JoshBroomberg

# Start an MLflow experiment run
with mlflow.start_run():
  
  lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
  lr.fit(train_x, train_y)

  predicted_qualities = lr.predict(test_x)

  rmse = np.sqrt(mean_squared_error(actual, pred))
  mae = mean_absolute_error(actual, pred)

JoshBroomberg

import mlflow
import os

REMOTE_SERVER_URI = "http://mlflow:5555"
S3_ENDPOINT_URL = "http://minio:9000"

# set the tracking server hostname/port
mlflow.set_tracking_uri(REMOTE_SERVER_URI)

# set the artifact backend to be the local minio backend

JoshBroomberg

if [ "$SPARK_MODE" == "worker" ]; then
  DRIVER_HOST="$(hostname | sed -E 's/spark-([a-z0-9]+)-(worker|master)-[0-9]+/spark-driver-\1.domino-compute.svc.cluster.local/g')"
  echo "Starting ray worker targeting head at \"${DRIVER_HOST}\" after head startup delay"
  for i in {1..10}; do ray start --address=${DRIVER_HOST}:6379 --redis-password=pass --num-gpus=1 && break || sleep 60; done
else
  echo "taking no action on master"
#   DRIVER_HOST="$(hostname | sed -E 's/spark-([a-z0-9]+)-(worker|master)-[0-9]+/spark-driver-\1.domino-compute.svc.cluster.local/g')"
#   PROXY_PATH="$(hostname | sed -E 's/spark-([a-z0-9]+)-(worker|master)-[0-9]+/kevin_flansburg\/Ray\/workspaces\/\1\/spark\/ui/g')"
#   echo "Starting ray ui proxying \"/${PROXY_PATH}\" to head at \"${DRIVER_HOST}\""
#   touch /usr/sbin/proxy.txt

JoshBroomberg

JoshBroomberg

JoshBroomberg

pragma solidity ^0.4.16;

contract Ownable {
    address public owner;

    constructor () public {
        owner = msg.sender;
    }

    modifier onlyOwner(){

JoshBroomberg

res <- rq(lalonde$re78 ~ X_all, tau=seq(0.2, 0.8, by=0.1))
sm <- summary(res)
plot(sm)

JoshBroomberg

compressed_image_data_train = pca.transform(image_data_train)
uncompressed_image_data_train = pca.inverse_transform(compressed_image_data_train)

fig=plt.figure(figsize=(12, 20))
columns = 4
rows = 8
for i in range(1, 33, 4):
    component_id = int(i/4)
    eigen_image = image_from_component_values(pca.components_[component_id])
    inverted_egein_image = PIL.ImageOps.invert(eigen_image)

JoshBroomberg

#!/usr/bin/env python
# -*- coding: utf-8 -*-
######################
# Speech recognition #
######################
# 1. Work through the following resources:
#     Viterbi algorithm. (n.d.). In Wikipedia. Retrieved November 9, 2016, from
#     https://en.wikipedia.org/wiki/Viterbi_algorithm
#     The 44 Phonemes in English. (n.d.). Retrieved November 9, 2016, from
#     http://www.dyslexia-reading-well.com/44-phonemes-in-english.html

JoshBroomberg

@celery.task
def async_training_task(data_file_name, k_iterations):
  train_model(file=data_file_name, iterations=k_iterations)
  
# This is the standard flask handler.
# Maybe you get some params from the request/
def get(self, request):
  async_training_task.delay(data_file_name=request.file_name, iterations=10)
  return Response("Training has been started!")