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AWS fastami GPU Image Setup

fastai.ai Part 1 v2
Notes from Lesson 2 live on 06-Nov-2017

Part I - Setting up AMI

AMI (Amazon Machine Image): a template for how your computer is created

Step 0: Getting Started

Log into AWS Console: http://console.aws.amazon.com/
Select Service: EC2
Launch Instance

Step 1: Choose an Amazon Machine Image (AMI)

  • Search Community AMIs [left menu]
  • Search: fastai
  • Select this image (for region N. Virginia): fastai-part1v2-p2 - ami-c6ac1cbc

NOTE: this AMI is available in a number of regions. Check your local region first. If it's not available, check next closest region.

  • Oregon: ami-8c4288f4
  • Sydney: ami-39ec055b
  • Mumbai: ami-c53975aa
  • N. Virginia: ami-c6ac1cbc
  • Ireland: ami-b93c9ec0

Step 2: Choose an Instance Type

(Note: it is the kind of computer we want to use.)

  • Filter by: GPU Compute
  • Select: p2.xlarge (this is the cheapeast, reasonably effective for deep learning type of instance available)
  • Select: Review and Launch at bottom

Step 2b: Select keypair

Note: you have already created a keypair in the past. Use one of those. For more specific instructions, see Create a Keypair.

And, voila! We have just created a new computer on AWS that we can log into 💥

Part II - Logging into our AWS Computer

Syntax for logging in and setting up tunnel for Jupyter Notebook

Note 1: Ensure you are in your .ssh directory on your local computer. (where your keypair file is located)
Note 2: You will put your Public IP address where mine is.
Note 3: This part -L8888:localhost:8888 connects Jupyter Notebook from AWS to your computer.

my current path

pwd
/Users/reshamashaikh/.ssh

my syntax for logging in

ssh -i aws_fastai_gpu.pem [email protected] -L8888:localhost:8888

my example

~/.ssh
▶ ssh -i aws_fastai_gpu.pem [email protected] -L8888:localhost:8888       
Welcome to Ubuntu 16.04.3 LTS (GNU/Linux 4.4.0-1039-aws x86_64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage

  Get cloud support with Ubuntu Advantage Cloud Guest:
    http://www.ubuntu.com/business/services/cloud

2 packages can be updated.
0 updates are security updates.

*** System restart required ***
(fastai) ubuntu@ip-172-31-10-243:~$

Working on our AWS Computer

List what files are loaded on the AWS computer:
ls

my example

(fastai) ubuntu@ip-172-31-10-243:~$ ls
data  fastai  src
(fastai) ubuntu@ip-172-31-10-243:~$

cd into the fastai repo

cd fastai

my example

(fastai) ubuntu@ip-172-31-10-243:~$ cd fastai
(fastai) ubuntu@ip-172-31-10-243:~/fastai$ ls -alt
total 76
drwxr-xr-x 18 ubuntu ubuntu  4096 Nov  7 16:25 ..
drwxrwxr-x  8 ubuntu ubuntu  4096 Nov  5 00:35 .git
drwxrwxr-x  6 ubuntu ubuntu  4096 Nov  5 00:35 fastai
drwxrwxr-x  6 ubuntu ubuntu  4096 Nov  5 00:29 .
-rw-rw-r--  1 ubuntu ubuntu  1273 Nov  5 00:29 environment.yml
drwxrwxr-x  3 ubuntu ubuntu  4096 Nov  1 21:30 tutorials
-rw-rw-r--  1 ubuntu ubuntu   905 Nov  1 21:30 requirements.txt
drwxrwxr-x  4 ubuntu ubuntu  4096 Nov  1 21:30 courses
-rw-rw-r--  1 ubuntu ubuntu  1173 Nov  1 21:30 .gitignore
-rw-rw-r--  1 ubuntu ubuntu 35141 Nov  1 21:30 LICENSE
-rw-rw-r--  1 ubuntu ubuntu   280 Nov  1 21:30 README.md
(fastai) ubuntu@ip-172-31-10-243:~/fastai$

