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sayantann11/all-classification-templetes-for-ML
Classification - Machine Learning This is ‘Classification’ tutorial which is a part of the Machine Learning course offered by Simplilearn. We will learn Classification algorithms, types of classification algorithms, support vector machines(SVM), Naive Bayes, Decision Tree and Random Forest Classifier in this tutorial. Objectives Let us look at some of the objectives covered under this section of Machine Learning tutorial. Define Classification and list its algorithms Describe Logistic Regression and Sigmoid Probability Explain K-Nearest Neighbors and KNN classification Understand Support Vector Machines, Polynomial Kernel, and Kernel Trick Analyze Kernel Support Vector Machines with an example Implement the Naïve Bayes Classifier Demonstrate Decision Tree Classifier Describe Random Forest Classifier Classification: Meaning Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are 2 types of Classification: Binomial Multi-Class Classification: Use Cases Some of the key areas where classification cases are being used: To find whether an email received is a spam or ham To identify customer segments To find if a bank loan is granted To identify if a kid will pass or fail in an examination Classification: Example Social media sentiment analysis has two potential outcomes, positive or negative, as displayed by the chart given below. https://www.simplilearn.com/ice9/free_resources_article_thumb/classification-example-machine-learning.JPG This chart shows the classification of the Iris flower dataset into its three sub-species indicated by codes 0, 1, and 2. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-flower-dataset-graph.JPG The test set dots represent the assignment of new test data points to one class or the other based on the trained classifier model. Types of Classification Algorithms Let’s have a quick look into the types of Classification Algorithm below. Linear Models Logistic Regression Support Vector Machines Nonlinear models K-nearest Neighbors (KNN) Kernel Support Vector Machines (SVM) Naïve Bayes Decision Tree Classification Random Forest Classification Logistic Regression: Meaning Let us understand the Logistic Regression model below. This refers to a regression model that is used for classification. This method is widely used for binary classification problems. It can also be extended to multi-class classification problems. Here, the dependent variable is categorical: y ϵ {0, 1} A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc In this case, you model the probability distribution of output y as 1 or 0. This is called the sigmoid probability (σ). If σ(θ Tx) > 0.5, set y = 1, else set y = 0 Unlike Linear Regression (and its Normal Equation solution), there is no closed form solution for finding optimal weights of Logistic Regression. Instead, you must solve this with maximum likelihood estimation (a probability model to detect the maximum likelihood of something happening). It can be used to calculate the probability of a given outcome in a binary model, like the probability of being classified as sick or passing an exam. https://www.simplilearn.com/ice9/free_resources_article_thumb/logistic-regression-example-graph.JPG Sigmoid Probability The probability in the logistic regression is often represented by the Sigmoid function (also called the logistic function or the S-curve): https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-function-machine-learning.JPG In this equation, t represents data values * the number of hours studied and S(t) represents the probability of passing the exam. Assume sigmoid function: https://www.simplilearn.com/ice9/free_resources_article_thumb/sigmoid-probability-machine-learning.JPG g(z) tends toward 1 as z -> infinity , and g(z) tends toward 0 as z -> infinity K-nearest Neighbors (KNN) K-nearest Neighbors algorithm is used to assign a data point to clusters based on similarity measurement. It uses a supervised method for classification. The steps to writing a k-means algorithm are as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-distribution-graph-machine-learning.JPG Choose the number of k and a distance metric. (k = 5 is common) Find k-nearest neighbors of the sample that you want to classify Assign the class label by majority vote. KNN Classification A new input point is classified in the category such that it has the most number of neighbors from that category. For example: https://www.simplilearn.com/ice9/free_resources_article_thumb/knn-classification-machine-learning.JPG Classify a patient as high risk or low risk. Mark email as spam or ham. Keen on learning about Classification Algorithms in Machine Learning? Click here! Support Vector Machine (SVM) Let us understand Support Vector Machine (SVM) in detail below. SVMs are classification algorithms used to assign data to various classes. They involve detecting hyperplanes which segregate data into classes. SVMs are very versatile and are also capable of performing linear or nonlinear classification, regression, and outlier detection. Once ideal hyperplanes are discovered, new data points can be easily classified. https://www.simplilearn.com/ice9/free_resources_article_thumb/support-vector-machines-graph-machine-learning.JPG The optimization objective is to find “maximum margin hyperplane” that is farthest from the closest points in the two classes (these points are called support vectors). In the given figure, the middle line represents the hyperplane. SVM Example Let’s look at this image below and have an idea about SVM in general. Hyperplanes with larger margins have lower generalization error. The positive and negative hyperplanes are represented by: https://www.simplilearn.com/ice9/free_resources_article_thumb/positive-negative-hyperplanes-machine-learning.JPG Classification of any new input sample xtest : If w0 + wTxtest > 1, the sample xtest is said to be in the class toward the right of the positive hyperplane. If w0 + wTxtest < -1, the sample xtest is said to be in the class toward the left of the negative hyperplane. When you subtract the two equations, you get: https://www.simplilearn.com/ice9/free_resources_article_thumb/equation-subtraction-machine-learning.JPG Length of vector w is (L2 norm length): https://www.simplilearn.com/ice9/free_resources_article_thumb/length-of-vector-machine-learning.JPG You normalize with the length of w to arrive at: https://www.simplilearn.com/ice9/free_resources_article_thumb/normalize-equation-machine-learning.JPG SVM: Hard Margin Classification Given below are some points to understand Hard Margin Classification. The left side of equation SVM-1 given above can be interpreted as the distance between the positive (+ve) and negative (-ve) hyperplanes; in other words, it is the margin that can be maximized. Hence the objective of the function is to maximize with the constraint that the samples are classified correctly, which is represented as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-machine-learning.JPG This means that you are minimizing ‖w‖. This also means that all positive samples are on one side of the positive hyperplane and all negative samples are on the other side of the negative hyperplane. This can be written concisely as : https://www.simplilearn.com/ice9/free_resources_article_thumb/hard-margin-classification-formula.JPG Minimizing ‖w‖ is the same as minimizing. This figure is better as it is differentiable even at w = 0. The approach listed above is called “hard margin linear SVM classifier.” SVM: Soft Margin Classification Given below are some points to understand Soft Margin Classification. To allow for linear constraints to be relaxed for nonlinearly separable data, a slack variable is introduced. (i) measures how much ith instance is allowed to violate the margin. The slack variable is simply added to the linear constraints. https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-machine-learning.JPG Subject to the above constraints, the new objective to be minimized becomes: https://www.simplilearn.com/ice9/free_resources_article_thumb/soft-margin-calculation-formula.JPG You have two conflicting objectives now—minimizing slack variable to reduce margin violations and minimizing to increase the margin. The hyperparameter C allows us to define this trade-off. Large values of C correspond to larger error penalties (so smaller margins), whereas smaller values of C allow for higher misclassification errors and larger margins. https://www.simplilearn.com/ice9/free_resources_article_thumb/machine-learning-certification-video-preview.jpg SVM: Regularization The concept of C is the reverse of regularization. Higher C means lower regularization, which increases bias and lowers the variance (causing overfitting). https://www.simplilearn.com/ice9/free_resources_article_thumb/concept-of-c-graph-machine-learning.JPG IRIS Data Set The Iris dataset contains measurements of 150 IRIS flowers from three different species: Setosa Versicolor Viriginica Each row represents one sample. Flower measurements in centimeters are stored as columns. These are called features. IRIS Data Set: SVM Let’s train an SVM model using sci-kit-learn for the Iris dataset: https://www.simplilearn.com/ice9/free_resources_article_thumb/svm-model-graph-machine-learning.JPG Nonlinear SVM Classification There are two ways to solve nonlinear SVMs: by adding polynomial features by adding similarity features Polynomial features can be added to datasets; in some cases, this can create a linearly separable dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/nonlinear-classification-svm-machine-learning.JPG In the figure on the left, there is only 1 feature x1. This dataset is not linearly separable. If you add x2 = (x1)2 (figure on the right), the data becomes linearly separable. Polynomial Kernel In sci-kit-learn, one can use a Pipeline class for creating polynomial features. Classification results for the Moons dataset are shown in the figure. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-machine-learning.JPG Polynomial Kernel with Kernel Trick Let us look at the image below and understand Kernel Trick in detail. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-with-kernel-trick.JPG For large dimensional datasets, adding too many polynomial features can slow down the model. You can apply a kernel trick with the effect of polynomial features without actually adding them. The code is shown (SVC class) below trains an SVM classifier using a 3rd-degree polynomial kernel but with a kernel trick. https://www.simplilearn.com/ice9/free_resources_article_thumb/polynomial-kernel-equation-machine-learning.JPG The hyperparameter coefθ controls the influence of high-degree polynomials. Kernel SVM Let us understand in detail about Kernel SVM. Kernel SVMs are used for classification of nonlinear data. In the chart, nonlinear data is projected into a higher dimensional space via a mapping function where it becomes linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-machine-learning.JPG In the higher dimension, a linear separating hyperplane can be derived and used for classification. A reverse projection of the higher dimension back to original feature space takes it back to nonlinear shape. As mentioned previously, SVMs can be kernelized to solve nonlinear classification problems. You can create a sample dataset for XOR gate (nonlinear problem) from NumPy. 100 samples will be assigned the class sample 1, and 100 samples will be assigned the class label -1. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-graph-machine-learning.JPG As you can see, this data is not linearly separable. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-non-separable.JPG You now use the kernel trick to classify XOR dataset created earlier. https://www.simplilearn.com/ice9/free_resources_article_thumb/kernel-svm-xor-machine-learning.JPG Naïve Bayes Classifier What is Naive Bayes Classifier? Have you ever wondered how your mail provider implements spam filtering or how online news channels perform news text classification or even how companies perform sentiment analysis of their audience on social media? All of this and more are done through a machine learning algorithm called Naive Bayes Classifier. Naive Bayes Named after Thomas Bayes from the 1700s who first coined this in the Western literature. Naive Bayes classifier works on the principle of conditional probability as given by the Bayes theorem. Advantages of Naive Bayes Classifier Listed below are six benefits of Naive Bayes Classifier. Very simple and easy to implement Needs less training data Handles both continuous and discrete data Highly scalable with the number of predictors and data points As it is fast, it can be used in real-time predictions Not sensitive to irrelevant features Bayes Theorem We will understand Bayes Theorem in detail from the points mentioned below. According to the Bayes model, the conditional probability P(Y|X) can be calculated as: P(Y|X) = P(X|Y)P(Y) / P(X) This means you have to estimate a very large number of P(X|Y) probabilities for a relatively small vector space X. For example, for a Boolean Y and 30 possible Boolean attributes in the X vector, you will have to estimate 3 billion probabilities P(X|Y). To make it practical, a Naïve Bayes classifier is used, which assumes conditional independence of P(X) to each other, with a given value of Y. This reduces the number of probability estimates to 2*30=60 in the above example. Naïve Bayes Classifier for SMS Spam Detection Consider a labeled SMS database having 5574 messages. It has messages as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-machine-learning.JPG Each message is marked as spam or ham in the data set. Let’s train a model with Naïve Bayes algorithm to detect spam from ham. The message lengths and their frequency (in the training dataset) are as shown below: https://www.simplilearn.com/ice9/free_resources_article_thumb/naive-bayes-spam-spam-detection.JPG Analyze the logic you use to train an algorithm to detect spam: Split each message into individual words/tokens (bag of words). Lemmatize the data (each word takes its base form, like “walking” or “walked” is replaced with “walk”). Convert data to vectors using scikit-learn module CountVectorizer. Run TFIDF to remove common words like “is,” “are,” “and.” Now apply scikit-learn module for Naïve Bayes MultinomialNB to get the Spam Detector. This spam detector can then be used to classify a random new message as spam or ham. Next, the accuracy of the spam detector is checked using the Confusion Matrix. For the SMS spam example above, the confusion matrix is shown on the right. Accuracy Rate = Correct / Total = (4827 + 592)/5574 = 97.21% Error Rate = Wrong / Total = (155 + 0)/5574 = 2.78% https://www.simplilearn.com/ice9/free_resources_article_thumb/confusion-matrix-machine-learning.JPG Although confusion Matrix is useful, some more precise metrics are provided by Precision and Recall. