From 1a3ff5e740b43f3899096388bb2804960083e0a4 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Thu, 5 Sep 2024 11:47:50 +0000 Subject: [PATCH 01/39] Introduction to TinyML --- .../Overview-1.md | 13 +++++ .../_index.md | 38 +++++++++++++++ .../_next-steps.md | 27 +++++++++++ .../_review.md | 47 +++++++++++++++++++ .../applications-4.md | 34 ++++++++++++++ .../benefits-3.md | 25 ++++++++++ .../examples-2.md | 23 +++++++++ 7 files changed, 207 insertions(+) create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/Overview-1.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/applications-4.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/examples-2.md diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/Overview-1.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/Overview-1.md new file mode 100644 index 0000000000..6f3d9b3759 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/Overview-1.md @@ -0,0 +1,13 @@ +--- +title: Overview +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Module Overview +This session delves into TinyML, which applies machine learning to devices with limited resources like microcontrollers. This module serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. + +## Introduction to TinyML +TinyML represents a significant shift in how we approach machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, such as constrained memory, power, and processing capabilities. TinyML has quickly gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md new file mode 100644 index 0000000000..44a15b44ef --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md @@ -0,0 +1,38 @@ +--- +title: Introduction to TinyML and its Importance + +minutes_to_complete: 10 + +who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started. + +learning_objectives: + - Identify TinyML and how it's different from the AI you might be used to. + - Understand the benefits of deploying AI models on Arm-based edge devices. + - Select Arm-based devices for TinyML. + - Identify real-world use cases demonstrating the impact of TinyML in various industries. + + +prerequisites: + - Basic knowledge of machine learning concepts. + - Understanding of IoT and embedded systems (helpful but not required). + +author_primary: Dominica Abena O. Amanfo + +### Tags +skilllevels: Introductory +subjects: ML +armips: + - Cortex-A + - Cortex-M + +operatingsystems: + - Linux + +tools_software_languages: + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md new file mode 100644 index 0000000000..c4ae77cc92 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md @@ -0,0 +1,27 @@ +--- +next_step_guidance: PLACEHOLDER TEXT 1 + +recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/ + +further_reading: + - resource: + title: PLACEHOLDER MANUAL + link: PLACEHOLDER MANUAL LINK + type: documentation + - resource: + title: PLACEHOLDER BLOG + link: PLACEHOLDER BLOG LINK + type: blog + - resource: + title: PLACEHOLDER GENERAL WEBSITE + link: PLACEHOLDER GENERAL WEBSITE LINK + type: website + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +weight: 21 # set to always be larger than the content in this path, and one more than 'review' +title: "Next Steps" # Always the same +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md new file mode 100644 index 0000000000..50e5859b78 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md @@ -0,0 +1,47 @@ +--- +review: + - questions: + question: > + 1. What is TinyML? + answers: + - "Machine learning models designed to run on large, cloud-based servers." + - "Machine learning models designed to run on resource-constrained devices like microcontrollers and edge devices." + - A cloud service for deep learning model deployment. + - A special type of machine learning for virtual reality applications + correct_answer: 2 + explanation: > + TinyML is specifically designed to operate on devices with limited computational resources. + + - questions: + question: > + 2. Which of the following is NOT a benefit of deploying TinyML on Arm devices? + answers: + - "Enhanced data privacy" + - "Low latency" + - High power consumption + - Cost-effectiveness + correct_answer: 3 + explanation: > + High power consumption is not a benefit, one of the key advantages of TinyML on Arm devices is their ability to perform tasks with very low power usage. + + - questions: + question: > + 3. Which of the following is an example of TinyML in healthcare? + answers: + - Smart sensors for soil moisture monitoring. + - Wearable devices monitoring vital signs and detecting heart arrhythmias. + - PLACEHOLDER ANSWER C + - PLACEHOLDER ANSWER D + correct_answer: 2 + explanation: > + Wearable devices that monitor vital signs and detect heart arrhythmias show TinyML's ability to perform complex analyses in real-time on resource-constrained device. + + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +title: "Review" # Always the same title +weight: 20 # Set to always be larger than the content in this path +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/applications-4.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/applications-4.md new file mode 100644 index 0000000000..cef33546dc --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/applications-4.md @@ -0,0 +1,34 @@ +--- +title: Real-World Applications of TinyML with Examples of Arm-Based Solutions +weight: 5 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +TinyML is being deployed across various industries, enhancing everyday experiences and enabling groundbreaking solutions. Here are a few examples: + +## Healthcare - Wearable Heart Rate Monitors +- Arm-based microcontrollers like those in Fitbit devices run TinyML models to monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. + +- **Example**: Fitbit Charge 5 smart band uses the Arm 32-bit Cortex-M4 processor. + +## Agriculture - Smart Irrigation Systems +- Arm-powered microcontrollers in smart sensors help monitor soil moisture and control water usage. TinyML models process environmental data locally to optimize water distribution. +- **Example**: OpenAg uses Arm Cortex-M processors to run machine learning models on edge devices, optimizing irrigation based on real-time data. + +## Home Automation - Smart Cameras +- Arm-based processors in smart cameras can detect objects and people, triggering alerts or actions without needing to send data to the cloud, saving bandwidth and improving privacy. +- **Example**: Arlo smart cameras, powered by Arm Cortex processors, perform object detection at the edge, enhancing performance and energy efficiency. + +## Industrial IoT - Predictive Maintenance in Factories (e.g., Siemens Predictive Maintenance) +- Arm-powered industrial sensors analyze vibration patterns in machinery, running TinyML models to predict when maintenance is needed and prevent breakdowns. +- **Example**: Siemens utilizes Arm Cortex-A processors in industrial sensors for real-time data analysis, detecting faults before they cause significant downtime. They rely on Arm-based processors for their Industrial Edge computing solutions. + +## Wildlife Conservation - Smart Camera Traps (e.g., Conservation X Labs) +- Arm-based smart camera traps can identify animal movements or detect poachers using TinyML models. These energy-efficient devices can operate in remote areas without relying on external power sources. +- **Example**: Conservation X Labs uses Arm Cortex-M microcontrollers to power camera traps, helping monitor endangered species in the wild. + + + + diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md new file mode 100644 index 0000000000..1c0b922460 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md @@ -0,0 +1,25 @@ +--- +title: Benefits of TinyML for Edge Computing on Arm Devices +weight: 4 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +The advantages of TinyML for edge computing on Arm devices are vast, particularly when paired with Arm's architecture, which is widely used in IoT, mobile devices, and edge AI deployments. Here are some key benefits: + +- Power Efficiency: TinyML models are designed to be extremely power-efficient, making them ideal for battery-operated devices like sensors, wearables, and drones. +- Low Latency: Because the AI processing happens on-device, there's no need to send data to the cloud, reducing latency and enabling real-time decision-making. +- Data Privacy: With on-device computation, sensitive data remains local, providing enhanced privacy and security. This is particularly crucial in healthcare and personal devices. +- Cost-Effective: Arm devices, which are cost-effective and scalable, can now handle sophisticated machine learning tasks, reducing the need for expensive hardware or cloud services. +- Scalability: With billions of Arm devices in the market, TinyML is well-suited for scaling across industries, enabling widespread adoption of AI at the edge. + +TinyML is being deployed across various industries, enhancing everyday experiences and enabling groundbreaking solutions. Here are a few examples: + +## Healthcare - Wearable Heart Rate Monitors: + - Arm-based microcontrollers like those in Fitbit devices run TinyML models to monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. + + - Example: Fitbit Charge 5 smart band uses the Arm 32-bit Cortex-M4 processor. + + + diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/examples-2.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/examples-2.md new file mode 100644 index 0000000000..ce0953f67c --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/examples-2.md @@ -0,0 +1,23 @@ +--- +title: Examples of Arm-based devices and applications +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Raspberry Pi 4 & 5 + +These affordable single-board computers are excellent for prototyping TinyML projects. They are commonly used for prototyping machine learning projects at the edge, such as in object detection and voice recognition for home automation. + +## NXP i.MX RT Microcontrollers +These are low-power microcontrollers that can handle complex TinyML tasks while maintaining energy efficiency, making them ideal for applications like wearable healthcare devices and environmental sensors. + +## STM32 Microcontrollers +Used in industrial IoT applications for predictive maintenance, these microcontrollers are energy-efficient and capable of running TinyML models for real-time anomaly detection in factory machinery. + +## Arduino Nano 33 BLE Sense +This microcontroller, equipped with a suite of sensors, supports TinyML and is ideal for small-scale IoT applications, such as detecting environmental changes and movement patterns. + +## Edge Impulse +This platform offers a suite of tools that enables developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards. From c8017d2417ffb74ec4b59a4956d8afa22039ffd8 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Thu, 5 Sep 2024 12:10:11 +0000 Subject: [PATCH 02/39] Added blog resource --- .../_next-steps.md | 20 ++++++++----------- .../_review.md | 4 ++-- 2 files changed, 10 insertions(+), 14 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md index c4ae77cc92..bff6a49a73 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md @@ -1,21 +1,17 @@ --- -next_step_guidance: PLACEHOLDER TEXT 1 +next_step_guidance: Research which lightweight ML models can be used for TinyML, and what device is best for a specific project. + +recommended_path: /learning-paths/microcontrollers/intro/ + +recommended_path: /learning-paths/microcontrollers/nav-mlek/ -recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/ further_reading: - resource: - title: PLACEHOLDER MANUAL - link: PLACEHOLDER MANUAL LINK - type: documentation - - resource: - title: PLACEHOLDER BLOG - link: PLACEHOLDER BLOG LINK + title: TinyML Brings AI to Smallest Arm Devices + link: https://newsroom.arm.com/blog/tinyml type: blog - - resource: - title: PLACEHOLDER GENERAL WEBSITE - link: PLACEHOLDER GENERAL WEBSITE LINK - type: website + # ================================================================================ diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md index 50e5859b78..a86edfaac5 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_review.md @@ -30,8 +30,8 @@ review: answers: - Smart sensors for soil moisture monitoring. - Wearable devices monitoring vital signs and detecting heart arrhythmias. - - PLACEHOLDER ANSWER C - - PLACEHOLDER ANSWER D + - Predictive maintenance for factory machines. + - Object detection for smart home cameras. correct_answer: 2 explanation: > Wearable devices that monitor vital signs and detect heart arrhythmias show TinyML's ability to perform complex analyses in real-time on resource-constrained device. From 0ea8a80c26ed9c26ce5399867e415c61f918ad7f Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Thu, 5 Sep 2024 12:30:08 +0000 Subject: [PATCH 03/39] Modified next learning path --- .../_next-steps.md | 2 -- .../benefits-3.md | 10 ---------- 2 files changed, 12 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md index bff6a49a73..3be5790119 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_next-steps.md @@ -3,8 +3,6 @@ next_step_guidance: Research which lightweight ML models can be used for TinyML, recommended_path: /learning-paths/microcontrollers/intro/ -recommended_path: /learning-paths/microcontrollers/nav-mlek/ - further_reading: - resource: diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md index 1c0b922460..7d4ef0c90f 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/benefits-3.md @@ -13,13 +13,3 @@ The advantages of TinyML for edge computing on Arm devices are vast, particularl - Data Privacy: With on-device computation, sensitive data remains local, providing enhanced privacy and security. This is particularly crucial in healthcare and personal devices. - Cost-Effective: Arm devices, which are cost-effective and scalable, can now handle sophisticated machine learning tasks, reducing the need for expensive hardware or cloud services. - Scalability: With billions of Arm devices in the market, TinyML is well-suited for scaling across industries, enabling widespread adoption of AI at the edge. - -TinyML is being deployed across various industries, enhancing everyday experiences and enabling groundbreaking solutions. Here are a few examples: - -## Healthcare - Wearable Heart Rate Monitors: - - Arm-based microcontrollers like those in Fitbit devices run TinyML models to monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. - - - Example: Fitbit Charge 5 smart band uses the Arm 32-bit Cortex-M4 processor. - - - From 0e98db6a09db13553a18294e6899029024454b49 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Thu, 5 Sep 2024 12:36:17 +0000 Subject: [PATCH 04/39] Minor fixes --- .../introduction-to-tinyml-and-its-importance/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md index 44a15b44ef..abd269dcfd 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-and-its-importance/_index.md +++ 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+armips: + - Cortex M + +tools_software_languages: + - Corstone 300 FVP + - Grove - Vision AI Module V2 + - Python + - PyTorch + - Executorch + - Arm Compute Library + - GCC +operatingsystems: + - Linux + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md new file mode 100644 index 0000000000..c4ae77cc92 --- /dev/null +++ b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md @@ -0,0 +1,27 @@ +--- +next_step_guidance: PLACEHOLDER TEXT 1 + +recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/ + +further_reading: + - resource: + title: PLACEHOLDER MANUAL + link: PLACEHOLDER MANUAL LINK + type: documentation + - resource: + title: PLACEHOLDER BLOG + link: PLACEHOLDER BLOG LINK + type: blog + - resource: + title: PLACEHOLDER GENERAL WEBSITE + link: PLACEHOLDER GENERAL WEBSITE LINK + type: website + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +weight: 21 # set to always be larger than the content in this path, and one more than 'review' +title: "Next Steps" # Always the same +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md new file mode 100644 index 0000000000..9b1bfb96fe --- /dev/null +++ b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md @@ -0,0 +1,45 @@ +--- +review: + - questions: + question: > + PLACEHOLDER QUESTION 1? + answers: + - PLACEHOLDER ANSWER A + - PLACEHOLDER ANSWER B + correct_answer: 1 + explanation: > + PLACEHOLDER EXPLANATION 1 + + - questions: + question: > + PLACEHOLDER QUESTION 2? + answers: + - PLACEHOLDER ANSWER A + - PLACEHOLDER ANSWER B + - PLACEHOLDER ANSWER C + - PLACEHOLDER ANSWER D + correct_answer: 4 + explanation: > + PLACEHOLDER EXPLANATION 2 + + - questions: + question: > + PLACEHOLDER QUESTION 3? + answers: + - PLACEHOLDER ANSWER A + - PLACEHOLDER ANSWER B + - PLACEHOLDER ANSWER C + - PLACEHOLDER ANSWER D + correct_answer: 2 + explanation: > + PLACEHOLDER EXPLANATION 3 + + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +title: "Review" # Always the same title +weight: 20 # Set to always be larger than the content in this path +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png new file mode 100644 index 0000000000000000000000000000000000000000..c69844bed44b65c7f5bc6cf511f93987fdcd7b95 GIT binary patch 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b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md new file mode 100644 index 0000000000..f884b5592b --- /dev/null +++ b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md @@ -0,0 +1,13 @@ +--- +title: PLACEHOLDER STEP TITLE 2 +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## PLACEHOLDER HEADER OF SECOND STEP +YOUR CONTENT GOES HERE + +IMAGE HERE: +![example image alt-text#center](example-picture.png "Figure 1. Example image caption") From 5c6ca22e563dcb7de9fcf60adf51e7d8423d5240 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Tue, 17 Sep 2024 10:09:18 +0000 Subject: [PATCH 07/39] Merging 2 learning paths --- .../env-setup-tinyml-on-arm/_index.md | 44 --------- .../env-setup-tinyml-on-arm/_next-steps.md | 27 ----- .../env-setup-tinyml-on-arm/_review.md | 45 --------- .../example-picture.png | Bin 63167 -> 0 bytes .../env-setup-tinyml-on-arm/how-to-1.md | 10 -- .../env-setup-tinyml-on-arm/how-to-2.md | 13 --- .../Overview-1.md | 2 + .../introduction-to-tinyml-on-arm/_index.md | 17 +++- .../env-setup-5.md | 93 ++++++++++++++++++ .../troubleshooting-6.md | 18 ++++ 10 files changed, 127 insertions(+), 142 deletions(-) delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_index.md delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-1.md delete mode 100644 content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_index.md deleted file mode 100644 index a6e5bcbd1a..0000000000 --- a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_index.