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How to Improve the Parallelization of Torch Dataloaders Using Torch.multiprocessing

কিভাবে Torch.multiprocessing ব্যবহার করে টর্চ ডেটালোডারের সমান্তরালকরণ উন্নত করা যায়

So verbessern Sie die Parallelisierung von Torch-Dataloadern mit Torch.multiprocessing

Cómo mejorar la paralelización de los cargadores de datos de Torch mediante el multiprocesamiento de Torch.

Comment améliorer la parallélisation des chargeurs de données Torch à l'aide de Torch.multiprocessing

Torch.multiprocessing का उपयोग करके Torch डेटालोडर्स के समानांतरीकरण में सुधार कैसे करें

Torch.multiprocessing を使用して Torch データローダーの並列化を改善する方法

Torch.multiprocessing을 사용하여 Torch 데이터 로더의 병렬화를 개선하는 방법

Como melhorar a paralelização de dataloaders Torch usando Torch.multiprocessing

Как улучшить распараллеливание загрузчиков данных Torch с помощью Torch.multiprocessing

Torch.multiprocessing Kullanılarak Torch Veri Yükleyicilerinin Paralelleştirilmesi Nasıl Geliştirilir?

Cách cải thiện tính năng song song hóa của bộ tải dữ liệu Torch bằng Torch.multiprocessing

如何使用 Torch.multiprocessing 提高 Torch 数据加载器的并行化

Computer vision is a field experiencing significant growth and has numerous practical applications, ranging from self-driving cars to facial recognition systems. However, one of the major challenges in this field is obtaining high-quality datasets to train machine learning models. To address this challenge, torchvision provides access to pre-built datasets, models and transforms specifically designed for computer vision tasks. Torchvision also supports both CPU and GPU acceleration, making it a flexible and powerful tool for developing computer vision applications. What are “Torchvision Datasets”? Torchvision datasets are collections of popular datasets commonly used in computer vision for developing and testing machine learning models. With torchvision datasets, developers can train and test their machine learning models on a range of tasks, such as image classification, object detection, and segmentation. The datasets are also preprocessed, labeled and organized into formats that can be easily loaded and used. List of the Torchvision Datasets MNIST CIFAR-10 CIFAR-100 ImageNet COCO Fashion-MNIST SVHN STL-10 CelebA Pascal VOC Places365 1. MNIST This torchvision dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. To access this dataset, you can download it directly from or load the dataset using torchvision: Kaggle import torchvision.datasets as datasets
# Load the training dataset
train_dataset = datasets.MNIST(root='data/', train=True, transform=None, download=True)
# Load the testing dataset
test_dataset = datasets.MNIST(root='data/', train=False, transform=None, download=True) Code for loading MNIST dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST 2. CIFAR-10 The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. It has a total of 50,000 training images and 10,000 test images which is further divided into five training batches and one test batch, each with 10,000 images. This dataset can be downloaded from , or loaded here using torchvision: Kaggle import torch
import torchvision
import torchvision.transforms as transforms
 
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                 	                   download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
 
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2) Note that you can adjust the batch size and number of worker processes for the data loaders as needed. Code for loading CIFAR-10 dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html#torchvision.datasets.CIFAR10 3. CIFAR-100 The CIFAR-100 dataset has 60,000(50,000 training images and 10,000 test images) 32x32 colour images in 100 classes, with 600 images per class. The 100 classes are grouped into 20 super-classes, with a fine label to denote its class and a coarse label to represent the super-class that it belongs to. To download the torchvision dataset from Kaggle, please visit the Kaggle and follow the instructions provided there. Alternatively, if you prefer to load the dataset using the torchvision library, you can do this: website import torchvision.datasets as datasets
import torchvision.transforms as transforms
# Define transform to normalize data
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
 
# Load CIFAR-100 train and test datasets
trainset = datasets.CIFAR100(root='./data', train=True, download=True, transform=transform)
testset = datasets.CIFAR100(root='./data', train=False, download=True, transform=transform)
 
# Create data loaders for train and test datasets
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False) Code for loading CIFAR-100 dataset using PyTorch torchvision package. Retrieved from . on 20/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR100.html#torchvision.datasets.CIFAR100 4. ImageNet The ImageNet dataset in torchvision contains approximately 1.2 million training images, 50,000 validation images and 100,000 test images. Each image in the dataset is labeled with one of the 1,000 categories such as "cat," "dog," "car,", "airplane" etc. To download this torchvision dataset, you have to visit the or load in torchvision: website import torchvision.datasets as datasets
import torchvision.transforms as transforms
 
# Set the path to the ImageNet dataset on your machine
data_path = "/path/to/imagenet"
 
# Create the ImageNet dataset object with custom options
imagenet_train = datasets.ImageNet(
	root=data_path,
	split='train',
    transform=transforms.Compose([
        transforms.Resize(256),
        transforms.RandomCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225])
	]),
	download=False
)
 
imagenet_val = datasets.ImageNet(
	root=data_path,
	split='val',
    transform=transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(
            mean=[0.485, 0.456, 0.406],
            std=[0.229, 0.224, 0.225])
	]),
	download=False
)
 
