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Building Tensorflow from Source (TF 2.3.1, Ubuntu 20.04 & MacOS 10.15.7)

This should work for other TF versions 2.x, but mine was done for 2.3.1 specifically.

Hat tip to...

kmhoffmann's Gist about compiling TF 2.3.0 on Ubuntu 20.04 as that helped me get started with the steps I needed to start compling my own TF packaget started.

Introduction

There are many reasons to want to compile your own TF, but for me, I've been playing around with ML for a while myself and recently embarked on the TensorFlow journey. While trying to follow along to the [Predict Fuel Efficiency] regression problem, I ran into the following error: Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA and after a bit of Googling, I found that the default TF package that you install with pip is built without CPU extensions and so I had to compile my own if I wan

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Installing MongoDB on WSL

Even though Mongo says it doesn't support WSL, you can still make it work. Just install MongoDB as you would any regular Ubuntu system

If you tried to start it with systemctl start mongod, it will fail with an error like:

System has not been booted with systemd as init system (PID 1). Can't operate.

Well...if SystemD doesn't work, then use SystemV!

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Install pandoc

brew install pandoc

How-To

The following will convert markdown file to a PDF file:

pandoc myfile.md -s -o myfile.pdf

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Errors/Problems with Install

In attempting to install Ruby with rbenv, I ran into the following build failures:

BUILD FAILED (macOS 11.2.3 using ruby-build 20210423)

Inspect or clean up the working tree at /var/folders/58/16lnyx815c183j6wzcbl_thc0000gn/T/ruby-build.20210426232453.81982.XfAg0C
Results logged to /var/folders/58/16lnyx815c183j6wzcbl_thc0000gn/T/ruby-build.20210426232453.81982.log

Last 10 log lines:
 ^

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---
apiVersion: v1
kind: Namespace
metadata:
  name: infra-ns
  labels:
    shared-gateway-access: "true"
---
apiVersion: v1
kind: Namespace

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import string
from google.cloud import storage

def copy_to_gcs(event, context):
    print('Event ID: {}'.format(context.event_id))
    print('Event type: {}'.format(context.event_type))
    print('File: {}'.format(event['name']))

    storage_client = storage.Client()
    source_bucket = storage_client.bucket(event['bucket'])

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import string
from google.cloud import storage

def delete_from_gcs(event, context):
    print('Event ID: {}'.format(context.event_id))
    print('Event type: {}'.format(context.event_type))
    print('File: {}'.format(event['name']))
    
    storage_client = storage.Client()
    source_bucket = storage_client.bucket(event['bucket'])

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import base64
from google.cloud import asset_v1

def export_assets(event, context):
    # create client
    client = asset_v1.AssetServiceClient()
    print("""This Function was triggered by messageId {} published at {} to {}
    """.format(context.event_id, context.timestamp, context.resource["name"]))
    
    # bq partition spec

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def search_reserved_ips():
    # create client
    client = asset_v1.AssetServiceClient()
    
    # init request
    request = asset_v1.SearchAllResourcesRequest(
        scope="projects/myproject-123",
        asset_types=[
            "compute.googleapis.com/Address",
        ],

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---
apiVersion: v1
kind: Service
metadata:
  labels:
    app: web
  name: web
  annotations:
    cloud.google.com/neg: '{"ingress": true}'
spec: