train

#!/usr/bin/env python

# A sample training component that trains a simple scikit-learn decision tree model.
# This implementation works in File mode and makes no assumptions about the input file names.
# Input is specified as CSV with a data point in each row and the labels in the first column.

from __future__ import print_function

import json
import os
import pickle
import sys
import traceback

import neptune
import neptune.integrations.sklearn as npt_utils
import pandas as pd
from sklearn import tree

# These are the paths to where SageMaker mounts interesting things in your container.

prefix = "/opt/ml/"

input_path = prefix + "input/data"
output_path = os.path.join(prefix, "output")
model_path = os.path.join(prefix, "model")
param_path = os.path.join(prefix, "input/config/hyperparameters.json")

# This algorithm has a single channel of input data called 'training'. Since we run in
# File mode, the input files are copied to the directory specified here.
channel_name = "training"
training_path = os.path.join(input_path, channel_name)


# The function to execute the training.
def train():
    run = neptune.init_run(
        tags=["sagemaker"],
        source_files=[sys.argv[0]],
    )

    print("Starting the training.")
    try:
        # Read in any hyperparameters that the user passed with the training job
        with open(param_path, "r") as tc:
            trainingParams = json.load(tc)

        # Take the set of files and read them all into a single pandas dataframe
        input_files = [os.path.join(training_path, file) for file in os.listdir(training_path)]
        if len(input_files) == 0:
            raise ValueError(
                (
                    "There are no files in {}.\n"
                    + "This usually indicates that the channel ({}) was incorrectly specified,\n"
                    + "the data specification in S3 was incorrectly specified or the role specified\n"
                    + "does not have permission to access the data."
                ).format(training_path, channel_name)
            )
        raw_data = [pd.read_csv(file, header=None) for file in input_files if file.endswith(".csv")]
        train_data = pd.concat(raw_data)

        # labels are in the first column
        train_y = train_data.iloc[:, 0]
        train_X = train_data.iloc[:, 1:]

        # Here we only support a single hyperparameter. Note that hyperparameters are always passed in as
        # strings, so we need to do any necessary conversions.
        max_leaf_nodes = trainingParams.get("max_leaf_nodes", None)
        if max_leaf_nodes is not None:
            max_leaf_nodes = int(max_leaf_nodes)

        # Now use scikit-learn's decision tree classifier to train the model.
        clf = tree.DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
        clf = clf.fit(train_X, train_y)

        run["cls_summary"] = npt_utils.create_classifier_summary(
            clf, train_X, train_X, train_y, train_y
        )

        # save the model
        with open(os.path.join(model_path, "decision-tree-model.pkl"), "wb") as out:
            pickle.dump(clf, out)
        print("Training complete.")
    except Exception as e:
        # Write out an error file. This will be returned as the failureReason in the
        # DescribeTrainingJob result.
        trc = traceback.format_exc()
        with open(os.path.join(output_path, "failure"), "w") as s:
            s.write("Exception during training: " + str(e) + "\n" + trc)
        # Printing this causes the exception to be in the training job logs, as well.
        print("Exception during training: " + str(e) + "\n" + trc, file=sys.stderr)
        # A non-zero exit code causes the training job to be marked as Failed.
        sys.exit(255)


if __name__ == "__main__":
    train()

    # A zero exit code causes the job to be marked a Succeeded.
    sys.exit(0)