# Print the number of images in the training and validation sets
print("Number of images in the training set:", len(imagenet_train))
print("Number of images in the validation set:", len(imagenet_val)) Code for loading ImageNet dataset using PyTorch torchvision package. Retrieved from . on 21/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageNet.html#torchvision.datasets.ImageNet 5. MS Coco The Microsoft Common Objects in Context(MS Coco) dataset contains 328,000 high-quality visual images of everyday objects and humans, often used as a standard to compare the performance of algorithms in real-time object detection. To download this torchvision dataset, please visit the or load in torchvision: website import torch
from torchvision import datasets, transforms
# Define transformation
transform = transforms.Compose([
   transforms.Resize(256),
   transforms.CenterCrop(224),
   transforms.ToTensor(),
   transforms.Normalize(mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225])
])
 
# Load training dataset
train_dataset = datasets.CocoDetection(root='/path/to/dataset/train2017',
                                       annFile='/path/to/dataset/annotations/instances_train2017.json',
                                       transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
 
# Load validation dataset
val_dataset = datasets.CocoDetection(root='/path/to/dataset/val2017',
                                     annFile='/path/to/dataset/annotations/instances_val2017.json',
                                     transform=transform)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Make sure to replace the /path/to/dataset placeholders with the actual path to your dataset directory. Also, adjust the batch_size parameter to fit your needs. Code for loading MS Coco dataset using PyTorch torchvision package. Retrieved from . https://pytorch.org/vision/stable/generated/torchvision.datasets.CocoDetection.html#torchvision.datasets.CocoDetection on 21/3/2023 6. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images (training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. This torchvision dataset can be downloaded from , or loaded using this code: Kaggle import torch
import torchvision
import torchvision.transforms as transforms

# Define transformations
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])
# Load the dataset
trainset = torchvision.datasets.FashionMNIST(root='./data', train=True,
                                        download=True, transform=transform)

testset = torchvision.datasets.FashionMNIST(root='./data', train=False,
                                       download=True, transform=transform)

# Create data loaders
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2) Code for loading Fashion-MNIST dataset using PyTorch torchvision package. Retrieved from . on 21/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.FashionMNIST.html#torchvision.datasets.FashionMNIST 7. SVHN The SVHN (Street View House Numbers) dataset is an image dataset derived from Google’s Street View imagery, which consists of cropped images of house numbers taken from street-level images. It is available in a full format with all the house numbers and their bounding boxes and a cropped format with just the house numbers alone. The full format is often used for object detection tasks, while the cropped format is commonly used for classification tasks. The SVHN dataset is also included in the torchvision package and it contains 73,257 images for training, 26,032 images for testing and 531,131 additional images for extra training data. To download this torchvision dataset, you can go to or you can load it here: Kaggle import torchvision
import torch

# Load the train and test sets
train_set = torchvision.datasets.SVHN(root='./data', split='train', download=True, transform=torchvision.transforms.ToTensor())
test_set = torchvision.datasets.SVHN(root='./data', split='test', download=True, transform=torchvision.transforms.ToTensor())

# Create data loaders
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False) Code for loading SVHN dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.SVHN.html#torchvision.datasets.SVHN 8. STL-10 The STL-10 dataset is an image recognition dataset that consists of 10 classes, with a total of about 6,000+ images. The STL-10 stands for “Standard Training and Test Set for Image Recognition-10 classes” and the 10 classes in the dataset are: Airplane Bird Car Cat Deer Dog Horse Monkey Ship Truck To access this dataset, you can download it directly from or load the dataset using torchvision: Kaggle import torchvision.datasets as datasets
import torchvision.transforms as transforms

# Define the transformation to apply to the data
transform = transforms.Compose([
    transforms.ToTensor(),   

# Convert PIL image to PyTorch tensor
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))   # Normalize the data
])

# Load the STL-10 dataset
train_dataset = datasets.STL10(root='./data', split='train', download=True, transform=transform)
test_dataset = datasets.STL10(root='./data', split='test', download=True, transform=transform) Code for loading STL-10 dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.STL10.html#torchvision.datasets.STL10 9. CelebA This torchvision dataset is a popular large-scale face attributes dataset which comprises over 200,000 celebrity images. It was first released by researchers at the Chinese University of Hong Kong in 2015. An image in the CelebA consists of 40 facial attributes such as age, hair colour, facial expression and gender. Also, these images were retrieved from the internet and cover a wide range of facial appearances, including different races, ages and genders. Bounding box annotations for the location of the face in each image, as well as 5 landmark points for the eyes, nose and mouth. You can download this dataset on or load it using this code: Kaggle import torchvision.datasets as datasets
import torchvision.transforms as transforms

transform = transforms.Compose([
    transforms.CenterCrop(178),
    transforms.Resize(128),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
celeba_dataset = datasets.CelebA(root='./data', split='train', transform=transform, download=True) Code for loading CelebA dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.CelebA.html#torchvision.datasets.CelebA 10. PASCAL VOC The VOC dataset (Visual Object Classes) was first introduced in 2005 as part of the PASCAL VOC Challenge, which aimed at advancing the state of the art in visual recognition. It consists of images of 20 different object categories, including animals, vehicles, and common household objects. Each of these images is annotated with the locations and classifications of objects within the image. The annotations include both bounding boxes and pixel-level segmentation masks. The dataset is split into two main sets: the training and validation sets. The training set contains approximately 5,000 images with annotations, while the validation set contains around 5,000 images without annotations. In addition, the dataset also includes a test set with approximately 10,000 images, but the annotations for this set are not publicly available. To access the recent dataset, you can download from the , or load it in torchvision: website Kaggle import torch
import torchvision
from torchvision import transforms

