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abusufyanvu / 6S191 MIT DeepLearningMIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning
sankalpjain99 / Automatic Essay ScoringCreated a web app that can automatically score essays. The grading model was trained using HP Essays Dataset from Kaggle. Used Long Short Term Memory (LSTM) network and machine learning algorithms to train model. WebApp was created using Flask framework.
thstielow / Raspi Bme680 IaqBasic IAQ calculator for the Bosch bme680 sensor, compensating the humidity dependency and long-term drifts. Outputs a gas quality score on a range of 0-100%.
royalosyin / Calculate Precipitation Based Agricultural Drought Indices With PythonPrecipitation-based indices are generally considered as the simplest indices because they are calculated solely based on long-term rainfall records that are often available. The mostly used precipitation-based indices consist of Decile Index (DI) Hutchinson Drought Severity Index (HDSI) Percen of Normal Index (PNI) Z-Score Index (ZSI) China-Z Index (CZI) Modified China-Z Index (MCZI) Rainfall Anomaly Index (RAI) Effective Drought Index (EDI) Standardized Precipitation Index (SPI).
zhenglz / OnionNet SFCTImproving protein–ligand docking and screening accuracies by incorporating a scoring function correction term
xuangch / CVPR22 GDLTThe code for CVPR2022 paper "Likert Scoring with Grade Decoupling for Long-term Action Assessment".
lmh9507 / CASAOfficial implementation for "CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting" (IJCAI 2025)
RobotPsychologist / Bg ControlImproving short-term prandial blood glucose outcomes for people with type 1 diabetes, a complex disease that affects nearly 10 million people worldwide. We aim to leverage semi-supervised learning to identify unlabelled meals in time-series blood glucose data, develop meal-scoring functions, and explore causal machine-learning techniques.
jddeguia / Compare Forecast ModelsEnergy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns. In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM. Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.
LOVISHARYX / HRV And GSR As Viable Physiological Markers For Mental Health RecognitionMental stress has become a standard part of day-to-day life. However, experiencing long-term and high-level stress affects the daily life and wellness of the person. Consequently, an individual's performance and management ability degrade significantly in critical situations. Electrocardiogram (ECG), Galvanic Skin Response (GSR), Electromyogram (EMG), Skin Temperature (ST), and Respiration are essential physiological biomarkers to quantify stress effectively. This paper aims to classify the stress level with improved performance based on GSR and ECG-derived Heart Rate Variability (HRV) features using machine and deep learning algorithms. It uses the Stress Recognition in Automobile Drivers (SRAD) dataset, which contains a collection of multiparameter recordings from 17 healthy participants who drive on a prescribed route under various pressure conditions. The work uses Pearson's Correlation, RFECV, and LightGBM feature selection methods with different classifiers to reduce redundancy between features and enhance performance. The accuracy and F1-score for stress level classifications are computed and compared using machine and deep learning algorithms. For binary classification (stress vs. non-stress), Random Forest achieves the best classification accuracy of 93.96 % which is higher than previous works. It also provides an accuracy of 81.41 % for three-class (baseline vs. medium stress vs. high stress) stress level classification.
mongodb-partners / AI MemoryAn AI Memory Service that enhances AI agent with long-term memory capabilities, using MongoDB Atlas and AWS Bedrock to provide hierarchical memory structures with importance scoring, semantic search, and conversation summarization for personalized, contextually-aware interactions.
IkshitaMishra / TopicModelling LSA LDARetrieving 'Topics' (concept) from corpus using (1) Latent Dirichlet Allocation (Genism) for modelling. Perplexity and Coherence score were used as evaluation models. (2) Latent Semantic Analysis using Term Frequency- Inverse Document Frequency and Truncated Singular Value Decomposition.
