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# ML Core 
scikit-learn==0.22.1
pandas==1.01
torch==1.40
tb-nightly==2.2.0
# Graphs and param management
seaborn==0.10.0
future==0.18.2
wandb==0.8.25
# GCP dependencies

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class SimpleTransformer(torch.nn.Module):
    def __init__(self, n_time_series, seq_len, d_model=128):
        super().__init__()
        self.dense_shape = torch.nn.Linear(n_time_series, d_model)
        self.pe = SimplePositionalEncoding(d_model)
        self.transformer = Transformer(d_model, nhead=8)
        self.final_layer = torch.nn.Linear(d_model, 1)
        self.sequence_size = seq_len
    def forward(self, x, t, tgt_mask, src_mask=None):
        if src_mask:

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class AttendDiagnose(nn.Module):
  def __init__(self, number_measurements, filter_number):
    super().__init__()
    self.d_model = filter_number*number_measurements
    self.embedding_conv = nn.Conv1d(number_measurements, filter_number*number_measurements, 1) 
    self.pe = PositionalEncoding(filter_number*number_measurements)
    # embed_dim and attention_heads  
    self.masked_attn = nn.modules.activation.MultiheadAttention(filter_number*number_measurements, 8)
    self.norm = nn.modules.normalization.LayerNorm(self.d_model)
    self.final_layer = nn.Linear(self.d_model, 1)

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!allennlp train babi_train_meta.jsonnet -s /tmp/serialization_dir --include-package allennlp.training.metatrainer

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Serving a TTS model with Seldon

In this walkthrough you will learn to serve a multi-part PyTorch model with Seldon. +* +*

s2i build . seldonio/seldon-core-s2i-python3:0.10 kubeflow/tts_encoder

docker run --name "EncoderServe" --rm -p 5000:5000 kubeflow/tts_encoder

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""" Use torchMoji to predict emojis from a single text input
"""

from __future__ import print_function, division, unicode_literals
import examples.example_helper
import json
import csv
import argparse
import numpy as np

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for i,chunk in enumerate(pd.read_csv('bigfile.csv', chunksize=500000)):
    chunk.to_csv('chunk{}.csv'.format(i))

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import requests
from requests.auth import HTTPBasicAuth
import lxml.html
import requests, zipfile, io
user_name = "Replace this with your MIMIC username"
your_password = "Replace this with your MIMIC password"
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 
           (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
response = requests.get("https://physionet.org/works/MIMICIIIClinicalDatabase/files/", 
                        auth=HTTPBasicAuth(user_name, your_password), headers=headers)

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.followedBy("End").where(new IterativeCondition<Tuple2<String, Integer>>() {
            @Override
            public boolean filter(Tuple2<String, Integer> stringIntegerTuple2, Context<Tuple2<String, Integer>> context) throws Exception {
                List<Tuple2<String,Integer>> s = Lists.newArrayList(context.getEventsForPattern("End"));
                int i = s.size();
                int value = stringIntegerTuple2.getField(1);
                int prevValue = s.get(i-1).getField(1);
                return value>prevValue;
            }
        });

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 Pattern<Tuple2<String, Integer>, ?> pattern  =  
  Pattern.<Tuple2<String,Integer>>begin("first")
   .where(new SimpleCondition2(15)).followedBy("increasing")
   .where(new SimpleCondition2(20))
   
   PatternStream<Tuple2<String, Integer>> patternStream = 
  CEP.pattern(dataWindowKafka.keyBy(0), pattern);
        DataStream<String> manyMentions = patternStream
        .select(new PatternSelectFunction<Tuple2<String, Integer>, String>() {
            @Override