# Define transformations to apply to the images
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load the train and validation datasets
train_dataset = torchvision.datasets.VOCDetection(root='./data', year='2007', image_set='train', transform=transform)
val_dataset = torchvision.datasets.VOCDetection(root='./data', year='2007', image_set='val', transform=transform)
# Create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Code for loading PASCAL VOC dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.VOCDetection.html#torchvision.datasets.VOCDetection 11. Places365 The Places365 dataset is a large-scale scene recognition dataset with more than 1.8 million images covering 365 scene categories. The Places365 Standard dataset consists of around 1.8 million images, while the Places365-Challenge dataset contains 50,000 additional validation images that are more challenging for recognition models. To access this dataset, you can use or load in torchvision here: Kaggle import torch
import torchvision
from torchvision import transforms

# Define transformations to apply to the images
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Load the train and validation datasets
train_dataset = torchvision.datasets.Places365(root='./data', split='train-standard', transform=transform)
val_dataset = torchvision.datasets.Places365(root='./data', split='val', transform=transform)
# Create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False) Code for loading Places365 dataset using PyTorch torchvision package. Retrieved from . on 22/3/2023 https://pytorch.org/vision/stable/generated/torchvision.datasets.Places365.html#torchvision.datasets.Places365 Common Use Cases for Torchvision Datasets - This dataset is commonly used for image classification tasks, particularly for handwritten digit recognition. MNIST - These datasets are commonly used for image classification tasks, particularly for object recognition. and CIFAR-10 CIFAR-100 - This torchvision dataset contains millions of images of various objects and animals and is commonly used for object recognition tasks. ImageNet - This dataset is commonly used for object detection, segmentation, and captioning tasks. MS COCO - Similar to MNIST, this dataset is commonly used for image classification tasks, particularly for fashion item recognition. Fashion-MNIST - This dataset is commonly used for digit recognition tasks. It is particularly useful for developing and testing algorithms for recognizing numbers in real-world settings. SVHN - This torchvision dataset is commonly used for image classification tasks, particularly for object recognition. STL-10 - This dataset is commonly used for facial recognition and attribute classification tasks. It consists of images of various facial expressions, poses and backgrounds. CelebA - This dataset is commonly used for object detection and segmentation tasks. Pascal VOC - This dataset is commonly used for scene recognition tasks. It consists of images of various indoor and outdoor scenes such as bedrooms, beaches, and offices. Places365 Final Thoughts Torchvision datasets are often used for training and evaluating machine learning models such as convolutional neural networks (CNNs), which are commonly used in computer vision applications. They are also available for anyone to download and use freely. The lead image of this article was generated via model using the prompt 'thousands of images organized together in small frames'. HackerNoon's AI Stable Diffusion More Dataset Listicles: Excel Datasets Keras Datasets R Datasets PyTorch Datasets Hugging Datasets

11 Torchvision Datasets for Computer Vision You Need to Know

কম্পিউটার ভিশনের জন্য 11 টর্চভিশন ডেটাসেট আপনার জানা দরকার

11 Torchvision-Datensätze für Computer Vision, die Sie kennen müssen

11 conjuntos de datos de Torchvision para visión por computadora que necesita saber

11 ensembles de données Torchvision pour la vision par ordinateur que vous devez connaître

कंप्यूटर विजन के लिए 11 टॉर्चविजन डेटासेट आपको जानना जरूरी है

知っておくべきコンピューター ビジョン用の 11 の Torchvision データセット

당신이 알아야 할 11가지 컴퓨터 비전용 Torchvision 데이터 세트

11 conjuntos de dados Torchvision para visão computacional que você precisa conhecer