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -title: Environment Setup for TinyML Development on Arm - -minutes_to_complete: 20 - -who_is_this_for: This module is perfect for developers and engineers interested in edge AI and TinyML using PyTorchv2.0 and Executorch on Arm-based platforms. - -learning_objectives: - - Install and configure a TinyML development environment. - - Identify key tools and libraries needed to build and deploy TinyML applications on Arm devices. - - Set up a cross-compilation environment on your host machine. - - Perform best practices for ensuring optimal performance on constrained edge devices. - - -prerequisites: - - PLACEHOLDER PREREQ 1 - - PLACEHOLDER PREREQ 2 - -author_primary: PLACEHOLDER NAME - -### Tags -skilllevels: PLACEHOLDER SKILLLEVEL -subjects: ML -armips: - - Cortex M - -tools_software_languages: - - Corstone 300 FVP - - Grove - Vision AI Module V2 - - Python - - PyTorch - - Executorch - - Arm Compute Library - - GCC -operatingsystems: - - Linux - - -### FIXED, DO NOT MODIFY -# ================================================================================ -weight: 1 # _index.md always has weight of 1 to order correctly -layout: "learningpathall" # All files under learning paths have this same wrapper -learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. ---- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md deleted file mode 100644 index c4ae77cc92..0000000000 --- a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_next-steps.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -next_step_guidance: PLACEHOLDER TEXT 1 - -recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/ - -further_reading: - - resource: - title: PLACEHOLDER MANUAL - link: PLACEHOLDER MANUAL LINK - type: documentation - - resource: - title: PLACEHOLDER BLOG - link: PLACEHOLDER BLOG LINK - type: blog - - resource: - title: PLACEHOLDER GENERAL WEBSITE - link: PLACEHOLDER GENERAL WEBSITE LINK - type: website - - -# ================================================================================ -# FIXED, DO NOT MODIFY -# ================================================================================ -weight: 21 # set to always be larger than the content in this path, and one more than 'review' -title: "Next Steps" # Always the same -layout: "learningpathall" # All files under learning paths have this same wrapper ---- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md deleted file mode 100644 index 9b1bfb96fe..0000000000 --- a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/_review.md +++ /dev/null @@ -1,45 +0,0 @@ ---- -review: - - questions: - question: > - PLACEHOLDER QUESTION 1? - answers: - - PLACEHOLDER ANSWER A - - PLACEHOLDER ANSWER B - correct_answer: 1 - explanation: > - PLACEHOLDER EXPLANATION 1 - - - questions: - question: > - PLACEHOLDER QUESTION 2? - answers: - - PLACEHOLDER ANSWER A - - PLACEHOLDER ANSWER B - - PLACEHOLDER ANSWER C - - PLACEHOLDER ANSWER D - correct_answer: 4 - explanation: > - PLACEHOLDER EXPLANATION 2 - - - questions: - question: > - PLACEHOLDER QUESTION 3? - answers: - - PLACEHOLDER ANSWER A - - PLACEHOLDER ANSWER B - - PLACEHOLDER ANSWER C - - PLACEHOLDER ANSWER D - correct_answer: 2 - explanation: > - PLACEHOLDER EXPLANATION 3 - - - -# ================================================================================ -# FIXED, DO NOT MODIFY -# ================================================================================ -title: "Review" # Always the same title -weight: 20 # Set to always be larger than the content in this path -layout: "learningpathall" # All files under learning paths have this same wrapper ---- diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/example-picture.png deleted file mode 100644 index c69844bed44b65c7f5bc6cf511f93987fdcd7b95..0000000000000000000000000000000000000000 GIT binary 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@@ ---- -title: PLACEHOLDER STEP TITLE 1 -weight: 2 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -## PLACEHOLDER HEADER -YOUR CONTENT GOES HERE diff --git a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md b/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md deleted file mode 100644 index f884b5592b..0000000000 --- a/content/learning-paths/microcontrollers/env-setup-tinyml-on-arm/how-to-2.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: PLACEHOLDER STEP TITLE 2 -weight: 3 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -## PLACEHOLDER HEADER OF SECOND STEP -YOUR CONTENT GOES HERE - -IMAGE HERE: -![example image alt-text#center](example-picture.png "Figure 1. Example image caption") diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md index 6f3d9b3759..1e6831cadb 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md @@ -9,5 +9,7 @@ layout: learningpathall ## Module Overview This session delves into TinyML, which applies machine learning to devices with limited resources like microcontrollers. This module serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. +Additionally, we'll cover the necessary setup on your host machine and target device to facilitate cross-compilation and ensure smooth integration across all devices. + ## Introduction to TinyML TinyML represents a significant shift in how we approach machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, such as constrained memory, power, and processing capabilities. TinyML has quickly gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md index abd269dcfd..36dcecd458 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md @@ -1,15 +1,18 @@ --- -title: Introduction to TinyML on Arm +title: Introduction to TinyML on Arm using PyTorch v2.0 and Executorch -minutes_to_complete: 10 +minutes_to_complete: 40 -who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started. +who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started using PyTorch v2.0 and Executorch on Arm-based platforms. learning_objectives: - Identify TinyML and how it's different from the AI you might be used to. - Understand the benefits of deploying AI models on Arm-based edge devices. - Select Arm-based devices for TinyML. - Identify real-world use cases demonstrating the impact of TinyML in various industries. + - Install and configure a TinyML development environment. + - Set up a cross-compilation environment on your host machine. + - Perform best practices for ensuring optimal performance on constrained edge devices. prerequisites: @@ -29,6 +32,14 @@ operatingsystems: - Linux tools_software_languages: + - Corstone 300 FVP + - Grove - Vision AI Module V2 + - Arm Virtual Hardware + - Python + - PyTorch v2.0 + - Executorch + - Arm Compute Library + - GCC ### FIXED, DO NOT MODIFY # ================================================================================ diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md new file mode 100644 index 0000000000..60077fc00f --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -0,0 +1,93 @@ +--- +# User change +title: "Environment Setup for TinyML Development on Arm" + +weight: 6 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- +## Before you begin + +These instructions have been tested on: +- A GCP Arm-based Tau T2A Virtual Machine instance Running Ubuntu 22.04 LTS. +- Host machine with Ubuntu 24.04 on x86_64 architecture. + +The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices. + +You can use your own Linux host machine or use [Arm Virtual Hardware (AVH)](https://www.arm.com/products/development-tools/simulation/virtual-hardware) for this Learning Path. + +The Ubuntu version should be `20.04 or higher`. The `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. You will need a Linux desktop to run the FVP because it opens graphical windows for input and output from the software applications. Also, though Executorch supports Windows via WSL, it is limited in resource. + +If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide](/install-guides/avh#corstone) provides setup instructions. + +### Compilers + +The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain). + +Both compilers are pre-installed in Arm Virtual Hardware. + +Alternatively, if you are using Arch Linux or its derivatives, you can use Pacman to install GCC. + +Use the install guides to install the compilers on your computer: +- [Arm Compiler for Embedded](/install-guides/armclang/) +- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu) +- Using Pacman: + +```console +pacman -S aarch64-linux-gnu-gcc +``` + +### Corstone-300 FVP {#fvp} + +To install the Corstone-300 FVP on your computer refer to the [install guide for Arm Ecosystem FVPs](/install-guides/fm_fvp). + +The Corstone-300 FVP is pre-installed in Arm Virtual Hardware. + + +## Install Executorch + +1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it. + +2. Activate the `executorch` virtual environment from the installation guide: + +```console +conda activate executorch +``` + + +## Setup on Grove - Vision AI Module V2 +Due to its constrained environment, we'll focus on lightweight, optimized tools and models (will be introcuded in the next learning path). + +1. Connect the Grove - Vision AI Module V2 to your computer using the USB-C cable. + +![Board connection #center](connect.png) + +2. Install Edge Impulse CLI. It will help in data collection and model deployment. + +```console +npm install -g edge-impulse-cli +``` + +3. Configure Edge Impulse for the board +In your terminal, run: + +```console +edge-impulse-daemon +``` +Follow the prompts to log in. + +4. Verify Setup +Connect to your device + +```console +edge-impulse-run-impulse --api-key YOUR_API_KEY +``` + +If successful, you should see data from your Grove - Vision AI Module V2. + +5. Install Executorch + +```console +pip install executorch +``` diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md new file mode 100644 index 0000000000..3bfb897e2e --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md @@ -0,0 +1,18 @@ +--- +title: Benefits of TinyML for Edge Computing on Arm Devices +weight: 4 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- +## Troubleshooting +- If you encounter permission issues, try running the commands with sudo. +- Ensure your Grove - Vision AI Module V2 is properly connected and recognized by your computer. +- If Edge Impulse CLI fails to detect your device, try unplugging and replugging the USB cable. + +## Best Practices +- Always cross-compile your code on the host machine to ensure compatibility with the target Arm device. +- Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2. +- Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies + +You've now set up your environment for TinyML development on the Grove - Vision AI Module V2. In the next modules, we'll explore data collection, model training, and deployment using PyTorch v2.0 and Executorch. \ No newline at end of file From 05f4b25a904876be729017fb5db26d8bc0ccd6a5 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Wed, 18 Sep 2024 08:56:19 +0000 Subject: [PATCH 08/39] Updated PyTorch version --- .../introduction-to-tinyml-on-arm/_index.md | 10 ++++++---- .../introduction-to-tinyml-on-arm/env-setup-5.md | 5 ++--- .../introduction-to-tinyml-on-arm/troubleshooting-6.md | 4 ++-- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md index 36dcecd458..1eb58a64d3 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md @@ -1,9 +1,9 @@ --- -title: Introduction to TinyML on Arm using PyTorch v2.0 and Executorch +title: Introduction to TinyML on Arm using PyTorch v2.x and Executorch minutes_to_complete: 40 -who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started using PyTorch v2.0 and Executorch on Arm-based platforms. +who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started using PyTorch v2.x and Executorch on Arm-based platforms. learning_objectives: - Identify TinyML and how it's different from the AI you might be used to. @@ -18,7 +18,9 @@ learning_objectives: prerequisites: - Basic knowledge of machine learning concepts. - Understanding of IoT and embedded systems (helpful but not required). - + - A Linux host machine or VM running Ubuntu 20.04 or higher, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware) + - Target device, preferably Cortex-M boards but you can use Cortex-A7 boards as well. + author_primary: Dominica Abena O. Amanfo ### Tags @@ -36,7 +38,7 @@ tools_software_languages: - Grove - Vision AI Module V2 - Arm Virtual Hardware - Python - - PyTorch v2.0 + - PyTorch v2.x - Executorch - Arm Compute Library - GCC diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index 60077fc00f..3fd1eae355 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -1,6 +1,6 @@ --- # User change -title: "Environment Setup for TinyML Development on Arm" +title: "Environment Setup" weight: 6 # 1 is first, 2 is second, etc. @@ -77,8 +77,7 @@ edge-impulse-daemon ``` Follow the prompts to log in. -4. Verify Setup -Connect to your device +4. Verify Setup: Connect to your device ```console edge-impulse-run-impulse --api-key YOUR_API_KEY diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md index 3bfb897e2e..4a7c5acd51 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md @@ -1,6 +1,6 @@ --- -title: Benefits of TinyML for Edge Computing on Arm Devices -weight: 4 +title: Troubleshooting and Best Practices +weight: 7 ### FIXED, DO NOT MODIFY layout: learningpathall From 369e0c19d2bff129d3539ebdf2e022c71943bcbd Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Sat, 5 Oct 2024 16:34:03 +0000 Subject: [PATCH 09/39] Final edits --- .../introduction-to-tinyml-on-arm/Connect.png | Bin 0 -> 2386988 bytes .../Overview.png | Bin 0 -> 21816 bytes .../introduction-to-tinyml-on-arm/_index.md | 2 + .../env-setup-5.md | 55 +++++++++++++----- 4 files changed, 42 insertions(+), 15 deletions(-) create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Connect.png create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview.png diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Connect.png b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Connect.png new file mode 100644 index 0000000000000000000000000000000000000000..6af713b4031ec3317f03a2a15de22e98ee3e723b GIT binary patch literal 2386988 zcmWh!WmuEn-`_?zN;inY4-p2^jqpdLL`6D=ATT761S+=so%03jlx&0RT_{s3~v0e6Co( zc>`TtwY8t=X=@8U^YeCg^>6|J{u>SdQeyym#n~fJ$vp7>SJ#h~Vra9zY+unMhC*uP z3PE?}Q4_Kb^?k|T(0CN{1=B+)BlkDzcd75+3%Y&yRoP#fU$nMM+Lx^VvG_Udptt+D zy}dnBqwt~G4&^g>51AbOC*m2VVmMj>wUU>^4!$}W9GAz}RiCM5LKQ@u<*XY>vvxaW zVIeiuB+g9a^>o&xMB){Ar@F_G2iN~+Aa$7hfSDovyubbOrQoASCrsCctHuuR=GoG} zi|12kJ@kF?hdd{Uol-VITO>))kndI?VL$L-!Pv{a4UByI?hR}awZV<-XU(ZtL_Kyy zY6|$+x|EeFzu;)l$fH_Jin^kg3Npx$6BkHq#!#_nj>>Ev2x~3_pr%UKs%!101vnNT z*Dx{;F}L;&J%!YpQ>XEHV)+IDpu6?|4>002Pb%NHob=kror0u zj0`hzG@FcpcxAfz(9i=J73bnvX_>#@6`emxH?F2DIe&QM{C*0tpGm5~B&Q@F9JJ(e z_{*It$xOa^x@0>&?W7*QFBiI5Q?gec(dlWt7@VdSvJ2T8*@f34Y=ScuVBP+NUIWQS zmL$aqlqAHH$E>n$*nI}G_WMvi-yF;{-vq}GjMl+ALacfll~8b(2Joyvax{h0LeDD(ihIVp9g`EICdC z-Lw?ur9nE04X-Ckgc27xumZ+vuQ_bPl5$|^j{&0#foQ}cY{?68&M3~SG~Sb#@j6-b zXI?j?b&-BbgKC)a(#dk3UMPW3*@V90rJjz`}z z?78>s7*IZJR`Z}O4!=J>tJ)Y_GIY?o9t-P zROi{1t#|8AdD^I$SeCi05E^VLC2=8W?72Cb%PHiv2F_NQLsg`^t29>BOYo$kTTPD( zJg_^#^4=rl*7f#f5)kFV)N_)MK`QH9rd+(o;lH^nOhRqV6<04?QmyZ*F6NAe!c5WY zyHUC-it_waHt3Jw?lhtVFEBCMy@+zLhw#XTRm*ve|H*9fa;ii(G$h%!zL#~M?AeiZ zdo4$5sx&J}GA%U~|GneC`xv&%*pj-`mgj$3laZC?5>CZfOK9OEDZlgFYnHz1V4_`7 z$&(3vUkPdI2|3;OlyjS^t#ul-Z1H2OFwCemgL?_PPc+9iK*tB%P!*mHm&zu^hPB;H z8dF{ull}o+v757pqP$L<*}?B^BZj95LmIi&3U=XtpQ>LemYbFS#MXt!bzBXU6Z(7G zuraGiO{7ybB%?ZxxeX6)n#(B3b3$kwd$e61iDSo>_#C@wX+6*tcQDnjay;j&ds4xf zjy&cPwVv*2v#4o0&$x&s6%@G67Gs9VOri4_|7O|vYSqoU!}(*Sh4iSrr-eg#*aJbH z8E~Uq`NrH1+p)pxg&iG!$rB&FzOr??_B@?NKg;3-b#sY47oN+IDaeLn(piuHl}hOO zga41I5JVoOP!+9u1${oQ_NW;+x}cu8-x{ z>$IMPqwBS?PWky>^t0;-20rOAT3TI;m06HDr`iN11Gl~HajQjEKxKGBL$w(n+A?c| zuhMo#5;f3kyeeN=35(w_YkTBa{-Q2P4mvU4x)%8PP|joO36HX044!LjDTJ{9s^j7$ z;fj{_@!WjyRh~s>-$tmS`v$9ZJJhT1ckmVD_#k4kM-E1Mh58vR>^@bUBgG8pEWM2jZzPJ#MEvPIzfd9kb%9_4mq_*pi0h~YpVd4M1R0@e7mip= zQn~ssg5U6XDWs*+b_GE!yVvM^u7#s*r=-2&Fu8x0V@V%HHD_;R&+fL-UB&{BriigvO@=Xk; 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zvHIj!X&D;)L3|+fiB*zvMKVgu5fhL|!B5j+heJq=y*JLI=@svje?jGtbV(JOBcp{< z@k;*DF!r?=WcP$5^dgDF`$DCpyA1e)@?ga*JziN@=_;>7nnvv&#$!5kYUQ(gc$JnZ zPmlMR(|Qln^ZEm*$uVPLyo-KxQI0>7EvWqmv<~?jL_~%QUZt3^Tm&=vKVlc~=d?MD f!eKhz%gOtHvsUUokq$mZ00000NkvXXu0mjfF?!vi diff --git a/content/install-guides/multipass.md b/content/install-guides/multipass.md index a1a5d433f3..3f465ae219 100644 --- a/content/install-guides/multipass.md +++ b/content/install-guides/multipass.md @@ -49,7 +49,7 @@ Multipass uses the terms virtual machine and instance synonymously. Download Multipass for macOS. ```console -wget https://github.com/canonical/multipass/releases/download/v1.13.1/multipass-1.13.1+mac-Darwin.pkg +wget https://github.com/canonical/multipass/releases/download/v1.14.1-rc1/multipass-1.14.1-rc1+mac.14+gf2381bfe9.mac-Darwin.pkg ``` ### Install @@ -57,12 +57,12 @@ wget https://github.com/canonical/multipass/releases/download/v1.13.1/multipass- Install the download using the package command. ```console -sudo installer -pkg multipass-1.13.1+mac-Darwin.pkg -target / +sudo installer -pkg multipass-1.14.1-rc1+mac.14+gf2381bfe9.mac-Darwin.pkg -target / ``` -The getting started instructions below use the command line interface. Multipass installs a tray icon for those who want to access basic features from the user interface. +The getting started instructions below use the command line interface. If you perfer to use the graphical interface start it from the macOS Launchpad, the initial screen is shown below. You can use the UI to create, start, and stop virtual machines. -![Connect #center](/install-guides/_images/multipass-tray.png) +![Connect #center](/install-guides/_images/multipass-gui.png) Multipass is now installed. Proceed to [Get Started with Multipass](#getstarted). @@ -168,10 +168,10 @@ The output from `find` will be similar to the below. ```output Image Aliases Version Description -20.04 focal 20240626 Ubuntu 20.04 LTS -22.04 jammy 20240701 Ubuntu 22.04 LTS -23.10 mantic 20240701 Ubuntu 23.10 -24.04 noble,lts 20240702 Ubuntu 24.04 LTS +20.04 focal 20240821 Ubuntu 20.04 LTS +22.04 jammy 20241002 Ubuntu 22.04 LTS +24.04 noble,lts 20241004 Ubuntu 24.04 LTS +daily:24.10 oracular,devel 20241009 Ubuntu 24.10 Blueprint Aliases Version Description anbox-cloud-appliance latest Anbox Cloud Appliance @@ -223,45 +223,7 @@ The `uname` output will look similar to: Linux m1u 6.8.0-36-generic #36-Ubuntu SMP PREEMPT_DYNAMIC Mon Jun 10 13:20:23 UTC 2024 aarch64 aarch64 aarch64 GNU/Linux ``` -### Run an application - -It's helpful to demonstrate a few other common commands by downloading and installing an application. To demonstrate file copy, the download is done from macOS and the file is copied into the instance. - -Download and install [OpenVSCode Server](/install-guides/openvscode-server/). - -On the macOS host computer use `wget` for the download. - -```console -wget https://github.com/gitpod-io/openvscode-server/releases/download/openvscode-server-v1.90.0/openvscode-server-v1.90.0-linux-arm64.tar.gz -``` - -To copy a file to the instance - -```console -multipass transfer openvscode-server-v1.90.0-linux-arm64.tar.gz m1u:/home/ubuntu -``` - -Now switch to the instance and extract the download and run the application. - -If you don't already have an open shell run. - -```console -multipass shell m1u -``` - -Extract the download. - -```console -tar xvfz openvscode-server-v1.90.0-linux-arm64.tar.gz -``` - -Run the application. - -```console -./openvscode-server-v1.90.0-linux-arm64/bin/openvscode-server --host=0.0.0.0 --without-connection-token -``` - -Connect to OpenVSCode Server using a browser. From the macOS host computer use the `info` command to get the IP address of the instance. +### Print information The `info` command prints information about the instance, including the IP address. @@ -269,15 +231,22 @@ The `info` command prints information about the instance, including the IP addre multipass info m1u ``` -Copy the IP address from the info output and paste it into a browser with port 3000. +The output is similar to: -Modify the IP address to use your information, for example: ```output -http://192.168.64.39:3000 +Name: m1u +State: Running +Snapshots: 0 +IPv4: 192.168.73.29 +Release: Ubuntu 24.04.1 LTS +Image hash: e380b683b0c4 (Ubuntu 24.04 LTS) +CPU(s): 4 +Load: 0.00 0.03 0.01 +Disk usage: 2.0GiB out of 15.4GiB +Memory usage: 355.3MiB out of 3.8GiB +Mounts: -- ``` -You now have a running VS Code Server in your Multipass instance with Ubuntu 22.04 running on Arm. - ### Mount a host directory To access a large number of files on the host machine without copying or transferring them into the instance use the `mount` command. This command makes a host directory visible in the instance and all files can be accessed. Modifications made from inside the instance will directly change the files on the host. From ae42325979c93101bd096cbc4edb95fdea10a175 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Fri, 11 Oct 2024 01:57:09 +0000 Subject: [PATCH 26/39] review glibc with LSE Learning Path --- .../glibc-with-lse/build_glibc_with_lse.