From 1b82484e1a39f267107fd903cf95c2c028041974 Mon Sep 17 00:00:00 2001 From: Jacob Date: Thu, 3 Mar 2022 13:55:08 -0500 Subject: [PATCH 1/9] Update kafka.md --- tech/kafka/kafka.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tech/kafka/kafka.md b/tech/kafka/kafka.md index 2190fe5..eba5164 100644 --- a/tech/kafka/kafka.md +++ b/tech/kafka/kafka.md @@ -38,6 +38,9 @@ Topic1-->JoinApp Topic2-->JoinApp end + subgraph notify + Topic2-->notify_process + end ``` ## Core components @@ -147,4 +150,4 @@ There are a variety of functions you can take on a stream to transform it (map, ## Testing * Use [TopologyTestDriver](https://www.confluent.io/blog/test-kafka-streams-with-topologytestdriver/) -* Use [MockSchemaRegistry](https://medium.com/bakdata/transparent-schema-registry-for-kafka-streams-6b43a3e7a15c) \ No newline at end of file +* Use [MockSchemaRegistry](https://medium.com/bakdata/transparent-schema-registry-for-kafka-streams-6b43a3e7a15c) From c9328cea6d23c8a91c9344af84c7f8b7be544622 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Wed, 16 Mar 2022 15:58:19 -0400 Subject: [PATCH 2/9] updated --- tech/kafka/kafka.md | 28 +++------------------------- 1 file changed, 3 insertions(+), 25 deletions(-) diff --git a/tech/kafka/kafka.md b/tech/kafka/kafka.md index d3c7047..a2417c0 100644 --- a/tech/kafka/kafka.md +++ b/tech/kafka/kafka.md @@ -22,34 +22,12 @@ * Connector API - allows you to add applications or data systems to push/pull data to/from kafka ```mermaid -<<<<<<< HEAD graph TD; Producer_API-->Kafka_Broker Kafka_Broker-->Consumer_API Connector_Input_via_API-->Kafka_Broker Kafka_Broker-->Connector_Output_via_API Kafka_Broker<-->Streaming_Engine -======= - flowchart LR - Producer-->Broker - subgraph kafka - Broker-->Log - Log-->Topic1 - Topic2 - end - Consumer-->Topic1 - subgraph kStream - Topic1-->JavaApp - JavaApp-->Topic2 - end - subgraph join - Topic1-->JoinApp - Topic2-->JoinApp - end - subgraph notify - Topic2-->notify_process - end ->>>>>>> 1b82484e1a39f267107fd903cf95c2c028041974 ``` ## Core components @@ -106,9 +84,9 @@ * can do inner, outer, left-outer ## Example Usage -* A systems acts as a producer and sends a record to a Kafka Broker -* Kafka Broker is examined and written to a log that corresponds with one or more topics -* The record is published to a topic endpoint and available for Consumption +* A systems acts as a producer and sends a **Record** to a Kafka Broker +* **Record** is examined and written to a log/segment that corresponds with one or more topics +* The **Record** is published to a topic endpoint and available for Consumption * Consumption occurs by any number of consumers. Consumers are created via: * Consumption API * kStream From 474da01616e9aca5dcd7f1923bfbd39626cb73a7 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Fri, 18 Mar 2022 12:18:27 -0400 Subject: [PATCH 3/9] updated --- tech/aws/sns/push-notification.md | 12 ++++++++---- tech/kafka/kafka.md | 2 +- tech/tensorflow/machine-learning.md | 13 +++++++++++++ tech/tensorflow/tensorflow.md | 24 ++++++++++++++++++++++++ 4 files changed, 46 insertions(+), 5 deletions(-) create mode 100644 tech/tensorflow/machine-learning.md create mode 100644 tech/tensorflow/tensorflow.md diff --git a/tech/aws/sns/push-notification.md b/tech/aws/sns/push-notification.md index 7674482..7ad0942 100644 --- a/tech/aws/sns/push-notification.md +++ b/tech/aws/sns/push-notification.md @@ -1,10 +1,14 @@ # Push Notification +https://github.com/department-of-veterans-affairs/vanotify-team/tree/master/Architecture/push-notifications -### What -* Push notification is a notification directed at a mobile app. The mobile app receives a notification and decides how to present it to the user +## Background +Push notification is a notification directed at a mobile app. The mobile platform OS receives a notification and decides how to present it to the user. Apple and Android both have integration platforms to allow for push notifications. Apple has [Apple Push Notification Service](https://developer.apple.com/library/archive/documentation/NetworkingInternet/Conceptual/RemoteNotificationsPG/APNSOverview.html) and Android has [Firebase Cloud Messaging](https://firebase.google.com/docs/cloud-messaging). + +## Push Notifications via AWS +AWS has the SNS service which can be configured to send messages to both Android/Apple notification platforms. AWS assigns an ARN (Amazon Resource Name) to the mobile app itself that you can use to send a push notification to ALL mobile app installations. AWS also assigns an ARN to each individual device installation so that you can send notifications directly to a device. ### How -* Setup: +* AWS Manual Setup: * Go to SNS * Create platform application * Purpose: This creates an ARN for your application so that when you want to send a push notification to all users of your application, you send to the generated ARN @@ -13,7 +17,7 @@ * Get Platform ARN * Add