Bilgisayarlı Görme için Bilmeniz Gereken 11 Torchvision Veri Kümesi

11 bộ dữ liệu Torchvision cho thị giác máy tính bạn cần biết

你需要知道的 11 个用于计算机视觉的 Torchvision 数据集

Singapore, July 8, 2022-Torches Finance, a lending protocol on KCC, announced that it has received seed funding from KuCoin Ventures. KuCoin Ventures and KCC will jointly incubate and empower Torches Finance to build the infrastructure foundation for KCC. KuCoin Ventures is a leading investment arm of KuCoin, the world's leading crypto trading platform, aiming to discover and incubate high-quality projects in various fields such as DeFi, Web 3.0, and infrastructure. Torches Finance is the underlying lending protocol on KCC, which means users can supply or borrow assets on Torches. It helps users obtain passive income while maximizing asset utilization. Torches now has supported supply and borrow services of BTC, ETH, KCS, USDT, and USDC, and will officially start Torches Point Mining in Q3. Users can earn Torches Points by supplying or borrowing assets. Torches Points can be converted into Torches tokens ($TOR) according to certain coefficients after the token launch. It is reported that Torches will leverage the seed funding in product development, technology innovation, security, marketing, and other aspects to increase protocol adoption and market share. For example, in terms of products, Torches will further improve accessibility, optimize the user’s experience, upgrade product functions such as leveraged mining and NFTs, and continuously expand the richness of Torches services. In terms of technology, Torches will continue to improve the transparency of the protocol and work with KCC to increase protocol adoption. Joe, CEO of Torches said, "Receiving seed funding from KuCoin Ventures and the incubation from  KCC is a great integration of Torches with KCS ecosystem. Torches will maximize KCC's marketing, technology, and liquidity resources, and invest the funding into key areas such as product development and technology innovation. With the improvement of products and the launch of point mining, we hope to build Torches as the backbone of KCC’s DeFi ecosystem, and strive to become one of the most important infrastructures in the KCC ecosystem.” The Torches "Supply & Borrow to Win" campaign is still on, and users who provide liquidity (supply or borrow) to a certain amount will have the opportunity to win the whitelist of Torches Genesis NFTs - Torchlight. Torchlight will grant the owner multiple benefits, including but not limited to boosting buffs at Torches Point Mining, IDO allocation…etc. About Torches Finance Torches Finance is a decentralized lending protocol based on KCC, supporting mainstream crypto assets such as BTC, ETH, etc. Users can earn TOR tokens by depositing and lending assets on Torches Finance. Visit Torches Finance: https://www.torches.finance/en About KuCoin Ventures KuCoin Ventures is a leading investment arm of KuCoin Exchange that aims to invest in the most disruptive cryptocurrency and blockchain projects in Web3.0 era. With the commitment of empowering Crypto/Web 3.0 Builders with Deep Insights and Global Resources, it is also a community-friendly and research-driven full-stage corporate venture that focuses on DeFi, Game-Fi, Web 3.0, infrastructure, working closely with its portfolio projects throughout the journey of entrepreneurship.

KuCoin Ventures invests in Torches Finance, a Lending Protocol Based on KCC

Singapore, June 27, 2022 - Torches Finance, a decentralized lending protocol based on KuCoin Community Chian (KCC), announced the mainnet launch on KCC. Torches Finance has enabled supplying and borrowing services for BTC, ETH, KCS, USDT, and USDC. As a lending protocol, Torches Finance wants to achieve a win-win with KCC and leveraged assets on KCC. With the advantages of the low gas fee and gradually thriving KCC ecosystem, Torches Finance can further reduce the impermanent loss and increase the utilization of user assets. According to the official news, Torches Finance has made innovations in lendable assets, lending incentives, liquidation rules, risk control, and other aspects to differentiate Torches from other lending products in the market. Also, Torches Finance was negotiating with other projects of the KCC ecosystem, which will provide investors with more opportunities in the future. Torches have a unique economic incentive model where users can receive TOR tokens whenever they supply and borrow assets. It also revealed Torches Points Mining activities to motivate early users to contribute to Torches Finance. Users can gain a certain amount of Torches Points (TOP) by supplying, borrowing, and liquidating. As the contribution proof, TOP will be able to redeem to TOR after the official release of the TOR token at a specific ratio. The details will be announced at the token launch. Eric, the CMO of Torches Finance, said, “Torches' short-term goal is to become a DeFi project on KCC with massive hype. Torches Finance will meet the needs of asset management on the KCC. And it will work with KCC to expand the market space for DeFi to another level. ” With Torches Finance’s mainnet launch, it will hold a series of activities, including the Genesis NFT Collection, Torchlight, and Free Mint. Torches Finance will give out whitelists to communities. Users can follow Torches’ official Twitter and Discord to discover more. About Torches Finance Torches Finance is a decentralized lending protocol based on KCC, supporting mainstream crypto assets such as BTC, ETH, etc. Users can earn TOR tokens by both depositing and lending assets on Torches Finance. Visit Torches Finance: https://www.torches.finance/en