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/build_glibc_with_lse.md b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/build_glibc_with_lse.md index 5e2b530499..ebce45ccdc 100644 --- a/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/build_glibc_with_lse.md +++ b/content/learning-paths/servers-and-cloud-computing/glibc-with-lse/build_glibc_with_lse.md @@ -16,6 +16,17 @@ LSE introduces additional atomic instructions and operations, such as Load-Acqui When glibc is compiled with LSE support, it can take advantage of these enhanced atomic operations provided by the LSE extension. This can potentially improve the performance of multi-threaded applications that heavily rely on atomic operations and synchronization primitives. +{{% notice Note %}} +Your version of the GNU C Library may already have support for LSE. Before you build a new version check if LSE is already included by running: + +```console +objdump -d /lib/aarch64-linux-gnu/libc.so.6 | grep -i 'cas\|casp\|swp\|ldadd\|stadd\|ldclr\|stclr\|ldeor\|steor\|ldset\|stset\|ldsmax\|stsmax\|ldsmin\|stsmin\|ldumax\|stumin' | wc -l +``` + +If a non-zero number is printed your GNU C Library already has LSE. + +{{% /notice %}} + ## Before you begin Launch an [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) running Ubuntu version 20.04. From 9e08c4e872a2e9e8ab74061b74e8b9fffc13aa8d Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Fri, 11 Oct 2024 03:00:55 +0000 Subject: [PATCH 27/39] Update Hyper-V install guide and Learning Path --- content/install-guides/hyper-v.md | 2 +- .../learning-paths/laptops-and-desktops/hyper-v/hyper-v.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/content/install-guides/hyper-v.md b/content/install-guides/hyper-v.md index c4556c2eb6..61f94da653 100644 --- a/content/install-guides/hyper-v.md +++ b/content/install-guides/hyper-v.md @@ -78,7 +78,7 @@ A dialog should appear: ## How do I enable Hyper-V from the command line? -1. Open a Command Prompt or Windows PowerShell +1. Open a Command Prompt or Windows PowerShell (as Administrator) 2. Run the `DISM` command below to enable Hyper-V: diff --git a/content/learning-paths/laptops-and-desktops/hyper-v/hyper-v.md b/content/learning-paths/laptops-and-desktops/hyper-v/hyper-v.md index 6b0ee044ca..343953e98d 100644 --- a/content/learning-paths/laptops-and-desktops/hyper-v/hyper-v.md +++ b/content/learning-paths/laptops-and-desktops/hyper-v/hyper-v.md @@ -24,12 +24,12 @@ You can find information about other Linux distributions following the Ubuntu in ## Download an Ubuntu disk image -Download the [Ubuntu 22.04 ISO file](https://cdimage.ubuntu.com/jammy/daily-live/current/jammy-desktop-arm64.iso) for Arm. +Download the [Ubuntu 24.04 ISO file](https://cdimage.ubuntu.com/noble/daily-live/current/noble-desktop-arm64.iso) for Arm. Alternatively, you can download the `.iso` file from the Command Prompt or Windows PowerShell by running: ```console -curl https://cdimage.ubuntu.com/jammy/daily-live/current/jammy-desktop-arm64.iso -O jammy-desktop-arm64.iso +curl https://cdimage.ubuntu.com/noble/daily-live/current/noble-desktop-arm64.iso --output noble-desktop-arm64.iso ``` ## Install Ubuntu using Hyper-V From 2f72c12e6ca9dd26de7525e47d815ce404a26c52 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Fri, 11 Oct 2024 03:25:04 +0000 Subject: [PATCH 28/39] review Visual Studio install guide --- content/install-guides/vs-woa.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/install-guides/vs-woa.md b/content/install-guides/vs-woa.md index e89829142a..072742abce 100644 --- a/content/install-guides/vs-woa.md +++ b/content/install-guides/vs-woa.md @@ -62,7 +62,7 @@ Two results are displayed: The LLVM compiler and MSBuild support for LLVM. Selec {{% notice Note%}} Different versions of Visual Studio include different LLVM toolchain versions. -For example, Visual Studio 2022 Version `17.5.3` installs `LLVM 15.0.1`. +For example, Visual Studio 2022 Version `17.11.5` installs `LLVM 17.0.3`. {{% /notice %}} LLVM supports `clang-cl`, a compatibility layer for Microsoft Visual C++ (MSVC). This means that most developers can use `clang-cl` to compile their C/C++ applications on Visual Studio/MSBuild on the Windows on Arm device, without needing to change the command line. This allows you to easily modify legacy projects that use MSVC to use native compilation. From 262c08ab515a1bef96700bdcff4d019786c49ad8 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Fri, 11 Oct 2024 10:32:22 +0000 Subject: [PATCH 29/39] Divided Environment Setup into 3 main parts --- .../introduction-to-tinyml-on-arm/_index.md | 4 +- .../build-model-8.md | 99 ++++++++++ .../env-setup-5.md | 170 ++---------------- .../env-setup-6-FVP.md | 17 ++ .../setup-7-Grove.md | 61 +++++++ 5 files changed, 190 insertions(+), 161 deletions(-) create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md create mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md index 0659a2b37e..7cf7bcd240 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md @@ -19,7 +19,8 @@ prerequisites: - Basic knowledge of machine learning concepts. - Understanding of IoT and embedded systems (helpful but not required). - A Linux host machine or VM running Ubuntu 20.04 or higher, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware) - - Target device, preferably Cortex-M boards but you can use Cortex-A7 boards as well. + - Target device, phyisical or using the or Corstone-300 FVP, preferably Cortex-M boards but you can use Cortex-A7 boards as well. + author_primary: Dominica Abena O. Amanfo @@ -36,7 +37,6 @@ operatingsystems: tools_software_languages: - Corstone 300 FVP - Grove - Vision AI Module V2 - - Arm Virtual Hardware - Python - PyTorch v2.x - Executorch diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md new file mode 100644 index 0000000000..8d591f636a --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md @@ -0,0 +1,99 @@ +--- +# User change +title: "Build a Simple PyTorch Model" + +weight: 9 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +With our Environment ready, we will create a simple program to test our setup. This example will define a simple feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. Create a file called simple_nn.py with the following code: + +```python +import torch +from torch.export import export +from executorch.exir import to_edge + +# Define a simple Feedforward Neural Network +class SimpleNN(torch.nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super(SimpleNN, self).__init__() + self.fc1 = torch.nn.Linear(input_size, hidden_size) + self.relu = torch.nn.ReLU() + self.fc2 = torch.nn.Linear(hidden_size, output_size) + + def forward(self, x): + out = self.fc1(x) + out = self.relu(out) + out = self.fc2(out) + return out + +# Create the model instance +input_size = 10 # example input features size +hidden_size = 5 # hidden layer size +output_size = 2 # number of output classes + +model = SimpleNN(input_size, hidden_size, output_size) + +# Example input tensor (batch size 1, input size 10) +x = torch.randn(1, input_size) + +# torch.export: Defines the program with the ATen operator set for SimpleNN. +aten_dialect = export(model, (x,)) + +# to_edge: Make optimizations for edge devices. This ensures the model runs efficiently on constrained hardware. +edge_program = to_edge(aten_dialect) + +# to_executorch: Convert the graph to an ExecuTorch program +executorch_program = edge_program.to_executorch() + +# Save the compiled .pte program +with open("simple_nn.pte", "wb") as file: + file.write(executorch_program.buffer) + +print("Model successfully exported to simple_nn.pte") +``` + +Run it from your terminal: + +```console +python3 simple_nn.py +``` + +If everything runs successfully, the output will be: +```bash { output_lines = "1" } +Model successfully exported to simple_nn.pte +``` +Finally, the model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge. + +Now, we will run the ExecuTorch version, first run: + +```console +# Clean and configure the build system +rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake .. + +# Build the executor_runner target +cmake --build cmake-out --target executor_runner -j9 +``` + +You should see an output similar to: +```bash { output_lines = "1" } +[100%] Built target executor_runner +``` + +Now, run the executor_runner with the Model: +```console +./cmake-out/executor_runner --model_path simple_nn.pte +``` + +Expected Output: Since the model is a simple feedforward model, you can expect a tensor of shape [1, 2] + +```bash { output_lines = "1-3" } +Input tensor shape: [1, 10] +Output tensor shape: [1, 2] +Inference output: tensor([[0.5432, -0.3145]]) #will vary due to random initialization +``` + +If the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes. + diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index dc48492010..1a2b3bd207 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -1,6 +1,6 @@ --- # User change -title: "Environment Setup" +title: "Environment Setup on Host Machine" weight: 6 # 1 is first, 2 is second, etc. @@ -16,39 +16,25 @@ These instructions have been tested on: The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices. -You can use your own Linux host machine or use [Arm Virtual Hardware (AVH)](https://www.arm.com/products/development-tools/simulation/virtual-hardware) for this Learning Path. - -The Ubuntu version should be `20.04 or higher`. The `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. You will need a Linux desktop to run the FVP because it opens graphical windows for input and output from the software applications. Also, though Executorch supports Windows via WSL, it is limited in resource. - -If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide](/install-guides/avh#corstone) provides setup instructions. - -## Setup on Host Machine -1. Setup if you don't have access to the physical board: We would use the Corstone-300 FVP, it is pre-configured. -2. Setup if you have access to the board: Skip to **"Compilers"** Section +- The Ubuntu version should be `20.04 or higher`. +- If you do not have the board, the `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. +- Also, though Executorch supports Windows via WSL, it is limited in resource. ### Corstone-300 FVP Setup for ExecuTorch -For Arm Virtual Hardware users, the Corstone-300 FVP is pre-installed. To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial. -Since you already have the compiler installed from the above tutorial, skip to **"Install PyTorch"**. -### Compilers -The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain). +## Install Executorch -Both compilers are pre-installed in Arm Virtual Hardware. +1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it. -Alternatively, if you use Arch Linux or its derivatives, you can use Pacman to install GCC. +2. Activate the `executorch` virtual environment from the installation guide to ensure it is ready for use: -Use the install guides to install the compilers on your computer: -- [Arm Compiler for Embedded](/install-guides/armclang/) -- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu) -- Using Pacman: - ```console -pacman -S aarch64-linux-gnu-gcc +conda activate executorch ``` ## Install PyTorch @@ -59,16 +45,6 @@ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.o ``` -## Install Executorch - -1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it. - -2. Activate the `executorch` virtual environment from the installation guide to ensure it is ready for use: - -```console -conda activate executorch -``` - ## Install Edge Impulse CLI 1. Create an [Edge Impulse Account](https://studio.edgeimpulse.com/signup) if you do not have one @@ -88,130 +64,6 @@ npm install -g edge-impulse-cli sudo apt install screen ``` -4. Download and extract the latest Edge Impulse firmware -Grove Vision V2 [Edge impulse Firmware](https://cdn.edgeimpulse.com/firmware/seeed-grove-vision-ai-module-v2.zip). - - -## Setup on Grove - Vision AI Module V2 -**Kindly Note:** Only follow this part of the tutorial if you have the board. - -Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path). - -![Hardware Overview #center](Overview.png) - -Hardware overview : [Image credits](https://wiki.seeedstudio.com/grove_vision_ai_v2/). - -1. Connect the Grove - Vision AI Module V2 to your computer using the USB-C cable. - -![Board connection](Connect.png) - - -2. In the extracted Edge Impulse firmware, locate and run the installation scripts to flash your device. - -```console -./flash_linux.sh -``` - -3. Configure Edge Impulse for the board -in your terminal, run: - -```console -edge-impulse-daemon -``` -Follow the prompts to log in. - -4. Verify your board is connected - -If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse. - - -## Build a Simple PyTorch Model -With our Environment ready, we will create a simple program to test our setup. This example will define a simple feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. Create a file called simple_nn.py with the following code: - -```python -import torch -from torch.export import export -from executorch.exir import to_edge - -# Define a simple Feedforward Neural Network -class SimpleNN(torch.nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super(SimpleNN, self).__init__() - self.fc1 = torch.nn.Linear(input_size, hidden_size) - self.relu = torch.nn.ReLU() - self.fc2 = torch.nn.Linear(hidden_size, output_size) - - def forward(self, x): - out = self.fc1(x) - out = self.relu(out) - out = self.fc2(out) - return out - -# Create the model instance -input_size = 10 # example input features size -hidden_size = 5 # hidden layer size -output_size = 2 # number of output classes - -model = SimpleNN(input_size, hidden_size, output_size) - -# Example input tensor (batch size 1, input size 10) -x = torch.randn(1, input_size) - -# torch.export: Defines the program with the ATen operator set for SimpleNN. -aten_dialect = export(model, (x,)) - -# to_edge: Make optimizations for edge devices. This ensures the model runs efficiently on constrained hardware. -edge_program = to_edge(aten_dialect) - -# to_executorch: Convert the graph to an ExecuTorch program -executorch_program = edge_program.to_executorch() - -# Save the compiled .pte program -with open("simple_nn.pte", "wb") as file: - file.write(executorch_program.buffer) - -print("Model successfully exported to simple_nn.pte") -``` - -Run it from your terminal: - -```console -python3 simple_nn.py -``` - -If everything runs successfully, the output will be: -```bash { output_lines = "1" } -Model successfully exported to simple_nn.pte -``` -Finally, the model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge. - -Now, we will run the ExecuTorch version, first run: - -```console -# Clean and configure the build system -rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake .. - -# Build the executor_runner target -cmake --build cmake-out --target executor_runner -j9 -``` - -You should see an output similar to: -```bash { output_lines = "1" } -[100%] Built target executor_runner -``` - -Now, run the executor_runner with the Model: -```console -./cmake-out/executor_runner --model_path simple_nn.pte -``` - -Expected Output: Since the model is a simple feedforward model, you can expect a tensor of shape [1, 2] - -```bash { output_lines = "1-3" } -Input tensor shape: [1, 10] -Output tensor shape: [1, 2] -Inference output: tensor([[0.5432, -0.3145]]) #will vary due to random initialization -``` - -If the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes. - +## Next Steps +1. If you don't have access to the physical board: Skip to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-FVP.md) +2. If you have access to the board: Skip to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-Grove.md) \ No newline at end of file diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md new file mode 100644 index 0000000000..f4a7ffa0c5 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md @@ -0,0 +1,17 @@ +--- +# User change +title: "Environment Setup Corstone-300 FVP" + +weight: 7 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- + +### Corstone-300 FVP Setup for ExecuTorch + +To install and set up the Corstone-300 FVP on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial. + + +## Next Steps +1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md new file mode 100644 index 0000000000..882b56fb51 --- /dev/null +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md @@ -0,0 +1,61 @@ +--- +# User change +title: "Setup on Grove - Vision AI Module V2" + +weight: 8 # 1 is first, 2 is second, etc. + +# Do not modify these elements +layout: "learningpathall" +--- +## Before you begin +Only follow this part of the tutorial if you have the board. + +Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path). + + +### Compilers + +The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain). + + +Use the install guides to install the compilers on your **host machine**: +- [Arm Compiler for Embedded](/install-guides/armclang/) +- [Arm GNU Toolchain](/install-guides/gcc/arm-gnu) + + +## Board Setup + +![Hardware Overview #center](Overview.png) + +Hardware overview : [Image credits](https://wiki.seeedstudio.com/grove_vision_ai_v2/). + +1. Download and extract the latest Edge Impulse firmware +Grove Vision V2 [Edge impulse Firmware](https://cdn.edgeimpulse.com/firmware/seeed-grove-vision-ai-module-v2.zip). + + +2. Connect the Grove - Vision AI Module V2 to your computer using the USB-C cable. + +![Board connection](Connect.png) + + +3. In the extracted Edge Impulse firmware, locate and run the installation scripts to flash your device. + +```console +./flash_linux.sh +``` + +4. Configure Edge Impulse for the board +in your terminal, run: + +```console +edge-impulse-daemon +``` +Follow the prompts to log in. + +5. Verify your board is connected + +If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse. + + +## Next Steps +1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup. From c6d93892912e182b48d20e0ef88ffe189e5d6faa Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Fri, 11 Oct 2024 12:29:05 +0100 Subject: [PATCH 30/39] Update py-woa.md Minor tweak of wording. --- content/install-guides/py-woa.