endpoints * Purpose: This creates an endpoint for a specific device so that if you want to send a push notification to a specific device, you send to the generated ARN - * Add Device Token (UUID) + * Add Device Push Token * (Optional) - Enter Additional payload to send with each push * (Optional) - Enter a user identifier * Get Device ARN diff --git a/tech/kafka/kafka.md b/tech/kafka/kafka.md index a2417c0..948ba8a 100644 --- a/tech/kafka/kafka.md +++ b/tech/kafka/kafka.md @@ -101,7 +101,7 @@ Log-->Topic1 Topic2 end - Consumer-->Topic1 + Topic1-->Consumer subgraph kStream Topic1-->JavaApp JavaApp-->Topic2 diff --git a/tech/tensorflow/machine-learning.md b/tech/tensorflow/machine-learning.md new file mode 100644 index 0000000..9efd1e5 --- /dev/null +++ b/tech/tensorflow/machine-learning.md @@ -0,0 +1,13 @@ +# Machine Learning + +## What is it +* Numbers In -> Numbers Out. Computers only understand numbers so all inputs have to be numbers. So both input and output of a model will be numerical. +* All models are initially **untrained** + +## Types +* Supervised Learning - Train by labeled examples + * You provide data and label them in categories (i.e. you provide weight & color attributes for fruit and then you label the fruit with it's name.) + * Only works well if you provide diverse and well-labeled data. +* Unsupervised Learning - Finding patterns in unlabled data. Leverages clustering to group items that closely share similar measured attributes. +* Reinforcement Learning - Set a reward function and let ML program figure out the best way to solve. + diff --git a/tech/tensorflow/tensorflow.md b/tech/tensorflow/tensorflow.md new file mode 100644 index 0000000..e503db8 --- /dev/null +++ b/tech/tensorflow/tensorflow.md @@ -0,0 +1,24 @@ +# Tensorflow + + +## Common Terms +* Deep Learning results feed better Machine Learning Models. Better Machine Learning Models feed better AI. +* AI - Science of making things smart + * Narrow AI - A system that can do one or a few things well or better than a human +* Machine Learning - An approach to achieve AI by finding patterns in a set of data + * Examples: + * Computer Vision: + * Object vs Image Recognition + * Object Recognition - detects how many and where objects are in an image + * Image Recognition - detects what is in the image but does not tell you where or how many there are + * Numerical Prediction + * Regression: Predicting a numerical value from other numerical inputs (i.e. predicting house prices based on sq footage) + * Natural Language + * Semantic Analysis and Content Summary analaysis of naturally spoken Language + * Audio + * Speech recognition - Translate audio to text + * Generative + * Style Transfer/Creative - Generating realistic images of human faces or transofmring human faces or transforming human voices into musical instruments. +* Deep Learning - A technique for implementing machine Learning. Involves **Deep Neural Networks (DNN)** code strcutures that are arranged in layers. Each layer filters a pattern out of other patterns and continues to filter the pattern until an output is given. + + From 88f782cfd05112853c4d6abf1f3d9146f822e019 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Sat, 19 Mar 2022 12:30:19 -0400 Subject: [PATCH 4/9] updated --- tech/tensorflow/machine-learning.md | 29 +++++++++++++++ tech/tensorflow/tensorflow.md | 58 ++++++++++++++++++++++++++++- 2 files changed, 86 insertions(+), 1 deletion(-) diff --git a/tech/tensorflow/machine-learning.md b/tech/tensorflow/machine-learning.md index 9efd1e5..0b882c6 100644 --- a/tech/tensorflow/machine-learning.md +++ b/tech/tensorflow/machine-learning.md @@ -8,6 +8,35 @@ * Supervised Learning - Train by labeled examples * You provide data and label them in categories (i.e. you provide weight & color attributes for fruit and then you label the fruit with it's name.) * Only works well if you provide diverse and well-labeled data. + * Steps: + 1) Provide numerical input data representing the objects and their labels + 2) Train the model using a machine learning algorithim to learn a line that can separate the different types of objects + 3) Have the model classify each object as a labled object based on which side of the line it falls on. + * Used for: Spam detection, text categorization, object recognition * Unsupervised Learning - Finding patterns in unlabled data. Leverages clustering to group items that closely share similar measured attributes. + * Used for: Clustering problems like Product Recommendation Engines * Reinforcement Learning - Set a reward function and let ML program figure out the best way to solve. + * Trial and Error with accurate "trials" providing rewards. + * Used for Gaming development and robotics + +## How to Train ML systems +1) Identify features and attributes of the objects you want to train against + a) The # of features/attributes = # of dimensions + b) image recognition requires each pixel