/cse252c_hw3-release

# UCSD CSE252C

Primary LanguagePythonMIT LicenseMIT

CSE252C HW3

0. Homework instructions

  1. Attempt all questions.

  2. Please comment all your code adequately.

  3. Include all relevant information such as text answers, output images in notebook.

  4. Academic integrity: The homework must be completed individually.

  5. Submission instructions:
    (a) Submit the notebook and its PDF version on Gradescope.
    (b) Rename your submission files as Lastname_Firstname.ipynb and Lastname_Firstname.pdf.
    (c) Correctly select pages for each answer on Gradescope to allow proper grading.

  6. Due date: Please check the jupyter notebook.

Steps to access and complete homework

  • Clone the homework repository
    • git clone https://github.com/eric-yyjau/cse252c_hw3-release.git
  • The homework is in the Jupyter Notebook hw3_questions.ipynb
  • Follow the README (this file) for installation, data and compute instructions.

1. Installation instructions

1. Set up the environment

1. [Option 1] On your own machine

  • (local) SSH into your machine
  • Install Python 3.X and Pip
  • [Recommended] Create an environment (e.g. with Anaconda)
    • conda create --name py36 python=3.6 pip
    • conda activate py36
  • Install Jupyter Notebook
    • conda install jupyter
  • Install kernels for Jupter Notebook
    • conda install nb_conda
  • Launch Jupyter Notebook server in the conda env of the cluster
    • jupyter notebook
    • You will be provided with a URL that you can open locally
    • In a opened notebook, change the kernel (on Menu: Kernel -> Change Kernel) to the name of the conda env you just created (in the case of this documentation it should be py36)

2. [Option 2] On the ieng6.ucsd.edu server

  • (local) (IMPORTANT) Connect your UCSD VPN

  • (local) Login with your credentials

    • ssh {USERNAME}@ieng6.ucsd.edu

  • If you cannot launch a pod, set up the environment following these instructions

  • Launch your pod. You should enter a node with 1 GPU

    export K8S_PRIORITY_CLASS_NAME=normal
export K8S_TIMEOUT_SECONDS=43200
launch-scipy-ml.sh -i ucsdets/cse152-252-notebook:latest -g 1 -p normal

  • Disconnect during training

    launch-scipy-ml.sh -b # launch the environment with '-b', it should have 12 hour limit
kubectl get pods
kubesh <PODNAME> # connect to the pod
kubectl delete pod <PODNAME>

  • You will be provided with a URL that you can open locally:

    • Connect to UCSD VPN
    • Click on the link. Then natigate to the jupyter notebook for a question which you are going to git clone as follows

2. Pull the repo and install dependencies

  • In the homework base directory
  • Install dependencies (Python 3.X with Pip)
    • pip install -r requirements.txt --user

2. Data

On the ieng6.ucsd.edu server, the datasets are located at

  • /datasets/cse152-252-sp20-public/hw3_data

To fetch the files you can use commands like scp to transfer files from the cluster to your local machine:

From your local machine:

scp -r <USERNAME>@dsmlp-login.ucsd.edu:/datasets/cse152-252-sp20-public/hw3_data/ {LOCAL PATH}

Or from within server if your local machine has a fixed address or IP:

scp -r {REMOTE PATH TO THE vis FOLDER} <USERNAME>@<LOCAL ADDRESS>:{YOUR LOCAL PATH}

3. [Extra] How to run training sessions

1. Set up the environment

[Option 1] On the ieng6.ucsd.edu server

  • Login with your credentials

    • ssh {USERNAME}@ieng6.ucsd.edu

  • Launch TMUX

    • Reconmended for session management: you can come back anytime after you disconnect your session. Otherwise you have to keep your connection on for hours while training.
    • Just run tmux
    • To detach and come back later, use ctrl + b then d. To attach next time, use ctrl + b then a.
    • For more TMUX usages please refer to online tutorials like https://linuxize.com/post/getting-started-with-tmux/

  • Launch your pod

    • Follow Section 1.1.2

[Option 2] On your own server

Just launch TMUX.

2. Start training

Now you can create conda env and do your training in there following Section 1.1