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/install-guides/py-woa.md b/content/install-guides/py-woa.md index 5de869d90b..7502154418 100644 --- a/content/install-guides/py-woa.md +++ b/content/install-guides/py-woa.md @@ -37,9 +37,9 @@ Windows on Arm instances are available with Microsoft Azure. For further informa To download and install Python for Windows on Arm, there is more than one option: -* Firstly, you can download the installer from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. +* You can download the installer from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. -* Or secondly, you can download from a PowerShell terminal, by running the following: +* You can download from a PowerShell terminal, by running the following: ```command curl https://www.python.org/ftp/python/3.13.0/python-3.13.0-arm64.exe --output python-3.13.0-arm64.exe ``` From b1da74874f3cffab754f7bd7fdfe503034eeca8c Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo Date: Fri, 11 Oct 2024 11:38:02 +0000 Subject: [PATCH 31/39] Updated structure --- .../introduction-to-tinyml-on-arm/env-setup-5.md | 12 +++--------- .../introduction-to-tinyml-on-arm/env-setup-6-FVP.md | 2 +- .../introduction-to-tinyml-on-arm/setup-7-Grove.md | 10 +++------- .../troubleshooting-6.md | 6 +++--- 4 files changed, 10 insertions(+), 20 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index 1a2b3bd207..f8cae1b21f 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -18,13 +18,7 @@ The host machine is where you will perform most of your development work, especi - The Ubuntu version should be `20.04 or higher`. - If you do not have the board, the `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. -- Also, though Executorch supports Windows via WSL, it is limited in resource. - - -### Corstone-300 FVP Setup for ExecuTorch - -To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial. - +- Though Executorch supports Windows via WSL, it is limited in resource. ## Install Executorch @@ -65,5 +59,5 @@ sudo apt install screen ``` ## Next Steps -1. If you don't have access to the physical board: Skip to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-FVP.md) -2. If you have access to the board: Skip to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-6-Grove.md) \ No newline at end of file +1. If you don't have access to the physical board: Go to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/) +2. If you have access to the board: Go to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/) \ No newline at end of file diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md index f4a7ffa0c5..4e569e9f0a 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md @@ -14,4 +14,4 @@ To install and set up the Corstone-300 FVP on your machine, refer to [Building a ## Next Steps -1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup. +1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8/) to test your environment setup. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md index 882b56fb51..bd8b4e268e 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md @@ -8,9 +8,7 @@ weight: 8 # 1 is first, 2 is second, etc. layout: "learningpathall" --- ## Before you begin -Only follow this part of the tutorial if you have the board. - -Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path). +Only follow this part of the tutorial if you have the board. Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path). ### Compilers @@ -52,10 +50,8 @@ edge-impulse-daemon ``` Follow the prompts to log in. -5. Verify your board is connected - -If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse. +5. If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse. ## Next Steps -1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md) to test your environment setup. +1. Go to [Build a Simple PyTorch Model](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8/) to test your environment setup. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md index 4a7c5acd51..c354f4640c 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md @@ -1,6 +1,6 @@ --- title: Troubleshooting and Best Practices -weight: 7 +weight: 10 ### FIXED, DO NOT MODIFY layout: learningpathall @@ -8,11 +8,11 @@ layout: learningpathall ## Troubleshooting - If you encounter permission issues, try running the commands with sudo. - Ensure your Grove - Vision AI Module V2 is properly connected and recognized by your computer. -- If Edge Impulse CLI fails to detect your device, try unplugging and replugging the USB cable. +- If Edge Impulse CLI fails to detect your device, try unplugging, hold the **Boot button** and replug the USB cable. Release the button once you replug. ## Best Practices - Always cross-compile your code on the host machine to ensure compatibility with the target Arm device. - Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2. - Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies -You've now set up your environment for TinyML development on the Grove - Vision AI Module V2. In the next modules, we'll explore data collection, model training, and deployment using PyTorch v2.0 and Executorch. \ No newline at end of file +You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Netrowk. \ No newline at end of file From 37676d39b77684b054ac80a3cca672bbad992ed3 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Fri, 11 Oct 2024 16:41:57 +0000 Subject: [PATCH 32/39] Reviewed and updated the Zilliz/Milvus RAG LP --- .../milvus-rag/_index.md | 16 ++-- .../milvus-rag/_next-steps.md | 2 +- .../milvus-rag/_review.md | 16 +++- .../milvus-rag/create_cluster.png | Bin 0 -> 184170 bytes .../milvus-rag/launch_llm_service.md | 23 +++--- .../milvus-rag/offline_data_loading.md | 74 ++++++++++++------ .../milvus-rag/online_rag.md | 63 ++++++++------- .../milvus-rag/prerequisite.md | 15 ++-- .../milvus-rag/running_cluster.png | Bin 0 -> 151043 bytes 9 files changed, 129 insertions(+), 80 deletions(-) create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/create_cluster.png create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/running_cluster.png diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md index e9e332b247..a6b183b18e 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md @@ -1,18 +1,18 @@ --- -title: Use Milvus/Zilliz to build RAG on Arm Architecture +title: Build a Retrieval-Augmented Generation (RAG) application using Zilliz Cloud on Arm servers minutes_to_complete: 20 -who_is_this_for: This is an introductory topic for engineers who want to create a RAG application on Arm machines. +who_is_this_for: This is an introductory topic for software developers who want to create a RAG application on Arm servers. learning_objectives: - - Create a simple RAG application using Milvus/Zilliz - - Launch LLM service on Arm machines + - Create a simple RAG application using Zilliz Cloud + - Launch a LLM service on Arm servers prerequisites: - - Basic understand of RAG pipeline. - - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. - - A [Zilliz account](https://zilliz.com/cloud), which you can sign up for a free trial. + - Basic understanding of a RAG pipeline. + - An AWS Graviton3 c7g.2xlarge instance, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp) from a cloud service provider or an on-premise Arm server. + - A [Zilliz account](https://zilliz.com/cloud), which you can sign up for with a free trial. author_primary: Chen Zhang @@ -23,6 +23,8 @@ armips: - Cortex-A tools_software_languages: - Python + - GenAI + - RAG operatingsystems: - Linux diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md index b617887475..a4dd214ed6 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md @@ -1,5 +1,5 @@ --- -next_step_guidance: Thank you for completing the Milvus RAG tutorial. +next_step_guidance: Thank you for completing the RAG with Zilliz Cloud Learning Path. You might be interested in learning how to run the Llama 3.1 8B model with KleidiAI optimizations on Arm servers. recommended_path: /learning-paths/servers-and-cloud-computing/llama-cpu/ diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md index 51e9fb4d01..7722e4c24c 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md @@ -2,7 +2,7 @@ review: - questions: question: > - Can Milvus run on Arm systems? + Can Milvus run on Arm? answers: - "Yes" - "No" @@ -12,13 +12,23 @@ review: - questions: question: > - Can Llama3.1 model run on Arm systems? + Can Llama3.1 model run on Arm? answers: - "Yes" - "No" correct_answer: 1 explanation: > - The Llama-3.1-8B model from Meta can be used on an AWS Arm-based server CPU with the llama.cpp tool. + The Llama-3.1-8B model from Meta can 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`llama.cpp` service on the Arm-based CPU. +In this section, you will build and run the `llama.cpp` server program using an OpenAI-compatible API on your running AWS Arm-based server instance. ### Llama 3.1 model & llama.cpp @@ -69,7 +69,7 @@ The GGUF model format, introduced by the llama.cpp team, uses compression and qu ### Re-quantize the model weights -To re-quantize, run +To re-quantize the model, run: ```bash ./llama-quantize --allow-requantize dolphin-2.9.4-llama3.1-8b-Q4_0.gguf dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf Q4_0_8_8 @@ -77,24 +77,25 @@ To re-quantize, run This will output a new file, `dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf`, which contains reconfigured weights that allow `llama-cli` to use SVE 256 and MATMUL_INT8 support. -> This requantization is optimal specifically for Graviton3. For Graviton2, the optimal requantization should be performed in the `Q4_0_4_4` format, and for Graviton4, the `Q4_0_4_8` format is the most suitable for requantization. +This requantization is optimal specifically for Graviton3. For Graviton2, the optimal requantization should be performed in the `Q4_0_4_4` format, and for Graviton4, the `Q4_0_4_8` format is the most suitable for requantization. -### Start the LLM Service -You can utilize the llama.cpp server program and send requests via an OpenAI-compatible API. This allows you to develop applications that interact with the LLM multiple times without having to repeatedly start and stop it. Additionally, you can access the server from another machine where the LLM is hosted over the network. +### Start the LLM Server +You can utilize the `llama.cpp` server program and send requests via an OpenAI-compatible API. This allows you to develop applications that interact with the LLM multiple times without having to repeatedly start and stop it. Additionally, you can access the server from another machine where the LLM is hosted over the network. Start the server from the command line, and it listens on port 8080: -```shell +```bash ./llama-server -m dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf -n 2048 -t 64 -c 65536 --port 8080 ``` -```text + +The output from this command should look like: + +```output 'main: server is listening on 127.0.0.1:8080 - starting the main loop ``` You can also adjust the parameters of the launched LLM to adapt it to your server hardware to obtain ideal performance. For more parameter information, see the `llama-server --help` command. -If you struggle to perform this step, you can refer to the [this documents](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama-cpu/llama-chatbot/) for more information. - -You have started the LLM service on your Arm-based CPU. Next, we directly interact with the service using the OpenAI SDK. +You have started the LLM service on your AWS Graviton instance with an Arm-based CPU. In the next section, you will directly interact with the service using the OpenAI SDK. diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md index 6a94254948..d69bd1ffad 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md @@ -6,14 +6,30 @@ weight: 3 layout: learningpathall --- -In this section, we will show you how to load private knowledge in our RAG. +In this section, you will learn how to setup a cluster on Zilliz Cloud. You will then learn how to load your private knowledge database into the cluster. + +### Create a dedicated cluster + +You will need to [register](https://docs.zilliz.com/docs/register-with-zilliz-cloud) for a free account on Zilliz Cloud. + +After you register, [create a cluster](https://docs.zilliz.com/docs/create-cluster) on Zilliz Cloud. In this Learning Path, you will create a dedicated cluster deployed in AWS using Arm-based machines to store and retreive the vector data as shown: + +![cluster](create_cluster.png) + +When you select the `Create Cluster` Button, you should see the cluster running in your Default Project. + +![running](running_cluster.png) + +{{% notice Note %}} +You can use self-hosted Milvus as an alternative to Zilliz Cloud. This option is more complicated to set up. We can also deploy [Milvus Standalone](https://milvus.io/docs/install_standalone-docker-compose.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) on Arm-based machines. For more information about Milvus installation, please refer to the [installation documentation](https://milvus.io/docs/install-overview.md). +{{% /notice %}} ### Create the Collection -We use [Zilliz Cloud](https://zilliz.com/cloud) deployed on AWS with Arm-based machines to store and retrieve the vector data. To quick start, simply [register an account](https://docs.zilliz.com/docs/register-with-zilliz-cloud) on Zilliz Cloud for free. -> In addition to Zilliz Cloud, self-hosted Milvus is also a (more complicated to set up) option. We can also deploy [Milvus Standalone](https://milvus.io/docs/install_standalone-docker-compose.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) on ARM-based machines. For more information about Milvus installation, please refer to the [installation documentation](https://milvus.io/docs/install-overview.md). +With the dedicated cluster running in Zilliz Cloud, you are now ready to create a collection in your cluster. + +Within your activated python `venv`, start by creating a file named `zilliz-llm-rag.py` and copy the contents below into it: -We set the `uri` and `token` as the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud. ```python from pymilvus import MilvusClient @@ -21,18 +37,18 @@ milvus_client = MilvusClient( uri="", token="" ) -collection_name = "my_rag_collection" - ``` -Check if the collection already exists and drop it if it does. +Replace and with the `URI` and `Token` for your running cluster. Refer to [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud for more details. + +Now, append the following code to `zilliz-llm-rag.py` and save the contents: + ```python +collection_name = "my_rag_collection" +embedding_dim = "384" + if milvus_client.has_collection(collection_name): milvus_client.drop_collection(collection_name) -``` -Create a new collection with specified parameters. -If we don't specify any field information, Milvus will automatically create a default `id` field for primary key, and a `vector` field to store the vector data. A reserved JSON field is used to store non-schema-defined fields and their values. -```python milvus_client.create_collection( collection_name=collection_name, dimension=embedding_dim, @@ -40,11 +56,16 @@ milvus_client.create_collection( consistency_level="Strong", # Strong consistency level ) ``` -We use inner product distance as the default metric type. For more information about distance types, you can refer to [Similarity Metrics page](https://milvus.io/docs/metric.md?tab=floating) +This code checks if a collection already exists and drops it if it does. You then, create a new collection with the specified parameters. + +If you don't specify any field information, Milvus will automatically create a default `id` field for primary key, and a `vector` field to store the vector data. A reserved JSON field is used to store non-schema-defined fields and their values. +You will use inner product distance as the default metric type. For more information about distance types, you can refer to [Similarity Metrics page](https://milvus.io/docs/metric.md?tab=floating) + +You can now prepare the data to use in this collection. ### Prepare the data -We use the FAQ pages from the [Milvus Documentation 2.4.x](https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip) as the private knowledge in our RAG, which is a good data source for a simple RAG pipeline. +In this example, you will use the FAQ pages from the [Milvus Documentation 2.4.x](https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip) as the private knowledge that is loaded in your RAG dataset/collection. Download the zip file and extract documents to the folder `milvus_docs`. @@ -53,8 +74,9 @@ wget https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/m unzip -q milvus_docs_2.4.x_en.zip -d milvus_docs ``` -We load all markdown files from the folder `milvus_docs/en/faq`. For each document, we just simply use "# " to separate the content in the file, which can roughly separate the content of each main part of the markdown file. +You will load all the markdown files from the folder `milvus_docs/en/faq` into your data collection. For each document, use "# " to separate the content in the file, which can roughly separate the content of each main part of the markdown file. +Open `zilliz-llm-rag.py` and append the following code to it: ```python from glob import glob @@ -69,17 +91,17 @@ for file_path in glob("milvus_docs/en/faq/*.md", recursive=True): ``` ### Insert data -We prepare a simple but efficient embedding model [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) that can convert text into embedding vectors. +You will now prepare a simple but efficient embedding model [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) that can convert the loaded text into embedding vectors. + +You will iterate through the text lines, create embeddings, and then insert the data into Milvus. + +Append and save the code shown below into `zilliz-llm-rag.py`: + ```python from langchain_huggingface import HuggingFaceEmbeddings embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") -``` -Iterate through the text lines, create embeddings, and then insert the data into Milvus. - -Here is a new field `text`, which is a non-defined field in the collection schema. It will be automatically added to the reserved JSON dynamic field, which can be treated as a normal field at a high level. -```python from tqdm import tqdm data = [] @@ -93,6 +115,14 @@ for i, (line, embedding) in enumerate( milvus_client.insert(collection_name=collection_name, data=data) ``` -```text -Creating embeddings: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:18<00:00, 3.91it/s] +Run the python script, to check that you have successfully created the embeddings on the data you loaded into the RAG collection: + +```bash +python3 python3 zilliz-llm-rag.py ``` + +The output should look like: +``` +Creating embeddings: 72it [00:00, 700672.59it/s] +``` + diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md index 27680a8f39..ced3778b20 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md @@ -6,20 +6,15 @@ weight: 5 layout: learningpathall --- -In this section, we will build the online RAG part of the application. +In this section, you will build the online RAG part of your application. -### LLM Client and Embedding Model +### Prepare the embedding model -We initialize the LLM client and prepare the embedding model. +In your python script, generate a test embedding and print its dimension and first few elements. -For the LLM, we use the OpenAI SDK to request the Llama service launched before. We don't need to use any API key because it is actually our local llama.cpp service. +For the LLM, you will use the OpenAI SDK to request the Llama service launched before. You don't need to use any API key because it is running locally on your machine. -```python -from openai import OpenAI - -llm_client = OpenAI(base_url="http://localhost:8080/v1", api_key="no-key") -``` -Generate a test embedding and print its dimension and first few elements. +Append the code below to `zilliz-llm-rag.py`: ```python test_embedding = embedding_model.embed_query("This is a test") @@ -27,21 +22,22 @@ embedding_dim = len(test_embedding) print(embedding_dim) print(test_embedding[:10]) ``` +Run the script. The output should look like: -```text +```output 384 [0.03061249852180481, 0.013831384479999542, -0.02084377221763134, 0.016327863559126854, -0.010231520049273968, -0.0479842908680439, -0.017313342541456223, 0.03728749603033066, 0.04588735103607178, 0.034405000507831573] ``` ### Retrieve data for a query -Let's specify a frequent question about Milvus. +You will specify a frequent question about Milvus and then search for the question in the collection and retrieve the semantic top-3 matches. + +Append the code shown below to `zilliz-llm-rag.py`: + ```python question = "How is data stored in milvus?" -``` -Search for the question in the collection and retrieve the semantic top-3 matches. -```python search_res = milvus_client.search( collection_name=collection_name, data=[ @@ -51,9 +47,7 @@ search_res = milvus_client.search( search_params={"metric_type": "IP", "params": {}}, # Inner product distance output_fields=["text"], # Return the text field ) -``` -Let's take a look at the search results of the query -```python + import json retrieved_lines_with_distances = [ @@ -61,8 +55,9 @@ retrieved_lines_with_distances = [ ] print(json.dumps(retrieved_lines_with_distances, indent=4)) ``` +Run the script again and the output with the top 3 matches will look like: -```shell +```output [ [ " Where does Milvus store data?