I hereby claim:
- I am pbamotra on github.
- I am benzene (https://keybase.io/benzene) on keybase.
- I have a public key ASCnWFSySbSBalyy4SCcjIzFTkb2gGmffCtnRewv0sLT2wo
To claim this, I am signing this object:
# Assumes you've awk, jq, curl installed | |
calc() { awk "BEGIN{print $*}"; } | |
export PROPERTY_VALUE=1000000 | |
export LOAN_AMOUNT=`calc $PROPERTY_VALUE*0.8` | |
export PROPERTY_ZIP=94107 | |
alias bankrate="curl 'https://mortgage-api.bankrate.com/rates/v4/?loanType=purchase&propertyValue=$PROPERTY_VALUE&propertyType=SingleFamily&propertyUse=PrimaryResidence&cashOutAmount=0&zipCode=$PROPERTY_ZIP&loanAmount=$LOAN_AMOUNT&creditScore=770&debtToIncomeRatio=0&pointsRange=Zero&productFamilies\[\]=conventional&loanTerms\[\]=30yr&loanTerms\[\]=7-1arm&loanTerms\[\]=7-6arm&loanTerms\[\]=10-1arm&loanTerms\[\]=10-6arm&defaultSearch=true&pid=br3&veteranStatus=NoMilitaryService&hadPriorVaLoan=false&hasVaDisabilities=false&firstTimeHomeBuyer=false&displayTargets\[\]=mobileRateTable&displayTargets\[\]=featuredRateTable&deviceTypes\[\]=mobile&e2eTestEnabled=false&clientId=MortgageRateTable&includeSponsored=true&includeEditorial=true' -H 'authority: mortgage-api.bankrate.com' -H 'accept: application/json, text/plain, */*' -H 'accept |
!pip install cython==0.28.5 | |
!pip install mmdet==2.10.0 requests | |
!pip install torch==1.7.0+cu110 torchvision==0.8.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html | |
!pip install mmcv-full==1.2.7+torch1.7.0+cu110 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html | |
!pip install onnx onnxruntime onnxruntime-gpu onnxoptimizer | |
!git clone --branch v2.10.0 https://github.com/open-mmlab/mmdetection.git | |
# Download conf and weights from | |
# https://github.com/iiLaurens/CascadeTabNet/blob/mmdet2x/Demo/Cascade_Tabnet_mmdet_v2_cpu_only_demo.ipynb |
# Execute this in a Jupyter notebook | |
import os | |
import json | |
import base64 | |
import pandas as pd | |
from pprint import pprint | |
# Import USD 10B+, 1MM vol+, 25+ P/E, Buy/Strong Buy rated | |
buy_rated_tradingview = pd.read_csv('~/Downloads/america_2021-04-11.csv') |
I hereby claim:
To claim this, I am signing this object:
import datetime | |
import glob | |
from lxml import etree | |
import pandas as pd | |
def get_books(file): | |
doc = etree.HTMLParser() | |
tree = etree.parse(file, parser=doc) |
Calendar: https://iclr.cc/virtual/calendar.html#tab-calendar | |
Paper search: https://iclr.cc/virtual/papers.html?filter=keywords | |
Papers: | |
1. Title: | |
Tree-Structured Attention with Hierarchical Accumulation | |
Authority: | |
Richard Socher | |
Url: | |
https://iclr.cc/virtual/poster_HJxK5pEYvr.html |
# Source: https://github.com/opencv/openvino_training_extensions/blob/develop/pytorch_toolkit/nncf/examples/common/utils.py#L86 | |
import tarfile | |
from pathlib import Path | |
def create_code_snapshot(root, dst_path, extensions=(".py", ".json", ".cpp", ".cu")): | |
"""Creates tarball with the source code""" | |
with tarfile.open(str(dst_path), "w:gz") as tar: | |
for path in Path(root).rglob("*"): |
from nvidia.dali.plugin.pytorch import DALIGenericIterator | |
pipe = ExternalSourcePipeline(data_iterator=iterator, batch_size=16, num_threads=2, device_id=0) | |
pipe.build() | |
# first parameter is list of pipelines to run | |
# second pipeline is output_map that maps consecutive outputs to | |
# corresponding names | |
# last parameter is the number of iterations - number of examples you | |
# want to iterate on |
eii = ExternalInputIterator(batch_size=16, | |
data_file=processed_data_file, | |
image_dir=images_directory) | |
iterator = iter(eii) | |
class ExternalSourcePipeline(Pipeline): | |
def __init__(self, data_iterator, batch_size, num_threads, device_id): | |
super(ExternalSourcePipeline, self).__init__(batch_size, | |
num_threads, | |
device_id, |
import types | |
import numpy as np | |
import collections | |
import pandas as pd | |
from random import shuffle | |
import nvidia.dali.ops as ops | |
import nvidia.dali.types as types | |
from nvidia.dali.pipeline import Pipeline |