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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,

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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

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# 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("*"):

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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

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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)

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Keybase proof

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:

pbamotra

# 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')

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!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

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# 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

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[Design Doc] Your Project Name

Design docs are a way to propose future work and get detailed technical feedback.

Problem Context

Brief description of what the problem or opportunity is. Give an overview of the domain and pain points. What is the current solution? Give some details about what its shortcomings are.

Proposed Solution