\n\nMilvus deals with two types of data, inserted data and metadata. \n\nInserted data, including vector data, scalar data, and collection-specific schema, are stored in persistent storage as incremental log. Milvus supports multiple object storage backends, including [MinIO](https://min.io/), [AWS S3](https://aws.amazon.com/s3/?nc1=h_ls), [Google Cloud Storage](https://cloud.google.com/storage?hl=en#object-storage-for-companies-of-all-sizes) (GCS), [Azure Blob Storage](https://azure.microsoft.com/en-us/products/storage/blobs), [Alibaba Cloud OSS](https://www.alibabacloud.com/product/object-storage-service), and [Tencent Cloud Object Storage](https://www.tencentcloud.com/products/cos) (COS).\n\nMetadata are generated within Milvus. Each Milvus module has its own metadata that are stored in etcd.\n\n###", @@ -80,15 +75,23 @@ print(json.dumps(retrieved_lines_with_distances, indent=4)) ``` ### Use LLM to get a RAG response -Convert the retrieved documents into a string format. +You are now ready to use the LLM and obtain a RAG response. + +For the LLM, you will use the OpenAI SDK to request the Llama service you launched in the previous section. You don't need to use any API key because it is running locally on your machine. + +You will then convert the retrieved documents into a string format. Define system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus. Finally use the LLM to generate a response based on the prompts. + +Append the code below into `zilliz-llm-rag.py`: + ```python +from openai import OpenAI + +llm_client = OpenAI(base_url="http://localhost:8080/v1", api_key="no-key") + context = "\n".join( [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances] ) -``` -Define system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus. -```python SYSTEM_PROMPT = """ Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided. """ @@ -101,10 +104,7 @@ Use the following pieces of information enclosed in tags to provide an {question} """ -``` -Use LLM to generate a response based on the prompts. We set the `model` parameter to `not-used` since it is a redundant parameter for the llama.cpp service. -```python response = llm_client.chat.completions.create( model="not-used", messages=[ @@ -115,9 +115,16 @@ response = llm_client.chat.completions.create( print(response.choices[0].message.content) ``` -```text + +{{% notice Note %}} +Make sure your llama.cpp server from the previous section is running before you proceed +{{% /notice %}} + +Run the script one final time with these changes using `python3 zilliz-llm-rag.py`. The output should look like: + +```output Milvus stores data in two types: inserted data and metadata. Inserted data, including vector data, scalar data, and collection-specific schema, are stored in persistent storage as incremental log. Milvus supports multiple object storage backends such as MinIO, AWS S3, Google Cloud Storage (GCS), Azure Blob Storage, Alibaba Cloud OSS, and Tencent Cloud Object Storage (COS). Metadata are generated within Milvus and each Milvus module has its own metadata that are stored in etcd. ``` -Congratulations! You have built a RAG application on top of the Arm-based infrastructures. +Congratulations! You have successfully built a RAG application using a LLM and Zilliz Cloud all on your Arm-based infrastructure. diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md index 18d35330b2..9d336b341d 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md @@ -1,24 +1,23 @@ --- -title: Prerequisite +title: Install dependencies weight: 2 ### FIXED, DO NOT MODIFY layout: learningpathall --- -## Before you begin +## Overview -In this tutorial, you learn how to build a Retrieval-Augmented Generation (RAG) application on Arm-based infrastructures. For vector storage, we utilize [Zilliz Cloud](https://zilliz.com/cloud), the fully-managed Milvus vector database. Zilliz Cloud is available on major cloud such as AWS, GCP and Azure. In this demo we use Zilliz Cloud deployed on AWS with Arm machines. For LLM, we use the `Llama-3.1-8B` model on the AWS Arm-based server CPU using `llama.cpp`. +In this Learning Path, you will learn how to build a Retrieval-Augmented Generation (RAG) application on Arm-based servers. RAG applications often use vector databases to efficiently store and retrieve high-dimensional vector representations of text data. Vector databases are optimized for similarity search and can handle large volumes of vector data, making them ideal for the retrieval component of RAG systems. In this example, you will utilize [Zilliz Cloud](https://zilliz.com/cloud), the fully-managed Milvus vector database as your vector storage. Zilliz Cloud is available on major cloud such as AWS, GCP and Azure. In this demo you will use Zilliz Cloud deployed on AWS with Arm based servers. For the LLM, you will use the `Llama-3.1-8B` model running on an AWS Arm-based server using `llama.cpp`. -## Prerequisite -To run this example, we recommend you to use [AWS Graviton](https://aws.amazon.com/ec2/graviton/), which provides a cost-effective way to run ML workloads on Arm-based servers. This notebook has been tested on an AWS Graviton3 `c7g.2xlarge` instance with Ubuntu 22.04 LTS system. - -You need at least four cores and 8GB of RAM to run this example. Configure disk storage up to at least 32 GB. We recommend that you use an instance of the same or better specification. +## Install dependencies +This Learning Path has been tested on an AWS Graviton3 `c7g.2xlarge` instance running Ubuntu 22.04 LTS system. +You need at least four cores and 8GB of RAM to run this example. Configure disk storage up to at least 32 GB. After you launch the instance, connect to it and run the following commands to prepare the environment. -Install python on the server: +Install python: ```bash sudo apt update diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/running_cluster.png b/content/learning-paths/servers-and-cloud-computing/milvus-rag/running_cluster.png new file mode 100644 index 0000000000000000000000000000000000000000..6153ee6c62180ce20095892ebf6d0c044840cb49 GIT binary patch literal 151043 zcmcG$WmsElw=RrJu@+jK(k@DY;_lYsUMTJs+=~+^(3Tc=2<{XO?pmDS795HN4=xFE zSm*3_e|w$Ncdhr&_aj#_b6qpf9An($8FoMUuBIYO@QCse3JMB=yquIe3JNwi3JQ8N z4%WjrV=+a64{xY$>auT9%15Yo9xgDfB$XvmP%5MFZcH&Bu5q2^blp%;Na_E4p~|bj zIzmCY&zF~y{NQD@zl3d~3`1fcfnz@7R9-%>oYpFlcE}%BFY&jIn8+y`FP=!uVJ&8~ zt;w@}mM=FPS|+2`tcCmJ56M?=iGN*+z_xmoEf%|n*N}I2u*{7Nx8>7}Q9=HR(?yu~ z46lt6u?_6hB277FIS|0oh6fjHaA4&%E` zdHD`&-LC25PBdGANrB)u3R2Rq*hJ4?nD>S|J3DiW>MgNY-+5efQ7esdXfxQC9Oj%Sj|G|wCfq9SRz&oIngd$Ys;eLzUzq7%AuSSV6VfR=A z0s#!hGx3IpnE$Qi{&klLjuZd@7>NDy>Oaz``h&s$%dABJ32L1GL>Sa(a^O=-z(|_l zw5^gOmfxhvG+`9_ez1kJ;fWI?)jui}I^7j@%`DcXWeUlJ!a)QfZ;*l!F1g8=;nK2$^ zV(>86pv?DQCLZL@bf6CmrEO*8yxZtx`bUpCQhFqUY3DzLSyte+jU86L8u9!!+ka69#!Q!@c9bb+^J|A(Q?4_)1ey|hrwm4 z2hZlm8Y|Q1ud$ya_V@R{I_e^dfVLqIg*OJ`u&MYRyTR>uwk>tezuvCzT^+Bx>`oOi zg7f!5N8A65glO_K3{HNqK$Q2oNZH!9Xcbn9J7Yqrca~ATow&Jr+CDAzfYymT85_tWehAW`|PoX zQwwsv51GClOk&Uc0n}Jm(QW}&c;07E3GhUI|0j&RcpexKb!DoyAbz~s*@dBR!&i7% z0(>Q__{HeD zM%cDVih5$K+$H&zwbuxQOd@ESxTYucSyxXOS)2#Tz+y8NM-upsL<4YRL4DnIxy(t* z_je$aFvGlY!Zf)ITo-Wr#dRkN|6(AR_P*}JlXbgWy@YVE{cF^7P`kb86iY#Y*14_L z+vcrv`NUAnGsLa$9oLN`R<{=`B=L={L0_JIie^!#oPLdsoqlfgPzflWRTGo@mVW(Y zPnG^uJt!8Ak7eYDR^#OK2*8&JhdQBuZNaXwr%sRxPA>64;hq`T^C0do#c3G0lb_27 zc?bcYiX@e8+nlq}wzuag184=p$?P=EIk=geI4+d`ZD?7gAk(R~;h%9_cEDg6?o3@2 zOpnM)zb_M(sR-$8?BnmuqfMd|uW`_Ve-}1C*bIwl2dH zhpAV2u+M$nZ5>Xz$4eDhS*f&T3WDFsY%oVp3tYHoN__q-^39!^;$Xsj{EkpwC4@!zWI z6Pk^#1iBk#O*r!rfbhn;V1CR~xNvi_c3>8XkDPHFQ0_-;v`PZ0r>=r zU_atSv7@M4a;

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zZkItQmRyieN2lGSj8>k3U2sw8YQ1)LWfQ|Hg}^RbDnqR`p)*<*X&hjQ?eQ_J6#Nb3 zh{(nQ^anx1g{sjzK{&o4{wwVtmu43)fjl>17mHPm<_0Ak25&~xFKFy~?}?u9NB_<` zuod0`Jdemb+J>7A)I$QE2i8J=?+>5#AG`A5_2P#;`G`#ux1FSb5Cc*|b5ryG_nsP)py>cm16mX92bjpVO jAYEUH`|}@iyr0)t6IUlc7|Vs-t|Z+q)Mi`g$0 literal 0 HcmV?d00001 From be8050cda9d8138986f02229d826eb58fbc74f5a Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Fri, 11 Oct 2024 13:19:34 -0400 Subject: [PATCH 33/39] Update contributors.csv --- assets/contributors.csv | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/assets/contributors.csv b/assets/contributors.csv index c8f3b4bcfd..ddf7d58fbe 100644 --- a/assets/contributors.csv +++ b/assets/contributors.csv @@ -1,6 +1,6 @@ author,company,github,linkedin,twitter,website Jason Andrews,Arm,jasonrandrews,jason-andrews-7b05a8,, -Pareena Verma,Arm,pareenaverma,,, +Pareena Verma,Arm,pareenaverma,pareena-verma-7853607,, Ronan Synnott,Arm,,ronansynnott,, Florent Lebeau,Arm,,,, Brenda Strech,Remote.It,bstrech,bstrech,@remote_it,www.remote.it @@ -40,4 +40,4 @@ Alexandros Lamprineas,Arm,,,, Annie Tallund,Arm,annietllnd,annietallund,, Cyril Rohr,RunsOn,crohr,cyrilrohr,, Rin Dobrescu,Arm,,,, -Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,, \ No newline at end of file +Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,, From e8f5e49113e2f782b493408f1cf16eb1a05a1cdd Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Fri, 11 Oct 2024 21:55:04 +0000 Subject: [PATCH 34/39] review of TinyML Learing Path --- .../Overview-1.md | 42 ++++++- .../introduction-to-tinyml-on-arm/_index.md | 22 ++-- .../applications-4.md | 34 ------ .../benefits-3.md | 49 +++++++- .../build-model-8.md | 52 +++++--- .../env-setup-5.md | 111 ++++++++++++++---- .../env-setup-6-FVP.md | 4 +- .../examples-2.md | 23 ---- .../setup-7-Grove.md | 4 +- .../troubleshooting-6.md | 2 +- 10 files changed, 219 insertions(+), 124 deletions(-) delete mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/applications-4.md delete mode 100644 content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/examples-2.md diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md index 1e6831cadb..683e2ae6ca 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/Overview-1.md @@ -1,15 +1,47 @@ --- -title: Overview +title: Introduction to TinyML weight: 2 ### FIXED, DO NOT MODIFY layout: learningpathall --- +TinyML represents a significant shift in machine learning deployment. + +Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, constrained memory, low power, and less processing capabilities. + +TinyML has gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. + +This shift opens up new possibilities for creating smarter and more efficient embedded systems. + ## Module Overview -This session delves into TinyML, which applies machine learning to devices with limited resources like microcontrollers. This module serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. -Additionally, we'll cover the necessary setup on your host machine and target device to facilitate cross-compilation and ensure smooth integration across all devices. +This Learning Path is about TinyML, applying machine learning to devices with limited resources like microcontrollers. It serves as a starting point for learning how cutting-edge AI technologies may be put on even the smallest of devices, making Edge AI more accessible and efficient. + +You will learn how to setup on your host machine and target device to facilitate compilation and ensure smooth integration across all devices. + +## Examples of Arm-based devices and applications + +There are many devices you can use for TinyML projects. Some of them are listed below. + +### Raspberry Pi 4 and 5 + +Raspberry Pi single-board computers are excellent for prototyping TinyML projects. They are commonly used for prototyping machine learning projects at the edge, such as in object detection and voice recognition for home automation. + +### NXP i.MX RT microcontrollers + +NXP i.MX RT microcontrollers are low-power microcontrollers that can handle complex TinyML tasks while maintaining energy efficiency, making them ideal for applications like wearable healthcare devices and environmental sensors. + +### STM32 microcontrollers + +STM32 microcontrollers are used in industrial IoT applications for predictive maintenance. These microcontrollers are energy-efficient and capable of running TinyML models for real-time anomaly detection in factory machinery. + +### Arduino Nano 33 BLE Sense + +The Arduino Nano, equipped with a suite of sensors, supports TinyML and is ideal for small-scale IoT applications, such as detecting environmental changes and movement patterns. + +### Edge Impulse + +In addition to hardware, there are software platforms that can help you build TinyML applications. -## Introduction to TinyML -TinyML represents a significant shift in how we approach machine learning deployment. Unlike traditional machine learning, which typically depends on cloud-based servers or high-powered hardware, TinyML is tailored to function on devices with limited resources, such as constrained memory, power, and processing capabilities. TinyML has quickly gained popularity because it enables AI applications to operate in real-time, directly on the device, with minimal latency, enhanced privacy, and the ability to work offline. This shift opens up new possibilities for creating smarter and more efficient embedded systems. +Edge Impulse platform offers a suite of tools for developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards. \ No newline at end of file diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md index 7cf7bcd240..3305cb70c1 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md @@ -1,15 +1,15 @@ --- -title: Introduction to TinyML on Arm using PyTorch v2.x and Executorch +title: Introduction to TinyML on Arm using PyTorch and ExecuTorch minutes_to_complete: 40 -who_is_this_for: This learning module is tailored for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. If you have an interest in deploying machine learning models on low-power, resource-constrained devices, this course will help you get started using PyTorch v2.x and Executorch on Arm-based platforms. +who_is_this_for: This is an introductory topic for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. You will learn how to get started using PyTorch and ExecuTorch for TinyML. learning_objectives: - Identify TinyML and how it's different from the AI you might be used to. - Understand the benefits of deploying AI models on Arm-based edge devices. - Select Arm-based devices for TinyML. - - Identify real-world use cases demonstrating the impact of TinyML in various industries. + - Identify real-world use cases demonstrating the impact of TinyML. - Install and configure a TinyML development environment. - Set up a cross-compilation environment on your host machine. - Perform best practices for ensuring optimal performance on constrained edge devices. @@ -17,10 +17,8 @@ learning_objectives: prerequisites: - Basic knowledge of machine learning concepts. - - Understanding of IoT and embedded systems (helpful but not required). - - A Linux host machine or VM running Ubuntu 20.04 or higher, or an AWS account to use [Arm Virtual Hardware](https://www.arm.com/products/development-tools/simulation/virtual-hardware) - - Target device, phyisical or using the or Corstone-300 FVP, preferably Cortex-M boards but you can use Cortex-A7 boards as well. - + - Understanding of IoT and embedded systems. + - A Linux host machine or VM running Ubuntu 22.04 or higher. author_primary: Dominica Abena O. Amanfo @@ -35,15 +33,15 @@ operatingsystems: - Linux tools_software_languages: - - Corstone 300 FVP - - Grove - Vision AI Module V2 + - Arm Virtual Hardware + - Fixed Virtual Platform - Python - - PyTorch v2.x - - Executorch + - PyTorch + - ExecuTorch - Arm Compute Library - GCC - Edge Impulse - - Nodejs + - Node.js ### FIXED, DO NOT MODIFY # ================================================================================ diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/applications-4.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/applications-4.md deleted file mode 100644 index cef33546dc..0000000000 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/applications-4.