shaabhishek

name: environment-name
channels:
    - pytorch
    - defaults
    - anaconda
dependencies:
    - numpy
    - pandas
    - pytorch
    # - torchvision

shaabhishek

## imports and other stuff

# Boilerplate for setting things up
query_args = {'dbname': dbname, 'host': host, 'port': port, 'user': dbusername}
conn = psycopg2.connect(**query_args)
cur = conn.cursor()
cur.execute('SET search_path to ' + schema_name)

# Setup the query- note it is ALMOST the same as the sql file's query
query = \

shaabhishek

#questions to answer
# 1. Do the following sequences give the same hidden states: pad-rnn-hidden & pack-rnn-pad-hidden 
# 2. does the birnn use the (hidden states computed in the forward states) to 
# compute the (hidden states computed in the backward direction)


import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnnutils

shaabhishek

\ProvidesPackage{mystyle}

\usepackage[utf8]{inputenc}
\usepackage{amsmath, amssymb, bm}
\usepackage{mathtools} % for DeclarePairedDelimiter command
\usepackage{graphicx}
\usepackage{subcaption} %allows drawing subfigures

% Set page size and margins
% Replace `letterpaper' with`a4paper' for UK/EU standard size

shaabhishek

from urllib.request import urlretrieve
from pathlib import Path
import time
import random

slides_dir = Path('/blablabla/CS590M/slides')

url = lambda n: f"https://blabla.com/slecture{n:02}h.pdf"
fname = lambda n: slides_dir / f"slecture{n:02}h.pdf"

shaabhishek

shaabhishek

########### INSTALL ###########
# !pip install --upgrade cmdstanpy
import os
import urllib.request
import shutil
# Install pre-built CmdStan binary
# (faster than compiling from source via install_cmdstan() function)
tgz_file = 'colab-cmdstan-2.23.0.tar.gz'
tgz_url = 'https://github.com/stan-dev/cmdstan/releases/download/v2.23.0/colab-cmdstan-2.23.0.tar.gz'
if not os.path.exists(tgz_file):

shaabhishek

linux: sort -u filename to remove duplicates from file

linux: use LC_ALL=C x ... instead of x ... to speed up x in {grep, sed, awk, sort}

latex: use package and command bm for bold math symbols

linux: awk '{print $1}' your_file | sort | uniq | wc -l to get unique counts for a column in a file

slurm: sacct --starttime 2022-09-01 --format=User,JobID,Jobname,partition,state,time,start,end,elapsed,MaxRss,MaxVMSize,nnodes,ncpus,nodelist for list all jobs since a specific data

shaabhishek

DatasetAllocator:

1. Helps create the flattened representation of the timeseries tensor from the raw files

---

DatasetAllocator.record: 1.

---

shaabhishek

Step 1: Install Anaconda compatible with RHEL 6

The file I used was Anaconda3-2020.11-Linux-x86_64.sh from https://repo.anaconda.com/archive/

wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
bash Anaconda3-2020.11-Linux-x86_64.sh

Step 2: Create a new environment.

I tried lots of older and newer versions of python but 3.7.0 is the one that worked for me. The patchelf is necessary for the next step.