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-recall-matrix-machine-learning.JPG Precision refers to the accuracy of positive predictions. https://www.simplilearn.com/ice9/free_resources_article_thumb/precision-formula-machine-learning.JPG Recall refers to the ratio of positive instances that are correctly detected by the classifier (also known as True positive rate or TPR). https://www.simplilearn.com/ice9/free_resources_article_thumb/recall-formula-machine-learning.JPG Precision/Recall Trade-off To detect age-appropriate videos for kids, you need high precision (low recall) to ensure that only safe videos make the cut (even though a few safe videos may be left out). The high recall is needed (low precision is acceptable) in-store surveillance to catch shoplifters; a few false alarms are acceptable, but all shoplifters must be caught. Learn about Naive Bayes in detail. Click here! Decision Tree Classifier Some aspects of the Decision Tree Classifier mentioned below are. Decision Trees (DT) can be used both for classification and regression. The advantage of decision trees is that they require very little data preparation. They do not require feature scaling or centering at all. They are also the fundamental components of Random Forests, one of the most powerful ML algorithms. Unlike Random Forests and Neural Networks (which do black-box modeling), Decision Trees are white box models, which means that inner workings of these models are clearly understood. In the case of classification, the data is segregated based on a series of questions. Any new data point is assigned to the selected leaf node. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-machine-learning.JPG Start at the tree root and split the data on the feature using the decision algorithm, resulting in the largest information gain (IG). This splitting procedure is then repeated in an iterative process at each child node until the leaves are pure. This means that the samples at each node belonging to the same class. In practice, you can set a limit on the depth of the tree to prevent overfitting. The purity is compromised here as the final leaves may still have some impurity. The figure shows the classification of the Iris dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-classifier-graph.JPG IRIS Decision Tree Let’s build a Decision Tree using scikit-learn for the Iris flower dataset and also visualize it using export_graphviz API. https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-machine-learning.JPG The output of export_graphviz can be converted into png format: https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-output.JPG Sample attribute stands for the number of training instances the node applies to. Value attribute stands for the number of training instances of each class the node applies to. Gini impurity measures the node’s impurity. A node is “pure” (gini=0) if all training instances it applies to belong to the same class. https://www.simplilearn.com/ice9/free_resources_article_thumb/impurity-formula-machine-learning.JPG For example, for Versicolor (green color node), the Gini is 1-(0/54)2 -(49/54)2 -(5/54) 2 ≈ 0.168 https://www.simplilearn.com/ice9/free_resources_article_thumb/iris-decision-tree-sample.JPG Decision Boundaries Let us learn to create decision boundaries below. For the first node (depth 0), the solid line splits the data (Iris-Setosa on left). Gini is 0 for Setosa node, so no further split is possible. The second node (depth 1) splits the data into Versicolor and Virginica. If max_depth were set as 3, a third split would happen (vertical dotted line). https://www.simplilearn.com/ice9/free_resources_article_thumb/decision-tree-boundaries.JPG For a sample with petal length 5 cm and petal width 1.5 cm, the tree traverses to depth 2 left node, so the probability predictions for this sample are 0% for Iris-Setosa (0/54), 90.7% for Iris-Versicolor (49/54), and 9.3% for Iris-Virginica (5/54) CART Training Algorithm Scikit-learn uses Classification and Regression Trees (CART) algorithm to train Decision Trees. CART algorithm: Split the data into two subsets using a single feature k and threshold tk (example, petal length < “2.45 cm”). This is done recursively for each node. k and tk are chosen such that they produce the purest subsets (weighted by their size). The objective is to minimize the cost function as given below: https://www.simplilearn.com/ice9/free_resources_article_thumb/cart-training-algorithm-machine-learning.JPG The algorithm stops executing if one of the following situations occurs: max_depth is reached No further splits are found for each node Other hyperparameters may be used to stop the tree: min_samples_split min_samples_leaf min_weight_fraction_leaf max_leaf_nodes Gini Impurity or Entropy Entropy is one more measure of impurity and can be used in place of Gini. https://www.simplilearn.com/ice9/free_resources_article_thumb/gini-impurity-entrophy.JPG It is a degree of uncertainty, and Information Gain is the reduction that occurs in entropy as one traverses down the tree. Entropy is zero for a DT node when the node contains instances of only one class. Entropy for depth 2 left node in the example given above is: https://www.simplilearn.com/ice9/free_resources_article_thumb/entrophy-for-depth-2.JPG Gini and Entropy both lead to similar trees. DT: Regularization The following figure shows two decision trees on the moons dataset. https://www.simplilearn.com/ice9/free_resources_article_thumb/dt-regularization-machine-learning.JPG The decision tree on the right is restricted by min_samples_leaf = 4. The model on the left is overfitting, while the model on the right generalizes better. Random Forest Classifier Let us have an understanding of Random Forest Classifier below. A random forest can be considered an ensemble of decision trees (Ensemble learning). Random Forest algorithm: Draw a random bootstrap sample of size n (randomly choose n samples from the training set). Grow a decision tree from the bootstrap sample. At each node, randomly select d features. Split the node using the feature that provides the best split according to the objective function, for instance by maximizing the information gain. Repeat the steps 1 to 2 k times. (k is the number of trees you want to create, using a subset of samples) Aggregate the prediction by each tree for a new data point to assign the class label by majority vote (pick the group selected by the most number of trees and assign new data point to that group). Random Forests are opaque, which means it is difficult to visualize their inner workings. https://www.simplilearn.com/ice9/free_resources_article_thumb/random-forest-classifier-graph.JPG However, the advantages outweigh their limitations since you do not have to worry about hyperparameters except k, which stands for the number of decision trees to be created from a subset of samples. RF is quite robust to noise from the individual decision trees. Hence, you need not prune individual decision trees. The larger the number of decision trees, the more accurate the Random Forest prediction is. (This, however, comes with higher computation cost). Key Takeaways Let us quickly run through what we have learned so far in this Classification tutorial. Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. K-nearest Neighbors (KNN) algorithm uses similar features to classify data. Support Vector Machines (SVMs) classify data by detecting the maximum margin hyperplane between data classes. Naïve Bayes, a simplified Bayes Model, can help classify data using conditional probability models. Decision Trees are powerful classifiers and use tree splitting logic until pure or somewhat pure leaf node classes are attained. Random Forests apply Ensemble Learning to Decision Trees for more accurate classification predictions. Conclusion This completes ‘Classification’ tutorial. In the next tutorial, we will learn 'Unsupervised Learning with Clustering.'
⭐ 284 | 🍴 55molyswu/hand_detection
using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. 
Realtime detection on video stream from a webcam . 
Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). 
If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. 
Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! 
Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.
abusufyanvu/6S191_MIT_DeepLearning
MIT 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
⭐ 239 | 🍴 77Sfedfcv/redesigned-pancake
Skip to content github / docs Code Issues 80 Pull requests 35 Discussions Actions Projects 2 Security Insights Merge branch 'main' into 1862-Add-Travis-CI-migration-table 1862-Add-Travis-CI-migration-table (#1869, Iixixi/ZachryTylerWood#102, THEBOLCK79/docs#1, sbnbhk/docs#1) @martin389 martin389 committed on Dec 9, 2020 2 parents 2f9ec0c + 1588f50 commit 1a56ed136914e522f3a23ecc2be1c49f479a1a6a Showing 501 changed files with 5,397 additions and 1,362 deletions. 2 .github/allowed-actions.js @@ -30,7 +30,7 @@ module.exports = [ 'rachmari/labeler@832d42ec5523f3c6d46e8168de71cd54363e3e2e', 'repo-sync/github-sync@3832fe8e2be32372e1b3970bbae8e7079edeec88', 'repo-sync/pull-request@33777245b1aace1a58c87a29c90321aa7a74bd7d', 'rtCamp/action-slack-notify@e17352feaf9aee300bf0ebc1dfbf467d80438815', 'someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd', 'tjenkinson/gh-action-auto-merge-dependency-updates@cee2ac0', 'EndBug/add-and-commit@9358097a71ad9fb9e2f9624c6098c89193d83575' ] 72 .github/workflows/confirm-internal-staff-work-in-docs.yml @@ -0,0 +1,72 @@ name: Confirm internal staff meant to post in public on: issues: types: - opened - reopened - transferred pull_request_target: types: - opened - reopened jobs: check-team-membership: runs-on: ubuntu-latest continue-on-error: true if: github.repository == 'github/docs' steps: - uses: actions/github-script@626af12fe9a53dc2972b48385e7fe7dec79145c9 with: github-token: ${{ secrets.DOCUBOT_FR_PROJECT_BOARD_WORKFLOWS_REPO_ORG_READ_SCOPES }} script: | // Only perform this action with GitHub employees try { await github.teams.getMembershipForUserInOrg({ org: 'github', team_slug: 'employees', username: context.payload.sender.login, }); } catch(err) { // An error will be thrown if the user is not a GitHub employee // If a user is not a GitHub employee, we should stop here and // Not send a notification return } // Don't perform this action with Docs team members try { await github.teams.getMembershipForUserInOrg({ org: 'github', team_slug: 'docs', username: context.payload.sender.login, }); // If the user is a Docs team member, we should stop here and not send // a notification return } catch(err) { // An error will be thrown if the user is not a Docs team member // If a user is not a Docs team member we should continue and send // the notification } const issueNo = context.number || context.issue.number // Create an issue in our private repo await github.issues.create({ owner: 'github', repo: 'docs-internal', title: `@${context.payload.sender.login} confirm that \#${issueNo} should be in the public github/docs repo`, body: `@${context.payload.sender.login} opened https://github.com/github/docs/issues/${issueNo} publicly in the github/docs repo, instead of the private github/docs-internal repo.\n\n@${context.payload.sender.login}, please confirm that this belongs in the public repo and that no sensitive information was disclosed by commenting below and closing the issue.\n\nIf this was not intentional and sensitive information was shared, please delete https://github.com/github/docs/issues/${issueNo} and notify us in the \#docs-open-source channel.\n\nThanks! \n\n/cc @github/docs @github/docs-engineering` }); throw new Error('A Hubber opened an issue on the public github/docs repo'); - name: Send Slack notification if a GitHub employee who isn't on the docs team opens an issue in public if: ${{ failure() && github.repository == 'github/docs' }} uses: someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd with: channel: ${{ secrets.DOCS_OPEN_SOURCE_SLACK_CHANNEL_ID }} bot-token: ${{ secrets.SLACK_DOCS_BOT_TOKEN }} text: <@${{github.actor}}> opened https://github.com/github/docs/issues/${{ github.event.number || github.event.issue.number }} publicly on the github/docs repo instead of the private github/docs-internal repo. They have been notified via a new issue in the github/docs-internal repo to confirm this was intentional. 15 .github/workflows/js-lint.yml @@ -10,23 +10,8 @@ on: - translations jobs: see_if_should_skip: runs-on: ubuntu-latest outputs: should_skip: ${{ steps.skip_check.outputs.should_skip }} steps: - id: skip_check uses: fkirc/skip-duplicate-actions@36feb0d8d062137530c2e00bd278d138fe191289 with: cancel_others: 'false' github_token: ${{ github.token }} paths: '["**/*.js", "package*.json", ".github/workflows/js-lint.yml", ".eslint*"]' lint: runs-on: ubuntu-latest needs: see_if_should_skip if: ${{ needs.see_if_should_skip.outputs.should_skip != 'true' }} steps: - name: Check out repo uses: actions/checkout@5a4ac9002d0be2fb38bd78e4b4dbde5606d7042f 13 .github/workflows/repo-freeze-reminders.yml @@ -14,11 +14,10 @@ jobs: if: github.repository == 'github/docs-internal' steps: - name: Send Slack notification if repo is frozen uses: someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd if: ${{ env.FREEZE == 'true' }} uses: rtCamp/action-slack-notify@e17352feaf9aee300bf0ebc1dfbf467d80438815 env: SLACK_WEBHOOK: ${{ secrets.DOCS_ALERTS_SLACK_WEBHOOK }} SLACK_USERNAME: docs-repo-sync SLACK_ICON_EMOJI: ':freezing_face:' SLACK_COLOR: '#51A0D5' # Carolina Blue SLACK_MESSAGE: All repo-sync runs will fail for ${{ github.repository }} because the repo is currently frozen! with: channel: ${{ secrets.DOCS_ALERTS_SLACK_CHANNEL_ID }} bot-token: ${{ secrets.SLACK_DOCS_BOT_TOKEN }} color: info text: All repo-sync runs will fail for ${{ github.repository }} because the repo is currently frozen! 