andrewm4894

ARG BASE_CONTAINER=jupyter/scipy-notebook
FROM $BASE_CONTAINER

LABEL maintainer="[email protected]"
LABEL version="01"

USER $NB_UID

# install specific package versions i want to use here
RUN conda install --quiet --yes \

andrewm4894

import json
from my_utils.os_utils import subprocess_execute
from my_dev.dev import hello_world
import pandas as pd


def run(event=dict(), context=dict()):
    ''' Function to be called by serverless lambda
    '''
    # make a dummy df to ensure pandas available to the lambda function

andrewm4894

service: serverless-learn-lambda

provider:
  name: aws
  runtime: python3.6
  region: us-west-2
  stage: dev  
  role: arn:aws:iam::XXX:role/serverless-lambda

functions:

andrewm4894

ARG BASE_CONTAINER=gcr.io/kubeflow-images-public/tensorflow-1.13.1-notebook-cpu:v0.5.0
FROM $BASE_CONTAINER

LABEL maintainer="[email protected]"
LABEL version="01"

RUN pip3 install git+https://github.com/andrewm4894/my_utils.git#egg=my_utils

RUN pip3 install kfp --upgrade

andrewm4894

"""
GCP HTTP Cloud Function to handle github webhook events.

Some code stolen from here: https://github.com/carlos-jenkins/python-github-webhooks/blob/master/webhooks.py

"""
# -*- coding: utf-8 -*-
import hmac
import json
import datetime

andrewm4894

service: handle-github-events

frameworkVersion: ">=1.2.0 <2.0.0"

package:
  exclude:
    - node_modules/**
    - .gitignore
    - .git/**

andrewm4894

npm install serverless -g
serverless install -u https://github.com/serverless/examples/tree/master/google-python-simple-http-endpoint -n handle-github-events

andrewm4894

cd handle-github-errors
npm i -D serverless-dotenv-plugin
npm install

andrewm4894

def calc_batches(train_max: int, train_every: int, n: int) -> dict:
    batches = dict()
    # loop over up to as many records as you have
    for batch in range(n):
        # work out the start of the batch, with a max() to handle first batch
        start = max(train_every * batch, 1)
        # work out the end of the batch, with a min() to handle last batch
        end = min(train_max+(train_every * batch), n)
        # add batch info to the dictionary
        batches[batch+1] = {"start": start, "end": end}

andrewm4894

def calc_batches(train_min: int, train_max: int, train_every: int, n: int) -> dict:
    batches = dict()
    batch = 0
    for row in range(1,n+1):
        if row < train_min:
            pass
        elif row == train_min:
            batches[batch] = dict(start=0, end=row)
        elif row % train_every == 0:
            batch += 1