Torches Finance: A Decentralized Lending Protocol Launched on KCC

Torches Finance: un protocolo de préstamo descentralizado lanzado en KCC

Hi there! If you’re new to this series, I laid down some ground rules , and explain the idea for my first project . I document, a little every day, what I’m working on. in day zero on day one I’ve realized, especially with today’s update where I’m discussing torching some money on social advertising and Nginx boilerplate, that I sometimes throw a weird hodgepodge of stuff at you. I almost edited this post down to one topic, but I think this mix of random thoughts is the most honest representation of my day-to-day during this challenge. So let’s get into it. Image credit: CC0 https://pixabay.com/en/users/Intellectual-4717896/ Burning some cash, like the Joker but on a scale my wife won’t kick me out of bed for First, let me lead up to I’m burning a bit of cash on ad experiments. Today has been pretty lively for a Sunday: why Chat on is working. I’ve had amazing live chats today with lovely people. Now this may not sound like much. But this is a B2B SaaS landing page that’s been live for less than 24 hours and has no advertising or backlinks other than those related to this little series. bystander.io 3 I have new signups for the early access form on the landing page today. These exclude the few signups I’ve been able to battle for via cold outreach. 7 Traffic to the landing page and stats to this blog continue to grow. While the stats for this series may not mean much in terms of validating Bystander, it sure does mean a lot to me and keeps me incredibly motivated (seriously, thank you to everyone reading and messaging me) For those curious how this little series documenting my journey has grown on Medium from day zero: Moving the series into Hacker Noon has been great Now, back on topic the topic of throwing away some cash. This budding interest in Bystander gives me the confidence to move a bit farther. A big goal over the next two days is setting up an option to preorder. Preordering will save businesses 50% over the period they purchase. I’ve never tried the preordering method of validation, and I’d like to see if we can make it work here. My assumptions now, are, a business who makes the leap for a preorder is someone I’m in active communication with. I don’t expect a landing page alone to be able to convert someone into prepaying for a B2B SaaS solution. So why ads? They’re a cheap way for me to test my copy and assumptions at a larger scale than I currently can organically. For trying to get folks to preorder I want to have the best copy and value proposition I can possibly have with my current resources and traffic. Now, first time ad accounts usually offer bonuses that are quite generous for the scale were working with. I want to keep my spend below 200 dollars here. But , for example, will give you $50 credit to start with, taking advantage of this and similar bonuses stretches that small testing budget quite a bit farther. LinkedIn Each campaign on both and LinkedIn . Facebook are both running 3 different ads With each add set, I’m testing two different things: My headlines & subheadlines My audience’s by job title: user experience professionals VS. support leaders VS. project managers Here’s an example LinkedIn ad for the User Experience camp: I don’t expect conversions in this small run at advertising. The scale of my spend is tiny, it’s my first go ad advertising Bystander, and my primary goal is to see if I can pin down a measurable difference on my audience and headlines to help focus my early efforts. Plus, I can’t learn about advertising by just reading, I need to just start. And at the very least, I’m spending a bit of money for your entertainment 😂 Going into some details and small changes in Bystander’s stack Some folks have kindly expressed an interest in the stack I’m working with for . I touched on this briefly , but I thought it may interest some folks on a more detailed look at the stack and the tools I use on a daily basis. Bystander.io on day 2 I come from a front-end development background originally. But most recently spent time as a Senior Engineer in Customer Support. Support engineering is a strange beast, you get really wide knowledge on an unbelievable number of technologies and customer stacks. But it’s like exploring an ankle deep ocean of tech — you find things fast, in any setting, and fix them. You don’t generally have a need to further explore code (or the time!) Going solo on a project, I mostly stick to what I know, and may toss _just one_new piece into the stack that I’m interested in trying or learning. You do have to stay interested and excited after all! The hours are long