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简体中文|English

Introducing Propeller

This doc introduct Propeller, a high level paddle API for general ML, Propeller encapsulate the following actions:：

  • training
  • evaluation
  • prediction
  • export serving

Propeller provide the following benefits:

  • You can run Propeller-based models on a local host or on a distributed multi-server environment without changing your model. Furthermore, you can run Propeller-based models on CPUs, GPUs without recoding your model.
  • Propeller simplify sharing implementations between model developers.
  • Propeller do many things for you (logging, hot-start...)
  • Propeller buids Program and PyReader or you.
  • Propeller provide a safe distributed training loop that controls how and when to:
    • build the Program
    • initialize variables
    • create checkpoint files and recover from failures
    • save visualizable results

Getting Started

    #Define model
    class BowModel(propeller.Model):
        def __init__(self, config, mode):
            self.embedding = Embedding(config['emb_size'], config['vocab_size'])
            self.fc1 = FC(config['hidden_size'])
            self.fc2 = FC(config['hidden_size'])

        def forward(self, features):
            q, t = features 
            q_emb = softsign(self.embedding(q))
            t_emb = softsign(self.embedding(t))
            q_emb = self.fc1(q_emb)
            t_emb = self.fc2(t_emn)
            prediction = dot(q_emb,  emb)
            return prediction

        def loss(self, predictions, label):
            return sigmoid_cross_entropy_with_logits(predictions, label)

        def backward(self, loss):
            opt = AdamOptimizer(1.e-3)
            opt.mimize(loss)

        def metrics(self, predictions, label):
            auc = atarshi.metrics.Auc(predictions, label)
            return {'auc': auc}

    # hyper param comes from files/command line prompt/env vir
    run_config = propeller.parse_runconfig(args)
    hparams = propeller.parse_hparam(args)
    
    # Define data
    # `FeatureColumns` helps you to organize training/evluation files.
    feature_column = propeller.data.FeatureColumns(columns=[
            propeller.data.TextColumn('query', vocab='./vocab'),
            propeller.data.TextColumn('title', vocab='./vocab'),
            propeller.data.LabelColumn('label'),
        ])
    train_ds = feature_column.build_dataset(data_dir='./data',  shuffle=True, repeat=True)
    eval_ds = feature_column.build_dataset(data_dir='./data', shuffle=False, repeat=False)

    # Start training!
    propeller.train_and_eval(BowModel, hparams, run_config, train_ds, eval_ds)

More detail see example/toy/

Main Feature

  1. train_and_eval

    according to user-specified propeller.Modelclass，initialize training model in the following 2 modes: 1. TRAIN mode 2. EVAL mode and perform train_and_eval

  2. FeatureColumns

    FeatureColumnsis used to ogranize train data. With custmizable Column property, it can adaps to many ML tasks（NLP/CV...). FeatureColumns also do the preprocessing for you (tokenization, vocab lookup, serialization, batcing etc.)

  3. Dataset

    FeatureColumns generats Dataset，or you can call propeller.Dataset.from_generator_func to build your own Dataset.

  4. Summary To trace tensor histogram in training, simply：

    propeller.summary.histogram('loss', tensor)

Contributing

  1. This project is in alpha stage, any contribution is welcomed. Fill free to create a PR.