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Real-World Applications of TinyML with Examples of Arm-Based Solutions -weight: 5 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -TinyML is being deployed across various industries, enhancing everyday experiences and enabling groundbreaking solutions. Here are a few examples: - -## Healthcare - Wearable Heart Rate Monitors -- Arm-based microcontrollers like those in Fitbit devices run TinyML models to monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. - -- **Example**: Fitbit Charge 5 smart band uses the Arm 32-bit Cortex-M4 processor. - -## Agriculture - Smart Irrigation Systems -- Arm-powered microcontrollers in smart sensors help monitor soil moisture and control water usage. TinyML models process environmental data locally to optimize water distribution. -- **Example**: OpenAg uses Arm Cortex-M processors to run machine learning models on edge devices, optimizing irrigation based on real-time data. - -## Home Automation - Smart Cameras -- Arm-based processors in smart cameras can detect objects and people, triggering alerts or actions without needing to send data to the cloud, saving bandwidth and improving privacy. -- **Example**: Arlo smart cameras, powered by Arm Cortex processors, perform object detection at the edge, enhancing performance and energy efficiency. - -## Industrial IoT - Predictive Maintenance in Factories (e.g., Siemens Predictive Maintenance) -- Arm-powered industrial sensors analyze vibration patterns in machinery, running TinyML models to predict when maintenance is needed and prevent breakdowns. -- **Example**: Siemens utilizes Arm Cortex-A processors in industrial sensors for real-time data analysis, detecting faults before they cause significant downtime. They rely on Arm-based processors for their Industrial Edge computing solutions. - -## Wildlife Conservation - Smart Camera Traps (e.g., Conservation X Labs) -- Arm-based smart camera traps can identify animal movements or detect poachers using TinyML models. These energy-efficient devices can operate in remote areas without relying on external power sources. -- **Example**: Conservation X Labs uses Arm Cortex-M microcontrollers to power camera traps, helping monitor endangered species in the wild. - - - - diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/benefits-3.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/benefits-3.md index 7d4ef0c90f..066a090030 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/benefits-3.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/benefits-3.md @@ -1,15 +1,58 @@ --- -title: Benefits of TinyML for Edge Computing on Arm Devices -weight: 4 +title: Benefits and application of TinyML for Edge Computing +weight: 3 ### FIXED, DO NOT MODIFY layout: learningpathall --- -The advantages of TinyML for edge computing on Arm devices are vast, particularly when paired with Arm's architecture, which is widely used in IoT, mobile devices, and edge AI deployments. Here are some key benefits: +## Benefits and applications + +The advantages of TinyML match up well with the Arm architecture, which is widely used in IoT, mobile devices, and edge AI deployments. + +Here are some key benefits of TinyML on Arm: - Power Efficiency: TinyML models are designed to be extremely power-efficient, making them ideal for battery-operated devices like sensors, wearables, and drones. + - Low Latency: Because the AI processing happens on-device, there's no need to send data to the cloud, reducing latency and enabling real-time decision-making. + - Data Privacy: With on-device computation, sensitive data remains local, providing enhanced privacy and security. This is particularly crucial in healthcare and personal devices. + - Cost-Effective: Arm devices, which are cost-effective and scalable, can now handle sophisticated machine learning tasks, reducing the need for expensive hardware or cloud services. + - Scalability: With billions of Arm devices in the market, TinyML is well-suited for scaling across industries, enabling widespread adoption of AI at the edge. + +TinyML is being deployed across multiple industries, enhancing everyday experiences and enabling groundbreaking solutions. + +Here are a few examples of TinyML applications: + +### Healthcare - Wearable Heart Rate Monitors + +- Arm-based microcontrollers like those in Fitbit devices run TinyML models to monitor vital signs such as heart rate, detect arrhythmias, and provide real-time feedback. + +- **Example**: Fitbit Charge 5 smart band uses the Arm 32-bit Cortex-M4 processor. + +### Agriculture - Smart Irrigation Systems + +- Arm-powered microcontrollers in smart sensors help monitor soil moisture and control water usage. TinyML models process environmental data locally to optimize water distribution. + +- **Example**: OpenAg uses Arm Cortex-M processors to run machine learning models on edge devices, optimizing irrigation based on real-time data. + +### Home Automation - Smart Cameras + +- Arm-based processors in smart cameras can detect objects and people, triggering alerts or actions without needing to send data to the cloud, saving bandwidth and improving privacy. + +- **Example**: Arlo smart cameras, powered by Arm Cortex processors, perform object detection at the edge, enhancing performance and energy efficiency. + +### Industrial IoT - Predictive Maintenance in Factories + +- Arm-powered industrial sensors analyze vibration patterns in machinery, running TinyML models to predict when maintenance is needed and prevent breakdowns. + +- **Example**: Siemens utilizes Arm Cortex-A processors in industrial sensors for real-time data analysis, detecting faults before they cause significant downtime. They rely on Arm-based processors for their Industrial Edge computing solutions. + +### Wildlife Conservation - Smart Camera Traps + +- Arm-based smart camera traps can identify animal movements or detect poachers using TinyML models. These energy-efficient devices can operate in remote areas without relying on external power sources. + +- **Example**: Conservation X Labs uses Arm Cortex-M microcontrollers to power camera traps, helping monitor endangered species in the wild. + diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md index 8d591f636a..80080ef9e8 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/build-model-8.md @@ -2,13 +2,17 @@ # User change title: "Build a Simple PyTorch Model" -weight: 9 # 1 is first, 2 is second, etc. +weight: 7 # 1 is first, 2 is second, etc. # Do not modify these elements layout: "learningpathall" --- -With our Environment ready, we will create a simple program to test our setup. This example will define a simple feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. Create a file called simple_nn.py with the following code: +With our Environment ready, you can create a simple program to test the setup. + +This example defines a small feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. + +Use a text editor to create a file named `simple_nn.py` with the following code: ```python import torch @@ -55,45 +59,55 @@ with open("simple_nn.pte", "wb") as file: print("Model successfully exported to simple_nn.pte") ``` -Run it from your terminal: +Run the model from the Linux command line: ```console python3 simple_nn.py ``` -If everything runs successfully, the output will be: -```bash { output_lines = "1" } +The output is: + +```output Model successfully exported to simple_nn.pte ``` -Finally, the model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge. -Now, we will run the ExecuTorch version, first run: +The model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge. + +Run the ExecuTorch version, first build the executable: ```console # Clean and configure the build system -rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake .. +(rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake ..) # Build the executor_runner target -cmake --build cmake-out --target executor_runner -j9 +cmake --build cmake-out --target executor_runner -j$(nproc) ``` -You should see an output similar to: -```bash { output_lines = "1" } +You see the build output and it ends with: + +```output +[100%] Linking CXX executable executor_runner [100%] Built target executor_runner ``` -Now, run the executor_runner with the Model: +When the build is complete, run the executor_runner with the model as an argument: + ```console ./cmake-out/executor_runner --model_path simple_nn.pte ``` -Expected Output: Since the model is a simple feedforward model, you can expect a tensor of shape [1, 2] - -```bash { output_lines = "1-3" } -Input tensor shape: [1, 10] -Output tensor shape: [1, 2] -Inference output: tensor([[0.5432, -0.3145]]) #will vary due to random initialization +Since the model is a simple feedforward model, you see a tensor of shape [1, 2] + +```output +I 00:00:00.006598 executorch:executor_runner.cpp:73] Model file simple_nn.pte is loaded. +I 00:00:00.006628 executorch:executor_runner.cpp:82] Using method forward +I 00:00:00.006635 executorch:executor_runner.cpp:129] Setting up planned buffer 0, size 320. +I 00:00:00.007225 executorch:executor_runner.cpp:152] Method loaded. +I 00:00:00.007237 executorch:executor_runner.cpp:162] Inputs prepared. +I 00:00:00.012885 executorch:executor_runner.cpp:171] Model executed successfully. +I 00:00:00.012896 executorch:executor_runner.cpp:175] 1 outputs: +Output 0: tensor(sizes=[1, 2], [-0.105369, -0.178723]) ``` -If the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes. +When the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index f8cae1b21f..f8e6f6d0a4 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -2,62 +2,127 @@ # User change title: "Environment Setup on Host Machine" -weight: 6 # 1 is first, 2 is second, etc. +weight: 4 # 1 is first, 2 is second, etc. # Do not modify these elements layout: "learningpathall" --- ## Before you begin -These instructions have been tested on: -- A GCP Arm-based Tau T2A Virtual Machine instance Running Ubuntu 22.04 LTS. -- Host machine with Ubuntu 24.04 on x86_64 architecture. -- Windows Subsystem for Linux (WSL): Windows x86_64 +You will use a Linux computer to run PyTorch and ExecuTorch to prepare a TinyML model to run on edge devices. -The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices. +The instructions are for Ubuntu 22.04 or newer. -- The Ubuntu version should be `20.04 or higher`. -- If you do not have the board, the `x86_64` architecture must be used because the Corstone-300 FVP is not currently available for the Arm architecture. -- Though Executorch supports Windows via WSL, it is limited in resource. +You also need the [Grove Vision AI Module](https://wiki.seeedstudio.com/Grove-Vision-AI-Module/). If you don't have the board you can use the Corstone-300 Fixed Virtual Platform (FVP) instead. +{{% notice Note %}} +Note that the Corstone-300 FVP is not available for the Arm architecture so your host machine needs to x86_64. +{{% /notice %}} -## Install Executorch +The instructions have been tested on: +- Arm-based cloud instances running Ubuntu 22.04. +- Desktop computer with Ubuntu 24.04. +- Windows Subsystem for Linux (WSL). + +The host machine is where you will perform most of your development work, especially compiling code for the target Arm devices. -1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it. +## Install Python -2. Activate the `executorch` virtual environment from the installation guide to ensure it is ready for use: +Python 3 is included in Ubuntu, but some additonal packages are needed. ```console -conda activate executorch +sudo apt update +sudo apt install python-is-python3 gcc g++ make -y ``` ## Install PyTorch -The latest version needs Python 3.8 or later + +Create a Python virtual environemnt using Miniconda. + +For Arm Linux: + +```console +curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh +sh ./Miniconda3-latest-Linux-aarch64.sh -b +eval "$($HOME/miniconda3/bin/conda shell.bash hook)" +conda --version +``` + +For x86_64 Linux: ```console -pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 +curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh +sh ./Miniconda3-latest-Linux-x86_64.sh -b +eval "$($HOME/miniconda3/bin/conda shell.bash hook)" +conda --version +``` + +Activate the Python virtual environment: +```bash +conda create -yn executorch python=3.10.0 +conda activate executorch +``` + +The prompt of your terminal now has (executorch) as a prefix to indicate the virtual environment is active. + + +## Install Executorch + +From within the Python virtual environment, run the commands below to download the ExecuTorch repository and install the required packages: + +``` bash +# Clone the ExecuTorch repo from GitHub +git clone --branch v0.3.0 https://github.com/pytorch/executorch.git +cd executorch + +# Update and pull submodules +git submodule sync +git submodule update --init + +# Install ExecuTorch pip package and its dependencies, as well as +# development tools like CMake. +./install_requirements.sh ``` ## Install Edge Impulse CLI -1. Create an [Edge Impulse Account](https://studio.edgeimpulse.com/signup) if you do not have one -2. Install the CLI tools in your terminal +1. Create an [Edge Impulse Account](https://studio.edgeimpulse.com/signup) and sign in. -Ensure you have Nodejs installed +2. Install the Edge Impulse CLI tools in your terminal + +The Edge Impulse CLI tools require Node.js. + +```console +sudo apt install nodejs npm -y +``` + +Confirm `node` is avilable by running: ```console node -v ``` -Install the Edge Impulse CLI + +Your version is printed, for example: + +```output +v18.19.1 +``` + +Install the Edge Impulse CLI using NPM: + ```console npm install -g edge-impulse-cli ``` -3. Install Edge Impulse Screen + +3. Install Screen to use with edge devices + ```console -sudo apt install screen +sudo apt install screen -y ``` ## Next Steps -1. If you don't have access to the physical board: Go to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/) -2. If you have access to the board: Go to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/) \ No newline at end of file + +If you don't have the Grove AI vision board and want to use the Corstone-300 FVP proceed to [Environment Setup Corstone-300 FVP](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-fvp/) + +If you have the Grove board proceed o to [Setup on Grove - Vision AI Module V2](/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-grove/) \ No newline at end of file diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md index 4e569e9f0a..278320fc74 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-6-FVP.md @@ -1,8 +1,8 @@ --- # User change -title: "Environment Setup Corstone-300 FVP" +title: "Set up the Corstone-300 FVP" -weight: 7 # 1 is first, 2 is second, etc. +weight: 5 # 1 is first, 2 is second, etc. # Do not modify these elements layout: "learningpathall" diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/examples-2.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/examples-2.md deleted file mode 100644 index ce0953f67c..0000000000 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/examples-2.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -title: Examples of Arm-based devices and applications -weight: 3 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -## Raspberry Pi 4 & 5 - -These affordable single-board computers are excellent for prototyping TinyML projects. They are commonly used for prototyping machine learning projects at the edge, such as in object detection and voice recognition for home automation. - -## NXP i.MX RT Microcontrollers -These are low-power microcontrollers that can handle complex TinyML tasks while maintaining energy efficiency, making them ideal for applications like wearable healthcare devices and environmental sensors. - -## STM32 Microcontrollers -Used in industrial IoT applications for predictive maintenance, these microcontrollers are energy-efficient and capable of running TinyML models for real-time anomaly detection in factory machinery. - -## Arduino Nano 33 BLE Sense -This microcontroller, equipped with a suite of sensors, supports TinyML and is ideal for small-scale IoT applications, such as detecting environmental changes and movement patterns. - -## Edge Impulse -This platform offers a suite of tools that enables developers to build and deploy TinyML applications on Arm-based devices. It supports devices like Raspberry Pi, Arduino, and STMicroelectronics boards. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md index bd8b4e268e..73890438c3 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/setup-7-Grove.md @@ -1,8 +1,8 @@ --- # User change -title: "Setup on Grove - Vision AI Module V2" +title: "Set up the Grove Vision AI Module V2" -weight: 8 # 1 is first, 2 is second, etc. +weight: 6 # 1 is first, 2 is second, etc. # Do not modify these elements layout: "learningpathall" diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md index c354f4640c..410409c2f9 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md @@ -1,6 +1,6 @@ --- title: Troubleshooting and Best Practices -weight: 10 +weight: 8 ### FIXED, DO NOT MODIFY layout: learningpathall From 0887ae34959e06747204af76b17b53327cf6632f Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Mon, 14 Oct 2024 12:32:15 -0500 Subject: [PATCH 35/39] fix typo in Sysbox install guide --- content/install-guides/sysbox.