Jai-Agarwal-04 / Sentiment Analysis With InsightsSentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.
suzanv / TermprofilingImplementation of the term scoring algorithm in Tomokiyo & Hurst (2003), based on Kullback-Leibler Divergence (kldiv). Given a foreground and background corpus, it returns the most descriptive terms of the foreground corpus in the form of a termcloud
pratikgirigoswami / Exemplar Based Image Inpainting• Image inpainting is the process of seamlessly filling in holes of arbitrary topology in an image to preserve its overall continuity. It is an ancient art of fixing accidental damage and recreating lost information. • Object removal or modification in the original images can be carried out through image inpainting methods. • In this project, various algorithms of Partial Derivative Equation based and Exemplar-based families have been studied and implemented. Results using Total Variation (TV) and Curvature Driven Diffusion (CDD) methods show that CDD produces a better visual quality of results. However, it fails to restore texture information. • To solve this problem, Exemplar-based algorithms are studied and implemented. Traditionally, the data term present in this algorithm is based on the strength of the isophote found using the gradient. The problem with the gradient operator is studied, and a better contour preserving data term is proposed. The proposed data term uses the strength of structure line found using Infinite size Symmetric Exponential Filter (ISEF). This filter helps overcome the drawback of which overcomes the drawback of insensibility to noise and precision of edge localization present in traditional data term. • Results are compared by quantitative analysis using PSNR, SSIM, and FSIM. Subjective analysis is done using Mean Opinion Score. It is proved that the proposed method produces better visual results compared to few other existing exemplar-based methods. • Methods/Keywords: Exemplar-based Image Inpainting, PDE-based Image Inpainting, ISEF Filter, Priority Computation, Isophote, Curvature Driven Diffusion • Software/Tools/Programming Language Used: MATLAB, C
shreyash2610 / Convolutional Long Short Term Memory Based IOT Node For Violence DetectionAbstract— Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model’s sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of ConvLSTM model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized convolutional neural networks (CNNs) to learn spatial features from video’s frames that were applied to Long Short- Term Memory (LSTM) for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as playing football, basketball, tennis, swimming and eating. The experimental results show high performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %.
gaoalexander / Web Search EngineCreates Varbyte-Compressed Inverted Index of the Common Crawl dataset (https://commoncrawl.org/), as well as associated Lexicon, Term Dictionary, and Page Table. Allows real-time querying (both conjunctive and disjunctive) of millions of pages ranked by BM25 Score, with average query time < 1s.
ucuapps / Robust DL Pipeline For PVC LocalizationPremature ventricular contraction(PVC) is among the most frequently occurring types of arrhythmias. Along with other cardiovascular diseases, it may easily cause hazardous health conditions, making PVC detection task extremely important in cardiac care. However, the long-term nature of monitoring, sophisticated morphological features, and patient variability makes the manual observation of PVC an impractical task. Existing approaches for automated PVC identification suffer from a range of disadvantages. These include domain-specific handcrafted features, usage of manually delineated R peaks locations, tested on a tiny sample of PVC beats(usually a small subset of MIT-BIH database). We address some of these drawbacks in proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. It consists of two neural networks. The first one is an encoder-decoder architecture that localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model, adopted for ECG signal data, does the delineation of healthy versus PVC bits. We have performed the extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task.
