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Onboarding-Perception.md

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Onboarding - Perception

Hey ! Hey Welcome to our team :) So glad you join us in our adventure to build technology powering the future of mobility. Here’s all the information you’d need to get started. Incase you are blocked, feel free to ping your buddy or your teammates - Say hi! ! But first, add your photo in the table below 🎆

Your Team

Mo Dani Harsi Emna Francesco Nicolai

Your Workstation

You have your very one ML workstation ! Ask your buddy to provide you with your credentials (which you should change ASAP). Only you have access to your workstation. Each workstation is fitted with at least 1 Titan V which is yours for running inference/training locally. For training on larger datasets we have AWS credentials for you (more on this below), ping your buddy for more information. Workstations are pre-installed with 1. Ubuntu 18.04 2. Cuda 10.1 3. v418.67 Nvidia Drivers. You'll have to install standard libraries and python packages before starting running cool stuff. Follow the instructions here to get started.

Our perception@gitlab

All perception related repositories are here. If you can read this you already have access to our repositories:) A good place to start is our data catalogue. If you need access to our drive data you can use scripts within the catalogue. As our data sources keep growing, we'd like to make sure everyone has access to all the datasets they need 💃 . If you plan to use a public dataset please discuss with your team member regarding the LICENSE.

Your AWS account

Its easy to work your AWS account with aws cli set up both on your workstation and your notebook For Unix machine skip the steps below. For Mac you'd need to jump through few hoops before setting up awscli.

Install developer tools + Homebrew + python-pip on MacBook. Skip this step for your workstation.

/usr/bin/xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL https://github.com/raw/Homebrew/install/master/install)"
brew install python

Install AWS CLI

# Both Unix and Mac
pip install awscli

Follow the instructions in the infrastructure wiki to login to AWS with KeyCloak.

Now test your AWS set-up

# Both Unix and Mac
aws s3 ls s3://das-perception/perception_onboarding/ --profile DASPerceptionDev

2019-04-12 16:18:49      16756 dani.jpeg
2019-04-12 16:16:36     278860 harsi.jpg
2019-04-12 16:18:39     135149 mo.png
2019-04-12 16:18:24          6 team.txt

Our S3 Buckets

Location Usage Example
s3://das-perception/perception_data Drive Data/Open source Data Videos, GPS, Lidar
s3://das-perception/perception_experiments Training Data/Docker Images tf-records etc
s3://das-perception/perception_logs Logging your experiments tensorflow summary events
s3://das-perception/perception_models Pre-trained models/your models ResNet101 pre-trained on ImageNet

Now you can push data to S3 and start training your models ! Need an 8 GPU machine ? Here's how to get one !

Our Dockers ourselves

Our gitlab repositories have their dedicated docker registry. F.ex on the left side panel (after expanding) you'll see Registry. You can build and push docker images (tagged obviously) to the registry.

For accessing the registry follow the instructions below Here you can find instructions on accessing gitlab docker registry from you workstation or an AWS machine. You'd need to setup an personal access token and run the following for the first time

Clone this repo

git clone ssh://git@ssh.gitlab.mobilityservices.io:443/am/roam/perception/cv-ml-resources.git
git checkout docker-demo

Get your personal access token and save it at a secure location. You can use this token from your workstation or MacBook. When prompted for password below provide your personal access token.

docker login registry.mobilityservices.io -u <your_gitlab_username>

Build your docker image and push it to our registry as below. You can tag your docker image latest. Best practice would be to use tag from the commit history as commit_tag.

# We use the following conventions for our docker containers.
# docker build -t registry.mobilityservices.io/am/roam/perception/<repository>/<branch>:<commit_tag> .
docker build -t registry.mobilityservices.io/am/roam/perception/cv-ml-resources/docker-demo:<your_name> .
docker push docker push registry.mobilityservices.io/am/roam/perception/cv-ml-resources/docker-demo:<your_name>

You can see the docker file in our registry here.

Our Coding Conventions

Within python environment, we (try to) follow PEP8 coding conventions wherever possible. Easiest way to enforce this into your favourite text editor is to install a PEP8 package/plugins. Inconsistent coding practices between team members can lead to messy PRs (tabs v spaces etc) and code reviews. Here are the detailed guidelines

Our NAS storage

We have our own Network Attached Storage (NAS). This is one stop location for accessing all the drive data, annotations, results, experiments etc. You have an account to mount the drive onto your workstation and/or MacBook. You'd need to connect to our dedicated Wifi: DAS_NW. Pwd : N85qaDL9uXeKm5RG26m7TZNdErSLSb2ZB73T

For Macbook : Connect to our NAS with a ethernet cable.

Finder -> Network -> perception_nas -> connect (top left) -> Enter credentials and wait a bit

From Linux (CLI Only) : Follow the following steps

You can also mount the NFS share from the command line; if you do this you are on your own.

Our Roadmap

You can find our mission statement and roadmap here

Your first day

  • Team Photo : Add your photo above
  • Gitlab : Get access ping @peter
  • Meet the team : #das-perception slack channel
  • Workstation Setup : Login and setup
  • AWS Setup : Credentials + your EBS storage

Your first week

  • Intro to Drive Data catalogue
  • Aloha @ OKRs : Align progress
  • Hey - Your first task :)
  • Meet and Greet - Mapping
  • Say Hi to Routing
  • Salut Simulation

Your first month

  • Solve Computer Vision :dancer_tone2:
  • Help us improve the on-boarding wiki :)