being a feature + c) for a high # of features the computer can use a hyperplane (one dimension less than the features you have) to separate instead + +### Classification vs Regression +* Classification - trying to predict from features +* Regression - trying to predict a number from other numbers + +## Ways to use +* Use Pre-Trained models - resuse state of the art models that already exist (i.e. monitoring body movements models for skeletal tracking) +* Transfer Learning - take a pre-trained models and re-train it to do what you need +* Custom Models - Writing your own models from a blank canvas +* **Note** - Tensorflow.js supports the execution of other forms of Tensorflow models through the command line converter. This helps to see what has been done on other platforms. +* Tools: + * MediaPipe: a C++ framework for building applied ML pipelines that some researchers choose to use + * TensorFlow Hub: repository of trained ML models that can be reused + * TensorFlow Lite: Google's ML framework that focuses on mobile native and IoT devices. **Note:** TensorflowJS can also run on these platforms but TensorFlow Lite is optimized for them. + * ONNX: The Open Neural Network Exchange is an open-source AI ecosystem of tech companies and research orgs. ONNX establishes open standards for representing ML algorithms in a way agnostic to the library it was written in. diff --git a/tech/tensorflow/tensorflow.md b/tech/tensorflow/tensorflow.md index e503db8..1b053f8 100644 --- a/tech/tensorflow/tensorflow.md +++ b/tech/tensorflow/tensorflow.md @@ -1,12 +1,14 @@ # Tensorflow +## What? +JS and Python library to develop models, train them, and use them to run ML and AI on many platforms. It is written in C++ ## Common Terms * Deep Learning results feed better Machine Learning Models. Better Machine Learning Models feed better AI. * AI - Science of making things smart * Narrow AI - A system that can do one or a few things well or better than a human * Machine Learning - An approach to achieve AI by finding patterns in a set of data - * Examples: + * Types: * Computer Vision: * Object vs Image Recognition * Object Recognition - detects how many and where objects are in an image @@ -19,6 +21,60 @@ * Speech recognition - Translate audio to text * Generative * Style Transfer/Creative - Generating realistic images of human faces or transofmring human faces or transforming human voices into musical instruments. + * Deep Learning - A technique for implementing machine Learning. Involves **Deep Neural Networks (DNN)** code strcutures that are arranged in layers. Each layer filters a pattern out of other patterns and continues to filter the pattern until an output is given. +* Keras - This is actually a Python library that outputs models. Tensorflow can tell the difference and use Kera models in addition to Tensorflow trained models. + * This works by feeding the Kerans Model the Layers API. The Core/OPS API then translates the Keras Model to a TensorFlow SavedModel. + +## What is it +TensorflowJS - Javascript library that contains Google's machine learning library +* Python vs Javascript + * Running Tensorflow in NodeJS allows you to leverage CUDA and AVX acceleration to perform a LOT faster than Python. + * NodeJS supports integrating with Python Keras and Tensorflow SavedModels created in Python. +* Server vs Client + * Benefits to using Tensorflow Server Side: + * can use tensorflow savedmodel without conversion + * run larger models than client-side (GPU memory limits) + * Code in 1 language - if you are already using JS then you can continue using JS + * Performance boost because of C bindings / JIT boost for pre/post processing + * Benefits of running client side: + * Privacy - everything is run on the client's browser to build models so no information is transferred anywhere + * Lower Latency - if you need to use a camera or audio input then the processing happens closer to the input so result come quicker + * Lower Cost - with no server there is no infrastructure server side to pay for + * Interactivity - can directly interact with the library using the browser platform + * Reach & Scale - Running on a browser is basically omnipresent. + +## Structurte +Models --> Layers API --> Core/OPS API +* Layers API - high level api + * Allows you to make models without dealing with the mathematics (equivalent to Python's Keras API) +* Core/Ops API (mathematical) + * Can use linear algebra to build more cuting edge models + * Contains code with how to work with the platform (i.e. WebBrowser or NodeJS env) and the platform's backend (CPU, WebGL, WASM for Browser or CPU, GPU for NodeJS) it is being used on + * Backend Types + * CPU - + * always available + * slowest form of execution + * WASM - + * Fast CPU execution + * SIMD + Multithreading support for GPU like performance + * great for smaller models (<10MB) + * WebGL - + * Fast GPU execution + * 97.6% device support + * Executes on AMD, NVIDIA, Intel, and more + * great for larger models (>10MB) + * WebNN - Supported in the future + * New web standard in development + * Even faster native execution + * Web GPU - Supported in the future + * new web standard in development + * Even faster native execution on GPU + + + + +## Tools +* [Teachable Machine](https://teachablemachine.withgoogle.com/) From da1caab3b71ce50bc8c0b7736c296298fedcc682 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Tue, 22 Mar 2022 09:54:37 -0400 Subject: [PATCH 5/9] updated --- tech/tensorflow/pre-made-models.md | 45 ++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 tech/tensorflow/pre-made-models.md diff --git a/tech/tensorflow/pre-made-models.md b/tech/tensorflow/pre-made-models.md new file mode 100644 index 0000000..7dc4670 --- /dev/null +++ b/tech/tensorflow/pre-made-models.md @@ -0,0 +1,45 @@ +# Pre-Made Models + +## What are they? +ML Models that have been trained already. [Tensorflow JS Model Library](https://www.tensorflow.org/js/models) +Types: +* JS classes that wrap a model +* Raw models that you need to learn to use working at the "Tensor" level +Categories: +* [Object Detection Model](https://github.com/tensorflow/tfjs-models/tree/master/coco-ssd) + * [Demo](https://tensorflow-js-object-detection.glitch.me/) +* [Speech Recognition](https://github.com/tensorflow/tfjs-models/tree/master/speech-commands) +* [Text Toxicity](https://github.com/tensorflow/tfjs-models/tree/master/toxicity) + * [Demo](https://storage.googleapis.com/tfjs-models/demos/toxicity/index.html) +* [Face Mesh](https://github.com/tensorflow/tfjs-models/tree/master/face-landmarks-detection) + * [Demo](https://storage.googleapis.com/tfjs-models/demos/facemesh/index.html) +* [Body Detection](https://github.com/tensorflow/tfjs-models/tree/master/pose-detection) + * [PoseNet Demo](https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=posenet) + * [MoveNet Demo](https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=movenet) + * [BlazePose Demo](https://storage.googleapis.com/tfjs-models/demos/pose-detection/index.html?model=blazepose) +* [Hand Pose Detection](https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection) + * [Demo](https://storage.googleapis.com/tfjs-models/demos/hand-pose-detection/index.html?model=mediapipe_hands) +* [Body Segmentation](https://github.com/tensorflow/tfjs-models/tree/master/body-pix) + * [Demo](https://storage.googleapis.com/tfjs-models/demos/body-pix/index.html) + * [Selfie Segementation Demo](https://storage.googleapis.com/tfjs-models/demos/body-segmentation/index.html?model=selfie_segmentation) + + + +## How to use? +* include [tensorflowjs from cdn](https://cdn.jsdelivr.net/npm/@tensorflow/tfjs) and [model you want to use from cdn](https://www.tensorflow.org/js/models) +``` + + + + +``` + From 2d0aed10681c5fc4049199dd946c8d5751dc7b86 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Sat, 2 Apr 2022 15:34:53 -0400 Subject: [PATCH 6/9] update --- .../samples/webcam-obj-detection/_notes.md | 9 + .../samples/webcam-obj-detection/index.html | 56 + .../webcam-obj-detection/package-lock.json | 4181 +++++++++++++++++ .../samples/webcam-obj-detection/package.json | 15 + .../samples/webcam-obj-detection/script.js | 111 + .../samples/webcam-obj-detection/style.css | 44 + 6 files changed, 4416 insertions(+) create mode 100644 tech/tensorflow/samples/webcam-obj-detection/_notes.md create mode 100644 tech/tensorflow/samples/webcam-obj-detection/index.html create mode 100644 tech/tensorflow/samples/webcam-obj-detection/package-lock.json create mode 100644 tech/tensorflow/samples/webcam-obj-detection/package.json create mode 100644 tech/tensorflow/samples/webcam-obj-detection/script.js create mode 100644 tech/tensorflow/samples/webcam-obj-detection/style.css diff --git a/tech/tensorflow/samples/webcam-obj-detection/_notes.md b/tech/tensorflow/samples/webcam-obj-detection/_notes.md new file mode 100644 index 0000000..021697e --- /dev/null +++ b/tech/tensorflow/samples/webcam-obj-detection/_notes.md @@ -0,0 +1,9 @@ +# ObjDetection via WebCam + +**Notes** +* Uses [Coco-SSD](https://github.com/tensorflow/tfjs-models/tree/master/coco-ssd) pre-trained model + * COCO - [Common Objects in Context Dataset](https://cocodataset.org/#home) + * SSD - [Single Shot MultiBox Detection](https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab) + * Bounding Box - Defined by leftmost X, topmost Y, Height Width + * Above 66% is generally the accepted minimal acceptance rate that a classification is accurate + diff --git a/tech/tensorflow/samples/webcam-obj-detection/index.html b/tech/tensorflow/samples/webcam-obj-detection/index.html new file mode 100644 index 0000000..0140d01 --- /dev/null +++ b/tech/tensorflow/samples/webcam-obj-detection/index.html @@ -0,0 +1,56 @@ + 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/dev/null +++ b/tech/tensorflow/samples/webcam-obj-detection/script.js @@ -0,0 +1,111 @@ +// Store the resulting model in the global scope of our app. +var model = undefined; +var children = []; + +const video = document.getElementById("webcam"); // Stream. +const liveView = document.getElementById("liveView"); // Container. +const demosSection = document.getElementById("demos"); +const enableWebcamButton = document.getElementById("webcamButton"); + +// Before we can use COCO-SSD class we must wait for it to finish +// loading. Machine Learning models can be large and take a moment +// to get everything needed to run. +// Note: cocoSsd is an external object loaded from our index.html +// script tag import so ignore any warning in Glitch. +cocoSsd.load().then(function (loadedModel) { + model = loadedModel; + // Show demo section now model is ready to use. + demosSection.classList.remove("invisible"); +}); + +// Check if webcam access is supported. +function getUserMediaSupported() { + return !!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia); +} + +// If webcam supported, add event listener to button for when user +// wants to activate it to call enableCam function which we will +// define in the next step. +if (getUserMediaSupported()) { + enableWebcamButton.addEventListener("click", enableCam); +} else { + console.warn("getUserMedia() is not supported by your browser"); +} + +// Enable the live webcam view and start classification. +function enableCam(event) { + // Only continue if the COCO-SSD has finished loading. + if (!model) { + return; + } + // Hide the button once clicked. + event.target.classList.add("removed"); + // getUsermedia parameters to force video but not audio. + const constraints = { + video: true, + }; + + // Activate the webcam stream. + navigator.mediaDevices.getUserMedia(constraints).then(function (stream) { + video.srcObject = stream; + video.addEventListener("loadeddata", predictWebcam); + }); +} + +function predictWebcam() { + + // Now let's start classifying a frame in the stream. + model.detect(video).then(function (predictions) { + // Remove any highlighting we did previous frame. + for (let i = 0; i < children.length; i++) { + liveView.removeChild(children[i]); + } + + children.splice(0); + + // Now lets loop through predictions and draw them to the live view if + // they have a high confidence score. + for (let n = 0; n < predictions.length; n++) { + // If we are over 66% sure we are sure we classified it right, draw it! + if (predictions[n].score > 0.66) { + const p = document.createElement("p"); + p.innerText = + predictions[n].class + + " - with " + + Math.round(parseFloat(predictions[n].score) * 100) + + "% confidence."; + + p.style = + "margin-left: " + + predictions[n].bbox[0] + + "px; margin-top: " + + (predictions[n].bbox[1] - 10) + + "px; width: " + + (predictions[n].bbox[2] - 10) + + "px; top: 0; left: 0;"; + + const highlighter = document.createElement("div"); + highlighter.setAttribute("class", "highlighter"); + highlighter.style = + "left: " + + predictions[n].bbox[0] + + "px; top: " + + predictions[n].bbox[1] + + "px; width: " + + predictions[n].bbox[2] + + "px; height: " + + predictions[n].bbox[3] + + "px;"; + + liveView.appendChild(highlighter); + liveView.appendChild(p); + children.push(highlighter); + children.push(p); + } + } + // Call this function again to keep predicting when the browser is ready. + window.requestAnimationFrame(predictWebcam); + }); +} + +demosSection.classList.remove("invisible"); diff --git a/tech/tensorflow/samples/webcam-obj-detection/style.css b/tech/tensorflow/samples/webcam-obj-detection/style.css new file mode 100644 index 0000000..ba93b1e --- /dev/null +++ b/tech/tensorflow/samples/webcam-obj-detection/style.css @@ -0,0 +1,44 @@ +body { + font-family: helvetica, arial, sans-serif; + margin: 2em; + color: #3D3D3D; +} +h1 { + font-style: italic; + color: #FF6F00; +} +video { + display: block; +} +section { + opacity: 1; + transition: opacity 500ms ease-in-out; +} +.removed { + display: none; +} +.invisible { + opacity: 0.2; +} +.camView { + position: relative; + float: left; + width: calc(100% - 20px); + margin: 10px; + cursor: pointer; +} +.camView p { + position: absolute; + padding: 5px; + background-color: rgba(255, 111, 0, 0.85); + color: #FFF; + border: 1px dashed rgba(255, 255, 255, 0.7); + z-index: 2; + font-size: 12px; +} +.highlighter { + background: rgba(0, 255, 0, 0.25); + border: 1px dashed #fff; + z-index: 1; + position: absolute; +} \ No newline at end of file From 3fa477125cd317fc52d33dc49e47372955ecd716 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Sat, 2 Apr 2022 15:35:12 -0400 Subject: [PATCH 7/9] update --- tech/tensorflow/machine-learning.md | 28 +++++++ .../template/hello-world/index.html | 22 ++++++ .../tensorflow/template/hello-world/script.js | 20 +++++ .../tensorflow/template/hello-world/style.css | 29 +++++++ tech/tensorflow/tensor.md | 75 +++++++++++++++++++ tech/tensorflow/tensorflow.md | 