Lhagawajaw / 11 36 00 PM Build Ready To Start 11 36 02 PM Build Image Version 72a309a113b53ef075815b129953617811:36:00 PM: Build ready to start 11:36:02 PM: build-image version: 72a309a113b53ef075815b129953617827965e48 (focal) 11:36:02 PM: build-image tag: v4.8.2 11:36:02 PM: buildbot version: 72ebfe61ef7a5152002962d9129cc52f5b1bb560 11:36:02 PM: Fetching cached dependencies 11:36:02 PM: Failed to fetch cache, continuing with build 11:36:02 PM: Starting to prepare the repo for build 11:36:02 PM: No cached dependencies found. Cloning fresh repo 11:36:02 PM: git clone https://github.com/netlify-templates/gatsby-ecommerce-theme 11:36:03 PM: Preparing Git Reference refs/heads/main 11:36:04 PM: Parsing package.json dependencies 11:36:05 PM: Starting build script 11:36:05 PM: Installing dependencies 11:36:05 PM: Python version set to 2.7 11:36:06 PM: v16.15.1 is already installed. 11:36:06 PM: Now using node v16.15.1 (npm v8.11.0) 11:36:06 PM: Started restoring cached build plugins 11:36:06 PM: Finished restoring cached build plugins 11:36:06 PM: Attempting ruby version 2.7.2, read from environment 11:36:08 PM: Using ruby version 2.7.2 11:36:08 PM: Using PHP version 8.0 11:36:08 PM: No npm workspaces detected 11:36:08 PM: Started restoring cached node modules 11:36:08 PM: Finished restoring cached node modules 11:36:09 PM: Installing NPM modules using NPM version 8.11.0 11:36:09 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:36:09 PM: npm WARN config location in the cache, and they are managed by 11:36:09 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:36:09 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:36:09 PM: npm WARN config location in the cache, and they are managed by 11:36:09 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:36:24 PM: npm WARN deprecated source-map-url@0.4.1: See https://github.com/lydell/source-map-url#deprecated 11:36:25 PM: npm WARN deprecated source-map-resolve@0.5.3: See https://github.com/lydell/source-map-resolve#deprecated 11:36:26 PM: npm WARN deprecated uuid@3.4.0: Please upgrade to version 7 or higher. Older versions may use Math.random() in certain circumstances, which is known to be problematic. See https://v8.dev/blog/math-random for details. 11:36:28 PM: npm WARN deprecated querystring@0.2.1: The querystring API is considered Legacy. new code should use the URLSearchParams API instead. 11:36:33 PM: npm WARN deprecated subscriptions-transport-ws@0.9.19: The `subscriptions-transport-ws` package is no longer maintained. We recommend you use `graphql-ws` instead. For help migrating Apollo software to `graphql-ws`, see https://www.apollographql.com/docs/apollo-server/data/subscriptions/#switching-from-subscriptions-transport-ws For general help using `graphql-ws`, see https://github.com/enisdenjo/graphql-ws/blob/master/README.md 11:36:36 PM: npm WARN deprecated async-cache@1.1.0: No longer maintained. Use [lru-cache](http://npm.im/lru-cache) version 7.6 or higher, and provide an asynchronous `fetchMethod` option. 11:36:37 PM: npm WARN deprecated babel-eslint@10.1.0: babel-eslint is now @babel/eslint-parser. This package will no longer receive updates. 11:36:41 PM: npm WARN deprecated devcert@1.2.0: critical regex denial of service bug fixed in 1.2.1 patch 11:36:42 PM: npm WARN deprecated debug@4.1.1: Debug versions >=3.2.0 <3.2.7 || >=4 <4.3.1 have a low-severity ReDos regression when used in a Node.js environment. It is recommended you upgrade to 3.2.7 or 4.3.1. (https://github.com/visionmedia/debug/issues/797) 11:36:45 PM: npm WARN deprecated urix@0.1.0: Please see https://github.com/lydell/urix#deprecated 11:36:45 PM: npm WARN deprecated resolve-url@0.2.1: https://github.com/lydell/resolve-url#deprecated 11:36:53 PM: npm WARN deprecated puppeteer@7.1.0: Version no longer supported. Upgrade to @latest 11:37:30 PM: added 2044 packages, and audited 2045 packages in 1m 11:37:30 PM: 208 packages are looking for funding 11:37:30 PM: run `npm fund` for details 11:37:30 PM: 41 vulnerabilities (13 moderate, 25 high, 3 critical) 11:37:30 PM: To address issues that do not require attention, run: 11:37:30 PM: npm audit fix 11:37:30 PM: To address all issues possible (including breaking changes), run: 11:37:30 PM: npm audit fix --force 11:37:30 PM: Some issues need review, and may require choosing 11:37:30 PM: a different dependency. 