DmitryBe

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

DmitryBe

CMD+b open/close left panel

Ctrl + - [+ shit] go back/forward

Ctrl + CMD + left [right] split editor

CMD + T open search panel

CMD + down open file from explorer

DmitryBe

---
- name: Create Instance in AWS
  hosts: localhost
  connection: local
  gather_facts: false

  vars:
    aws_access_key: "xxxxxx"
    aws_secret_key: "xxxxxx"
    security_token: "xxxxxx"

DmitryBe

Setting up a GPU Enabled Kubernetes for Deep Learning

Kubernetes spread like a wildfire in 2017, No kidding! Here are some numbers from Scott's post:

“For companies with more than 5000 employees, Kubernetes is used by 48% and the primary orchestration tool for 33%.”

“79% of the sample chose Docker as their primary container technology.”

Riding the wave of Kubernetes, 2017 was a particular fun year for Infrastructure/DevOps folks. Finally we have some cool tools to play with after years of darkness. We started thinking what we could do with such paradigm shift. We tried to optimize the developer velocity with Jenkins and Helm Chart and many other more to come :D

One thing I hold dear in my heart is democratizing Kubernetes for Data team. It's a well known fact that today's Data teams have to muster an array of bleeding edge technologies in or

DmitryBe

# requried env
# KUBE_NAMESPACE
# patch.json

KUBE_TOKEN=$(cat /var/run/secrets/kubernetes.io/serviceaccount/token)
if [ -z "$KUBE_TOKEN" ]; then
  echo "Error: KUBE_TOKEN is empty"
  exit 1
fi

DmitryBe

import os
import io, os, sys, types
from IPython import get_ipython
from nbformat import read
from IPython.core.interactiveshell import InteractiveShell

class NotebookLoader(object):
    """Module Loader for Jupyter Notebooks"""
    def __init__(self, path=None):
        self.shell = InteractiveShell.instance()

DmitryBe

#!/bin/bash

if [[ -z ${MFA_DEVICE} ]]; then echo 'MFA_DEVICE is required'; exit -1; else echo 'MFA_DEVICE found'; fi
if [[ -z ${AWS_ACCESS_KEY_ID} ]]; then echo 'AWS_ACCESS_KEY_ID is required'; exit -1; else echo 'AWS_ACCESS_KEY_ID found'; fi
if [[ -z ${AWS_SECRET_ACCESS_KEY} ]]; then echo 'AWS_SECRET_ACCESS_KEY is required'; exit -1; else echo 'AWS_SECRET_ACCESS_KEY found'; fi

function aws_auth {
   CODE=$1
   RESPONSE=$2
   unset AWS_SESSION_TOKEN

DmitryBe

def load_app_config(config_file_path):
    '''
    load json config file, example:
    {
        "query": {
            "port": 8000,
            "env1": "${ENV1:-default_value}",
            "env2": "${ENV2:-12}"
        }
    }

DmitryBe

// avro schema path
val avroSchemaPath = "path-to/avro-schema.avsc"

val avroSchemaStr= scala.io.Source.fromFile(avroSchemaPath).mkString
val avroSchemaParser =  new Schema.Parser
val avroSchema = avroSchemaParser.parse(avroSchemaStr)

// create avro generic record reader
val avroGenericRecordReader = new GenericDatumReader[GenericRecord](avroSchema)

DmitryBe

"\e[A": history-search-backward
"\e[B": history-search-forward
set show-all-if-ambiguous on
set completion-ignore-case on
set bell-style none