nrupatunga

# Required packages: torch
# Desired packages: numpy, matplotlib
# Add missing imports:

def main():
    training_images = {x for x in range(1, 100001)}
    priority_images = {x * 100 for x in range(1, 11)}
    # Task:
    # 1. Dataloader needs to return a batch of size 10. Each element is a unique element from `training_images`.
    # 2. Elements from `priority_images` must be present in EVERY batch with the ratio 1:1, meaning that batch will have

nrupatunga

This is the video demonstration

nrupatunga

export CONTAINER_URI="gcr.io/deeplearning-platform-release/experimental.theia.1-7"
export INSTANCE_NAME=...
export PROJECT_NAME=...
export IMAGE_PROJECT="deeplearning-platform-release"
export IMAGE_FAMILY="theia-container-experimental"
export MACHINE_TYPE=... #"n1-standard-4" 
export ZONE=... #"us-central1-a"
gcloud compute instances create "${INSTANCE_NAME}" \
        --project="${PROJECT_NAME}" \
        --zone="${ZONE}" \

nrupatunga

export CONTAINER_URI="gcr.io/deeplearning-platform-release/experimental.theia.1-7"
export INSTANCE_NAME=...
export PROJECT_NAME=...
export IMAGE_PROJECT="deeplearning-platform-release"
export IMAGE_FAMILY="theia-container-experimental"
export MACHINE_TYPE=... #"n1-standard-4"
export ZONE=.... #"us-central1-a"
gcloud notebooks instances create "${INSTANCE_NAME}" \
        --project="${PROJECT_NAME}" \
        --location="${ZONE}" \

nrupatunga

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from torchvision.datasets import MNIST
from torch.utils.data import random_split, DataLoader
import pytorch_lightning as pl
class LitModel(pl.LightningModule):
    def __init__(self, channels, width, height, num_classes, hidden_size=64, learning_rate=2e-4):
        super().__init__()

nrupatunga

import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

nrupatunga

import os
import requests
import json

SLACK_WEBHOOK= os.environ.get("SLACK_WEBHOOK")


def send_message(messages, channel="abhishek", username="beast"):
    """
    :param messages: list of texts

nrupatunga

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. author2vec http://dl.acm.org/citation.cfm?id=2889382
5. bb2vec https://arxiv.org/pdf/1809.09621.pdf
6. behavior2vec https://dl.acm.org/citation.cfm?id=3184454
7. care2vec https://arxiv.org/abs/1812.00715
8. cat2vec http://104.155.136.4:3000/forum?id=HyNxRZ9xg

nrupatunga

import requests
from lxml import html

# conferences = ["CVPR2013","ICCV2013","CVPR2014","CVPR2015"]
# conferences = ["CVPR2017","ICCV2017","CVPR2016","ICCV2016"]
conferences = ["CVPR2018"]

for conf in conferences:
    # Get the HTML text and find the classes of type 'ptitle'
    response = requests.get("http://openaccess.thecvf.com/" + conf + ".py")

nrupatunga

# This is an example for the CIFAR-10 dataset.
# There's a function for creating a train and validation iterator.
# There's also a function for creating a test iterator.
# Inspired by https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4

from utils import plot_images

def get_train_valid_loader(data_dir,
                           batch_size,
                           augment,