18 ++++- tech/twistlock/twistlock.md | 14 ++++ tech/typescript/typescript.md | 12 +++ 8 files changed, 214 insertions(+), 4 deletions(-) create mode 100644 tech/tensorflow/template/hello-world/index.html create mode 100644 tech/tensorflow/template/hello-world/script.js create mode 100644 tech/tensorflow/template/hello-world/style.css create mode 100644 tech/tensorflow/tensor.md create mode 100644 tech/twistlock/twistlock.md create mode 100644 tech/typescript/typescript.md diff --git a/tech/tensorflow/machine-learning.md b/tech/tensorflow/machine-learning.md index 0b882c6..651a615 100644 --- a/tech/tensorflow/machine-learning.md +++ b/tech/tensorflow/machine-learning.md @@ -40,3 +40,31 @@ * TensorFlow Lite: Google's ML framework that focuses on mobile native and IoT devices. **Note:** TensorflowJS can also run on these platforms but TensorFlow Lite is optimized for them. * ONNX: The Open Neural Network Exchange is an open-source AI ecosystem of tech companies and research orgs. ONNX establishes open standards for representing ML algorithms in a way agnostic to the library it was written in. +## Selecting a Model +* Understand the problem by asking the client questions: + * What type of app does the customer need + * is it a web app? does it need to run on the client-side or the server-side? + * how important is privacy, speed, accuracy? +* Populate the list of models that can be used to solve the problem +* Use Inference speed (lower the better), file size of the model, RAM in MB to compare models + * calc inference speed: + ``` + async function calculateInferenceSpeed() { + // Record timestamp before model execution + const timeStart = performance.now(); + // Execute model + let results = await useSomeModel(); + // Record timestamp after model execution + const timeEnd = performance.now(); + // Calculate time taken for model execution + const timeTaken = timeEnd - timeStart; + console.log(`Time taken ${timeTaken} ms.`); + // Convert ms to FPS + console.log(`Frames per sec: ${1000 / timeTaken}`); + } + ``` + * file size: check model's documentation or pull up the model and look at file size on chrome dev tools + * models are json or bin files. add up all the json/bin files that re pulled down and you have your model size + * Use memory tab in chrome dev tools + * load the model, go to memory tab, click create snapshot => view total ram in the statistics drop down option + diff --git a/tech/tensorflow/template/hello-world/index.html b/tech/tensorflow/template/hello-world/index.html new file mode 100644 index 0000000..9b10d5d --- /dev/null +++ b/tech/tensorflow/template/hello-world/index.html @@ -0,0 +1,22 @@ + + + + Hello World - TensorFlow.js + + + + + + + +

TensorFlow.js Hello World

+ +

Awaiting TF.js load

+ + + + + + + + diff --git a/tech/tensorflow/template/hello-world/script.js b/tech/tensorflow/template/hello-world/script.js new file mode 100644 index 0000000..f792a07 --- /dev/null +++ b/tech/tensorflow/template/hello-world/script.js @@ -0,0 +1,20 @@ +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + + const status = document.getElementById('status'); + status.innerText = 'Loaded TensorFlow.js - version: ' + tf.version.tfjs; + \ No newline at end of file diff --git a/tech/tensorflow/template/hello-world/style.css b/tech/tensorflow/template/hello-world/style.css new file mode 100644 index 0000000..bac4868 --- /dev/null +++ b/tech/tensorflow/template/hello-world/style.css @@ -0,0 +1,29 @@ +/** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + +/* CSS files add styling rules to your content */ + +body { + font-family: helvetica, arial, sans-serif; + margin: 2em; + } + + h1 { + font-style: italic; + color: #FF6F00; + } + \ No newline at end of file diff --git a/tech/tensorflow/tensor.md b/tech/tensorflow/tensor.md new file mode 100644 index 0000000..9fe773b --- /dev/null +++ b/tech/tensorflow/tensor.md @@ -0,0 +1,75 @@ +# Tensor + +## What +* Primary data structure in tensorflow models +* Similar to arrays that can have multiple dimensions. **ONLY contain numerical data** +* ML models take tensors as inputs and output tensors +* Tensor is a class +* Need to explicitly dispose tensors when they are no longer needed + * tf.dispose() => to destroy a specific tensor + * tf.tidy() => dispose any tensor(s) created in the same function +* Terminology: + * DataType (DType) => either integer or floating point + * Shape => Number of elements present along each axis of the tensor (a cube would have a shape of [3,3,3] if it had 3 elements on each axis) + * Rank => # of "Axis" in the tensor. 