when you try something new. I’ve made the mistake of trying to build a project with all the new cool things a few times before, and progress ends up so slow I lose motivation and abandon ship. So! The stack for this … Bystander Backend: latest stable with . Node Express with MongoDB Mongoose for authentication and access control, authentication is something that’s been solved a million times. But working on a solo project, authentication-as-a-service saves me a ton of time. And it let’s me be confident in an otherwise scary space. Their content team is top notch too, if any of that interests you, check out . Auth0 their blog Express+Mongoose+Auth0 let me make APIs with protected endpoints incredibly fast. On the frontend: , which requires . I find Typescript an absolute joy to work with. It’s a superset of JS that let’s you develop with JS as a strongly typed language. So, working with Angular in and IDE like gives you typeahead on all the things and lets you catch compile errors. Angular 5 Typescript Webstorm For a design and component framework, my original plan was to go with , but the screenshots I currently made available actually leverage a , with components I’ve already sliced down into Angular components/services for a previous project. I’m still tempted by Nebular as it feels more modern, but really, I need to double down and focus on function rather than form. Nebular Bootstrap official theme The new (to me!) tech I’m playing with for this project is , although in the form of (Angular’s flavor of Redux) NgRx/Redux put your application’s state into a single object. So you get some reallllyyy cool benefits when it comes to debugging. is worth a watch. Plus, even without having an interest in Redux or NgRx, this is the best format I’ve ever seen for two developers presenting a talk. Brandon and Mike did an awesome job on this. Redux NgRx This talk by Ngrx maintainers Hosting Related: droplets: one for marketing+blog, one for the app’s node instance. Scalability comes when I actually have customers. Digital ocean Nginx . Cause it’s easy to use and their free services have a real impact. Cloudflare (Thanks to for recommending them over MLab) MongoDB’s own database-as-a-service a kind reader Applications I use daily: , still hard to beat once you get it set up with the plugins needed for your project. Although, because of Angular 5 I’m spending less time in there and more time in… Sublime Text Webstorm which I touched on earlier. makes it so your terminal is awesome. If you don’t already use iTerm2, I’d Google around a bit to see how other folks set it up. That will save you some time on getting the visual side going. I LOVE tabs and color coding so I can see where I have different services running locally: iTerm2 Red, Blue two different running stances of an app on different ports so I can test two branches live. Which are my active tabs where I actually need to work in the console. to take screenshots and annotate them for documenting this journey. It’s free and amazing. Skitch when I need to quickly record a Gif of part of my screen. Also free and super simple to use. LICEcap My own Slack (free) workspace to pipe notifications and data to me. Checking different emails, chat messages from Bystander.io, keeping up with Tweets, and follows on medium… Pretty much anything I ever need to tab over to, to check any sort of notification, I just yank that into a slack channel to avoid distractions and save a ton of time. Plus I don’t have room for new tabs when I’m working: I know I need to close some tabs when the icons go away, or my laptop’s fan is giving me a headache Spotify to listen to weird stuff. And I work until 11pm/midnight, so I keep Night Shift turned on, it definitely helps me. For any screen that isn’t iOS based, I use for the same purpose. Flux Reduce some blue light to help me get to sleep after a late night working And that’s about everything! What tools are essential in your workflow? Is there anything you use that might save me some time? ’Cause I could certainly do with a bit more time! Tomorrow, Day 10! Begin hooking up Stripe to add a pre-order option. Lots and lots of follow up. I need to touch base with new subscribers, folks I’m cold emailing, and the few people from live chat. More cold outreach (it never stops) Rollover from today’s list, to get Bystander’s Ghost blog into a template I like 5 million more things that Ican’tfitintotheday Thanks for reading, this turned out quite long! And of course, feel free to email me with any questions or feedback! where this was originally published. And I’ll see you all tomorrow! My email is on my homepage : Forward to day 9: Let’s talk pricing Back to day 8 Please rip my landing page to shreds