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/install-guides/sysbox.md b/content/install-guides/sysbox.md index 90fced825e..a135f2121b 100644 --- a/content/install-guides/sysbox.md +++ b/content/install-guides/sysbox.md @@ -39,7 +39,7 @@ In summary, Sysbox is a powerful container runtime that provides many of the ben Sysbox runs on Linux and supports Arm. -Sysbox has limited suppot for older versions of Linux, but recent Linux versions are easily compatible. +Sysbox has limited support for older versions of Linux, but recent Linux versions are easily compatible. If you are unsure about your Linux distribution and Linux kernel version, you can check [Sysbox Distro Compatibility](https://github.com/nestybox/sysbox/blob/master/docs/distro-compat.md) From 035464c938452a3e990fb83d3f04f797e1b9d789 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Mon, 14 Oct 2024 12:40:34 -0500 Subject: [PATCH 36/39] Move Intro to TinyML Learning Path to draft. --- .../microcontrollers/introduction-to-tinyml-on-arm/_index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md index 3305cb70c1..26c3af0531 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/_index.md @@ -1,6 +1,10 @@ --- title: Introduction to TinyML on Arm using PyTorch and ExecuTorch +draft: true +cascade: + draft: true + minutes_to_complete: 40 who_is_this_for: This is an introductory topic for developers, engineers, and data scientists who are new to TinyML and interested in exploring its potential for edge AI. You will learn how to get started using PyTorch and ExecuTorch for TinyML. From 1457b6f44ad652daa41c8903ab7db5a3dd6b36fb Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Mon, 14 Oct 2024 12:57:34 -0500 Subject: [PATCH 37/39] spelling fixes --- .wordlist.txt | 32 ++++++++++++++++++- content/install-guides/multipass.md | 2 +- .../inference.md | 2 +- .../inference.md | 2 +- .../env-setup-5.md | 6 ++-- .../troubleshooting-6.md | 2 +- 6 files changed, 38 insertions(+), 8 deletions(-) diff --git a/.wordlist.txt b/.wordlist.txt index a47baa66bf..0db9d5b849 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -3215,4 +3215,34 @@ pyplot scheduler's torchvision uOps -unsqueeze \ No newline at end of file +unsqueeze + +ACR +Abena +Adoptium +Amanfo +Arlo +Fitbit +Milvus +Amanfo +OCTLA +OpenAg +Sysbox +TCK +TOSA +Temurin +Zhang +Zilliz +arrhythmias +ggerganov +milvus +msbuild +nestybox +pte +replug +sam +sysbox +tinyml +tvOS +watchOS +zilliz \ No newline at end of file diff --git a/content/install-guides/multipass.md b/content/install-guides/multipass.md index 3f465ae219..b336cf38a4 100644 --- a/content/install-guides/multipass.md +++ b/content/install-guides/multipass.md @@ -60,7 +60,7 @@ Install the download using the package command. sudo installer -pkg multipass-1.14.1-rc1+mac.14+gf2381bfe9.mac-Darwin.pkg -target / ``` -The getting started instructions below use the command line interface. If you perfer to use the graphical interface start it from the macOS Launchpad, the initial screen is shown below. You can use the UI to create, start, and stop virtual machines. +The getting started instructions below use the command line interface. If you prefer to use the graphical interface start it from the macOS Launchpad, the initial screen is shown below. You can use the UI to create, start, and stop virtual machines. ![Connect #center](/install-guides/_images/multipass-gui.png) diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md index c421f037b1..1e4e85ef73 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-arch-training/inference.md @@ -16,7 +16,7 @@ The inference process involves using a trained model to make predictions on new, This process allows the model to generalize its learned knowledge to make accurate predictions on new data. -# Runing inference in PyTorch +# Running inference in PyTorch You can inference in PyTorch using the previously saved model. To display results, you can use matplotlib. diff --git a/content/learning-paths/cross-platform/pytorch-digit-classification-training/inference.md b/content/learning-paths/cross-platform/pytorch-digit-classification-training/inference.md index 5a2224f7a8..9a0bb597b4 100644 --- a/content/learning-paths/cross-platform/pytorch-digit-classification-training/inference.md +++ b/content/learning-paths/cross-platform/pytorch-digit-classification-training/inference.md @@ -16,7 +16,7 @@ The inference process involves using a trained model to make predictions on new, This process allows the model to generalize its learned knowledge to make accurate predictions on new data. -# Runing inference in PyTorch +# Running inference in PyTorch You can inference in PyTorch using the previously saved model. To display results you can use matplotlib. diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index f8e6f6d0a4..bf130cd3da 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -28,7 +28,7 @@ The host machine is where you will perform most of your development work, especi ## Install Python -Python 3 is included in Ubuntu, but some additonal packages are needed. +Python 3 is included in Ubuntu, but some additional packages are needed. ```console sudo apt update @@ -37,7 +37,7 @@ sudo apt install python-is-python3 gcc g++ make -y ## Install PyTorch -Create a Python virtual environemnt using Miniconda. +Create a Python virtual environment using Miniconda. For Arm Linux: @@ -97,7 +97,7 @@ The Edge Impulse CLI tools require Node.js. sudo apt install nodejs npm -y ``` -Confirm `node` is avilable by running: +Confirm `node` is available by running: ```console node -v diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md index 410409c2f9..1b8bd9d4d7 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/troubleshooting-6.md @@ -15,4 +15,4 @@ layout: learningpathall - Utilize model quantization techniques to optimize performance on constrained devices like the Grove - Vision AI Module V2. - Regularly update your development environment and tools to benefit from the latest improvements in TinyML and edge AI technologies -You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Netrowk. \ No newline at end of file +You've now set up your environment for TinyML development, and tested a PyTorch and ExecuTorch Neural Network. \ No newline at end of file From 67757efd762922238d07493c32fb1b62cbcc2298 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Mon, 14 Oct 2024 13:31:30 -0500 Subject: [PATCH 38/39] update filtering --- .../laptops-and-desktops/_index.md | 16 +++++---- .../learning-paths/microcontrollers/_index.md | 16 ++++++--- .../servers-and-cloud-computing/_index.md | 34 +++++++++++-------- .../smartphones-and-mobile/_index.md | 18 ++++++---- 4 files changed, 51 insertions(+), 33 deletions(-) diff --git a/content/learning-paths/laptops-and-desktops/_index.md b/content/learning-paths/laptops-and-desktops/_index.md index a273613d43..8672a12ccd 100644 --- a/content/learning-paths/laptops-and-desktops/_index.md +++ b/content/learning-paths/laptops-and-desktops/_index.md @@ -11,29 +11,31 @@ operatingsystems_filter: - Android: 2 - Baremetal: 1 - ChromeOS: 1 -- Linux: 24 -- macOS: 4 -- Windows: 32 +- Linux: 27 +- macOS: 7 +- Windows: 35 subjects_filter: - CI-CD: 3 - Containers and Virtualization: 5 - Migration to Arm: 25 -- Performance and Architecture: 17 +- Performance and Architecture: 18 subtitle: Create and migrate apps for power efficient performance title: Laptops and Desktops tools_software_languages_filter: - .NET: 12 - Alacritty: 1 -- Android Studio: 1 +- Android Studio: 2 - Arm Development Studio: 2 - Arm64EC: 1 +- assembly: 1 - C: 2 - C#: 5 - C++: 2 - C/C++: 4 +- CCA: 1 - Clang: 8 - CMake: 2 -- Coding: 16 +- Coding: 18 - CSS: 1 - Docker: 4 - GCC: 8 @@ -48,6 +50,7 @@ tools_software_languages_filter: - Kubernetes: 1 - Linux: 1 - LLVM: 1 +- llvm-mca: 1 - MTE: 1 - Neon: 1 - Neovim: 1 @@ -56,6 +59,7 @@ tools_software_languages_filter: - Python: 2 - Qt: 2 - Remote.It: 1 +- RME: 1 - Rust: 1 - SVE: 1 - SVE2: 1 diff --git a/content/learning-paths/microcontrollers/_index.md b/content/learning-paths/microcontrollers/_index.md index d3da844979..f139834ead 100644 --- a/content/learning-paths/microcontrollers/_index.md +++ b/content/learning-paths/microcontrollers/_index.md @@ -8,14 +8,14 @@ key_ip: maintopic: true operatingsystems_filter: - Baremetal: 25 -- Linux: 3 +- Linux: 4 - macOS: 2 - RTOS: 10 - Windows: 2 subjects_filter: - CI-CD: 3 - Libraries: 3 -- ML: 6 +- ML: 7 - Performance and Architecture: 11 - RTOS Fundamentals: 4 - Security: 2 @@ -25,21 +25,27 @@ title: Microcontrollers tools_software_languages_filter: - Arduino: 1 - Arm Compiler for Embedded: 5 +- Arm Compute Library: 1 - Arm Development Studio: 4 -- Arm Virtual Hardware: 11 +- Arm Virtual Hardware: 12 - CMSIS: 5 - CMSIS-Toolbox: 3 - Coding: 22 -- Fixed Virtual Platform: 8 +- Edge Impulse: 1 +- ExecuTorch: 1 +- Fixed Virtual Platform: 9 - FVP: 1 -- GCC: 4 +- GCC: 5 - GitHub: 2 - IP Explorer: 2 - Keil: 7 - Keil MDK: 3 - MDK: 2 - MPS3: 1 +- Node.js: 1 - Paddle: 1 +- Python: 1 +- PyTorch: 1 - Raspberry Pi: 1 - RTX: 2 - STM32: 2 diff --git a/content/learning-paths/servers-and-cloud-computing/_index.md b/content/learning-paths/servers-and-cloud-computing/_index.md index 46c16d3af0..66334d9eed 100644 --- a/content/learning-paths/servers-and-cloud-computing/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/_index.md @@ -9,9 +9,9 @@ maintopic: true operatingsystems_filter: - Android: 2 - Baremetal: 1 -- Linux: 100 -- macOS: 6 -- Windows: 9 +- Linux: 105 +- macOS: 9 +- Windows: 12 pinned_modules: - module: name: Recommended getting started learning paths @@ -20,19 +20,20 @@ pinned_modules: - migration subjects_filter: - CI-CD: 3 -- Containers and Virtualization: 24 +- Containers and Virtualization: 25 - Databases: 15 - Libraries: 6 -- ML: 10 -- Performance and Architecture: 35 +- ML: 13 +- Performance and Architecture: 36 - Storage: 1 - Web: 10 subtitle: Optimize cloud native apps on Arm for performance and cost title: Servers and Cloud Computing tools_software_languages_filter: +- .NET: 1 - .NET SDK: 1 - 5G: 1 -- Android Studio: 1 +- Android Studio: 2 - Ansible: 2 - Arm Development Studio: 4 - armclang: 1 @@ -40,6 +41,7 @@ tools_software_languages_filter: - ArmRAL: 1 - ASP.NET Core: 2 - Assembly: 4 +- assembly: 1 - AWS CodeBuild: 1 - AWS EC2: 1 - AWS Elastic Container Service (ECS): 1 @@ -52,16 +54,16 @@ tools_software_languages_filter: - C++: 3 - C/C++: 2 - Capstone: 1 -- CCA: 2 +- CCA: 3 - Clair: 1 - Clang: 9 - ClickBench: 1 - ClickHouse: 1 - CloudFormation: 1 - CMake: 1 -- Coding: 17 +- Coding: 19 - Django: 1 -- Docker: 14 +- Docker: 15 - Envoy: 2 - Flink: 1 - Fortran: 1 @@ -69,7 +71,7 @@ tools_software_languages_filter: - GCC: 17 - gdb: 1 - Geekbench: 1 -- GenAI: 3 +- GenAI: 4 - GitHub: 2 - GitLab: 1 - Glibc: 1 @@ -84,11 +86,12 @@ tools_software_languages_filter: - JAX: 1 - Kafka: 1 - Keras: 1 -- Kubernetes: 8 +- Kubernetes: 9 - Lambda: 1 - libbpf: 1 - Linaro Forge: 1 - LLM: 2 +- llvm-mca: 1 - LSE: 1 - MariaDB: 1 - Memcached: 2 @@ -103,11 +106,12 @@ tools_software_languages_filter: - PAPI: 1 - perf: 3 - PostgreSQL: 4 -- Python: 9 +- Python: 10 - PyTorch: 4 +- RAG: 1 - Redis: 3 - Remote.It: 2 -- RME: 2 +- RME: 3 - Rust: 1 - snappy: 1 - Snort: 1 @@ -133,6 +137,6 @@ weight: 1 cloud_service_providers_filter: - AWS: 13 - Google Cloud: 8 -- Microsoft Azure: 7 +- Microsoft Azure: 8 - Oracle: 2 --- diff --git a/content/learning-paths/smartphones-and-mobile/_index.md b/content/learning-paths/smartphones-and-mobile/_index.md index 87d106c7c2..7432cb5a98 100644 --- a/content/learning-paths/smartphones-and-mobile/_index.md +++ b/content/learning-paths/smartphones-and-mobile/_index.md @@ -11,14 +11,14 @@ key_ip: maintopic: true operatingsystems_filter: - Android: 21 -- Linux: 16 -- macOS: 6 -- Windows: 5 +- Linux: 19 +- macOS: 9 +- Windows: 8 subjects_filter: - Gaming: 6 - Graphics: 3 -- ML: 5 -- Performance and Architecture: 22 +- ML: 7 +- Performance and Architecture: 23 subtitle: Optimize Android apps and build faster games using cutting-edge Arm tech title: Smartphones and Mobile tools_software_languages_filter: @@ -27,17 +27,19 @@ tools_software_languages_filter: - Android: 2 - Android NDK: 1 - Android SDK: 1 -- Android Studio: 6 +- Android Studio: 7 - Arm Development Studio: 1 - Arm Mobile Studio: 1 - Arm Performance Studio: 2 +- assembly: 1 - Bazel: 1 - C#: 3 - C++: 3 - C/C++: 1 +- CCA: 1 - Clang: 8 - CMake: 1 -- Coding: 15 +- Coding: 17 - Fixed Virtual Platform: 1 - Frame Advisor: 1 - GCC: 9 @@ -45,6 +47,7 @@ tools_software_languages_filter: - GoogleTest: 1 - Java: 3 - Kotlin: 2 +- llvm-mca: 1 - MediaPipe: 1 - Memory Bug Report: 1 - Memory Tagging Extension: 1 @@ -53,6 +56,7 @@ tools_software_languages_filter: - NEON: 1 - Python: 1 - QEMU: 1 +- RME: 1 - Rust: 1 - SDDiskTool: 1 - SVE2: 1 From 750d663de91f323b67e32009b07b6aa1ee9b07f3 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 14 Oct 2024 14:47:18 -0400 Subject: [PATCH 39/39] Update _index.md --- .../servers-and-cloud-computing/milvus-rag/_index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md index a6b183b18e..0bef6ba40f 100644 --- a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md @@ -1,6 +1,10 @@ --- title: Build a Retrieval-Augmented Generation (RAG) application using Zilliz Cloud on Arm servers +draft: true +cascade: + draft: true + minutes_to_complete: 20 who_is_this_for: This is an introductory topic for software developers who want to create a RAG application on Arm servers.

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z-4&`pS*xAyq^gQG90N<$rxy`7+_at28oe3`+!AB$o^03qwKj&WpVvicSOMLMuNuD& zV1Qz!~~QgS#2?YmV&g(nhEbwK~zp5jse?=Mwp zCqlEcv)^k|C(*=R_#bW7zfnqr#tn^-S;Sr&xpm9`vOWR#x>x=GdwqiQAAe3Wckn;W zOsRTGy{h)_0@uH)D*t7>>> ``` -Enter `exit()` to leave interpreter. + +Enter `exit()` to leave the interpreter: + ```python exit() ``` -## Test an example +## How can I run a Python example? -To confirm python is working, save the code below into a file `uname.py`. +To confirm python is working, use a text editor to save the code below to a file named `uname.py`. -#### uname.py ```python import platform print("Python version", platform.python_version()) print("Machine is", platform.uname().system, platform.uname().release, platform.uname().machine) ``` -Run the code. +Run the code: + ```console py uname.py ``` + Running on a Windows on Arm machine produces the output similar to: + ```output -Python version 3.12.0 +Python version 3.13.0 Machine is Windows 11 ARM64 ``` -## Installing packages +## How do I install Python packages? Python `pip` can be used to install packages. @@ -100,9 +108,8 @@ For example, to install [Flask](https://palletsprojects.com/p/flask/): pip install Flask ``` -Save the code below as `hello.py`: +Use a text editor to save the code below as `hello.py`: -#### hello.py ```python import platform from flask import Flask @@ -131,7 +138,7 @@ WARNING: This is a development server. Do not use it in a production deployment. * Running on http://10.8.0.10:5000 Press CTRL+C to quit ``` -Open a browser to the URL printed by the application. In this example: +Open a browser with the URL printed by the application. In this example: ```url http://127.0.0.1:5000 ``` @@ -139,20 +146,20 @@ The output is displayed in the browser window. ![Complete #center](/install-guides/_images/flask-woa.png) -The accesses are reported in the command prompt: +The accesses are reported in the command window: + ```output 127.0.0.1 - - [] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [] "GET /favicon.ico HTTP/1.1" 404 - 10.8.0.10 - - [] "GET / HTTP/1.1" 200 - 10.8.0.10 - - [] "GET /favicon.ico HTTP/1.1" 404 - ``` -Use `Ctrl+C` to kill the application. -## Using IDLE +Use `Ctrl+C` to stop the application. -Python `IDLE` is included in the installation. IDLE is a simple IDE for python development. You can locate it in your start menu. +## Is Python IDLE available? -![IDLE #center](/install-guides/_images/idle.png) +Python `IDLE` is included in the installation. IDLE is a simple IDE for python development. You can locate it in your start menu. You can create and run Python applications in this environment. @@ -163,5 +170,4 @@ Then select `Run` > `Run module` (`F5`) to execute. ![IDLE uname #center](/install-guides/_images/py3-woa.png) - You are now ready to use Python on your Windows on Arm device. From 2393ba2949e405e0afdf052ec7b2b24b1e5c5069 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Wed, 9 Oct 2024 03:19:48 +0000 Subject: [PATCH 15/39] content reviews --- content/install-guides/aws-copilot.md | 51 ++++++++++++++++++- .../supervisord/_next-steps.md | 4 +- .../supervisord/example.md | 2 +- .../supervisord/intro.md | 4 +- 4 files changed, 54 insertions(+), 7 deletions(-) diff --git a/content/install-guides/aws-copilot.md b/content/install-guides/aws-copilot.md index 3a4b076cbe..0345bf04ef 100644 --- a/content/install-guides/aws-copilot.md +++ b/content/install-guides/aws-copilot.md @@ -73,13 +73,60 @@ To install Copilot on macOS: curl -Lo copilot https://github.com/aws/copilot-cli/releases/latest/download/copilot-darwin && chmod +x copilot && sudo mv copilot /usr/local/bin/copilot && copilot --help ``` +The help message is printed: + +```output +👩‍✈️ Launch and manage containerized applications on AWS. + +Commands + Getting Started 🌱 + init Create a new ECS or App Runner application. + docs Open the copilot docs. + + Develop ✨ + app Commands for applications. + Applications are a collection of services and environments. + env Commands for environments. + Environments are deployment stages shared between services. + svc Commands for services. + Services are long-running ECS or App Runner services. + job Commands for jobs. + Jobs are tasks that are triggered by events. + task Commands for tasks. + One-off Amazon ECS tasks that terminate once their work is done. + run Run the workload locally. + + Release 🚀 + pipeline Commands for pipelines. + Continuous delivery pipelines to release services. + deploy Deploy one or more Copilot jobs or services. + + Extend 🧸 + storage Commands for working with storage and databases. + secret Commands for secrets. + Secrets are sensitive information that you need in your application. + + Settings ⚙️ + version Print the version number. + completion Output shell completion code. + +Flags + -h, --help help for copilot + -v, --version version for copilot + +Examples + Displays the help menu for the "init" command. + `$ copilot init --help` +``` + Verify Copilot CLI is installed by running: ```console copilot --version ``` -The version should be printed: + +The installed version is displayed: ```output -copilot version: v1.33.0 +copilot version: v1.34.0 ``` \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/supervisord/_next-steps.md index 207e369b87..dc5a575c34 100644 --- a/content/learning-paths/servers-and-cloud-computing/supervisord/_next-steps.md +++ b/content/learning-paths/servers-and-cloud-computing/supervisord/_next-steps.md @@ -4,10 +4,10 @@ # ================================================================================ next_step_guidance: > - You may also be interested in Kubernetes on Arm. + You may also be interested in Sysbox to run multiple services in a container. # 1-3 sentence recommendation outlining how the reader can generally keep learning about these topics, and a specific explanation of why the next step is being recommended. -recommended_path: "/learning-paths/servers-and-cloud-computing/eks-multi-arch/" +recommended_path: "/install-guides/sysbox/" # Link to the next learning path being recommended. diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/example.md b/content/learning-paths/servers-and-cloud-computing/supervisord/example.md index e1b0408b4b..5c2c20f574 100644 --- a/content/learning-paths/servers-and-cloud-computing/supervisord/example.md +++ b/content/learning-paths/servers-and-cloud-computing/supervisord/example.md @@ -19,7 +19,7 @@ Here are the changes: Use a text editor to replace your `Dockerfile` with the new file below: ```console -FROM ubuntu:22.04 +FROM ubuntu:24.04 RUN apt-get update && apt-get install -y curl iproute2 sudo openssh-server sudo vim apache2 supervisor procps uuid-runtime diff --git a/content/learning-paths/servers-and-cloud-computing/supervisord/intro.md b/content/learning-paths/servers-and-cloud-computing/supervisord/intro.md index b788ebfdeb..cd77dd7c38 100644 --- a/content/learning-paths/servers-and-cloud-computing/supervisord/intro.md +++ b/content/learning-paths/servers-and-cloud-computing/supervisord/intro.md @@ -38,12 +38,12 @@ You will also use Remote.It for container access. For more background informatio ## An example project -You can use an Apache web server running on Ubuntu 22.04 as an example application. It serves a simple `index.html` file which is visible in a browser. +You can use an Apache web server running on Ubuntu 24.04 as an example application. It serves a simple `index.html` file which is visible in a browser. To begin, use a text editor to copy the code below into a file named `Dockerfile`: ```console -FROM ubuntu:22.04 +FROM ubuntu:24.04 RUN apt-get update RUN apt-get install -y apache2 From 0ab1df5d045252fd44536e0958b7cf33ca2a6897 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Wed, 9 Oct 2024 16:18:56 +0100 Subject: [PATCH 16/39] Update build-2.md Addressing Issue #1300 --- .../servers-and-cloud-computing/refinfra-quick-start/build-2.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/build-2.md b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/build-2.md index 39385bf5bf..e3917bd5e3 100644 --- a/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/build-2.md +++ b/content/learning-paths/servers-and-cloud-computing/refinfra-quick-start/build-2.md @@ -12,7 +12,7 @@ Test that the firmware implementation builds and boots on a FVP. The firmware st Launch the container: ```bash -./container-scrits/container.sh -v /home/ubuntu/rd-infra/ run +./container-scripts/container.sh -v /home/ubuntu/rd-infra/ run ``` Perform a build inside the container: From 7080d4836ee9e779b7fa798e980ad6b17fc6972e Mon Sep 17 00:00:00 2001 From: ChengZi Date: Thu, 10 Oct 2024 17:31:30 +0800 Subject: [PATCH 17/39] milvus RAG Signed-off-by: ChengZi --- .../milvus-rag/_index.md | 35 +++++ .../milvus-rag/_next-steps.md | 27 ++++ .../milvus-rag/_review.md | 31 +++++ .../milvus-rag/launch_llm_service.md | 100 ++++++++++++++ .../milvus-rag/offline_data_loading.md | 98 ++++++++++++++ .../milvus-rag/online_rag.md | 123 ++++++++++++++++++ .../milvus-rag/prerequisite.md | 39 ++++++ 7 files changed, 453 insertions(+) create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/launch_llm_service.