6 Axis is max number tensorflow handles + * Size => total elements in the tensor +* Example: + * 0 Dimensions: + ``` + // js 0 dimension + let value = 6; + + // tf 0 dimension + let tensor = tf.scalar(6); + ``` + * 1 Dimensional: example as a x,y,x coordinates on a 3 dimensional plane + ``` + // js 1 dimension + let value = [1,2,3]; + + // tf 1 dimension. + let tensor = tf.tensor1d([1,2,3]); + ``` + * 2 Dimensional: example grayscale image + ``` + // js 2 dimension + let values = [ + [1,2,3], + [4,5,6], + [7,8,9] + ] + + // tf 2 dimensional + let tensor = tf.tensor2d([ + [1,2,3], + [4,5,6], + [7,8,9] + ]); + ``` + * 3 Dimensiona: exmaple rgb image where each pixel would need a rgb array value + * 4 Dimensional: example video where the rgb images above now have a time component associated with it + +## Code +``` +/** + * Line below creates a 2D tensor. + * Printing its values at this stage would give you: + * Rank: 2, shape: [2,3], values: [[1, 2, 3], [4, 5, 6]] + **/ +let tensor = tf.tensor2d([[1, 2, 3], [4, 5 ,6]]); + +// Create a Tensor holding a single scalar value: +let scalar = tf.scalar(2); + +// Multiply all values in tensor by the scalar value 2. +let newTensor = tensor.mul(scalar); + +// Values of newTensor would be: [[2, 4, 6], [8, 10 ,12]] +newTensor.print(); + +// You can even change the shape of the Tensor. +// This would convert the 2D tensor to a 1D version with +// 6 elements instead of a 2D 2 by 3 shape. +let reshaped = tensor.reshape([6]); +``` + diff --git a/tech/tensorflow/tensorflow.md b/tech/tensorflow/tensorflow.md index 1b053f8..80ac00a 100644 --- a/tech/tensorflow/tensorflow.md +++ b/tech/tensorflow/tensorflow.md @@ -1,8 +1,5 @@ # Tensorflow -## What? -JS and Python library to develop models, train them, and use them to run ML and AI on many platforms. It is written in C++ - ## Common Terms * Deep Learning results feed better Machine Learning Models. Better Machine Learning Models feed better AI. * AI - Science of making things smart @@ -27,7 +24,8 @@ JS and Python library to develop models, train them, and use them to run ML and * This works by feeding the Kerans Model the Layers API. The Core/OPS API then translates the Keras Model to a TensorFlow SavedModel. ## What is it -TensorflowJS - Javascript library that contains Google's machine learning library +* JS and Python library to develop models, train them, and use them to run ML and AI on many platforms. It is written in C++ +* TensorflowJS - Javascript library that contains Google's machine learning library * Python vs Javascript * Running Tensorflow in NodeJS allows you to leverage CUDA and AVX acceleration to perform a LOT faster than Python. * NodeJS supports integrating with Python Keras and Tensorflow SavedModels created in Python. @@ -44,6 +42,18 @@ TensorflowJS - Javascript library that contains Google's machine learning librar * Interactivity - can directly interact with the library using the browser platform * Reach & Scale - Running on a browser is basically omnipresent. +## Setup +**Note:** If using typescript, then set "skipLibCheck: true" in tsconfig.json file. +**Note:** NPM is shipped with ES Modules and ES2017 syntax +* JS + * npm install @tensorflow/tfjs +* Node: + * npm install @tensorflow/tfjs-node +* Linux with CUDA support (NVIDIA GPU) + * npm install @tensorflow/tfjs-node-gpu +* Script tag + * + ## Structurte Models --> Layers API --> Core/OPS API * Layers API - high level api diff --git a/tech/twistlock/twistlock.md b/tech/twistlock/twistlock.md new file mode 100644 index 0000000..01e2cf6 --- /dev/null +++ b/tech/twistlock/twistlock.md @@ -0,0 +1,14 @@ +# Twistlock + +## Background +When using containers, there are three primary challenges that need to be solved to ensure that your deployment is secure: +1) Access Control - Ensuring that each container follows least-access privelage +2) Inter-Container Communication - Ensuring that communication between containers is secure (authenticated) +3) App within Container - Ensuring that code within your container doesnt violate key security, that exceptions are handled properly, or contain malicious code + +Twistlock is a full suite of tools to address those challenges. + +On VA Notify, Twistlock is used to address challenge #3; container scanning. + +## How +Twistlock is integrated into our CI/CD pipeline. When a developer commits their code to the notify repository, Github triggers a pre-commit hook that launches Twistlock in a github VM environment. Twistlock then does a scan of the user's commit and the resultant container. If no issues are found and the container score is above a specified threshold, then the code is committed and Github kicks off any post commit actions. If there ARE issues found, then the commit is rejected and the dev is notified that an error was found. \ No newline at end of file diff --git a/tech/typescript/typescript.md b/tech/typescript/typescript.md new file mode 100644 index 0000000..0c6c93a --- /dev/null +++ b/tech/typescript/typescript.md @@ -0,0 +1,12 @@ +# Typescript + +## Common Commands: +* Setup a typescript project using [GTS](https://github.com/google/gts) + * creates a code formatter, linter, and code fixer for typescript projects. + * npx gts init + * command to compile: npm run compile + * command to lin +* Compile to JS + * npx tsc index.ts +* Activate watch command + * npx tsc -w \ No newline at end of file From 7b72fe7e20173c7d819aabb8e596b97dffe9d428 Mon Sep 17 00:00:00 2001 From: jacob cheriathundam Date: Mon, 4 Apr 2022 15:33:30 -0400 Subject: [PATCH 8/9] udpated --- tech/tensorflow/machine-learning.md | 2 + tech/tensorflow/raw-models.md | 73 +++++++++++++++++++++++++++++ 2 files changed, 75 insertions(+) create mode 100644 tech/tensorflow/raw-models.md diff --git a/tech/tensorflow/machine-learning.md b/tech/tensorflow/machine-learning.md index 651a615..b250cfe 100644 --- a/tech/tensorflow/machine-learning.md +++ b/tech/tensorflow/machine-learning.md @@ -67,4 +67,6 @@ * models are json or bin files. add up all the json/bin files that re pulled down and you have your model size * Use memory tab in chrome dev tools * load the model, go to memory tab, click create snapshot => view total ram in the statistics drop down option + + diff --git a/tech/tensorflow/raw-models.md b/tech/tensorflow/raw-models.md new file mode 100644 index 0000000..3db9ecb --- /dev/null +++ b/tech/tensorflow/raw-models.md @@ -0,0 +1,73 @@ +# Raw Models + +## Types +* Layers Model + * retain higher level building blocks for ease of use and debugging + * NOT optimized +* Graph Model + * highly optimized + * can combine multiple poerations in one batch + +## How +* [Documentation](https://js.tensorflow.org/api/latest/#Models-Loading) +* Stored in two file + * model.json + * metadata about model type, architecture, config details + * declares a "format" key that has either layers model or graph model + * one or more ".bin" files. Typically have "shard<#>of<#>.bin" + * each bin file is 4MB or less + +### Loading a Raw Model + * tf.loadGraphModel() + * tf.loadLayerModel() + + +### Saving a model + * Can always save a model for use offline using the command: model.save() + +## Inspect +* can use ```model.summary()``` command to provide information about the model. Provides: + * model layers + * output shapes at each layer + * number of params in each layer + * total number of params + * number of trainable and non-trainable params + + +## Sample Code +``` +const MODEL_PATH = 'https://storage.googleapis.com/jmstore/TensorFlowJS/EdX/SavedModels/sqftToPropertyPrice/model.json'; +let model = undefined; + +async function loadModel() { + model = await tf.loadLayersModel(MODEL_PATH); + model.summary(); + + // Create a batch of 1. + const input = tf.tensor2d([[870]]); + + // Create a batch of 3 + const inputBatch = tf.tensor2d([[500], [1100], [970]]); + + // Actually make the predictions for each batch. + const result = model.predict(input); + const resultBatch = model.predict(inputBatch); + + // Print results to console. + result.print(); // Or use .arraySync() to get results back as array. + resultBatch.print(); // Or use .arraySync() to get results back as array. + + input.dispose(); + inputBatch.dispose(); + result.dispose(); + resultBatch.dispose(); + model.dispose(); +} + +loadModel(); +``` +* In this example: + * A total of 6 tensors are created when the code is run. + * 2 tensors are created when the model loads. The 'model.dispose()' method disposes of this tensor. + * 2 tensors are created, one for each input. The input.dispose() and inputBatch.dispose() methods dispose of these tensors. + * 2 tensors are created and returned as outputs. The result.dispose() and 'resultBatch.dispose()' methods dispose of these tensors. From 5f8319cf0f20b5e96d1e00919c9b9decbbe63418 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 7 Dec 2022 17:22:21 +0000 Subject: [PATCH 9/9] Bump decode-uri-component Bumps [decode-uri-component](https://github.com/SamVerschueren/decode-uri-component) from 0.2.0 to 0.2.2. - [Release notes](https://github.com/SamVerschueren/decode-uri-component/releases) - [Commits](https://github.com/SamVerschueren/decode-uri-component/compare/v0.2.0...v0.2.2) --- updated-dependencies: - dependency-name: decode-uri-component dependency-type: indirect ... Signed-off-by: dependabot[bot] --- .../angular/sample/fem-prod-angular/package-lock.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tech/frontend/angular/sample/fem-prod-angular/package-lock.json b/tech/frontend/angular/sample/fem-prod-angular/package-lock.json index 27fb727..235e863 100644 --- a/tech/frontend/angular/sample/fem-prod-angular/package-lock.json +++ b/tech/frontend/angular/sample/fem-prod-angular/package-lock.json @@ -6843,9 +6843,9 @@ "dev": true }, "decode-uri-component": { - "version": "0.2.0", - "resolved": "https://registry.npmjs.org/decode-uri-component/-/decode-uri-component-0.2.0.tgz", - "integrity": "sha1-6zkTMzRYd1y4TNGh+uBiEGu4dUU=" + "version": "0.2.2", + "resolved": "https://registry.npmjs.org/decode-uri-component/-/decode-uri-component-0.2.2.tgz", + "integrity": "sha512-FqUYQ+8o158GyGTrMFJms9qh3CqTKvAqgqsTnkLI8sKu0028orqBhxNMFkFen0zGyg6epACD32pjVk58ngIErQ==" }, "dedent": { "version": "0.7.0",