11:37:30 PM: Run `npm audit` for details. 11:37:30 PM: NPM modules installed 11:37:31 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:37:31 PM: npm WARN config location in the cache, and they are managed by 11:37:31 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:37:31 PM: Started restoring cached go cache 11:37:31 PM: Finished restoring cached go cache 11:37:31 PM: Installing Go version 1.17 (requested 1.17) 11:37:36 PM: unset GOOS; 11:37:36 PM: unset GOARCH; 11:37:36 PM: export GOROOT='/opt/buildhome/.gimme/versions/go1.17.linux.amd64'; 11:37:36 PM: export PATH="/opt/buildhome/.gimme/versions/go1.17.linux.amd64/bin:${PATH}"; 11:37:36 PM: go version >&2; 11:37:36 PM: export GIMME_ENV="/opt/buildhome/.gimme/env/go1.17.linux.amd64.env" 11:37:37 PM: go version go1.17 linux/amd64 11:37:37 PM: Installing missing commands 11:37:37 PM: Verify run directory 11:37:38 PM: 11:37:38 PM: ──────────────────────────────────────────────────────────────── 11:37:38 PM: Netlify Build 11:37:38 PM: ──────────────────────────────────────────────────────────────── 11:37:38 PM: 11:37:38 PM: ❯ Version 11:37:38 PM: @netlify/build 27.3.0 11:37:38 PM: 11:37:38 PM: ❯ Flags 11:37:38 PM: baseRelDir: true 11:37:38 PM: buildId: 62b9ce60232d3454599e9b1c 11:37:38 PM: deployId: 62b9ce60232d3454599e9b1e 11:37:38 PM: 11:37:38 PM: ❯ Current directory 11:37:38 PM: /opt/build/repo 11:37:38 PM: 11:37:38 PM: ❯ Config file 11:37:38 PM: /opt/build/repo/netlify.toml 11:37:38 PM: 11:37:38 PM: ❯ Context 11:37:38 PM: production 11:37:38 PM: 11:37:38 PM: ❯ Loading plugins 11:37:38 PM: - @netlify/plugin-gatsby@3.2.4 from netlify.toml and package.json 11:37:38 PM: - netlify-plugin-cypress@2.2.0 from netlify.toml and package.json 11:37:40 PM: 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 1. @netlify/plugin-gatsby (onPreBuild event) 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 11:37:40 PM: No Gatsby cache found. Building fresh. 11:37:40 PM: 11:37:40 PM: (@netlify/plugin-gatsby onPreBuild completed in 17ms) 11:37:40 PM: 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 2. netlify-plugin-cypress (onPreBuild event) 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 11:37:41 PM: [STARTED] Task without title. 11:37:44 PM: [SUCCESS] Task without title. 11:37:46 PM: [2266:0627/153746.716704:ERROR:zygote_host_impl_linux.cc(263)] Failed to adjust OOM score of renderer with pid 2420: Permission denied (13) 11:37:46 PM: [2420:0627/153746.749095:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process. 11:37:46 PM: [2420:0627/153746.764711:ERROR:gpu_memory_buffer_support_x11.cc(44)] dri3 extension not supported. 11:37:46 PM: Displaying Cypress info... 11:37:46 PM: Detected no known browsers installed 11:37:46 PM: Proxy Settings: none detected 11:37:46 PM: Environment Variables: 11:37:46 PM: CYPRESS_CACHE_FOLDER: ./node_modules/.cache/CypressBinary 11:37:46 PM: Application Data: /opt/buildhome/.config/cypress/cy/development 11:37:46 PM: Browser Profiles: /opt/buildhome/.config/cypress/cy/development/browsers 11:37:46 PM: Binary Caches: /opt/build/repo/node_modules/.cache/CypressBinary 11:37:46 PM: Cypress Version: 10.2.0 (stable) 11:37:46 PM: System Platform: linux (Ubuntu - 20.04) 11:37:46 PM: System Memory: 32.8 GB free 27.9 GB 11:37:47 PM: 11:37:47 PM: (netlify-plugin-cypress onPreBuild completed in 6.2s) 11:37:47 PM: 11:37:47 PM: ──────────────────────────────────────────────────────────────── 11:37:47 PM: 3. build.command from