[Day 9] Zero to MVP in 30 Days — Torching some cash

ATLAS congratulates Torchlight Energy on a successful Annual Shareholder Meeting I recently had the pleasure of attending Torchlight Energy’s . With Torchlight Energy being a large portfolio holding and with Torchlight being a multi-year investment for ATLAS — a considerable time, opportunity, capital, and — the was an important checkpoint. Very rarely are executive management, the Board of Directors, and key investors in such an intimate and real-time response environment. Torchlight’s offered a unique opportunity to update our investment thesis; as well as to consider optimization strategies, potential value-add scenarios, and exit timelines. Annual Shareholder Meeting reputation commitment Annual Shareholder Meeting Annual Shareholder Meeting We’re happy to report that we were more than satisfied with the and that we continue to believe in the disruptive potential of Torchlight Energy’s technology-first approach. Never before have we been more confident that Torchlight has the as well as the underlying to be able to execute its investment thesis. While Torchlight very much so still has venture level execution risk, we believe that it has the operating strategy to execute — even given the historic degree of difficulty it faces in attacking its assets. While every potentially industry disrupting endeavor has unique risks and unique uncertainties, post- we’re confident that Torchlight’s wildcatting attempts in both its Hazel Project and its Orogrande Project will yield productive results. Annual Shareholder Meeting; technology geology Orogrande Project Annual Shareholder Meeting With Torchlight being just months away from a drilling and completion program launch — Torchlight has already launched in the Hazel Project, it’s paramount that Torchlight be sure of its operational program. In hosting numerous multi-hour meetings with both the executive management team and the Board of Directors, I’m confident that the execution strategy is optimized. multi-play Torchlight management, impressively so, presented a point-to-point, logical, and decisive technical-strategy for attacking both the as well as the [the first horizontal Well attempts in the Orogrande Project and Hazel Project, respectively]. In meeting with Torchlight’s operations and technical team, I was impressed with the degree of granularity that each technical risk had been assessed and that each technical risk had been resolved . Torchlight, in staying to its core competency, has found ways to deploy modern Frac Tech to tailor a scheme-based solution for each anticipated complexity. University Founders A25 #1 Well Flying B Ranch #3H Well through technology-first, engineered solutions It’s in these engineered solutions that I derive my conviction in [1] the operational approach being deployed and [2] the probability of ultimate success. Seemingly, Torchlight has the architecture, design, landing capacity, and technology to be able to execute. Torchlight’s ultimate success or failure will be entirely dependent on its ability to maximize the utility of current Frac Tech; effectively, the Torchlight investment thesis and company “DNA”. “ aving done very detailed mapping and avoiding areas with intense faulting, volcanism, high clay contents, and which were overly mature [thermally], Torchlight Energy zoomed in on a 168,000 net acres within the oil window in Texas” — Wunderlich Securities [New Permutations to the Permian Theme: The Alpine High and Orogrande Basin] H But Torchlight isn’t simply an operational story, it’s also very much so a story. Torchlight’s geology — its underlying acreage, collected through a unique combination of relationships as well as a willingness to attack difficult complexities, is absolutely sound; a belief that was reinforced in multiple presentations at the Torchlight has collected libraries of data — data which it has been able to refine, cleanse, model, and analyze in ways that have never been applied to either the Hazel Project or the Orogrande Project. Post- I’m convinced that Torchlight has an unprecedented geological insight into its two resource plays; one that it believes has helped it identify productive acreage within undeveloped Projects. Quite possibly Torchlight’s most valuable asset might be its geological expertise — the collective geological intellectual capacity of its combined management team, Board of Directors, and consulting geologist. geology Annual Shareholder Meeting. Annual Shareholder Meeting Torchlight’s unique ability to read and assess complex geology — including subsets of geology within larger Projects or plays— has allowed it to aggregate a uniquely large acreage position for an E&P its size. We challenged Torchlight on [i] , [ii] , [iii] , [iv] , [v] , [vi] , [vii] , [viii] , [ix] , and [x] . In every instance we were met with more than adequate rebuttal. Geology, in our opinion, was the single largest risk to the investment thesis at Torchlight — given that geology can’t be altered, iterated, or changed [as nearly all other parts of the operational process can]. Having performed multiple interviews and sat through multiple presentations, we believe Torchlight’s geology to be greatly derisked to our prior evaluations; and it should be noted that we were more than satisfied to be invested at “prior evaluations”. thickness of reservoir porosity permeability brittleness clay content TOC thermal maturity matrix permeability pore throat sizing overall complexity translating to operational risk orchlight Energy is looking to challenge the unique geological complexities of the Orogrande Basin — one of its two major Projects; to challenge the extraordinary Well-design difficulty presented by the Orogrande Basin, and to challenge the nearly limitless down hole variables it is sure to encounter upon launching its drilling and exploration program T Having completed initial data-collection and analysis efforts, Torchlight Energy is proceeding with first horizontal-Well deployment; including the first horizontal effort in the history of the Orogrande Basin. With these horizontal efforts, Torchlight moves beyond vertical Well-testing and vertical Well data collection and into thesis prove-out. With these horizontal efforts, Torchlight will be able to deploy the full war chest of its technology, its geological, geophysical, and Well-design learnings, as well as its [vertical-Well tested] operating learnings. Torchlight Energy is ready to prove concept. We’ve never been more confident in Torchlight or in the strategy for attacking both the Hazel Project and the Orogrande Project; and we’re extremely excited that Torchlight has moved forward with horizontal Well deployment. Through each stage of maturation and through each stage of optimization we’ve been proud to work with Torchlight Energy management and we’ve been proud to support the E&P’s development efforts. We continue to believe in the team at Torchlight Energy and we continue to believe in the disruptive potential of its technology-first approach to revolutionizing the Energy Complex. DISCLOSURE: [1] Torchlight Energy Resources, Inc. was not involved in any aspect of the above note preparation or in any aspect of the preparation of any data or information presented [2] I have not received compensation from Torchlight Energy Resources, Inc. or any other party for the writing of this note; and/or any other note written prior [regarding Torchlight Energy Resources, Inc.] [3] I may or may not have received compensation for providing research and/or consulting regarding Torchlight Energy Resources, Inc. from third-parties at some point prior to writing this note and/or I may receive compensation for providing research and/or consulting regarding Torchlight Energy Resources, Inc. at a later date. This includes providing subscription-based research and/or consulting services to both institutional and retail-based capital market participants. [4] Dallas Salazar: I own, or my family or company owns, or I have influence over some amount of the outstanding shares of the following companies mentioned in this note: Torchlight Energy Resources, Inc. [5] The article does not constitute investment advice. Each reader is encouraged to consult with his or her individual financial professional and any action a reader takes as a result of information presented here is his or her own responsibility. [6] Torchlight Energy Resources, Inc. holds the license to certain images included in the note above; to which the company has allowed me a one-time publishing right. I did not pay for this right nor will I be providing a fee of any sort for this right into the future.