❗ Update the fastai repo

git pull

my example

(fastai) ubuntu@ip-172-31-10-243:~/fastai$ git pull
remote: Counting objects: 21, done.
remote: Total 21 (delta 12), reused 12 (delta 12), pack-reused 9
Unpacking objects: 100% (21/21), done.
From https://github.com/fastai/fastai
   9ae40be..d64a103  master     -> origin/master
Updating 9ae40be..d64a103
Fast-forward
 courses/dl1/excel/collab_filter.xlsx   | Bin 0 -> 90259 bytes
 courses/dl1/excel/conv-example.xlsx    | Bin 0 -> 101835 bytes
 courses/dl1/excel/entropy_example.xlsx | Bin 0 -> 10228 bytes
 courses/dl1/excel/graddesc.xlsm        | Bin 0 -> 124265 bytes
 courses/dl1/excel/layers_example.xlsx  | Bin 0 -> 17931 bytes
 courses/dl1/lesson1-rxt50.ipynb        |   4 +++-
 fastai/conv_learner.py                 |   5 +++--
 fastai/dataset.py                      |   2 ++
 fastai/imports.py                      |   1 +
 fastai/model.py                        |   4 +++-
 fastai/plots.py                        |  26 ++++++++++++++++++++++++++
 fastai/structured.py                   |   4 ++--
 fastai/torch_imports.py                |   4 ++++
 13 files changed, 44 insertions(+), 6 deletions(-)
 create mode 100755 courses/dl1/excel/collab_filter.xlsx
 create mode 100644 courses/dl1/excel/conv-example.xlsx
 create mode 100644 courses/dl1/excel/entropy_example.xlsx
 create mode 100644 courses/dl1/excel/graddesc.xlsm
 create mode 100644 courses/dl1/excel/layers_example.xlsx
(fastai) ubuntu@ip-172-31-10-243:~/fastai$

Note 1: The fastai repo is available in your home directory, in the fastai folder.
Note 2: The dogscats dataset is already there for you, and the data folder is linked to ~/data.

Update the conda libraries (do this once a month, or if you run into errors)
conda env update
conda update --all

Optional

  • check which version of python is running
    • python --version
  • check which python path is being used
    • which python
  • see what packages are installed
    • pip list --format=legacy

my example

(fastai) ubuntu@ip-172-31-10-243:~/fastai$ python --version
Python 3.6.3 :: Anaconda, Inc.
(fastai) ubuntu@ip-172-31-10-243:~/fastai$ which python
/home/ubuntu/src/anaconda3/envs/fastai/bin/python
(fastai) ubuntu@ip-172-31-10-243:~/fastai$

Part III - Jupyter Notebook

Create a Jupyter config

jupyter notebook --generate-config

Open the config file generated and add the following line

c.NotebookApp.ip = '*'

Launch Jupyter Notebook
jupyter notebook

my example

(fastai) ubuntu@ip-172-31-10-243:~/fastai$ jupyter notebook
[I 17:00:22.985 NotebookApp] Writing notebook server cookie secret to /run/user/1000/jupyter/notebook_cookie_secret
[I 17:00:30.584 NotebookApp] [jupyter_nbextensions_configurator] enabled 0.2.8
[I 17:00:30.950 NotebookApp] Serving notebooks from local directory: /home/ubuntu/fastai
[I 17:00:30.950 NotebookApp] 0 active kernels
[I 17:00:30.950 NotebookApp] The Jupyter Notebook is running at:
[I 17:00:30.950 NotebookApp] http://localhost:8888/?token=04089b6ccf89e723321097c9089ab52550f408c86f533608
[I 17:00:30.950 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 17:00:30.951 NotebookApp] No web browser found: could not locate runnable browser.
[C 17:00:30.951 NotebookApp] 
    
    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=04089b6ccf89e723321097c9089ab52550f408c86f533608
[I 17:01:49.931 NotebookApp] 302 GET /?token=04089b6ccf89e723321097c9089ab52550f408c86f533608 (127.0.0.1) 0.58ms

Going to "MY URL" for Jupyter Notebook

http://localhost:8888/?token=04089b6ccf89e723321097c9089ab52550f408c86f533608

Note: you will want to edit the above url with YOUR TOKEN.
http://localhost:8888/?token=token_url

This notebook is running on AWS GPU machine.
The p2 instance costs $0.90 per hour 💰
The p3 instance costs $3.00 per hour 💰 💰 💰
Storage: ~ $8 per month for storing data files (80GB GP2 SSD @ $0.10 GB/month)

🔴 Remember to shut the notebook down and STOP Instance! 💰 🔴

Workflow

I opened the Lesson 1 notebook, made a copy with the name tmp-reshama-lesson1.ipynb and was able to run all the code! 💥

Note on Tmux

On the fastai AWS AMI, tmux mouse mode is enabled, so hold down shift while selecting to copy to your local clipboard.

References

Optional: Elastic IP