netlify.toml 11:37:47 PM: ──────────────────────────────────────────────────────────────── 11:37:47 PM: 11:37:47 PM: $ gatsby build 11:37:49 PM: success open and validate gatsby-configs, load plugins - 0.298s 11:37:49 PM: success onPreInit - 0.003s 11:37:49 PM: success initialize cache - 0.107s 11:37:49 PM: success copy gatsby files - 0.044s 11:37:49 PM: success Compiling Gatsby Functions - 0.251s 11:37:49 PM: success onPreBootstrap - 0.259s 11:37:50 PM: success createSchemaCustomization - 0.000s 11:37:50 PM: success Checking for changed pages - 0.000s 11:37:50 PM: success source and transform nodes - 0.154s 11:37:50 PM: info Writing GraphQL type definitions to /opt/build/repo/.cache/schema.gql 11:37:50 PM: success building schema - 0.402s 11:37:50 PM: success createPages - 0.000s 11:37:50 PM: success createPagesStatefully - 0.312s 11:37:50 PM: info Total nodes: 49, SitePage nodes: 26 (use --verbose for breakdown) 11:37:50 PM: success Checking for changed pages - 0.000s 11:37:50 PM: success onPreExtractQueries - 0.000s 11:37:54 PM: success extract queries from components - 3.614s 11:37:54 PM: success write out redirect data - 0.006s 11:37:54 PM: success Build manifest and related icons - 0.468s 11:37:54 PM: success onPostBootstrap - 0.469s 11:37:54 PM: info bootstrap finished - 7.967s 11:37:54 PM: success write out requires - 0.009s 11:38:19 PM: success Building production JavaScript and CSS bundles - 24.472s 11:38:38 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'mini-css-extract-plugin /opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Footer/Footer.module.css|0|Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Footer/Footer.module.css': No serializer registered for Warning 11:38:38 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:38 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'mini-css-extract-plugin /opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Header/Header.module.css|0|Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Header/Header.module.css': No serializer registered for Warning 11:38:38 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:39 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[0]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[1]!/opt/build/repo/src/components/Footer/Footer.module.css': No serializer registered for Warning 11:38:39 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:39 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[0]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[1]!/opt/build/repo/src/components/Header/Header.module.css': No serializer registered for Warning 11:38:39 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:41 PM: success Building HTML renderer - 21.648s 11:38:41 PM: success Execute page configs - 0.024s 11:38:41 PM: success Caching Webpack compilations - 0.000s 11:38:41 PM: success run queries in workers - 0.042s - 26/26 621.26/s 11:38:41 PM: success Merge worker state - 0.001s 11:38:41 PM: success Rewriting compilation hashes - 0.001s 11:38:41 PM: success Writing page-data.json files to public directory - 0.014s - 26/26 1818.92/s 11:38:45 PM: success Building static HTML for pages - 4.353s - 26/26 5.97/s 11:38:45 PM: info [gatsby-plugin-netlify] Creating SSR/DSG redirects... 11:38:45 PM: info [gatsby-plugin-netlify] Created 0 SSR/DSG redirects... 11:38:45 PM: success onPostBuild - 0.011s 11:38:45 PM: 11:38:45 PM: Pages 11:38:45 PM: ┌ src/pages/404.js 11:38:45 PM: │ ├ /404/ 11:38:45 PM: │ └ /404.html 11:38:45 PM: ├ src/pages/about.js 11:38:45 PM: │ └ /about/ 11:38:45 PM: ├ src/pages/accountSuccess.js 11:38:45 PM: │ └ /accountSuccess/ 11:38:45 PM: ├ src/pages/cart.js 11:38:45 PM: │ └ /cart/ 11:38:45 PM: ├ src/pages/faq.js 11:38:45 PM: │ └ /faq/ 11:38:45 PM: ├ src/pages/forgot.js 11:38:45 PM: │ └ /forgot/ 11:38:45 PM: ├ src/pages/how-to-use.js 11:38:45 PM: │ └ /how-to-use/ 11:38:45 PM: ├ src/pages/index.js 11:38:45 PM: │ └ / 11:38:45 PM: ├ src/pages/login.js 11:38:45 PM: │ └ /login/ 11:38:45 PM: ├ src/pages/orderConfirm.js 11:38:45 PM: │ └ /orderConfirm/ 11:38:45 PM: ├ src/pages/search.js 11:38:45 PM: │ └ /search/ 11:38:45 PM: ├ src/pages/shop.js 11:38:45 PM: │ └ /shop/ 11:38:45 PM: ├ src/pages/shopV2.js 11:38:45 PM: │ └ /shopV2/ 11:38:45 PM: ├ src/pages/signup.js 11:38:45 PM: │ └ /signup/ 11:38:45 PM: ├ src/pages/styling.js 11:38:45 PM: │ └ /styling/ 11:38:45 PM: ├ src/pages/support.js 11:38:45 PM: │ └ /support/ 11:38:45 PM: ├ src/pages/account/address.js 11:38:45 PM: │ └ /account/address/ 11:38:45 PM: ├ src/pages/account/favorites.js 11:38:45 PM: │ └ /account/favorites/ 11:38:45 PM: ├ src/pages/account/index.js 11:38:45 PM: │ └ /account/ 11:38:45 PM: ├ src/pages/account/orders.js 11:38:45 PM: │ └ /account/orders/ 11:38:45 PM: ├ src/pages/account/settings.js 11:38:45 PM: │ └ /account/settings/ 11:38:45 PM: ├ src/pages/account/viewed.js 11:38:45 PM: │ └ /account/viewed/ 11:38:45 PM: ├ src/pages/blog/index.js 11:38:45 PM: │ └ /blog/ 11:38:45 PM: ├ src/pages/blog/sample.js 11:38:45 PM: │ └ /blog/sample/ 11:38:45 PM: └ src/pages/product/sample.js 11:38:45 PM: └ /product/sample/ 11:38:45 PM: ╭────────────────────────────────────────────────────────────────────╮ 11:38:45 PM: │ │ 11:38:45 PM: │ (SSG) Generated at build time │ 11:38:45 PM: │ D (DSG) Deferred static generation - page generated at runtime │ 11:38:45 PM: │ ∞ (SSR) Server-side renders at runtime (uses getServerData) │ 11:38:45 PM: │ λ (Function) Gatsby function │ 11:38:45 PM: │ │ 11:38:45 PM: ╰────────────────────────────────────────────────────────────────────╯ 11:38:45 PM: info Done building in 58.825944508 sec 11:38:46 PM: 11:38:46 PM: (build.command completed in 59s) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 4. @netlify/plugin-gatsby (onBuild event) 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:46 PM: Skipping Gatsby Functions and SSR/DSG support 11:38:46 PM: 11:38:46 PM: (@netlify/plugin-gatsby onBuild completed in 9ms) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 5. Functions bundling 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:46 PM: The Netlify Functions setting targets a non-existing directory: netlify/functions 11:38:46 PM: 11:38:46 PM: (Functions bundling completed in 3ms) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 6. @netlify/plugin-gatsby (onPostBuild event) 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:47 PM: Skipping Gatsby Functions and SSR/DSG support 11:38:47 PM: 11:38:47 PM: (@netlify/plugin-gatsby onPostBuild completed in 1.4s) 11:38:47 PM: 11:38:47 PM: ──────────────────────────────────────────────────────────────── 11:38:47 PM: 7. netlify-plugin-cypress (onPostBuild event) 11:38:47 PM: ──────────────────────────────────────────────────────────────── 11:38:47 PM: 11:38:49 PM: [2557:0627/153849.751277:ERROR:zygote_host_impl_linux.cc(263)] Failed to adjust OOM score of renderer with pid 2711: Permission denied (13) 11:38:49 PM: [2711:0627/153849.770005:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process. 11:38:49 PM: [2711:0627/153849.773016:ERROR:gpu_memory_buffer_support_x11.cc(44)] dri3 extension not supported. 11:38:52 PM: Couldn't find tsconfig.json. tsconfig-paths will be skipped 11:38:52 PM: tput: No value for $TERM and no -T specified 11:38:52 PM: ==================================================================================================== 11:38:52 PM: (Run Starting) 11:38:52 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:38:52 PM: │ Cypress: 10.2.0 │ 11:38:52 PM: │ Browser: Custom Chromium 90 (headless) │ 11:38:52 PM: │ Node Version: v16.15.1 (/opt/buildhome/.nvm/versions/node/v16.15.1/bin/node) │ 11:38:52 PM: │ Specs: 1 found (basic.cy.js) │ 11:38:52 PM: │ Searched: cypress/e2e/**/*.cy.