prthamesh pratos

Build a docker image

Make sure you are in the folder that contains the Dockerfile
If your folder is /my-docker-image/, there should be 2 files in your folder:
```
/my-docker-image
|
|---Dockerfile
|---requirements.txt
```

•100% ➜ docker build -t tensorflow-av .
Sending build context to Docker daemon  3.584kB
Step 1/6 : FROM python:3.6
 ---> 968120d8cbe8
Step 2/6 : RUN apt-get update
 ---> Running in 20271eafe0d0
Get:1 http://security.debian.org jessie/updates InRelease [63.1 kB]
Ign http://deb.debian.org jessie InRelease
Get:2 http://deb.debian.org jessie-updates InRelease [145 kB]

Generate ssh keys:
- ssh-keygen -t rsa -C "<emai-id>@gmail.com"
Add them, delete the previous ones:
- ssh-add ~/.ssh/id_rsa_prato_git
- ssh-add -D
- ssh-add -l
Change the config file:
- vim config
- Add below:

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

How Do I Into Git?

a helpful primer for users sick of git's poorly-named commands

I've used Git since 2011, and this is the stuff that I've always had to Google to remember. I hope it helps you not hate Git so much.

Learning About the Repo

Learning About History

lsblk
sudo file -s /dev/xvdf
sudo file -s /dev/xvda1
sudo mkfs -t ext4 /dev/xvdf
sudo mkdir datasets
sudo mount /dev/xvdf datasets 

ls
cd datasets/

Moving docker machines around in different systems

There might be a case when you need to work at home or any remote place away from your own office. You have all the docker-machines created and want to get them to your laptop/home system.

Follow the steps below:

Copy all the machines from .docker/machine/machines to your .docker/machine/machines
Change the config.json in each machine: eg .docker/machine/machines/docker1m/config.json
- All the paths from /Users/office/.docker/... to /Users/home/.docker/...
Save them and run docker-machine ls to confirm.

Creating docker application from local system using Docker CLI

To create a machine using Docker CLI:
- docker-machine create --driver azure --azure-subscription-id <subs-id> --azure-location <location:eg. eastus> --azure-resource-group <resource-group-name if created/otherwise created automatically> --azure-size <vm-name> <machine-name>

NOTE: Every Resource group has upto 10 CPU Core limit, so if you get issues creating newer instances solution is to create a new resource group.

VM Sizes:

Listed here: VM Sizes

Converting list of lists/zip(list) to a CSV

kaggle_submission = zip(image_id, label_predict, predictions)
    
    with open("kaggle_submission.csv", "w") as f:
        fileWriter = csv.writer(f, dialect='excel')
        for img_id, pred, label in kaggle_submission:
            row = [img_id, pred, label]
 print(row)

	bleach==1.5.0
	certifi==2016.2.28
	cycler==0.10.0
	decorator==4.1.2
	entrypoints==0.2.3
	html5lib==0.9999999
	ipykernel==4.6.1
	ipython==6.2.1
	ipython-genutils==0.2.0
	ipywidgets==7.0.3