54 .github/workflows/repo-sync-stalls.yml @@ -0,0 +1,54 @@ name: Repo Sync Stalls on: workflow_dispatch: schedule: - cron: '*/30 * * * *' jobs: check-freezer: name: Check for deployment freezes runs-on: ubuntu-latest steps: - name: Exit if repo is frozen if: ${{ env.FREEZE == 'true' }} run: | echo 'The repo is currently frozen! Exiting this workflow.' exit 1 # prevents further steps from running repo-sync-stalls: runs-on: ubuntu-latest steps: - name: Check if repo sync is stalled uses: actions/github-script@626af12fe9a53dc2972b48385e7fe7dec79145c9 with: github-token: ${{ secrets.DOCUBOT_FR_PROJECT_BOARD_WORKFLOWS_REPO_ORG_READ_SCOPES }} script: | let pulls; const owner = context.repo.owner const repo = context.repo.repo try { pulls = await github.pulls.list({ owner: owner, repo: repo, head: `${owner}:repo-sync`, state: 'open' }); } catch(err) { throw err return } pulls.data.forEach(pr => { const timeDelta = Date.now() - Date.parse(pr.created_at); const minutesOpen = timeDelta / 1000 / 60; if (minutesOpen > 30) { core.setFailed('Repo sync appears to be stalled') } }) - name: Send Slack notification if workflow fails uses: someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd if: failure() with: channel: ${{ secrets.DOCS_ALERTS_SLACK_CHANNEL_ID }} bot-token: ${{ secrets.SLACK_DOCS_BOT_TOKEN }} color: failure text: Repo sync appears to be stalled for ${{github.repository}}. See https://github.com/${{github.repository}}/pulls?q=is%3Apr+is%3Aopen+repo+sync 16 .github/workflows/repo-sync.yml @@ -7,6 +7,7 @@ name: Repo Sync on: workflow_dispatch: schedule: - cron: '*/15 * * * *' # every 15 minutes @@ -70,11 +71,10 @@ jobs: number: ${{ steps.find-pull-request.outputs.number }} - name: Send Slack notification if workflow fails uses: rtCamp/action-slack-notify@e17352feaf9aee300bf0ebc1dfbf467d80438815 if: ${{ failure() }} env: SLACK_WEBHOOK: ${{ secrets.DOCS_ALERTS_SLACK_WEBHOOK }} SLACK_USERNAME: docs-repo-sync SLACK_ICON_EMOJI: ':ohno:' SLACK_COLOR: '#B90E0A' # Crimson SLACK_MESSAGE: The last repo-sync run for ${{github.repository}} failed. See https://github.com/${{github.repository}}/actions?query=workflow%3A%22Repo+Sync%22 uses: someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd if: failure() with: channel: ${{ secrets.DOCS_ALERTS_SLACK_CHANNEL_ID }} bot-token: ${{ secrets.SLACK_DOCS_BOT_TOKEN }} color: failure text: The last repo-sync run for ${{github.repository}} failed. See https://github.com/${{github.repository}}/actions?query=workflow%3A%22Repo+Sync%22 10 .github/workflows/sync-algolia-search-indices.yml @@ -33,8 +33,10 @@ jobs: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: npm run sync-search - name: Send slack notification if workflow run fails uses: rtCamp/action-slack-notify@e17352feaf9aee300bf0ebc1dfbf467d80438815 uses: someimportantcompany/github-actions-slack-message@0b470c14b39da4260ed9e3f9a4f1298a74ccdefd if: failure() env: SLACK_WEBHOOK: ${{ secrets.DOCS_ALERTS_SLACK_WEBHOOK }} SLACK_MESSAGE: The last Algolia workflow run for ${{github.repository}} failed. Search actions for `workflow:Algolia` with: channel: ${{ secrets.DOCS_ALERTS_SLACK_CHANNEL_ID }} bot-token: ${{ secrets.SLACK_DOCS_BOT_TOKEN }} color: failure text: The last Algolia workflow run for ${{github.repository}} failed. Search actions for `workflow:Algolia` 15 .github/workflows/yml-lint.yml @@ -10,23 +10,8 @@ on: - translations jobs: see_if_should_skip: runs-on: ubuntu-latest outputs: should_skip: ${{ steps.skip_check.outputs.should_skip }} steps: - id: skip_check uses: fkirc/skip-duplicate-actions@36feb0d8d062137530c2e00bd278d138fe191289 with: cancel_others: 'false' github_token: ${{ github.token }} paths: '["**/*.yml", "**/*.yaml", "package*.json", ".github/workflows/yml-lint.yml"]' lint: runs-on: ubuntu-latest needs: see_if_should_skip if: ${{ needs.see_if_should_skip.outputs.should_skip != 'true' }} steps: - name: Check out repo uses: actions/checkout@5a4ac9002d0be2fb38bd78e4b4dbde5606d7042f 4 README.md @@ -28,7 +28,7 @@ If you've found a problem, you can open an issue using a [template](https://gith #### Solve an issue If you have a solution to one of the open issues, you will need to fork the repository and submit a PR using the [template](https://github.com/github/docs/blob/main/CONTRIBUTING.md#pull-request-template) that is visible automatically in the pull request body. For more details about this process, please check out [Getting Started with Contributing](/CONTRIBUTING.md). If you have a solution to one of the open issues, you will need to fork the repository and submit a pull request using the [template](https://github.com/github/docs/blob/main/CONTRIBUTING.md#pull-request-template) that is visible automatically in the pull request body. For more details about this process, please check out [Getting Started with Contributing](/CONTRIBUTING.md). #### Join us in discussions @@ -50,6 +50,8 @@ There are a few more things to know when you're getting started with this repo: In addition to the README you're reading right now, this repo includes other READMEs that describe the purpose of each subdirectory in more detail: - [content/README.md](content/README.md) - [content/graphql/README.md](content/graphql/README.md) - [content/rest/README.md](content/rest/README.md) - [contributing/README.md](contributing/README.md) - [data/README.md](data/README.md) - [data/reusables/README.md](data/reusables/README.md) BIN +164 KB assets/images/help/classroom/assignment-group-hero.png Binary file not shown. BIN +75.5 KB assets/images/help/classroom/assignment-ide-go-grant-access-button.png Binary file not shown. BIN +175 KB assets/images/help/classroom/assignment-individual-hero.png Binary file not shown. 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You should see "Hello Mona the Octocat" or the name you used for the `who-to-greet` input and the timestamp printed in the log. From your repository, click the **Actions** tab, and select the latest workflow run. {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}Under **Jobs** or in the visualization graph, click **A job to say hello**. {% endif %}You should see "Hello Mona the Octocat" or the name you used for the `who-to-greet` input and the timestamp printed in the log. {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}  {% else %}  {% endif %} 6 content/actions/creating-actions/creating-a-javascript-action.md @@ -261,9 +261,11 @@ jobs: ``` {% endraw %} From your repository, click the **Actions** tab, and select the latest workflow run. You should see "Hello Mona the Octocat" or the name you used for the `who-to-greet` input and the timestamp printed in the log. From your repository, click the **Actions** tab, and select the latest workflow run. {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}Under **Jobs** or in the visualization graph, click **A job to say hello**. {% endif %}You should see "Hello Mona the Octocat" or the name you used for the `who-to-greet` input and the timestamp printed in the log. {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@2.22" %} {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}  {% elsif currentVersion ver_gt "enterprise-server@2.22" %}  {% else %}  4 content/actions/guides/about-packaging-with-github-actions.md @@ -25,7 +25,11 @@ Creating a package at the end of a continuous integration workflow can help duri Now, when reviewing a pull request, you'll be able to look at the workflow run and download the artifact that was produced. {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}  {% else %}  {% endif %} This will let you run the code in the pull request on your machine, which can help with debugging or testing the pull request. 4 content/actions/guides/building-and-testing-powershell.md @@ -60,7 +60,11 @@ jobs: * `run: Test-Path resultsfile.log` - Check whether a file called `resultsfile.log` is present in the repository's root directory. * `Should -Be $true` - Uses Pester to define an expected result. If the result is unexpected, then {% data variables.product.prodname_actions %} flags this as a failed test. For example: {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}  {% else %}  {% endif %} * `Invoke-Pester Unit.Tests.ps1 -Passthru` - Uses Pester to execute tests defined in a file called `Unit.Tests.ps1`. For example, to perform the same test described above, the `Unit.Tests.ps1` will contain the following: ``` 7 content/actions/guides/storing-workflow-data-as-artifacts.md @@ -108,8 +108,6 @@ jobs: path: output/test/code-coverage.html ```  {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@2.22" %} ### Configuring a custom artifact retention period @@ -238,7 +236,12 @@ jobs: echo The result is $value ``` The workflow run will archive any artifacts that it generated. For more information on downloading archived artifacts, see "[Downloading workflow artifacts](/actions/managing-workflow-runs/downloading-workflow-artifacts)." {% if currentVersion == "free-pro-team@latest" or currentVersion ver_gt "enterprise-server@3.0" %}  {% else %}  {% endif %} {% if currentVersion == "free-pro-team@latest" %} 8 content/actions/index.md @@ -68,18 +68,18 @@ versions:
Code examples
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Learn about requirements, guidelines, and the app submission process. 1. Add webhook events to the app to track user billing requests. For more information about the {% data variables.product.prodname_marketplace %} API, webhook events, and billing requests, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." 1. [Integrating with the {% data variables.product.prodname_marketplace %} API](/marketplace/integrating-with-the-github-marketplace-api/)
Before you can list your app on {% data variables.product.prodname_marketplace %}, you'll need to integrate billing flows using the {% data variables.product.prodname_marketplace %} API and webhook events. 1. Create a draft {% data variables.product.prodname_marketplace %} listing. For more information, see "[Drafting a listing for your app](/developers/github-marketplace/drafting-a-listing-for-your-app)." 1. [Listing on {% data variables.product.prodname_marketplace %}](/marketplace/listing-on-github-marketplace/)
Create a draft {% data variables.product.prodname_marketplace %} listing, configure webhook settings, and set up pricing plans. 1. Add a pricing plan. For more information, see "[Setting pricing plans for your listing](/developers/github-marketplace/setting-pricing-plans-for-your-listing)." 1. [Selling your app](/marketplace/selling-your-app/)
Learn about pricing plans, billing cycles, and how to receive payment from {% data variables.product.prodname_dotcom %} for your app. 1. Check whether your app meets the requirements for listing on {% data variables.product.prodname_marketplace %} as a free or a paid app. For more information, see "[Requirements for listing an app](/developers/github-marketplace/requirements-for-listing-an-app)." 1. [{% data variables.product.prodname_marketplace %} Insights](/marketplace/github-marketplace-insights/)
See how your app is performing in {% data variables.product.prodname_marketplace %}. You can use metrics collected by {% data variables.product.prodname_dotcom %} to guide your marketing campaign and be successful in {% data variables.product.prodname_marketplace %}. 1. Read and accept the terms of the "[{% data variables.product.prodname_marketplace %} Developer Agreement](/articles/github-marketplace-developer-agreement/)." 1. [{% data variables.product.prodname_marketplace %} transactions](/marketplace/github-marketplace-transactions/)
Download and view transaction data for your {% data variables.product.prodname_marketplace %} listing. 1. Submit your listing for publication in {% data variables.product.prodname_marketplace %}, requesting verification if you want to sell the app. For more information, see "[Submitting your listing for publication](/developers/github-marketplace/submitting-your-listing-for-publication)." ### Reviewing your app An onboarding expert will contact you with any questions or further steps. For example, if you have added a paid plan, you will need to complete the verification process and complete financial onboarding. As soon as your listing is approved the app is published to {% data variables.product.prodname_marketplace %}. We want to make sure that the apps offered on {% data variables.product.prodname_marketplace %} are safe, secure, and well tested. The {% data variables.product.prodname_marketplace %} onboarding specialists will review your app to ensure that it meets all requirements. Follow the guidelines in these articles before submitting your app: ### Seeing how your app is performing You can access metrics and transactions for your listing. For more information, see: * [Requirements for listing an app on {% data variables.product.prodname_marketplace %}](/marketplace/getting-started/requirements-for-listing-an-app-on-github-marketplace/) * [Security review process](/marketplace/getting-started/security-review-process/) - "[Viewing metrics for your listing](/developers/github-marketplace/viewing-metrics-for-your-listing)" - "[Viewing transactions for your listing](/developers/github-marketplace/viewing-transactions-for-your-listing)" 43 content/developers/github-marketplace/about-verified-creators.md @@ -0,0 +1,43 @@ --- title: About verified creators intro: 'Each organization that wants to sell apps on {% data variables.product.prodname_marketplace %} must follow a verification process. Their identity is checked and their billing process reviewed.' versions: free-pro-team: '*' --- ### About verified creators A verified creator is an organization that {% data variables.product.company_short %} has checked. Anyone can share their apps with other users on {% data variables.product.prodname_marketplace %} but only organizations that are verified by {% data variables.product.company_short %} can sell apps. For more information about organizations, see "[About organizations](/github/setting-up-and-managing-organizations-and-teams/about-organizations)." The verification process aims to protect users. For example, it verifies the seller's identity, checks that their {% data variables.product.product_name %} organization is set up securely, and that they can be contacted for support. After passing the verification checks, any apps that the organization lists on {% data variables.product.prodname_marketplace %} are shown with a verified creator badge {% octicon "verified" aria-label="Verified creator badge" %}. The organization can now add paid plans to any of their apps. Each app with a paid plan also goes through a financial onboarding process to check that it's set up to handle billing correctly.  In addition to the verified creator badge, you'll also see badges for unverified and verified apps. These apps were published using the old method for verifying individual apps.  For information on finding apps to use, see "[Searching {% data variables.product.prodname_marketplace %}](/github/searching-for-information-on-github/searching-github-marketplace)." ### About the verification process The first time you request verification for a listing of one of your apps, you will enter the verification process. An onboarding expert will guide you through the process. This includes checking: - Profile information - The basic profile information is populated accurately and appropriately. - Security - The organization has enabled two-factor authentication. - Verified domain - The organization has verified the domain of the site URL. - Purchase webhook event - The event is handled correctly by the app. When your organization is verified, all your apps are shown with a verified creator badge. You are now able to offer paid plans for any of your apps. For more information about the requirements for listing an app on {% data variables.product.prodname_marketplace %}, see "[Requirements for listing an app on {% data variables.product.prodname_marketplace %}](/marketplace/getting-started/requirements-for-listing-an-app-on-github-marketplace/)." {% data reusables.marketplace.app-transfer-to-org-for-verification %} For information on how to do this, see: "[Submitting your listing for publication](/developers/github-marketplace/submitting-your-listing-for-publication#transferring-an-app-to-an-organization-before-you-submit)." {% note %} **Note:** This verification process for apps replaces the previous process where individual apps were verified. The current process is similar to the verification process for actions. If you have apps that were verified under the old process, these will not be affected by the changes. The {% data variables.product.prodname_marketplace %} team will contact you with details of how to migrate to organization-based verification. {% endnote %} 12 content/developers/github-marketplace/billing-customers.md @@ -13,17 +13,17 @@ versions: ### Understanding the billing cycle Customers can choose a monthly or yearly billing cycle when they purchase your app. All changes customers make to the billing cycle and plan selection will trigger a `marketplace_purchase` event. You can refer to the `marketplace_purchase` webhook payload to see which billing cycle a customer selects and when the next billing date begins (`effective_date`). For more information about webhook payloads, see "[{% data variables.product.prodname_marketplace %} webhook events](/marketplace/integrating-with-the-github-marketplace-api/github-marketplace-webhook-events/)." Customers can choose a monthly or yearly billing cycle when they purchase your app. All changes customers make to