{js,jsx,ts,tsx} │ 11:38:52 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:38:52 PM: ──────────────────────────────────────────────────────────────────────────────────────────────────── 11:38:52 PM: Running: basic.cy.js (1 of 1) 11:38:56 PM: 11:38:56 PM: sample render test 11:38:58 PM: ✓ displays the title text (2517ms) 11:38:58 PM: 1 passing (3s) 11:39:00 PM: (Results) 11:39:00 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:39:00 PM: │ Tests: 1 │ 11:39:00 PM: │ Passing: 1 │ 11:39:00 PM: │ Failing: 0 │ 11:39:00 PM: │ Pending: 0 │ 11:39:00 PM: │ Skipped: 0 │ 11:39:00 PM: │ Screenshots: 0 │ 11:39:00 PM: │ Video: true │ 11:39:00 PM: │ Duration: 2 seconds │ 11:39:00 PM: │ Spec Ran: basic.cy.js │ 11:39:00 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:39:00 PM: (Video) 11:39:00 PM: - Started processing: Compressing to 32 CRF 11:39:01 PM: - Finished processing: /opt/build/repo/cypress/videos/basic.cy.js.mp4 (1 second) 11:39:01 PM: tput: No value for $TERM and no -T specified 11:39:01 PM: ==================================================================================================== 11:39:01 PM: (Run Finished) 11:39:01 PM: Spec Tests Passing Failing Pending Skipped 11:39:01 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:39:01 PM: Creating deploy upload records 11:39:01 PM: │ ✔ basic.cy.js 00:02 1 1 - - - │ 11:39:01 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:39:01 PM: ✔ All specs passed! 00:02 1 1 - - - 11:39:01 PM: 11:39:01 PM: (netlify-plugin-cypress onPostBuild completed in 14s) 11:39:01 PM: 11:39:01 PM: ──────────────────────────────────────────────────────────────── 11:39:01 PM: 8. Deploy site 11:39:01 PM: ──────────────────────────────────────────────────────────────── 11:39:01 PM: 11:39:01 PM: Starting to deploy site from 'public' 11:39:01 PM: Creating deploy tree 11:39:01 PM: 0 new files to upload 11:39:01 PM: 0 new functions to upload 11:39:02 PM: Starting post processing 11:39:02 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:02 PM: Post processing - HTML 11:39:02 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing - header rules 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing - redirect rules 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing done 11:39:07 PM: Site is live ✨ 11:39:07 PM: Finished waiting for live deploy in 6.137803722s 11:39:07 PM: Site deploy was successfully initiated 11:39:07 PM: 11:39:07 PM: (Deploy site completed in 6.4s) 11:39:07 PM: 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 9. @netlify/plugin-gatsby (onSuccess event) 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 11:39:07 PM: 11:39:07 PM: (@netlify/plugin-gatsby onSuccess completed in 5ms) 11:39:07 PM: 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 10. netlify-plugin-cypress (onSuccess event) 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 11:39:07 PM: 11:39:07 PM: (netlify-plugin-cypress onSuccess completed in 6ms) 11:39:08 PM: 11:39:08 PM: ──────────────────────────────────────────────────────────────── 11:39:08 PM: Netlify Build Complete 11:39:08 PM: ──────────────────────────────────────────────────────────────── 11:39:08 PM: 11:39:08 PM: (Netlify Build completed in 1m 29.4s) 11:39:08 PM: Caching artifacts 11:39:08 PM: Started saving node modules 11:39:08 PM: Finished saving node modules 11:39:08 PM: Started saving build plugins 11:39:08 PM: Finished saving build plugins 11:39:08 PM: Started saving pip cache 11:39:08 PM: Finished saving pip cache 11:39:08 PM: Started saving emacs cask dependencies 11:39:08 PM: Finished saving emacs cask dependencies 11:39:08 PM: Started saving maven dependencies 11:39:08 PM: Finished saving maven dependencies 11:39:08 PM: Started saving boot dependencies 11:39:08 PM: Finished saving boot dependencies 11:39:08 PM: Started saving rust rustup cache 11:39:08 PM: Finished saving rust rustup cache 11:39:08 PM: Started saving go dependencies 11:39:08 PM: Finished saving go dependencies 11:39:10 PM: Build script success 11:39:10 PM: Pushing to repository git@github.com:Lhagawajaw/hymd-baraa 11:40:32 PM: Finished processing build request in 4m30.278982258s
timotta / Positional VectorizerThe Positional Vectorizer is a scikit-learn transformer designed to convert text into a bag of words vector. It achieves this using a ranking algorithm based on the term position in the text to assign scores.