Torchlight Energy: A Billion Barrel Vision Nears Proof of Concept

ATLAS congratulates the Torchlight Energy team on permitting its first horizontal Well in the Orogrande Project Nearly two years ago, into the heart of what would eventually prove out to be the longest and most catastrophic commodity crisis in the history of Energy, we at ATLAS made a data-based bet on a pre-production oil and gas E&P [i.e., “exploration and production company”]. At the time, prior to an aggressive and historic asset restructuring effort, the E&P we had bet on had substantially one asset we were interested in: its [based in the — a completely unproven West Texas resource play]. Orogrande Project Orogrande Basin The Orogrande Basin, effectively leased in its entirety by Torchlight Energy, closely resembled its tethered-E&P — it had no established production and it presented its own set of unique stress points. This is why Torchlight had the opportunity to lease the Orogrande Basin acreage [1] in its entirety and [2] at economics that made sense to a pre-production, stressed-E&P. Nobody else wanted the Orogrande Basin. The Orogrande Basin had a long, established, and something of infamous track record of turning back E&Ps — of leaving them wanting. Of course, it only turned these E&Ps back after the E&Ps had spent millions in trying to develop what could potentially be a billion barrel resource play — the reason many an E&P had tried their hand at wildcatting the play in the first place [the potential payload locked within the rocks]. The Orogrande Basin had become a pariah; one that not only beat its challengers — but embarrassed them. Littered with geological complexities thanks to intense geological-deformations caused by millions of years of uplifts, faulting, igneous intrusions and volcanism — looking for prospective pockets is infinitely harder in the Orogrande Basin than, say, the Midland Basin; which is an eastern located, much friendlier, much more famous peer of the Orogrande Basin But Torchlight wasn’t coming to the Orogrande Basin — which houses its Orogrande Project — looking to replicate the efforts of its predecessors. Torchlight was optimizing an Orogrande Project-tailored Well-strategy that would see it deploy twenty years of macro-technology evolution; of translated evolution that it believed gave it unprecedented geological and Well-design insight as well as unprecedented technical prowess. Torchlight was looking to challenge the unique geological complexities of the Orogrande Basin, to challenge the extraordinary Well-design difficulty presented by the Orogrande Basin, and to challenge the nearly limitless down hole variables it was sure to encounter upon launching its drilling and exploration program. Frac Tech We believed that Torchlight, led into the Orogrande Basin by famed prospector-geologist Rich Masterson, could execute — if it were able to collect enough data prior to launching a wildcatting program — and we believed that Torchlight had the geological, geophysical, and reservoir engineering team in place to do what had never been done before. In short, we believed that Torchlight could by successfully proving out its Orogrande Project efforts. Torchlight, with its last vertical Well-test, has gathered the necessary data to move the Orogrande Project thesis forward. disrupt the global Energy Complex Nearly two years later, Torchlight Energy has permitted the first horizontal Well for its Orogrande Project; the first horizontal effort in the history of the Orogrande Basin. With this horizontal effort, Torchlight moves beyond vertical Well-testing and vertical Well data collection and into thesis prove-out. With this horizontal effort, Torchlight will be able to deploy the full war chest of its technology, its geological, geophysical, and Well-design learnings, as well as its vertical-Well tested operating-learnings. Torchlight, more so than any of its predecessors and more so than any E&P in the history of the Orogrande Basin, is ready to prove concept; it’s ready to unlock its Orogrande Project. We met with the Torchlight team at Torchlight HQ just two weeks ago to review overall strategy, Well-location, and the geological complexities of the Orogrande Project. We’ve never been more confident in Torchlight or in the strategy for attacking the Orogrande Project; and we’re extremely excited that Torchlight has moved forward with a horizontal permit. “Having done very detailed mapping and avoiding areas with intense faulting, volcanism, high clay contents, and which were overly mature [thermally], Torchlight Energy zoomed in on a 168,000 net acres within the oil window in Texas” — Wunderlich Securities [New Permutations to the Permian Theme: The Alpine High and Orogrande Basin] Through each stage of maturation and through each stage of optimization we’ve been proud to work with Torchlight management and we’ve been proud to support its development efforts. We continue to believe in the team at Torchlight and we continue to believe in the disruptive potential of its technology-first approach to revolutionizing the Energy Complex. Congratulations on first horizontal Well permit, Torchlight Energy. DISCLOSURE: [1] Torchlight Energy Resources, Inc. was not involved in any aspect of the above note preparation or in any aspect of the preparation of any data or information presented [2] I have not received compensation from Torchlight Energy Resources, Inc. or any other party for the writing of this note; and/or any other note written prior [regarding Torchlight Energy Resources, Inc.] [3] I may or may not have received compensation for providing research and/or consulting regarding Torchlight Energy Resources, Inc. from third-parties at some point prior to writing this note and/or I may receive compensation for providing research and/or consulting regarding Torchlight Energy Resources, Inc. at a later date. This includes providing subscription-based research and/or consulting services to both institutional and retail-based capital market participants. [4] Dallas Salazar: I own, or my family or company owns, or I have influence over some amount of the outstanding shares of the following companies mentioned in this note: Torchlight Energy Resources, Inc. [5] The article does not constitute investment advice. Each reader is encouraged to consult with his or her individual financial professional and any action a reader takes as a result of information presented here is his or her own responsibility. [6] Torchlight Energy Resources, Inc. holds the license to the title image included in the note title; to which the company has allowed me a one-time publishing right. I did not pay for this right nor will I be providing a fee of any sort for this right into the future.

Texas Wildcatter Torchlight Energy Takes Frac Tech to the Orogrande Project

The Crowded AI Spaces Still Have Room for One More Genius

জনাকীর্ণ এআই স্পেসগুলিতে এখনও আরও একটি প্রতিভা থাকার জায়গা রয়েছে

In den überfüllten KI-Räumen ist noch Platz für ein weiteres Genie

Los abarrotados espacios de IA todavía tienen espacio para un genio más

Les espaces IA bondés ont encore de la place pour un génie supplémentaire

भीड़-भाड़ वाले एआई स्पेस में अभी भी एक और प्रतिभा के लिए जगह है

混雑したAI空間にはまだもう一人の天才の余地がある

혼잡한 AI 공간에는 아직 천재가 한 명 더 들어갈 여지가 있습니다

Os lotados espaços de IA ainda têm espaço para mais um gênio

В переполненных пространствах ИИ все еще есть место для еще одного гения

Kalabalık Yapay Zeka Alanlarında Hala Bir Dahiye Daha Yer Var

Không gian AI đông đúc vẫn còn chỗ cho một thiên tài nữa

拥挤的人工智能领域仍有空间容纳更多天才

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