the billing cycle and plan selection will trigger a `marketplace_purchase` event. You can refer to the `marketplace_purchase` webhook payload to see which billing cycle a customer selects and when the next billing date begins (`effective_date`). For more information about webhook payloads, see "[Webhook events for the {% data variables.product.prodname_marketplace %} API](/developers/github-marketplace/webhook-events-for-the-github-marketplace-api)." ### Providing billing services in your app's UI Customers must be able to perform the following actions from your app's website: - Customers must be able to modify or cancel their {% data variables.product.prodname_marketplace %} plans for personal and organizational accounts separately. Customers should be able to perform the following actions from your app's website: - Customers should be able to modify or cancel their {% data variables.product.prodname_marketplace %} plans for personal and organizational accounts separately. {% data reusables.marketplace.marketplace-billing-ui-requirements %} ### Billing services for upgrades, downgrades, and cancellations Follow these guidelines for upgrades, downgrades, and cancellations to maintain a clear and consistent billing process. For more detailed instructions about the {% data variables.product.prodname_marketplace %} purchase events, see "[Billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows)." Follow these guidelines for upgrades, downgrades, and cancellations to maintain a clear and consistent billing process. For more detailed instructions about the {% data variables.product.prodname_marketplace %} purchase events, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." You can use the `marketplace_purchase` webhook's `effective_date` key to determine when a plan change will occur and periodically synchronize the [List accounts for a plan](/rest/reference/apps#list-accounts-for-a-plan). @@ -33,7 +33,7 @@ When a customer upgrades their pricing plan or changes their billing cycle from {% data reusables.marketplace.marketplace-failed-purchase-event %} For information about building upgrade and downgrade workflows into your app, see "[Upgrading and downgrading plans](/marketplace/integrating-with-the-github-marketplace-api/upgrading-and-downgrading-plans/)." For information about building upgrade and downgrade workflows into your app, see "[Handling plan changes](/developers/github-marketplace/handling-plan-changes)." #### Downgrades and cancellations @@ -45,4 +45,4 @@ When a customer cancels a plan, you must: {% data reusables.marketplace.cancellation-clarification %} - Enable them to upgrade the plan through GitHub if they would like to continue the plan at a later time. For information about building cancellation workflows into your app, see "[Cancelling plans](/marketplace/integrating-with-the-github-marketplace-api/cancelling-plans/)." For information about building cancellation workflows into your app, see "[Handling plan cancellations](/developers/github-marketplace/handling-plan-cancellations)." 20 ...nt/developers/github-marketplace/customer-experience-best-practices-for-apps.md @@ -0,0 +1,20 @@ --- title: Customer experience best practices for apps intro: 'Guidelines for creating an app that will be easy to use and understand.' shortTitle: Customer experience best practice versions: free-pro-team: '*' --- If you follow these best practices it will help you to provide a good customer experience. ### Customer communication - Marketing materials for the app should accurately represent the app's behavior. - Apps should include links to user-facing documentation that describe how to set up and use the app. - Customers should be able to see what type of plan they have in the billing, profile, or account settings section of the app. - Customers should be able to install and use your app on both a personal account and an organization account. They should be able to view and manage the app on those accounts separately. ### Plan management {% data reusables.marketplace.marketplace-billing-ui-requirements %} 4 content/developers/github-marketplace/drafting-a-listing-for-your-app.md @@ -59,8 +59,8 @@ Once you've created a {% data variables.product.prodname_marketplace %} draft li ### Submitting your app Once you've completed your {% data variables.product.prodname_marketplace %} listing, you can submit your listing for review from the **Overview** page. You'll need to read and accept the "[{% data variables.product.prodname_marketplace %} Developer Agreement](/articles/github-marketplace-developer-agreement/)," and then you can click **Submit for review**. After you submit your app for review, the {% data variables.product.prodname_marketplace %} onboarding team will contact you with additional information about the onboarding process. You can learn more about the onboarding and security review process in "[Getting started with {% data variables.product.prodname_marketplace %}](/marketplace/getting-started/)." Once you've completed your {% data variables.product.prodname_marketplace %} listing, you can submit your listing for review from the **Overview** page. You'll need to read and accept the "[{% data variables.product.prodname_marketplace %} Developer Agreement](/articles/github-marketplace-developer-agreement/)," and then you can click **Submit for review**. After you submit your app for review, an onboarding expert will contact you with additional information about the onboarding process. You can learn more about the onboarding and security review process in "[Getting started with {% data variables.product.prodname_marketplace %}](/marketplace/getting-started/)." ### Removing a {% data variables.product.prodname_marketplace %} listing If you no longer want to list your app in {% data variables.product.prodname_marketplace %}, contact [marketplace@github.com](mailto:marketplace@github.com) to remove your listing. If you no longer want to list your app in {% data variables.product.prodname_marketplace %}, contact {% data variables.contact.contact_support %} to remove your listing. 2 content/developers/github-marketplace/handling-new-purchases-and-free-trials.md @@ -28,7 +28,7 @@ GitHub then sends the [`marketplace_purchase`](/webhooks/event-payloads/#marketp Read the `effective_date` and `marketplace_purchase` object from the `marketplace_purchase` webhook to determine which plan the customer purchased, when the billing cycle starts, and when the next billing cycle begins. If your app offers a free trial, read the `marketplace_purchase[on_free_trial]` attribute from the webhook. If the value is `true`, your app will need to track the free trial start date (`effective_date`) and the date the free trial ends (`free_trial_ends_on`). Use the `free_trial_ends_on` date to display the remaining days left in a free trial in your app's UI. You can do this in either a banner or in your [billing UI](/marketplace/selling-your-app/billing-customers-in-github-marketplace/#providing-billing-services-in-your-apps-ui). To learn how to handle cancellations before a free trial ends, see "[Cancelling plans](/marketplace/integrating-with-the-github-marketplace-api/cancelling-plans/)." See "[Upgrading and downgrading plans](/marketplace/integrating-with-the-github-marketplace-api/upgrading-and-downgrading-plans/)" to find out how to transition a free trial to a paid plan when a free trial expires. If your app offers a free trial, read the `marketplace_purchase[on_free_trial]` attribute from the webhook. If the value is `true`, your app will need to track the free trial start date (`effective_date`) and the date the free trial ends (`free_trial_ends_on`). Use the `free_trial_ends_on` date to display the remaining days left in a free trial in your app's UI. You can do this in either a banner or in your [billing UI](/marketplace/selling-your-app/billing-customers-in-github-marketplace/#providing-billing-services-in-your-apps-ui). To learn how to handle cancellations before a free trial ends, see "[Handling plan cancellations](/developers/github-marketplace/handling-plan-cancellations)." See "[Handling plan changes](/developers/github-marketplace/handling-plan-changes)" to find out how to transition a free trial to a paid plan when a free trial expires. See "[{% data variables.product.prodname_marketplace %} webhook events](/marketplace/integrating-with-the-github-marketplace-api/github-marketplace-webhook-events/)" for an example of the `marketplace_purchase` event payload. 6 content/developers/github-marketplace/index.md @@ -11,8 +11,10 @@ versions: {% topic_link_in_list /creating-apps-for-github-marketplace %} {% link_in_list /about-github-marketplace %} {% link_in_list /about-verified-creators %} {% link_in_list /requirements-for-listing-an-app %} {% link_in_list /security-review-process-for-submitted-apps %} {% link_in_list /security-best-practices-for-apps %} {% link_in_list /customer-experience-best-practices-for-apps %} {% link_in_list /viewing-metrics-for-your-listing %} {% link_in_list /viewing-transactions-for-your-listing %} {% topic_link_in_list /using-the-github-marketplace-api-in-your-app %} @@ -27,7 +29,7 @@ versions: {% link_in_list /writing-a-listing-description-for-your-app %} {% link_in_list /setting-pricing-plans-for-your-listing %} {% link_in_list /configuring-a-webhook-to-notify-you-of-plan-changes %} {% link_in_list /submitting-your-listing-for-review %} {% link_in_list /submitting-your-listing-for-publication %} {% topic_link_in_list /selling-your-app-on-github-marketplace %} {% link_in_list /pricing-plans-for-github-marketplace-apps %} {% link_in_list /billing-customers %} 32 content/developers/github-marketplace/pricing-plans-for-github-marketplace-apps.md @@ -10,35 +10,45 @@ versions: {% data variables.product.prodname_marketplace %} pricing plans can be free, flat rate, or per-unit, and GitHub lists the price in US dollars. Customers purchase your app using a payment method attached to their {% data variables.product.product_name %} account, without having to leave GitHub.com. You don't have to write code to perform billing transactions, but you will have to handle [billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows) for purchase events. {% data variables.product.prodname_marketplace %} pricing plans can be free, flat rate, or per-unit. Prices are set, displayed, and processed in US dollars. Paid plans are restricted to verified listings. Customers purchase your app using a payment method attached to their {% data variables.product.product_name %} account, without having to leave {% data variables.product.prodname_dotcom_the_website %}. You don't have to write code to perform billing transactions, but you will have to handle events from the {% data variables.product.prodname_marketplace %} API. For more information, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." If the app you're listing on {% data variables.product.prodname_marketplace %} has multiple plan options, you can set up corresponding pricing plans. For example, if your app has two plan options, an open source plan and a pro plan, you can set up a free pricing plan for your open source plan and a flat pricing plan for your pro plan. Each {% data variables.product.prodname_marketplace %} listing must have an annual and a monthly price for every plan that's listed. For more information on how to create a pricing plan, see "[Setting a {% data variables.product.prodname_marketplace %} listing's pricing plan](/marketplace/listing-on-github-marketplace/setting-a-github-marketplace-listing-s-pricing-plan/)." {% note %} {% data reusables.marketplace.free-plan-note %} **Note:** If you're listing an app on {% data variables.product.prodname_marketplace %}, you can't list your app with a free pricing plan if you offer a paid service outside of {% data variables.product.prodname_marketplace %}. ### Types of pricing plans {% endnote %} #### Free pricing plans ### Types of pricing plans {% data reusables.marketplace.free-apps-encouraged %} Free plans are completely free for users. If you set up a free pricing plan, you cannot charge users that choose the free pricing plan for the use of your app. You can create both free and paid plans for your listing. All apps need to handle events for new purchases and cancellations. Apps that only have free plans do not need to handle events for free trials, upgrades, and downgrades. For more information, see: "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." If you add a paid plan to an app that you've already listed in {% data variables.product.prodname_marketplace %} as a free service, you'll need to request verification for the app and go through financial onboarding. #### Paid pricing plans **Free pricing plans** are completely free for users. If you set up a free pricing plan, you cannot charge users that choose the free pricing plan for the use of your app. You can create both free and paid plans for your listing. Unverified free apps do not need to implement any billing flows. Free apps that are verified by Github need to implement billing flows for new purchases and cancellations, but do not need to implement billing flows for free trials, upgrades, and downgrades. If you add a paid plan to an app that you've already listed in {% data variables.product.prodname_marketplace %} as a free service, you'll need to resubmit the app for review. There are two types of paid pricing plan: **Flat rate pricing plans** charge a set fee on a monthly and yearly basis. - Flat rate pricing plans charge a set fee on a monthly and yearly basis. **Per-unit pricing plans** charge a set fee on either a monthly or yearly basis for a unit that you specify. A "unit" can be anything you'd like (for example, a user, seat, or person). - Per-unit pricing plans charge a set fee on either a monthly or yearly basis for a unit that you specify. A "unit" can be anything you'd like (for example, a user, seat, or person). **Marketplace free trials** provide 14-day free trials of OAuth or GitHub Apps to customers. When you [set up a Marketplace pricing plan](/marketplace/listing-on-github-marketplace/setting-a-github-marketplace-listing-s-pricing-plan/), you can select the option to provide a free trial for flat-rate or per-unit pricing plans. You may also want to offer free trials. These provide free, 14-day trials of OAuth or GitHub Apps to customers. When you set up a Marketplace pricing plan, you can select the option to provide a free trial for flat-rate or per-unit pricing plans. ### Free trials Customers can start a free trial for any available paid plan on a Marketplace listing, but will not be able to create more than one free trial for a Marketplace product. Customers can start a free trial for any paid plan on a Marketplace listing that includes free trials. However, customers cannot create more than one free trial per marketplace product. Free trials have a fixed length of 14 days. Customers are notified 4 days before the end of their trial period (on day 11 of the free trial) that their plan will be upgraded. At the end of a free trial, customers will be auto-enrolled into the plan they are trialing if they do not cancel. See "[New purchases and free trials](/marketplace/integrating-with-the-github-marketplace-api/handling-new-purchases-and-free-trials/)" for details on how to handle free trials in your app. For more information, see: "[Handling new purchases and free trials](/developers/github-marketplace/integrating-with-the-github-marketplace-api/handling-new-purchases-and-free-trials/)." {% note %} 61 content/developers/github-marketplace/requirements-for-listing-an-app.md @@ -1,6 +1,6 @@ --- title: Requirements for listing an app intro: 'Apps on {% data variables.product.prodname_marketplace %} must meet the requirements outlined on this page before our {% data variables.product.prodname_marketplace %} onboarding specialists will approve the listing.' intro: 'Apps on {% data variables.product.prodname_marketplace %} must meet the requirements outlined on this page before the listing can be published.' redirect_from: - /apps/adding-integrations/listing-apps-on-github-marketplace/requirements-for-listing-an-app-on-github-marketplace/ - /apps/marketplace/listing-apps-on-github-marketplace/requirements-for-listing-an-app-on-github-marketplace/ @@ -12,49 +12,62 @@ versions: free-pro-team: '*' --- The requirements for listing an app on {% data variables.product.prodname_marketplace %} vary according to whether you want to offer a free or a paid app. Before you submit your app for review, you must read and accept the terms of the "[{% data variables.product.prodname_marketplace %} Developer Agreement](/articles/github-marketplace-developer-agreement/)." You'll accept the terms within your [draft listing](/marketplace/listing-on-github-marketplace/creating-a-draft-github-marketplace-listing/) on {% data variables.product.product_name %}. Once you've submitted your app, one of the {% data variables.product.prodname_marketplace %} onboarding specialists will reach out to you with more information about the onboarding process, and review your app to ensure it meets these requirements: ### Requirements for all {% data variables.product.prodname_marketplace %} listings ### User experience All listings on {% data variables.product.prodname_marketplace %} should be for tools that provide value to the {% data variables.product.product_name %} community. When you submit your listing for publication, you must read and accept the terms of the "[{% data variables.product.prodname_marketplace %} Developer Agreement](/articles/github-marketplace-developer-agreement/)." - {% data variables.product.prodname_github_app %}s should have a minimum of 100 installations. - {% data variables.product.prodname_oauth_app %}s should have a minimum of 200 users. #### User experience requirements for all apps All listings should meet the following requirements, regardless of whether they are for a free or paid app. - Listings must not actively persuade users away from {% data variables.product.product_name %}. - Listings must include valid contact information for the publisher. - Listings must have a relevant description of the application. - Listings must specify a pricing plan. - Apps must provide value to customers and integrate with the platform in some way beyond authentication. - Apps must be publicly available in {% data variables.product.prodname_marketplace %} and cannot be in beta or available by invite only. - Apps cannot actively persuade users away from {% data variables.product.product_name %}. - Marketing materials for the app must accurately represent the app's behavior. - Apps must include links to user-facing documentation that describe how to set up and use the app. - When a customer purchases an app and GitHub redirects them to the app's installation URL, the app must begin the OAuth flow immediately. For details, see "[Handling new purchases and free trials](/marketplace/integrating-with-the-github-marketplace-api/handling-new-purchases-and-free-trials/#step-3-authorization)." - Apps must have webhook events set up to notify the publisher of any plan changes or cancellations using the {% data variables.product.prodname_marketplace %} API. For more information, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." - Customers must be able to install your app and select repositories on both a personal and organization account. They should be able to view and manage those accounts separately. For more information on providing a good customer experience, see "[Customer experience best practices for apps](/developers/github-marketplace/customer-experience-best-practices-for-apps)." ### Brand and listing #### Brand and listing requirements for all apps - Apps that use GitHub logos must follow the "[{% data variables.product.product_name %} Logos and Usage](https://github.com/logos)" guidelines. - Apps that use GitHub logos must follow the {% data variables.product.company_short %} guidelines. For more information, see "[{% data variables.product.company_short %} Logos and Usage](https://github.com/logos)." - Apps must have a logo, feature card, and screenshots images that meet the recommendations provided in "[Writing {% data variables.product.prodname_marketplace %} listing descriptions](/marketplace/listing-on-github-marketplace/writing-github-marketplace-listing-descriptions/)." - Listings must include descriptions that are well written and free of grammatical errors. For guidance in writing your listing, see "[Writing {% data variables.product.prodname_marketplace %} listing descriptions](/marketplace/listing-on-github-marketplace/writing-github-marketplace-listing-descriptions/)." ### Security To protect your customers, we recommend that you also follow security best practices. For more information, see "[Security best practices for apps](/developers/github-marketplace/security-best-practices-for-apps)." ### Considerations for free apps Apps will go through a security review before being listed on {% data variables.product.prodname_marketplace %}. A successful review will meet the requirements and follow the security best practices listed in "[Security review process](/marketplace/getting-started/security-review-process/)." For information on the review process, contact [marketplace@github.com](mailto:marketplace@github.com). {% data reusables.marketplace.free-apps-encouraged %} ### Requirements for paid apps In addition to the requirements for all apps above, each app that you offer as a paid service on {% data variables.product.prodname_marketplace %} must also meet the following requirements: - {% data variables.product.prodname_github_app %}s should have a minimum of 100 installations. - {% data variables.product.prodname_oauth_app %}s should have a minimum of 200 users. - All paid apps must handle {% data variables.product.prodname_marketplace %} purchase events for new purchases, upgrades, downgrades, cancellations, and free trials. For more information, see "[Billing requirements for paid apps](#billing-requirements-for-paid-apps)" below. - Publishing organizations must have a verified domain and must enable two-factor authentication. For more information, see "[Requiring two-factor authentication in your organization](/github/setting-up-and-managing-organizations-and-teams/requiring-two-factor-authentication-in-your-organization.") ### Billing flows When you are ready to publish the app on {% data variables.product.prodname_marketplace %} you must request verification for the listing. Your app must integrate [billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows) using the [{% data variables.product.prodname_marketplace %} webhook event](/marketplace/integrating-with-the-github-marketplace-api/github-marketplace-webhook-events/). {% note %} #### Free apps The verification process is open to organizations. {% data reusables.marketplace.app-transfer-to-org-for-verification %} For information on how to do this, see: "[Submitting your listing for publication](/developers/github-marketplace/submitting-your-listing-for-publication#transferring-an-app-to-an-organization-before-you-submit)." {% data reusables.marketplace.free-apps-encouraged %} If you are listing a free app, you'll need to meet these requirements: {% endnote %} - Customers must be able to see that they have a free plan in the billing, profile, or account settings section of the app. - When a customer cancels your app, you must follow the flow for [cancelling plans](/marketplace/integrating-with-the-github-marketplace-api/cancelling-plans/). ### Billing requirements for paid apps #### Paid apps Your app does not need to handle payments but does need to use {% data variables.product.prodname_marketplace %} purchase events to manage new purchases, upgrades, downgrades, cancellations, and free trials. For information about how integrate these events into your app, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." To offer your app as a paid service, you'll need to meet these requirements to list your app on {% data variables.product.prodname_marketplace %}: Using GitHub's billing API allows customers to purchase an app without leaving GitHub and to pay for the service with the payment method already attached to their {% data variables.product.product_name %} account. - To sell your app in {% data variables.product.prodname_marketplace %}, it must use GitHub's billing system. Your app does not need to handle payments but does need to use "[{% data variables.product.prodname_marketplace %} purchase events](/marketplace/integrating-with-the-github-marketplace-api/github-marketplace-webhook-events/)" to manage new purchases, upgrades, downgrades, cancellations, and free trials. See "[Billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows)" to learn about how to integrate these events into your app. Using GitHub's billing system allows customers to purchase an app without leaving GitHub and pay for the service with the payment method already attached to their {% data variables.product.product_name %} account. - Apps must support both monthly and annual billing for paid subscriptions purchases. - Listings may offer any combination of free and paid plans. Free plans are optional but encouraged. For more information, see "[Setting a {% data variables.product.prodname_marketplace %} listing's pricing plan](/marketplace/listing-on-github-marketplace/setting-a-github-marketplace-listing-s-pricing-plan/)." {% data reusables.marketplace.marketplace-billing-ui-requirements %} 60 content/developers/github-marketplace/security-best-practices-for-apps.md @@ -0,0 +1,60 @@ --- title: Security best practices for apps intro: 'Guidelines for preparing a secure app to share on {% data variables.product.prodname_marketplace %}.' redirect_from: - /apps/marketplace/getting-started/security-review-process/ - /marketplace/getting-started/security-review-process - /developers/github-marketplace/security-review-process-for-submitted-apps shortTitle: Security best practice versions: free-pro-team: '*' --- If you follow these best practices it will help you to provide a secure user experience. ### Authorization, authentication, and access control We recommend creating a GitHub App rather than an OAuth App. {% data reusables.marketplace.github_apps_preferred %}. See "[Differences between GitHub Apps and OAuth Apps](/apps/differences-between-apps/)" for more details. - Apps should use the principle of least privilege and should only request the OAuth scopes and GitHub App permissions that the app needs to perform its intended functionality. For more information, see [Principle of least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege) in Wikipedia. - Apps should provide customers with a way to delete their account, without having to email or call a support person. - Apps should not share tokens between different implementations of the app. For example, a desktop app should have a separate token from a web-based app. Individual tokens allow each app to request the access needed for GitHub resources separately. - Design your app with different user roles, depending on the functionality needed by each type of user. For example, a standard user should not have access to admin functionality, and billing managers might not need push access to repository code. - Apps should not share service accounts such as email or database services to manage your SaaS service. - All services used in your app should have unique login and password credentials. - Admin privilege access to the production hosting infrastructure should only be given to engineers and employees with administrative duties. - Apps should not use personal access tokens to authenticate and should authenticate as an [OAuth App](/apps/about-apps/#about-oauth-apps) or a [GitHub App](/apps/about-apps/#about-github-apps): - OAuth Apps should authenticate using an [OAuth token](/apps/building-oauth-apps/authorizing-oauth-apps/). - GitHub Apps should authenticate using either a [JSON Web Token (JWT)](/apps/building-github-apps/authenticating-with-github-apps/#authenticating-as-a-github-app), [OAuth token](/apps/building-github-apps/identifying-and-authorizing-users-for-github-apps/), or [installation access token](/apps/building-github-apps/authenticating-with-github-apps/#authenticating-as-an-installation). ### Data protection - Apps should encrypt data transferred over the public internet using HTTPS, with a valid TLS certificate, or SSH for Git. - Apps should store client ID and client secret keys securely. We recommend storing them as [environmental variables](http://en.wikipedia.org/wiki/Environment_variable#Getting_and_setting_environment_variables). - Apps should delete all GitHub user data within 30 days of receiving a request from the user, or within 30 days of the end of the user's legal relationship with GitHub. - Apps should not require the user to provide their GitHub password. - Apps should encrypt tokens, client IDs, and client secrets. ### Logging and monitoring Apps should have logging and monitoring capabilities. App logs should be retained for at least 30 days and archived for at least one year. A security log should include: - Authentication and authorization events - Service configuration changes - Object reads and writes - All user and group permission changes - Elevation of role to admin - Consistent timestamping for each event - Source users, IP addresses, and/or hostnames for all logged actions ### Incident response workflow To provide a secure experience for users, you should have a clear incident response plan in place before listing your app. We recommend having a security and operations incident response team in your company rather than using a third-party vendor. You should have the capability to notify {% data variables.product.product_name %} within 24 hours of a confirmed incident. For an example of an incident response workflow, see the "Data Breach Response Policy" on the [SANS Institute website](https://www.sans.org/information-security-policy/). A short document with clear steps to take in the event of an incident is more valuable than a lengthy policy template. ### Vulnerability management and patching workflow You should conduct regular vulnerability scans of production infrastructure. You should triage the results of vulnerability scans and define a period of time in which you agree to remediate the vulnerability. If you are not ready to set up a full vulnerability management program, it's useful to start by creating a patching process. For guidance in creating a patch management policy, see this TechRepublic article "[Establish a patch management policy](https://www.techrepublic.com/blog/it-security/establish-a-patch-management-policy-87756/)." 94 ...ent/developers/github-marketplace/security-review-process-for-submitted-apps.md This file was deleted. 