# REST Service¶

In this section we’ll take the RNN sentiment classifer from the example Predicting sentiment on the IMDB dataset and use it to demonstrate how to easily expose your PyTorch module on the web using skorch and another library called Palladium.

With Palladium, you define the Palladium dataset, the model, and Palladium provides the framework to fit, test, and serve your model on the web. Palladium comes with its own documentation and a tutorial, which you may want to check out to learn more about what you can do with it.

The way to make the dataset and model known to Palladium is through its configuration file. Here’s the part of the configuration that defines the dataset and model:

{
'path': 'aclImdb/train/',
},

'path': 'aclImdb/test/',
},

'model': {
'__factory__': 'model.create_pipeline',
'use_cuda': True,
},

'model_persister': {
'path': 'rnn-model-{version}',
},

'scoring': 'accuracy',
}


You can save this configuration as palladium-config.py.

The dataset_loader_train and dataset_loader_test entries define where the data comes from. They refer to a Python class defined inside the model module. Let’s create a file and call it model.py, put it in the same directory as the configuration file. We’ll start off with defining the dataset loader:

import os
from urllib.request import urlretrieve
import tarfile

import numpy as np
from sklearn.datasets import load_files

DATA_URL = 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'
DATA_FN = DATA_URL.rsplit('/', 1)[1]

if not os.path.exists('aclImdb'):
# unzip data if it does not exist
if not os.path.exists(DATA_FN):
urlretrieve(DATA_URL, DATA_FN)
with tarfile.open(DATA_FN, 'r:gz') as f:
f.extractall()

def __init__(self, path='aclImdb/train/'):
self.path = path

def __call__(self):
dataset = load_files(self.path, categories=['pos', 'neg'])
X, y = dataset['data'], dataset['target']
X = np.asarray([x.decode() for x in X])  # decode from bytes
return X, y


The most interesting bit here is that our Palladium DatasetLoader defines a __call__ method that will return the data and the target (X and y). Easy. Note that in the configuration file, we refer to our DatasetLoader twice, once for the training set and once for the test set.

Our configuration also refers to a function create_pipeline which we’ll create next:

from dstoolbox.transformers import Padder2d
from dstoolbox.transformers import TextFeaturizer
from sklearn.pipeline import Pipeline
from skorch import NeuralNetClassifier
import torch

def create_pipeline(
vocab_size=1000,
max_len=50,
use_cuda=False,
**kwargs
):
return Pipeline([
('to_idx', TextFeaturizer(max_features=vocab_size)),
('net', NeuralNetClassifier(
RNNClassifier,
device=('cuda' if use_cuda else 'cpu'),
max_epochs=5,
lr=0.01,
optimizer=torch.optim.RMSprop,
module__vocab_size=vocab_size,
**kwargs,
))
])


You’ve noticed that this function’s job is to create the model and return it. Here, we’re defining a pipeline that wraps skorch’s NeuralNetClassifier, which in turn is a wrapper around our PyTorch module, as it’s defined in the predicting sentiment tutorial. We’ll also add the RNNClassifier to model.py:

from torch import nn
F = nn.functional

class RNNClassifier(nn.Module):
def __init__(
self,
embedding_dim=128,
rec_layer_type='lstm',
num_units=128,
num_layers=2,
dropout=0,
vocab_size=1000,
):
super().__init__()
self.embedding_dim = embedding_dim
self.rec_layer_type = rec_layer_type.lower()
self.num_units = num_units
self.num_layers = num_layers
self.dropout = dropout

self.emb = nn.Embedding(
vocab_size + 1, embedding_dim=self.embedding_dim)

rec_layer = {'lstm': nn.LSTM, 'gru': nn.GRU}[self.rec_layer_type]
# We have to make sure that the recurrent layer is batch_first,
# since sklearn assumes the batch dimension to be the first
self.rec = rec_layer(
self.embedding_dim, self.num_units,
num_layers=num_layers, batch_first=True,
)

self.output = nn.Linear(self.num_units, 2)

def forward(self, X):
embeddings = self.emb(X)
# from the recurrent layer, only take the activities from the
# last sequence step
if self.rec_layer_type == 'gru':
_, rec_out = self.rec(embeddings)
else:
_, (rec_out, _) = self.rec(embeddings)
rec_out = rec_out[-1]  # take output of last RNN layer
drop = F.dropout(rec_out, p=self.dropout)
# Remember that the final non-linearity should be softmax, so
# that our predict_proba method outputs actual probabilities!
out = F.softmax(self.output(drop), dim=-1)
return out


You can find the full contents of the model.py file in the skorch/examples/rnn_classifer folder of skorch’s source code.

Now with dataset and model in place, it’s time to try Palladium out. You can install Palladium and another dependency we use with pip install palladium dstoolbox.

From within the directory that contains model.py and palladium-config.py now run the following command:

PALLADIUM_CONFIG=palladium-config.py pld-fit --evaluate


You should see output similar to this:

INFO:palladium:Loading data...
epoch    train_loss    valid_acc    valid_loss     dur
-------  ------------  -----------  ------------  ------
1        0.7679       0.5008        0.7617  3.1300
2        0.6385       0.7100        0.5840  3.1247
3        0.5430       0.7438        0.5518  3.1317
4        0.4736       0.7480        0.5424  3.1373
5        0.4253       0.7448        0.5832  3.1433
INFO:palladium:Fitting model done in 29.060 sec.
DEBUG:palladium:Evaluating model on train set...
DEBUG:palladium:Evaluating model on train set done in 6.743 sec.
DEBUG:palladium:Evaluating model on test set...
DEBUG:palladium:Evaluating model on test set done in 6.476 sec.
INFO:palladium:Writing model done in 0.694 sec.
INFO:palladium:Wrote model with version 1.


Congratulations, you’ve trained your first model with Palladium! Note that in the output you see a train score (accuracy) of 0.83 and a test score of about 0.75. These refer to how well your model did on the training set (defined by dataset_loader_train in the configuration) and on the test set (dataset_loader_test).

You’re ready to now serve the model on the web. Add this piece of configuration to the palladium-config.py configuration file (and make sure it lives within the outermost brackets:

{
# ...

'predict_service': {
'mapping': [
('text', 'str'),
],
'predict_proba': True,
'unwrap_sample': True,
},

# ...
}


With this piece of information inside the configuration, we’re ready to launch the web server using:

PALLADIUM_CONFIG=palladium-config.py pld-devserver


You can now try out the web service at this address: http://localhost:5000/predict?text=this+movie+was+brilliant

You should see a JSON string returned that looks something like this:

{
"metadata": {"error_code": 0, "status": "OK"},
"result": [0.326442807912827, 0.673557221889496],
}


The result entry has the probabilities. Our model assigns 67% probability to the sentence “this movie was brilliant” to be positive. By the way, the skorch tutorial itself has tips on how to improve this model.

The take away is Palladium helps you reduce the boilerplate code that’s needed to get your machine learning project started. Palladium has routines to fit, test, and serve models so you don’t have to worry about that, and you can concentrate on the actual machine learning part. Configuration and code are separated with Palladium, which helps organize your experiments and work on ideas in parallel. Check out the Palladium documentation for more.