peakBreaker

"""
From documents to clusters

This script will run through a list of docs and process out the groups the docs may belong to using
cluster analysis, NMF and TF*IDF for preprocessing.  These are some basic techniques for unsupervised NLP
which may be very handy.

"""

# For creating the data structure to process

peakBreaker

FROM archlinux/base:latest

# Basic dependencies
RUN pacman -Syu --noconfirm
RUN pacman -S tar curl sudo grep base-devel --noconfirm
RUN mkdir -p /opt/yay/
WORKDIR /opt/yay/

# Add the builduser (cant install yay as root)
RUN useradd builduser -m # Create the builduser

peakBreaker

resource "google_service_account" "sa" {
  account_id   = "my-service-account"
  display_name = "A service account that Jane can use, with some pubsub capabilities"
}

# Allow SA service account use of the default GCE account
# https://www.terraform.io/docs/providers/google/r/google_service_account_iam.html
resource "google_service_account_iam_member" "gce-default-account-iam" {
  service_account_id = data.google_compute_default_service_account.default.name
  role               = "roles/iam.serviceAccountUser"

peakBreaker

def train_model():
    # ... Define the model and training data etc here

    def my_callback(epoch, logs):
        # Optionally clear the file if it exists here? or use on_train_begin cb
        # Focus on the point here
        with open('train_logs.json', 'a') as l:
            data =json.dumps({'epoch': epoch, **logs})  
            l.write(data + '\n')

peakBreaker

#!/usr/bin/env python

"""Logging setup

To use this module, just import it to set up the logger.  It should persist from import across the runtime

"""

import logging