with sample num of different sessions
* "mean_by_positive_num": weighted mean with positive sample num of different sessions Default: mean

batch size used for exported model, -1 indicates batch_size is None
which is only supported by classification model right now, while
other models support static batch_size Default: -1

type of exporter [final | latest | best | none] when train_and_evaluation
final: performs a single export in the end of training
latest: regularly exports the serving graph and checkpoints
best: export the best model according to best_exporter_metric
none: do not perform export Default: final

the metric used to determine the best checkpoint Default: auc

metric value the bigger the best Default: true

enable early stop Default: false

custom early stop function, format:
   early_stop_func(eval_results, early_stop_params)
return True if should stop 

custom early stop parameters 

early stop max check steps Default: 10000

each feature has a placeholder Default: true

export to keep, only for exporter_type in [best, latest] Default: 1

multi value field list 

auto analyze multi value fields Default: false

is placeholder named by input Default: false

filter out inputs, only keep effective ones Default: true

export the original feature values as string Default: false

export the outputs required by RTP Default: false

export asset files 

 Default: 0

 Default: 0

use embedding cache, only for sok hybrid embedding Default: false

for sok hybrid key value embedding Default: 8388608

 Default: 16777216

input field names: must be included in DatasetConfig.input_fields 

 Default: IdFeature

 Default: 0

 Default: 0

for categorical_column_with_identity Default: 0

only for raw features 

separator with in features Default: |

delimeter to separator key from value 

delimeter to separate sequence multi-values 

truncate sequence data to max_seq_len 

many other field share this config 

lookup max select element number, default 10 Default: 10

max_partitions Default: 1

combiner Default: sum

embedding initializer 

number of digits kept after dot in format float/double to string
scientific format is not used.
in default it is not allowed to convert float/double to string Default: -1

normalize raw feature to [0-1] Default: 0

 Default: 0

normalization function for raw features:
  such as: tf.math.log1p 

raw feature of multiple dimensions Default: 1

sequence feature combiner 

sub feature type for sequence feature Default: IdFeature

sequence length Default: 1

for expr feature 

embedding variable params 

for combo feature:
  if not set, use cross_column
  otherwise, the input features are first joined
    and then passed to categorical_column 

separator for each inputs
if not set, combo inputs will not be split 

 Default: DEEP

 Default: false

 Default: false

 Default: false

 Default: true

 Default: false

 Default: false

 Default: 0.0001

hive master's ip 

hive port Default: 10000

hive username Default: admin

hive database Default: default

 Default: 1

 Default: 1

 Default: 1

 Default: 1

 Default: 0

 Default: 1

 Default: 0

 Default: 1

 Default: 0.8

 Default: interaction

 Default: true

activation function Default: relu

use batch normalization Default: false

 Default: gelu

number of tasks 

mmoe expert mlp layer definition 

number of mmoe experts 

 Default: false

 Default: true

 Default: true

run mode: eager, lazy Default: eager

 Default: true

 Default: true

 Default: relu

 Default: 4

 Default: 2

 Default: true

 Default: true

 Default: 2

 Default: 1

 Default: 0

 Default: 0.25

 Default: 32

 Default: 1

 Default: 0

 Default: 0.5

 Default: true

 Default: fixed

 Default: 1

 Default: false

 Default: 2

 Default: 50

 Default: 1

 Default: 0.1

 Default: 2

 Default: 1

 Default: 1

 Default: 1

 Default: 1

 Default: 1

 Default: 1

 Default: 0

 Default: 1

 Default: 0

 Default: 1

 Default: 1

max number of high capsules Default: 5

max behaviour sequence length 

high capsule embedding vector dimension 

number EM iterations Default: 3

routing logits scale Default: 20

routing logits initial stddev Default: 1

squash power Default: 1

output ratio Default: 1

constant interest number
in default, use log(seq_len) Default: false

preprocessing dnn before entering capsule layer 

dnn layers applied on user_context(none sequence features) 

concat user and capsule dnn 

method to combine several user sequences
such as item_ids, category_ids Default: SUM