53 content/developers/github-marketplace/setting-pricing-plans-for-your-listing.md @@ -1,6 +1,6 @@ --- title: Setting pricing plans for your listing intro: 'When [listing your app on {% data variables.product.prodname_marketplace %}](/marketplace/listing-on-github-marketplace/), you can choose to provide your app as a free service or sell your app. If you plan to sell your app, you can create different pricing plans for different feature tiers.' intro: 'When you list your app on {% data variables.product.prodname_marketplace %}, you can choose to provide your app as a free service or sell your app. If you plan to sell your app, you can create different pricing plans for different feature tiers.' redirect_from: - /apps/adding-integrations/managing-pricing-and-payments-for-a-github-marketplace-listing/setting-a-github-marketplace-listing-s-pricing-plan/ - /apps/marketplace/managing-pricing-and-payments-for-a-github-marketplace-listing/setting-a-github-marketplace-listing-s-pricing-plan/ @@ -17,57 +17,52 @@ versions: free-pro-team: '*' --- ### About setting pricing plans If you want to sell an app on {% data variables.product.prodname_marketplace %}, you need to request verification when you publish the listing for your app. During the verification process, an onboarding expert checks the organization's identity and security settings. The onboarding expert will also take the organization through financial onboarding. For more information, see: "[Requirements for listing an app on {% data variables.product.prodname_marketplace %}](/marketplace/getting-started/requirements-for-listing-an-app-on-github-marketplace/)." ### Creating pricing plans To learn about the types of pricing plans that {% data variables.product.prodname_marketplace %} offers, see "[{% data variables.product.prodname_marketplace %} Pricing Plans](/marketplace/selling-your-app/github-marketplace-pricing-plans/)." You'll also find helpful billing guidelines in "[Selling your app](/marketplace/selling-your-app/)." Pricing plans can be in the draft or published state. If you haven't submitted your {% data variables.product.prodname_marketplace %} listing for approval, a published listing will function the same way as draft listings until your app is approved and listed on {% data variables.product.prodname_marketplace %}. Draft listings allow you to create and save new pricing plans without making them available on your {% data variables.product.prodname_marketplace %} listing page. Once you publish the pricing plan, it's available for customers to purchase immediately. You can publish up to 10 pricing plans. {% data reusables.marketplace.app-transfer-to-org-for-verification %} For information on how to do this, see: "[Submitting your listing for publication](/developers/github-marketplace/submitting-your-listing-for-publication#transferring-an-app-to-an-organization-before-you-submit)." To create a pricing plan for your {% data variables.product.prodname_marketplace %} listing, click **Plans and pricing** in the left sidebar of your [{% data variables.product.prodname_marketplace %} listing page](https://github.com/marketplace/manage). If you haven't created a {% data variables.product.prodname_marketplace %} listing yet, read "[Creating a draft {% data variables.product.prodname_marketplace %} listing](/marketplace/listing-on-github-marketplace/creating-a-draft-github-marketplace-listing/)" to learn how. When you click **New draft plan**, you'll see a form that allows you to customize your pricing plan. You'll need to configure the following fields to create a pricing plan: {% data variables.product.prodname_marketplace %} offers several different types of pricing plan. For detailed information, see "[Pricing plans for {% data variables.product.prodname_marketplace %}](/developers/github-marketplace/pricing-plans-for-github-marketplace-apps)." #### Plan name ### About saving pricing plans Your pricing plan's name will appear on your {% data variables.product.prodname_marketplace %} app's landing page. You can customize the name of your pricing plan to align to the plan's resources, the size of the company that will use the plan, or anything you'd like. You can save pricing plans in a draft or published state. If you haven't submitted your {% data variables.product.prodname_marketplace %} listing for approval, a published plan will function in the same way as a draft plan until your listing is approved and shown on {% data variables.product.prodname_marketplace %}. Draft plans allow you to create and save new pricing plans without making them available on your {% data variables.product.prodname_marketplace %} listing page. Once you publish a pricing plan on a published listing, it's available for customers to purchase immediately. You can publish up to 10 pricing plans. #### Pricing models For guidelines on billing customers, see "[Billing customers](/developers/github-marketplace/billing-customers)." ##### Free plans {% data reusables.marketplace.free-apps-encouraged %} A free plan still requires you to handle [new purchase](/marketplace/integrating-with-the-github-marketplace-api/handling-new-purchases-and-free-trials/) and [cancellation](/marketplace/integrating-with-the-github-marketplace-api/cancelling-plans/) billing flows. See "[Billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows)" for more details. ##### Flat-rate plans ### Creating pricing plans Flat-rate pricing plans allow you to offer your service to customers for a flat-rate fee. {% data reusables.marketplace.marketplace-pricing-free-trials %} To create a pricing plan for your {% data variables.product.prodname_marketplace %} listing, click **Plans and pricing** in the left sidebar of your [{% data variables.product.prodname_marketplace %} listing page](https://github.com/marketplace/manage). For more information, see "[Creating a draft {% data variables.product.prodname_marketplace %} listing](/marketplace/listing-on-github-marketplace/creating-a-draft-github-marketplace-listing/)." You must set a price for both monthly and yearly subscriptions in U.S. Dollars for flat-rate plans. When you click **New draft plan**, you'll see a form that allows you to customize your pricing plan. You'll need to configure the following fields to create a pricing plan: ##### Per-unit plans - **Plan name** - Your pricing plan's name will appear on your {% data variables.product.prodname_marketplace %} app's landing page. You can customize the name of your pricing plan to align with the plan's resources, the size of the company that will use the plan, or anything you'd like. Per-unit pricing allows you to offer your app in units. For example, a unit can be a person, seat, or user. You'll need to provide a name for the unit and set a price for both monthly and yearly subscriptions, in U.S. Dollars. - **Pricing models** - There are three types of pricing plan: free, flat-rate, and per-unit. All plans require you to process new purchase and cancellation events from the marketplace API. In addition, for paid plans: #### Available for - You must set a price for both monthly and yearly subscriptions in US dollars. - Your app must process plan change events. - You must request verification to publish a listing with a paid plan. - {% data reusables.marketplace.marketplace-pricing-free-trials %} {% data variables.product.prodname_marketplace %} pricing plans can apply to **Personal and organization accounts**, **Personal accounts only**, or **Organization accounts only**. For example, if your pricing plan is per-unit and provides multiple seats, you would select **Organization accounts only** because there is no way to assign seats to people in an organization from a personal account. For detailed information, see "[Pricing plans for {% data variables.product.prodname_marketplace %} apps](/developers/github-marketplace/pricing-plans-for-github-marketplace-apps)" and "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." #### Short description - **Available for** - {% data variables.product.prodname_marketplace %} pricing plans can apply to **Personal and organization accounts**, **Personal accounts only**, or **Organization accounts only**. For example, if your pricing plan is per-unit and provides multiple seats, you would select **Organization accounts only** because there is no way to assign seats to people in an organization from a personal account. Write a brief summary of the details of the pricing plan. The description might include the type of customer the plan is intended for or the resources the plan includes. - **Short description** - Write a brief summary of the details of the pricing plan. The description might include the type of customer the plan is intended for or the resources the plan includes. #### Bullets - **Bullets** - You can write up to four bullets that include more details about your pricing plan. The bullets might include the use cases of your app or list more detailed information about the resources or features included in the plan. You can write up to four bullets that include more details about your pricing plan. The bullets might include the use cases of your app or list more detailed information about the resources or features included in the plan. {% data reusables.marketplace.free-plan-note %} ### Changing a {% data variables.product.prodname_marketplace %} listing's pricing plan If a pricing plan for your {% data variables.product.prodname_marketplace %} plan is no longer needed or if you need to adjust pricing details, you can remove it. If a pricing plan for your {% data variables.product.prodname_marketplace %} listing is no longer needed, or if you need to adjust pricing details, you can remove it.  Once you publish a pricing plan for an app already listed in the {% data variables.product.prodname_marketplace %}, you can't make changes to the plan. Instead, you'll need to remove the pricing plan. Customers who already purchased the removed pricing plan will continue to use it until they opt out and move onto a new pricing plan. For more on pricing plans, see "[{% data variables.product.prodname_marketplace %} pricing plans](/marketplace/selling-your-app/github-marketplace-pricing-plans/)." Once you publish a pricing plan for an app that is already listed in {% data variables.product.prodname_marketplace %}, you can't make changes to the plan. Instead, you'll need to remove the pricing plan and create a new plan. Customers who already purchased the removed pricing plan will continue to use it until they opt out and move onto a new pricing plan. For more on pricing plans, see "[{% data variables.product.prodname_marketplace %} pricing plans](/marketplace/selling-your-app/github-marketplace-pricing-plans/)." Once you remove a pricing plan, users won't be able to purchase your app using that plan. Existing users on the removed pricing plan will continue to stay on the plan until they cancel their plan subscription. 37 content/developers/github-marketplace/submitting-your-listing-for-publication.md @@ -0,0 +1,37 @@ --- title: Submitting your listing for publication intro: 'You can submit your listing for the {% data variables.product.prodname_dotcom %} community to use.' redirect_from: - /marketplace/listing-on-github-marketplace/submitting-your-listing-for-review - /developers/github-marketplace/submitting-your-listing-for-review versions: free-pro-team: '*' --- Once you've completed the listing for your app, you'll see two buttons that allow you to request publication of the listing with or without verification. The **Request** button for "Publish without verification" is disabled if you have published any paid pricing plans in the listing.  {% data reusables.marketplace.launch-with-free %} After you submit your listing for review, an onboarding expert will reach out to you with additional information. For an overview of the process for creating and submitting a listing, see "[About {% data variables.product.prodname_marketplace %}](/developers/github-marketplace/about-github-marketplace#publishing-an-app-to-github-marketplace)." ### Prerequisites for publishing with verification Before you request verification of your listing, you'll need to integrate the {% data variables.product.prodname_marketplace %} billing flows and webhook into your app. For more information, see "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." If you've met the requirements for listing and you've integrated with the {% data variables.product.prodname_marketplace %} API, go ahead and submit your listing. For more information, see "[Requirements for listing an app](/developers/github-marketplace/requirements-for-listing-an-app)." {% data reusables.marketplace.app-transfer-to-org-for-verification %} For information on how to do this, see: "[Transferring an app to an organization before you submit](#transferring-an-app-to-an-organization-before-you-submit)" below. ### Transferring an app to an organization before you submit You cannot sell an app that's owned by a user account. You need to transfer the app to an organization that is already a verified creator, or that can request verification for a listing for the app. For details, see: 1. "[Creating an organization from scratch](/github/setting-up-and-managing-organizations-and-teams/creating-a-new-organization-from-scratch)" 1. "[Transferring ownership of a GitHub App](/developers/apps/transferring-ownership-of-a-github-app)" or "[Transferring ownership of an OAuth App](/developers/apps/transferring-ownership-of-an-oauth-app)" 22 content/developers/github-marketplace/submitting-your-listing-for-review.md This file was deleted. 