maxbrenner-ai

$.ajax({
    type: "POST",
    url: "https://a3bqc81dv1.execute-api.us-east-2.amazonaws.com/test/myLambdaFunction",
    data: JSON.stringify({'hyperparmeter_1': 0, 'hyperparameter_2': 0,'data': csv_text}),
    contentType: 'text/plain',
    success: function(success){
        console.log(success);
    },
    error: function(error){
        console.log(error)

maxbrenner-ai

import os
import io
import boto3
import json
import csv


# grab environment variables
ENDPOINT_NAME = os.environ['ENDPOINT_NAME']
# grab runtime client

maxbrenner-ai

endpoint_name = 'anomaly-detection-endpoint'
anomaly_detection.deploy(initial_instance_count=1, 
                                             instance_type='ml.t2.medium', 
                                             endpoint_name=endpoint_name)
# Delete endpoint when you don't need your model deployed
sm.delete_endpoint(EndpointName=endpoint_name)

maxbrenner-ai

anomaly_detection.fit()

maxbrenner-ai

sess = sagemaker.session.Session()
anomaly_detection = sagemaker.estimator.Estimator(image_name=docker_image_name,
                                    role=role,
                                    train_instance_count=1, 
                                    train_instance_type='ml.m4.xlarge',
                                    output_path='s3://{}/{}/output'.format(bucket, prefix),
                                    base_job_name="anomaly-detection",
                                    sagemaker_session=sess)

maxbrenner-ai

# Install sagemaker (version 1.72)
import sys
!{sys.executable} -m pip install --quiet sagemaker==1.72 -U

# Imports
import io
import os
import sys
import time
import json

maxbrenner-ai

prefix = '/opt/ml/'

input_path = prefix + 'input/data'
output_path = os.path.join(prefix, 'output')
model_path = os.path.join(prefix, 'model')

# The function to execute the training.
def train():
    print('\nStarting the training.')
    with open(os.path.join(model_path, 'model'), 'w') as f:

maxbrenner-ai

For example, say rule_requests = ['moviename', 'starttime', 'city'], length: 3

Round 1:
rule_current_slot_index = 0 (< 3)
           |
[request 'moviename', request 'starttime', request 'city', mach_found, done]

Round 2:
                  rule_current_slot_index = 1 (< 3)
                             |

maxbrenner-ai

# Agent action examples:
{'intent': 'request', 'inform_slots': {}, 'request_slots': {'theater': 'UNK'}}
{'intent': 'inform', 'inform_slots': {'moviename': 'the witch'}, 'request_slots': {}}
{'intent': 'done', 'inform_slots': {}, 'request_slots': {}}

# User action examples:
{'intent': 'request', 'inform_slots': {'city': 'seattle'}, 'request_slots': {'theater': 'UNK', 'starttime': 'UNK'}}
{'intent': 'inform', 'inform_slots': {'moviename': 'the witch'}, 'request_slots': {}}
{'intent': 'done', 'inform_slots': {}, 'request_slots': {}}
{'intent': 'request', 'inform_slots': {}, 'request_slots': {'moviename': 'UNK'}}

maxbrenner-ai

{'intent': 'request', 'inform_slots': {'city': 'seattle'}, 'request_slots': {'theater': 'UNK', 'starttime': 'UNK'}}
{'intent': 'inform', 'inform_slots': {'moviename': 'the witch'}, 'request_slots': {}}
{'intent': 'done', 'inform_slots': {}, 'request_slots': {}}
{'intent': 'request', 'inform_slots': {}, 'request_slots': {'moviename': 'UNK'}}
{'intent': 'thanks', 'inform_slots': {}, 'request_slots': {'theater': 'UNK'}}