pythondataanalysis

 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── base_deepfake (1) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── both_deepfake (3) │   ├── fake │   │   └── frames │   ├── real │   │   └── frames │   └── val │   ├── fake │   └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │   ├── DeepFakeDetection │   ├── Deepfakes │   ├── Face2Face │   ├── FaceSwap │   └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.

Project Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.

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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

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