4 content/developers/github-marketplace/testing-your-app.md @@ -1,6 +1,6 @@ --- title: Testing your app intro: 'GitHub recommends testing your app with APIs and webhooks before submitting your listing to {% data variables.product.prodname_marketplace %} so you can provide an ideal experience for customers. Before the {% data variables.product.prodname_marketplace %} onboarding team approves your app, it must adequately handle the [billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows).' intro: 'GitHub recommends testing your app with APIs and webhooks before submitting your listing to {% data variables.product.prodname_marketplace %} so you can provide an ideal experience for customers. Before an onboarding expert approves your app, it must adequately handle the billing flows.' redirect_from: - /apps/marketplace/testing-apps-apis-and-webhooks/ - /apps/marketplace/integrating-with-the-github-marketplace-api/testing-github-marketplace-apps/ @@ -13,7 +13,7 @@ versions: ### Testing apps You can use a [draft {% data variables.product.prodname_marketplace %} listing](/marketplace/listing-on-github-marketplace/creating-a-draft-github-marketplace-listing/) to simulate each of the [billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows). A listing in the draft state means that it has not been submitted for approval. Any purchases you make using a draft {% data variables.product.prodname_marketplace %} listing will _not_ create real transactions, and GitHub will not charge your credit card. You can use a draft {% data variables.product.prodname_marketplace %} listing to simulate each of the billing flows. A listing in the draft state means that it has not been submitted for approval. Any purchases you make using a draft {% data variables.product.prodname_marketplace %} listing will _not_ create real transactions, and GitHub will not charge your credit card. For more information, see "[Drafting a listing for your app](/developers/github-marketplace/drafting-a-listing-for-your-app)" and "[Using the {% data variables.product.prodname_marketplace %} API in your app](/developers/github-marketplace/using-the-github-marketplace-api-in-your-app)." #### Using a development app with a draft listing to test changes 2 .../developers/github-marketplace/webhook-events-for-the-github-marketplace-api.md @@ -1,6 +1,6 @@ --- title: Webhook events for the GitHub Marketplace API intro: 'A {% data variables.product.prodname_marketplace %} app receives information about changes to a user''s plan from the Marketplace purchase event webhook. A Marketplace purchase event is triggered when a user purchases, cancels, or changes their payment plan. For details on how to respond to each of these types of events, see "[Billing flows](/marketplace/integrating-with-the-github-marketplace-api/#billing-flows)."' intro: 'A {% data variables.product.prodname_marketplace %} app receives information about changes to a user''s plan from the Marketplace purchase event webhook. A Marketplace purchase event is triggered when a user purchases, cancels, or changes their payment plan.' redirect_from: - /apps/marketplace/setting-up-github-marketplace-webhooks/about-webhook-payloads-for-a-github-marketplace-listing/ - /apps/marketplace/integrating-with-the-github-marketplace-api/github-marketplace-webhook-events/ 4 content/developers/webhooks-and-events/webhook-events-and-payloads.md @@ -445,7 +445,7 @@ Key | Type | Description #### Webhook payload object {% data reusables.webhooks.installation_properties %} {% data reusables.webhooks.app_desc %} {% data reusables.webhooks.app_always_desc %} {% data reusables.webhooks.sender_desc %} #### Webhook payload example @@ -469,7 +469,7 @@ Key | Type | Description #### Webhook payload object {% data reusables.webhooks.installation_repositories_properties %} {% data reusables.webhooks.app_desc %} {% data reusables.webhooks.app_always_desc %} {% data reusables.webhooks.sender_desc %} #### Webhook payload example 54 ...ssions/collaborating-with-your-community-using-discussions/about-discussions.md @@ -0,0 +1,54 @@ --- title: About discussions intro: Use discussions to ask and answer questions, share information, make announcements, and conduct or participate in a conversation about a project on {% data variables.product.product_name %}. versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### About discussions With {% data variables.product.prodname_discussions %}, the community for your project can create and participate in conversations within the project's repository. Discussions empower a project's maintainers, contributors, and visitors to gather and accomplish the following goals in a central location, without third-party tools. - Share announcements and information, gather feedback, plan, and make decisions - Ask questions, discuss and answer the questions, and mark the discussions as answered - Foster an inviting atmosphere for visitors and contributors to discuss goals, development, administration, and workflows  You don't need to close a discussion like you close an issue or a pull request. If a repository administrator or project maintainer enables discussions for a repository, anyone who visits the repository can create and participate in discussions for the repository. Repository administrators and project maintainers can manage discussions and discussion categories in a repository, and pin discussions to increase the visibility of the discussion. Moderators and collaborators can mark comments as answers, lock discussions, and convert issues to discussions. For more information, see "[Repository permission levels for an organization](/github/setting-up-and-managing-organizations-and-teams/repository-permission-levels-for-an-organization)." For more information about management of discussions for your repository, see "[Managing discussions in your repository](/discussions/managing-discussions-for-your-community/managing-discussions-in-your-repository)." ### About categories and formats for discussions {% data reusables.discussions.you-can-categorize-discussions %} {% data reusables.discussions.about-categories-and-formats %} {% data reusables.discussions.repository-category-limit %} For discussions with a question/answer format, an individual comment within the discussion can be marked as the discussion's answer. {% data reusables.discussions.github-recognizes-members %} For more information, see "[Managing categories for discussions in your repository](/discussions/managing-discussions-for-your-community/managing-categories-for-discussions-in-your-repository)." ### Best practices for discussions As a community member or maintainer, start a discussion to ask a question or discuss information that affects the community. For more information, see "[Collaborating with maintainers using discussions](/discussions/collaborating-with-your-community-using-discussions/collaborating-with-maintainers-using-discussions)." Participate in a discussion to ask and answer questions, provide feedback, and engage with the project's community. For more information, see "[Participating in a discussion](/discussions/collaborating-with-your-community-using-discussions/participating-in-a-discussion)." You can spotlight discussions that contain important, useful, or exemplary conversations among members in the community. For more information, see "[Managing discussions in your repository](/discussions/managing-discussions-for-your-community/managing-discussions-in-your-repository#pinning-a-discussion)." {% data reusables.discussions.you-can-convert-an-issue %} For more information, see "[Moderating discussions in your repository](/discussions/managing-discussions-for-your-community/moderating-discussions#converting-an-issue-to-a-discussion)." ### Sharing feedback You can share your feedback about {% data variables.product.prodname_discussions %} with {% data variables.product.company_short %}. To join the conversation, see [`github/feedback`](https://github.com/github/feedback/discussions?discussions_q=category%3A%22Discussions+Feedback%22). ### Further reading - "[About writing and formatting on {% data variables.product.prodname_dotcom %}](/github/writing-on-github/about-writing-and-formatting-on-github)" - "[Searching discussions](/github/searching-for-information-on-github/searching-discussions)" - "[About notifications](/github/managing-subscriptions-and-notifications-on-github/about-notifications)" - "[Moderating comments and conversations](/github/building-a-strong-community/moderating-comments-and-conversations)" - "[Maintaining your safety on {% data variables.product.prodname_dotcom %}](/github/building-a-strong-community/maintaining-your-safety-on-github)" 50 ...community-using-discussions/collaborating-with-maintainers-using-discussions.md @@ -0,0 +1,50 @@ --- title: Collaborating with maintainers using discussions shortTitle: Collaborating with maintainers intro: You can contribute to the goals, plans, health, and community for a project on {% data variables.product.product_name %} by communicating with the maintainers of the project in a discussion. permissions: People with read permissions to a repository can start and participate in discussions in the repository. versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### About collaboration with maintainers using discussions {% data reusables.discussions.about-discussions %} If you use or contribute to a project, you can start a discussion to make suggestions and engage with maintainers and community members about your plans, questions, ideas, and feedback. For more information, see "[About discussions](/discussions/collaborating-with-your-community-using-discussions/about-discussions)." {% data reusables.discussions.about-categories-and-formats %} Repository administrators and project maintainers can delete a discussion. For more information, see "[Managing discussions in your repository](/discussions/managing-discussions-for-your-community/managing-discussions-in-your-repository#deleting-a-discussion)." {% data reusables.discussions.github-recognizes-members %} These members appear in a list of the most helpful contributors to the project's discussions. As your project grows, you can grant higher access permissions to active members of your community. For more information, see "[Granting higher permissions to top contributors](/discussions/guides/granting-higher-permissions-to-top-contributors)"  For more information about participation in discussions, see "[Participating in a discussion](/discussions/collaborating-with-your-community-using-discussions/participating-in-a-discussion)." ### Prerequisites To collaborate with maintainers using discussions, a repository administrator or project maintainer must enable discussions for the repository. For more information, see "[Enabling or disabling discussions for a repository](/github/administering-a-repository/enabling-or-disabling-github-discussions-for-a-repository)." ### Starting a discussion {% data reusables.discussions.starting-a-discussion %} ### Filtering the list of discussions You can search for discussions and filter the list of discussions in a repository. For more information, see "[Searching discussions](/github/searching-for-information-on-github/searching-discussions)." {% data reusables.repositories.navigate-to-repo %} {% data reusables.discussions.discussions-tab %} 1. In the **Search all discussions** field, type a search query. Optionally, to the right of the search field, click a button to further filter the results.  1. In the list of discussions, click the discussion you want to view.  ### Converting an issue to a discussion {% data reusables.discussions.you-can-convert-an-issue %} For more information, see "[Moderating discussions in your repository](/discussions/managing-discussions-for-your-community/moderating-discussions#converting-an-issue-to-a-discussion#converting-an-issue-to-a-discussion)." ### Further reading - "[About writing and formatting on {% data variables.product.prodname_dotcom %}](/github/writing-on-github/about-writing-and-formatting-on-github)" - "[Maintaining your safety on {% data variables.product.prodname_dotcom %}](/github/building-a-strong-community/maintaining-your-safety-on-github)" 14 content/discussions/collaborating-with-your-community-using-discussions/index.md @@ -0,0 +1,14 @@ --- title: Collaborating with your community using discussions shortTitle: Collaborating using discussions intro: Gather and discuss your project with community members and other maintainers. versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} {% link_in_list /about-discussions %} {% link_in_list /participating-in-a-discussion %} {% link_in_list /collaborating-with-maintainers-using-discussions %} 31 ...borating-with-your-community-using-discussions/participating-in-a-discussion.md @@ -0,0 +1,31 @@ --- title: Participating in a discussion intro: You can converse with the community and maintainers in a forum within the repository for a project on {% data variables.product.product_name %}. permissions: People with read permissions to a repository can participate in discussions in the repository. versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### About participation in a discussion {% data reusables.discussions.about-discussions %} For more information, see "[About discussions](/discussions/collaborating-with-your-community-using-discussions/about-discussions)." In addition to starting or viewing a discussion, you can comment in response to the original comment from the author of the discussion. You can also create a comment thread by replying to an individual comment that another community member made within the discussion, and react to comments with emoji. For more information about reactions, see "[About conversations on {% data variables.product.prodname_dotcom %}](/github/collaborating-with-issues-and-pull-requests/about-conversations-on-github#reacting-to-ideas-in-comments)." You can block users and report disruptive content to maintain a safe and pleasant environment for yourself on {% data variables.product.product_name %}. For more information, see "[Maintaining your safety on {% data variables.product.prodname_dotcom %}](/github/building-a-strong-community/maintaining-your-safety-on-github)." ### Prerequisites Discussions must be enabled for the repository for you to participate in a discussion in the repository. For more information, see "[Enabling or disabling discussions for a repository](/github/administering-a-repository/enabling-or-disabling-github-discussions-for-a-repository)." ### Creating a discussion {% data reusables.discussions.starting-a-discussion %} ### Marking a comment as an answer Discussion authors and users with the triage role or greater for a repository can mark a comment as the answer to a discussion in the repository. {% data reusables.discussions.marking-a-comment-as-an-answer %} 49 content/discussions/guides/best-practices-for-community-conversations-on-github.md @@ -0,0 +1,49 @@ --- title: Best practices for community conversations on GitHub shortTitle: Best practices for community conversations intro: 'You can use discussions to brainstorm with your team, and eventually move the conversation to a discussion when you are ready to scope out the work.' versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### Community conversations in {% data variables.product.prodname_discussions %} Since {% data variables.product.prodname_discussions %} is an open forum, there is an opportunity to bring non-code collaboration into a project's repository and gather diverse feedback and ideas more quickly. You can help drive a productive conversation by: - Asking pointed questions and follow-up questions to garner specific feedback - Capture a diverse experience and distill it down to main points - Open an issue to take action based on the conversation, where applicable For more information about opening an issue and cross-referencing a discussion, see "[Opening an issue from a comment](/github/managing-your-work-on-github/opening-an-issue-from-a-comment)." ### Learning about conversations on GitHub You can create and participate in discussions, issues, and pull requests, depending on the type of conversation you'd like to have. You can use {% data variables.product.prodname_discussions %} to discuss big picture ideas, brainstorm, and spike out a project's specific details before committing it to an issue, which can then be scoped. Discussions are useful for teams if: - You are in the discovery phase of a project and are still learning which director your team wants to go in - You want to collect feedback from a wider community about a project - You want to keep bug fixes, feature requests, and general conversations separate Issues are useful for discussing specific details of a project such as bug reports and planned improvements. For more information, see "[About issues](/articles/about-issues)." Pull requests allow you to comment directly on proposed changes. For more information, see "[About pull requests](/articles/about-pull-requests)" and "[Commenting on a pull request](/articles/commenting-on-a-pull-request)." {% data reusables.organizations.team-discussions-purpose %} For more information, see "[About team discussions](/articles/about-team-discussions)." ### Following contributing guidelines Before you open a discussion, check to see if the repository has contributing guidelines. The CONTRIBUTING file includes information about how the repository maintainer would like you to contribute ideas to the project. For more information, see "[Setting up your project for healthy contributions](/github/building-a-strong-community/setting-up-your-project-for-healthy-contributions)." ### Next steps To continue learning about {% data variables.product.prodname_discussions %} and quickly create a discussion for your community, see "[Quickstart for {% data variables.product.prodname_discussions %}](/discussions/quickstart)." ### Further reading - "[Setting up your project for healthy contributions](/articles/setting-up-your-project-for-healthy-contributions)" - "[Using templates to encourage useful issues and pull requests](/github/building-a-strong-community/using-templates-to-encourage-useful-issues-and-pull-requests)" - "[Moderating comments and conversations](/articles/moderating-comments-and-conversations)" - "[Writing on {% data variables.product.prodname_dotcom %}](/articles/writing-on-github)" 21 content/discussions/guides/finding-discussions-across-multiple-repositories.md @@ -0,0 +1,21 @@ --- title: Finding discussions across multiple repositories intro: 'You can easily access every discussion you''ve created or participated in across multiple repositories.' versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### Finding discussions 1. Navigate to {% data variables.product.prodname_dotcom_the_website %}. 