dnn layers applied on item features 

similarity power, the paper says that the big
the better Default: 10

 Default: COSINE

add a layer for scaling the similarity Default: true

 Default: 0.0001

 Default: false

if small than 1.0, then a loss will be added to
limit the maximal interest similarities, but
in experiments, setup such a loss leads to low hitrate. Default: 1

deprecated: original mmoe experts config 

mmoe expert dnn layer definition 

number of mmoe experts Default: 0

task tower 

l2 regularization Default: 0.0001

 Default: 5

 Default: 4

 Default: 0.0001

 Default: 0.0001

 Default: false

 Default: 0.1

 Default: 0.9

 Default: 0.999

 Default: 1e-06

 Default: 0.9

 Default: 0.999

 Default: 0.9

 Default: 0.999

 Default: 1e-06

 Default: 0.9

 Default: 0.999

 Default: 0.002

 Default: 0.002

 Default: 4000000

 Default: 0.0002

 Default: 10000

 Default: 0

 Default: 0.002

 Default: 4000000

 Default: 0.95

 Default: true

 Default: 0

 Default: 0

 Default: 0

optional float learning_rate = 1 [default=1e-4]; 

 Default: -0.5

 Default: 0.1

 Default: 0

 Default: 0

 Default: 0

 Default: 0.9

 Default: 0.999

 Default: 0.002

Whether to linearly interpolate learning rates for steps in
[0, schedule[0].step]. Default: false

 Default: 0.002

 Default: 0.9

 Default: 1e-06

 Default: 0.9

 Default: false

 Default: 0.9999

 Default: 0

 Default: 0.9

 Default: 0.9

 Default: 1

 Default: 1

encode user info 

encode target item info 

encode u2i seq info 

produce trigger score 

encode trigger item seqs to target item co-occurance info 

produce sim score 

direct net user_dnn 

direct net item_dnn 

for direct net, similar to DSSM Default: COSINE

for direct net Default: true

bias net dnn 

 Default: 1e-06

train config, including optimizer, weight decay, num_steps and so on 

for compatibility 

recommendation model config 

Json file[RTP FG] to define input data and features:
* In easy_rec.python.utils.fg_util.load_fg_json_to_config:
  data_config and feature_config will be generated
  based on fg_json.
* After generation, a prefix '!' is added:
  fg_json_path = '!' + fg_json_path
  indicates config update is already done, and should not
  be updated anymore. In this way, we make load_fg_json_to_config
  function reentrant.
This step is done before edit_config_json to take effect. 

number of experts per task 

number of experts for share
For the last extraction_network, no need to configure this 

dnn network of experts per task 

dnn network of experts for share
For the last extraction_network, no need to configure this 

extraction network 

task tower 

l2 regularization Default: 0.0001

debug mode 

user features 

item ids 

context features for each item 

results 

item features 

generate features 

context features 

all fg input features 

tf output tensors 

 Default: 0.0001

 Default: false

COSINE = 0; EUCLID = 1; Default: COSINE

Size of the encoder layers and the pooler layer 

Number of hidden layers in the Transformer encoder 

Number of attention heads for each attention layer in the Transformer encoder 

The size of the "intermediate" (i.e. feed-forward) layer in the Transformer encoder 

The non-linear activation function (function or string) in the encoder and pooler.

"gelu", "relu", "tanh" and "swish" are supported. Default: gelu

The dropout probability for all fully connected layers in the embeddings, encoder, and pooler Default: 0.1

The dropout ratio for the attention probabilities Default: 0.1

The maximum sequence length that this model might ever be used with Default: 512

Whether to add position embeddings for the position of each token in the text sequence Default: true

The stddev of the truncated_normal_initializer for initializing all weight matrices Default: 0.02

Whether to output all token embedding, if set to false, then only output the first token embedding Default: true

The position of target item (i.e. head, tail, ignore) Default: head

Whether to preserve a position for target Default: true

din attention layer 

whether to keep target item feature Default: true

option: softmax, sigmoid Default: softmax

Percentage length of mask original sequence Default: 0.6

Percentage left of crop original sequence Default: 0.2

Percentage length of reorder original sequence Default: 0.6