Configuration files

An NLP pipeline config is a JSON file that contains one required element chainer:

{
  "chainer": {
    "in": ["x"],
    "in_y": ["y"],
    "pipe": [
      ...
    ],
    "out": ["y_predicted"]
  }
}

Chainer is a core concept of DeepPavlov library: chainer builds a pipeline from heterogeneous components (Rule-Based/ML/DL) and allows to train or infer from pipeline as a whole. Each component in the pipeline specifies its inputs and outputs as arrays of names, for example: "in": ["tokens", "features"] and "out": ["token_embeddings", "features_embeddings"] and you can chain outputs of one components with inputs of other components:

{
  "class_name": "deeppavlov.models.preprocessors.str_lower:StrLower",
  "in": ["x"],
  "out": ["x_lower"]
},
{
  "class_name": "nltk_tokenizer",
  "in": ["x_lower"],
  "out": ["x_tokens"]
},

Each Component in the pipeline must implement method __call__() and has class_name parameter, which is its registered codename, or full name of any python class in the form of "module_name:ClassName". It can also have any other parameters which repeat its __init__() method arguments. Default values of __init__() arguments will be overridden with the config values during the initialization of a class instance.

You can reuse components in the pipeline to process different parts of data with the help of id and ref parameters:

{
  "class_name": "nltk_tokenizer",
  "id": "tokenizer",
  "in": ["x_lower"],
  "out": ["x_tokens"]
},
{
  "ref": "tokenizer",
  "in": ["y"],
  "out": ["y_tokens"]
},

Variables

As of version 0.1.0 every string value in a configuration file is interpreted as a format string where fields are evaluated from metadata.variables element:

{
  "chainer": {
    "in": ["x"],
    "pipe": [
      {
        "class_name": "my_component",
        "in": ["x"],
        "out": ["x"],
        "load_path": "{MY_PATH}/file.obj"
      },
      {
        "in": ["x"],
        "out": ["y_predicted"],
        "config_path": "{CONFIGS_PATH}/classifiers/intents_snips.json"
      }
    ],
    "out": ["y_predicted"]
  },
  "metadata": {
    "variables": {
      "MY_PATH": "/some/path",
      "CONFIGS_PATH": "{DEEPPAVLOV_PATH}/configs"
    }
  }
}

Variable DEEPPAVLOV_PATH is always preset to be a path to the deeppavlov python module.

One can override configuration variables using environment variables with prefix DP_. So environment variable DP_VARIABLE_NAME will override VARIABLE_NAME inside a configuration file.

For example, adding DP_ROOT_PATH=/my_path/to/large_hard_drive will make most configs use this path for downloading and reading embeddings/models/datasets.

Training

There are two abstract classes for trainable components: Estimator and NNModel.

Estimator are fit once on any data with no batching or early stopping, so it can be safely done at the time of pipeline initialization. fit() method has to be implemented for each Estimator. One example is Vocab.

NNModel requires more complex training. It can only be trained in a supervised mode (as opposed to Estimator which can be trained in both supervised and unsupervised settings). This process takes multiple epochs with periodic validation and logging. train_on_batch() method has to be implemented for each NNModel.

Training is triggered by train_evaluate_model_from_config() function.

Train config

Estimator s that are trained should also have fit_on parameter which contains a list of input parameter names. An NNModel should have the in_y parameter which contains a list of ground truth answer names. For example:

[
  {
    "id": "classes_vocab",
    "class_name": "default_vocab",
    "fit_on": ["y"],
    "level": "token",
    "save_path": "vocabs/classes.dict",
    "load_path": "vocabs/classes.dict"
  },
  {
    "in": ["x"],
    "in_y": ["y"],
    "out": ["y_predicted"],
    "class_name": "intent_model",
    "save_path": "classifiers/intent_cnn",
    "load_path": "classifiers/intent_cnn",
    "classes_vocab": {
      "ref": "classes_vocab"
    }
  }
]

The config for training the pipeline should have three additional elements: dataset_reader, dataset_iterator and train:

{
  "dataset_reader": {
    "class_name": ...,
    ...
  },
  "dataset_iterator": {
    "class_name": ...,
    ...
  },
  "chainer": {
    ...
  },
  "train": {
    ...
  }
}

Simplified version of training pipeline contains two elements: dataset and train. The dataset element currently can be used for train from classification data in csv and json formats. You can find complete examples of how to use simplified training pipeline in intents_sample_csv.json and intents_sample_json.json config files.

Train Parameters

  • epochs — maximum number of epochs to train NNModel, defaults to -1 (infinite)

  • batch_size,

  • metric_optimization — maximize or minimize a metric, defaults to maximize

  • validation_patience — how many times in a row the validation metric has to not improve for early stopping, defaults to 5

  • val_every_n_epochs — how often to validate the pipe, defaults to -1 (never)

  • log_every_n_batches, log_every_n_epochs — how often to calculate metrics for train data, defaults to -1 (never)

  • validate_best, test_best flags to infer the best saved model on valid and test data, defaults to true

  • tensorboard_log_dir — path to write logged metrics during training. Use tensorboard to visualize metrics plots.

  • metrics — list of metrics to evaluate the model.

Metrics

"train": {
  "metrics": [
    "f1",
    {
      "name": "accuracy",
      "inputs": ["y", "y_labels"]
    },
    {
      "name": "roc_auc",
      "inputs": ["y", "y_probabilities"]
    }
  ],
  ...
}
The first metric in the list is used for early stopping.

Each metric can be described as a JSON object with name and inputs properties, where name is a registered name of a metric function and inputs is a list of parameter names from chainer’s inner memory that will be passed to the metric function.

If a metric is described as a single string, this string is interpreted as a registered name.

Default value for inputs parameter is a concatenation of chainer’s in_y and out parameters.

DatasetReader

DatasetReader class reads data and returns it in a specified format. A concrete DatasetReader class should be inherited from this base class and registered with a codename:

from deeppavlov.core.common.registry import register
from deeppavlov.core.data.dataset_reader import DatasetReader

@register('dstc2_datasetreader')
class DSTC2DatasetReader(DatasetReader):

DataLearningIterator and DataFittingIterator

DataLearningIterator forms the sets of data (‘train’, ‘valid’, ‘test’) needed for training/inference and divides them into batches. A concrete DataLearningIterator class should be registered and can be inherited from deeppavlov.data.data_learning_iterator.DataLearningIterator class. This is a base class and can be used as a DataLearningIterator as well.

DataFittingIterator iterates over provided dataset without train/valid/test splitting and is useful for Estimator s that do not require training.

Inference

All components inherited from Component abstract class can be used for inference. The __call__() method should return standard output of a component. For example, a tokenizer should return tokens, a NER recognizer should return recognized entities, a bot should return an utterance. A particular format of returned data should be defined in __call__().

Inference is triggered by interact_model() function. There is no need in a separate JSON for inference.