1. In the top-right corner of {% data variables.product.prodname_dotcom_the_website %}, click your profile photo, then click **Your enterprises**.  1. Toggle between **Created** and **Commented** to see the discussions you've created or participated in. ### Further reading - "[Searching discussions](/github/searching-for-information-on-github/searching-discussions)" - "[About discussions](/discussions/collaborating-with-your-community-using-discussions/about-discussions)" - "[Managing discussions for your community](/discussions/managing-discussions-for-your-community)" 32 content/discussions/guides/granting-higher-permissions-to-top-contributors.md @@ -0,0 +1,32 @@ --- title: Granting higher permissions to top contributors intro: 'Repository administrators can promote any community member to a moderator and maintainer.' versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### Introduction The most helpful contributors for the past 30 days are highlighted on the {% data variables.product.prodname_discussions %} dashboard, based on how many comments were marked as answers by other community members. Helpful contributors can help drive a healthy community and moderate and guide the community space in addition to maintainers. ### Step 1: Audit your discussions top contributors {% data reusables.repositories.navigate-to-repo %} {% data reusables.discussions.discussions-tab %} 1. Compare the list of contributors with their access permissions to see who qualifies to moderate the discussion. ### Step 2: Review permission levels for discussions People with triage permissions for a repository can help moderate a project's discussions by marking comments as answers, locking discussions that are not longer useful or are damaging to the community, and converting issues to discussions when an idea is still in the early stages of development. For more information, see "[Moderating discussions](/discussions/managing-discussions-for-your-community/moderating-discussions)." For more information about repository permission levels and {% data variables.product.prodname_discussions %}, see "[Repository permissions levels for an organization](/github/setting-up-and-managing-organizations-and-teams/repository-permission-levels-for-an-organization)." ### Step 3: Change permissions levels for top contributors You can change a contributor's permission levels to give them more access to the tooling they need to moderate GitHub Discussions. To change a person's or team's permission levels, see "[Managing teams and people with access to your repository](/github/administering-a-repository/managing-teams-and-people-with-access-to-your-repository)." ### Step 4: Notify community members of elevated access When you change a collaborators permission level, they will receive a notification for the change. 29 content/discussions/guides/index.md @@ -0,0 +1,29 @@ --- title: Discussions guides shortTitle: Guides intro: 'Discover pathways to get started or learn best practices for participating or monitoring your community''s discussions.' versions: free-pro-team: '*' --- {% data reusables.discussions.beta %} ### Getting started with discussions {% link_in_list /about-discussions %} {% link_in_list /best-practices-for-community-conversations-on-github %} {% link_in_list /finding-discussions-across-multiple-repositories %} ### Administering discussions {% link_in_list /granting-higher-permissions-to-top-contributors %} 55 content/discussions/index.md @@ -0,0 +1,55 @@ --- title: GitHub Discussions Documentation beta_product: true shortTitle: GitHub Discussions intro: '{% data variables.product.prodname_discussions %} is a collaborative communication forum for the community around an open source project. Community members can ask and answer questions, share updates, have open-ended conversations, and follow along on decisions affecting the community''s way of working.' introLinks: quickstart: /discussions/quickstart featuredLinks: guides: - /discussions/collaborating-with-your-community-using-discussions/about-discussions - /discussions/collaborating-with-your-community-using-discussions/participating-in-a-discussion - /discussions/managing-discussions-for-your-community/moderating-discussions gettingStarted: - /discussions/quickstart guideCards: - /discussions/collaborating-with-your-community-using-discussions/about-discussions - /discussions/collaborating-with-your-community-using-discussions/participating-in-a-discussion - /discussions/managing-discussions-for-your-community/moderating-discussions popular: - /discussions/guides/granting-higher-permissions-to-top-contributors - /discussions/guides/best-practices-for-community-conversations-on-github - /discussions/guides/finding-discussions-across-multiple-repositories - /discussions/collaborating-with-your-community-using-discussions/collaborating-with-maintainers-using-discussions - /discussions/managing-discussions-for-your-community/managing-categories-for-discussions-in-your-repository product_video: https://www.youtube-nocookie.com/embed/DbTWBP3_RbM layout: product-landing versions: free-pro-team: '*' --- {% assign discussionsCommunityExamples = site.data.variables.discussions_community_examples %} {% if discussionsCommunityExamples %}
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**Note**: You can use any name, but we suggest this value for clarity. | | **Tool URL** | Launch URL from {% data variables.product.prodname_classroom %} | | **LTI version** | LTI 1.0/1.1 | | **Default launch container** | New window | | **Consumer key** | Consumer key from {% data variables.product.prodname_classroom %} | | **Shared secret** | Shared secret from {% data variables.product.prodname_classroom %} | 1. Scroll to and click **Services**. 1. To the right of "IMS LTI Names and Role Provisioning", select the drop-down menu and click **Use this service to retrieve members' information as per privacy settings**. 1. Scroll to and click **Privacy**. 1. To the right of **Share launcher's name with tool** and **Share launcher's email with tool**, select the drop-down menus to click **Always**. 1. At the bottom of the page, click **Save changes**. 1. In the **Preconfigure tool** menu, click **GitHub Classroom - _YOUR CLASSROOM NAME_**. 1. Under "Common module settings", to the right of "Availability", select the drop-down menu and click **Hide from students**. 1. At the bottom of the page, click **Save and return to course**. 1. Navigate to anywhere you chose to display {% data variables.product.prodname_classroom %}, and click the {% data variables.product.prodname_classroom %} activity. ### Importing a roster from your LMS For more information about importing the roster from your LMS into {% data variables.product.prodname_classroom %}, see "[Manage classrooms](/education/manage-coursework-with-github-classroom/manage-classrooms#creating-a-roster-for-your-classroom)." ### Disconnecting your LMS {% data reusables.classroom.sign-into-github-classroom %} {% data reusables.classroom.click-classroom-in-list %} {% data reusables.classroom.click-settings %} 1. Under "Connect to a learning management system (LMS)", click **Connection Settings**.  1. Under "Delete Connection to your learning management system", click **Disconnect from your learning management system**.  145 .../education/manage-coursework-with-github-classroom/create-a-group-assignment.md @@ -0,0 +1,145 @@ --- title: Create a group assignment intro: 'You can create a collaborative assignment for teams of students who participate in your course.' versions: free-pro-team: '*' redirect_from: - /education/manage-coursework-with-github-classroom/create-group-assignments --- ### About group assignments {% data reusables.classroom.assignments-group-definition %} Students can work together on a group assignment in a shared repository, like a team of professional developers. When a student accepts a group assignment, the student can create a new team or join an existing team. {% data variables.product.prodname_classroom %} saves the teams for an assignment as a set. You can name the set of teams for a specific assignment when you create the assignment, and you can reuse that set of teams for a later assignment. {% data reusables.classroom.classroom-creates-group-repositories %} {% data reusables.classroom.about-assignments %} You can decide how many teams one assignment can have, and how many members each team can have. Each team that a student creates for an assignment is a team within your organization on {% data variables.product.product_name %}. The visibility of the team is secret. Teams that you create on {% data variables.product.product_name %} will not appear in {% data variables.product.prodname_classroom %}. For more information, see "[About teams](/github/setting-up-and-managing-organizations-and-teams/about-teams)." For a video demonstration of the creation of a group assignment, see "[Basics of setting up {% data variables.product.prodname_classroom %}](/education/manage-coursework-with-github-classroom/basics-of-setting-up-github-classroom)." ### Prerequisites {% data reusables.classroom.assignments-classroom-prerequisite %} ### Creating an assignment {% data reusables.classroom.assignments-guide-create-the-assignment %} ### Setting up the basics for an assignment Name your assignment, decide whether to assign a deadline, define teams, and choose the visibility of assignment repositories. - [Naming an assignment](#naming-an-assignment) - [Assigning a deadline for an assignment](#assigning-a-deadline-for-an-assignment) - [Choosing an assignment type](#choosing-an-assignment-type) - [Defining teams for an assignment](#defining-teams-for-an-assignment) - [Choosing a visibility for assignment repositories](#choosing-a-visibility-for-assignment-repositories) #### Naming an assignment For a group assignment, {% data variables.product.prodname_classroom %} names repositories by the repository prefix and the name of the team. By default, the repository prefix is the assignment title. For example, if you name an assignment "assignment-1" and the team's name on {% data variables.product.product_name %} is "student-team", the name of the assignment repository for members of the team will be `assignment-1-student-team`. {% data reusables.classroom.assignments-type-a-title %} #### Assigning a deadline for an assignment {% data reusables.classroom.assignments-guide-assign-a-deadline %} #### Choosing an assignment type Under "Individual or group assignment", select the drop-down menu, then click **Group assignment**. You can't change the assignment type after you create the assignment. If you'd rather create a individual assignment, see "[Create an individual assignment](/education/manage-coursework-with-github-classroom/create-an-individual-assignment)." #### Defining teams for an assignment If you've already created a group assignment for the classroom, you can reuse a set of teams for the new assignment. To create a new set with the teams that your students create for the assignment, type the name for the set. Optionally, type the maximum number of team members and total teams. {% tip %} **Tips**: - We recommend including details about the set of teams in the name for the set. For example, if you want to use the set of teams for one assignment, name the set after the assignment. If you want to reuse the set throughout a semester or course, name the set after the semester or course. - If you'd like to assign students to a specific team, give your students a name for the team and provide a list of members. {% endtip %}  #### Choosing a visibility for assignment repositories {% data reusables.classroom.assignments-guide-choose-visibility %} {% data reusables.classroom.assignments-guide-click-continue-after-basics %} ### Adding starter code and configuring a development environment {% data reusables.classroom.assignments-guide-intro-for-environment %} - [Choosing a template repository](#choosing-a-template-repository) - [Choosing an online integrated development environment (IDE)](#choosing-an-online-integrated-development-environment-ide) #### Choosing a template repository By default, a new assignment will create an empty repository for each team that a student creates. {% data reusables.classroom.you-can-choose-a-template-repository %} For more information about template repositories, see "[Creating a template repository](/github/creating-cloning-and-archiving-repositories/creating-a-template-repository)." {% data reusables.classroom.assignments-guide-choose-template-repository %} #### Choosing an online integrated development environment (IDE) {% data reusables.classroom.about-online-ides %} For more information, see "[Integrate {% data variables.product.prodname_classroom %} with an IDE](/education/manage-coursework-with-github-classroom/integrate-github-classroom-with-an-ide)." {% data reusables.classroom.assignments-guide-choose-an-online-ide %} {% data reusables.classroom.assignments-guide-click-continue-after-starter-code-and-feedback %} ### Providing feedback Optionally, you can automatically grade assignments and create a space for discussing each submission with the team. - [Testing assignments automatically](#testing-assignments-automatically) - [Preventing changes to important files](#preventing-changes-to-important-files) - [Creating a pull request for feedback](#creating-a-pull-request-for-feedback) #### Testing assignments automatically {% data reusables.classroom.assignments-guide-using-autograding %} #### Preventing changes to important files {% data reusables.classroom.assignments-guide-prevent-changes %} #### Creating a pull request for feedback {% data reusables.classroom.you-can-create-a-pull-request-for-feedback %} {% data reusables.classroom.assignments-guide-create-review-pull-request %} {% data reusables.classroom.assignments-guide-click-create-assignment-button %} ### Inviting students to an assignment {% data reusables.classroom.assignments-guide-invite-students-to-assignment %} You can see the teams that are working on or have submitted an assignment in the **Teams** tab for the assignment. {% data reusables.classroom.assignments-to-prevent-submission %}