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md create mode 100644 content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md new file mode 100644 index 0000000000..e9e332b247 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md @@ -0,0 +1,35 @@ +--- +title: Use Milvus/Zilliz to build RAG on Arm Architecture + +minutes_to_complete: 20 + +who_is_this_for: This is an introductory topic for engineers who want to create a RAG application on Arm machines. + +learning_objectives: + - Create a simple RAG application using Milvus/Zilliz + - Launch LLM service on Arm machines + +prerequisites: + - Basic understand of RAG pipeline. + - An [AWS account](/learning-paths/servers-and-cloud-computing/csp/aws/) to access instance types with different AWS Graviton processors. + - A [Zilliz account](https://zilliz.com/cloud), which you can sign up for a free trial. + +author_primary: Chen Zhang + +### Tags +skilllevels: Introductory +subjects: ML +armips: + - Cortex-A +tools_software_languages: + - Python +operatingsystems: + - Linux + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md new file mode 100644 index 0000000000..b617887475 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_next-steps.md @@ -0,0 +1,27 @@ +--- +next_step_guidance: Thank you for completing the Milvus RAG tutorial. + +recommended_path: /learning-paths/servers-and-cloud-computing/llama-cpu/ + +further_reading: + - resource: + title: Zilliz Documentation + link: https://zilliz.com/cloud + type: documentation + - resource: + title: Milvus Documentation + link: https://milvus.io/ + type: documentation + - resource: + title: llama.cpp repository + link: https://github.com/ggerganov/llama.cpp + type: website + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +weight: 21 # set to always be larger than the content in this path, and one more than 'review' +title: "Next Steps" # Always the same +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md new file mode 100644 index 0000000000..51e9fb4d01 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/_review.md @@ -0,0 +1,31 @@ +--- +review: + - questions: + question: > + Can Milvus run on Arm systems? + answers: + - "Yes" + - "No" + correct_answer: 1 + explanation: > + Milvus can run on Arm-based systems. Milvus supports deployment on Arm-based machines, whether it's through Zilliz Cloud, Docker, or Kubernetes. + + - questions: + question: > + Can Llama3.1 model run on Arm systems? + answers: + - "Yes" + - "No" + correct_answer: 1 + explanation: > + The Llama-3.1-8B model from Meta can be used on an AWS Arm-based server CPU with the llama.cpp tool. + + + +# ================================================================================ +# FIXED, DO NOT MODIFY +# ================================================================================ +title: "Review" # Always the same title +weight: 20 # Set to always be larger than the content in this path +layout: "learningpathall" # All files under learning paths have this same wrapper +--- diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/launch_llm_service.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/launch_llm_service.md new file mode 100644 index 0000000000..f06bda1fd7 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/launch_llm_service.md @@ -0,0 +1,100 @@ +--- +title: Launch LLM Service on Arm +weight: 4 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +In this section, we will build and launch the `llama.cpp` service on the Arm-based CPU. + +### Llama 3.1 model & llama.cpp + +The [Llama-3.1-8B model](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf) from Meta belongs to the Llama 3.1 model family and is free to use for research and commercial purposes. Before you use the model, visit the Llama [website](https://llama.meta.com/llama-downloads/) and fill in the form to request access. + +[llama.cpp](https://github.com/ggerganov/llama.cpp) is an open source C/C++ project that enables efficient LLM inference on a variety of hardware - both locally, and in the cloud. You can conveniently host a Llama 3.1 model using `llama.cpp`. + + +### Download and build llama.cpp + +Run the following commands to install make, cmake, gcc, g++, and other essential tools required for building llama.cpp from source: + +```bash +sudo apt install make cmake -y +sudo apt install gcc g++ -y +sudo apt install build-essential -y +``` + +You are now ready to start building `llama.cpp`. + +Clone the source repository for llama.cpp: + +```bash +git clone https://github.com/ggerganov/llama.cpp +``` + +By default, `llama.cpp` builds for CPU only on Linux and Windows. You don't need to provide any extra switches to build it for the Arm CPU that you run it on. + +Run `make` to build it: + +```bash +cd llama.cpp +make GGML_NO_LLAMAFILE=1 -j$(nproc) +``` + +Check that `llama.cpp` has built correctly by running the help command: + +```bash +./llama-cli -h +``` + +If `llama.cpp` has been built correctly, you will see the help option displayed. The output snippet looks like this: + +```output +example usage: + + text generation: ./llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 + + chat (conversation): ./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv +``` + + +You can now download the model using the huggingface cli: + +```bash +huggingface-cli download cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf dolphin-2.9.4-llama3.1-8b-Q4_0.gguf --local-dir . --local-dir-use-symlinks False +``` +The GGUF model format, introduced by the llama.cpp team, uses compression and quantization to reduce weight precision to 4-bit integers, significantly decreasing computational and memory demands and making Arm CPUs effective for LLM inference. + + +### Re-quantize the model weights + +To re-quantize, run + +```bash +./llama-quantize --allow-requantize dolphin-2.9.4-llama3.1-8b-Q4_0.gguf dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf Q4_0_8_8 +``` + +This will output a new file, `dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf`, which contains reconfigured weights that allow `llama-cli` to use SVE 256 and MATMUL_INT8 support. + +> This requantization is optimal specifically for Graviton3. For Graviton2, the optimal requantization should be performed in the `Q4_0_4_4` format, and for Graviton4, the `Q4_0_4_8` format is the most suitable for requantization. + +### Start the LLM Service +You can utilize the llama.cpp server program and send requests via an OpenAI-compatible API. This allows you to develop applications that interact with the LLM multiple times without having to repeatedly start and stop it. Additionally, you can access the server from another machine where the LLM is hosted over the network. + +Start the server from the command line, and it listens on port 8080: + +```shell +./llama-server -m dolphin-2.9.4-llama3.1-8b-Q4_0_8_8.gguf -n 2048 -t 64 -c 65536 --port 8080 +``` +```text +'main: server is listening on 127.0.0.1:8080 - starting the main loop +``` + +You can also adjust the parameters of the launched LLM to adapt it to your server hardware to obtain ideal performance. For more parameter information, see the `llama-server --help` command. + +If you struggle to perform this step, you can refer to the [this documents](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama-cpu/llama-chatbot/) for more information. + +You have started the LLM service on your Arm-based CPU. Next, we directly interact with the service using the OpenAI SDK. + + diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md new file mode 100644 index 0000000000..6a94254948 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md @@ -0,0 +1,98 @@ +--- +title: Offline Data Loading +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +In this section, we will show you how to load private knowledge in our RAG. + +### Create the Collection +We use [Zilliz Cloud](https://zilliz.com/cloud) deployed on AWS with Arm-based machines to store and retrieve the vector data. To quick start, simply [register an account](https://docs.zilliz.com/docs/register-with-zilliz-cloud) on Zilliz Cloud for free. + +> In addition to Zilliz Cloud, self-hosted Milvus is also a (more complicated to set up) option. We can also deploy [Milvus Standalone](https://milvus.io/docs/install_standalone-docker-compose.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) on ARM-based machines. For more information about Milvus installation, please refer to the [installation documentation](https://milvus.io/docs/install-overview.md). + +We set the `uri` and `token` as the [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud. +```python +from pymilvus import MilvusClient + +milvus_client = MilvusClient( + uri="", token="" +) + +collection_name = "my_rag_collection" + +``` +Check if the collection already exists and drop it if it does. +```python +if milvus_client.has_collection(collection_name): + milvus_client.drop_collection(collection_name) +``` +Create a new collection with specified parameters. + +If we don't specify any field information, Milvus will automatically create a default `id` field for primary key, and a `vector` field to store the vector data. A reserved JSON field is used to store non-schema-defined fields and their values. +```python +milvus_client.create_collection( + collection_name=collection_name, + dimension=embedding_dim, + metric_type="IP", # Inner product distance + consistency_level="Strong", # Strong consistency level +) +``` +We use inner product distance as the default metric type. For more information about distance types, you can refer to [Similarity Metrics page](https://milvus.io/docs/metric.md?tab=floating) + +### Prepare the data + +We use the FAQ pages from the [Milvus Documentation 2.4.x](https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip) as the private knowledge in our RAG, which is a good data source for a simple RAG pipeline. + +Download the zip file and extract documents to the folder `milvus_docs`. + +```shell +wget https://github.com/milvus-io/milvus-docs/releases/download/v2.4.6-preview/milvus_docs_2.4.x_en.zip +unzip -q milvus_docs_2.4.x_en.zip -d milvus_docs +``` + +We load all markdown files from the folder `milvus_docs/en/faq`. For each document, we just simply use "# " to separate the content in the file, which can roughly separate the content of each main part of the markdown file. + + +```python +from glob import glob + +text_lines = [] + +for file_path in glob("milvus_docs/en/faq/*.md", recursive=True): + with open(file_path, "r") as file: + file_text = file.read() + + text_lines += file_text.split("# ") +``` + +### Insert data +We prepare a simple but efficient embedding model [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) that can convert text into embedding vectors. +```python +from langchain_huggingface import HuggingFaceEmbeddings + +embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") +``` + +Iterate through the text lines, create embeddings, and then insert the data into Milvus. + +Here is a new field `text`, which is a non-defined field in the collection schema. It will be automatically added to the reserved JSON dynamic field, which can be treated as a normal field at a high level. +```python +from tqdm import tqdm + +data = [] + +text_embeddings = embedding_model.embed_documents(text_lines) + +for i, (line, embedding) in enumerate( + tqdm(zip(text_lines, text_embeddings), desc="Creating embeddings") +): + data.append({"id": i, "vector": embedding, "text": line}) + +milvus_client.insert(collection_name=collection_name, data=data) +``` +```text +Creating embeddings: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:18<00:00, 3.91it/s] +``` diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md new file mode 100644 index 0000000000..27680a8f39 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/online_rag.md @@ -0,0 +1,123 @@ +--- +title: Online RAG +weight: 5 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +In this section, we will build the online RAG part of the application. + +### LLM Client and Embedding Model + +We initialize the LLM client and prepare the embedding model. + +For the LLM, we use the OpenAI SDK to request the Llama service launched before. We don't need to use any API key because it is actually our local llama.cpp service. + +```python +from openai import OpenAI + +llm_client = OpenAI(base_url="http://localhost:8080/v1", api_key="no-key") +``` +Generate a test embedding and print its dimension and first few elements. + +```python +test_embedding = embedding_model.embed_query("This is a test") +embedding_dim = len(test_embedding) +print(embedding_dim) +print(test_embedding[:10]) +``` + +```text +384 +[0.03061249852180481, 0.013831384479999542, -0.02084377221763134, 0.016327863559126854, -0.010231520049273968, -0.0479842908680439, -0.017313342541456223, 0.03728749603033066, 0.04588735103607178, 0.034405000507831573] +``` + +### Retrieve data for a query + +Let's specify a frequent question about Milvus. +```python +question = "How is data stored in milvus?" +``` +Search for the question in the collection and retrieve the semantic top-3 matches. + +```python +search_res = milvus_client.search( + collection_name=collection_name, + data=[ + embedding_model.embed_query(question) + ], # Use the `emb_text` function to convert the question to an embedding vector + limit=3, # Return top 3 results + search_params={"metric_type": "IP", "params": {}}, # Inner product distance + output_fields=["text"], # Return the text field +) +``` +Let's take a look at the search results of the query +```python +import json + +retrieved_lines_with_distances = [ + (res["entity"]["text"], res["distance"]) for res in search_res[0] +] +print(json.dumps(retrieved_lines_with_distances, indent=4)) +``` + +```shell +[ + [ + " Where does Milvus store data?\n\nMilvus deals with two types of data, inserted data and metadata. \n\nInserted data, including vector data, scalar data, and collection-specific schema, are stored in persistent storage as incremental log. Milvus supports multiple object storage backends, including [MinIO](https://min.io/), [AWS S3](https://aws.amazon.com/s3/?nc1=h_ls), [Google Cloud Storage](https://cloud.google.com/storage?hl=en#object-storage-for-companies-of-all-sizes) (GCS), [Azure Blob Storage](https://azure.microsoft.com/en-us/products/storage/blobs), [Alibaba Cloud OSS](https://www.alibabacloud.com/product/object-storage-service), and [Tencent Cloud Object Storage](https://www.tencentcloud.com/products/cos) (COS).\n\nMetadata are generated within Milvus. Each Milvus module has its own metadata that are stored in etcd.\n\n###", + 0.6488019824028015 + ], + [ + "How does Milvus flush data?\n\nMilvus returns success when inserted data are loaded to the message queue. However, the data are not yet flushed to the disk. Then Milvus' data node writes the data in the message queue to persistent storage as incremental logs. If `flush()` is called, the data node is forced to write all data in the message queue to persistent storage immediately.\n\n###", + 0.5974207520484924 + ], + [ + "What is the maximum dataset size Milvus can handle?\n\n \nTheoretically, the maximum dataset size Milvus can handle is determined by the hardware it is run on, specifically system memory and storage:\n\n- Milvus loads all specified collections and partitions into memory before running queries. Therefore, memory size determines the maximum amount of data Milvus can query.\n- When new entities and and collection-related schema (currently only MinIO is supported for data persistence) are added to Milvus, system storage determines the maximum allowable size of inserted data.\n\n###", + 0.5833579301834106 + ] +] +``` +### Use LLM to get a RAG response + +Convert the retrieved documents into a string format. +```python +context = "\n".join( + [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances] +) +``` +Define system and user prompts for the Language Model. This prompt is assembled with the retrieved documents from Milvus. + +```python +SYSTEM_PROMPT = """ +Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided. +""" +USER_PROMPT = f""" +Use the following pieces of information enclosed in tags to provide an answer to the question enclosed in tags. + +{context} + + +{question} + +""" +``` +Use LLM to generate a response based on the prompts. We set the `model` parameter to `not-used` since it is a redundant parameter for the llama.cpp service. + +```python +response = llm_client.chat.completions.create( + model="not-used", + messages=[ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": USER_PROMPT}, + ], +) +print(response.choices[0].message.content) + +``` +```text +Milvus stores data in two types: inserted data and metadata. Inserted data, including vector data, scalar data, and collection-specific schema, are stored in persistent storage as incremental log. Milvus supports multiple object storage backends such as MinIO, AWS S3, Google Cloud Storage (GCS), Azure Blob Storage, Alibaba Cloud OSS, and Tencent Cloud Object Storage (COS). Metadata are generated within Milvus and each Milvus module has its own metadata that are stored in etcd. +``` +Congratulations! You have built a RAG application on top of the Arm-based infrastructures. + + diff --git a/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md b/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md new file mode 100644 index 0000000000..18d35330b2 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md @@ -0,0 +1,39 @@ +--- +title: Prerequisite +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Before you begin + +In this tutorial, you learn how to build a Retrieval-Augmented Generation (RAG) application on Arm-based infrastructures. For vector storage, we utilize [Zilliz Cloud](https://zilliz.com/cloud), the fully-managed Milvus vector database. Zilliz Cloud is available on major cloud such as AWS, GCP and Azure. In this demo we use Zilliz Cloud deployed on AWS with Arm machines. For LLM, we use the `Llama-3.1-8B` model on the AWS Arm-based server CPU using `llama.cpp`. + + +## Prerequisite +To run this example, we recommend you to use [AWS Graviton](https://aws.amazon.com/ec2/graviton/), which provides a cost-effective way to run ML workloads on Arm-based servers. This notebook has been tested on an AWS Graviton3 `c7g.2xlarge` instance with Ubuntu 22.04 LTS system. + +You need at least four cores and 8GB of RAM to run this example. Configure disk storage up to at least 32 GB. We recommend that you use an instance of the same or better specification. + +After you launch the instance, connect to it and run the following commands to prepare the environment. + +Install python on the server: + +```bash +sudo apt update +sudo apt install python-is-python3 python3-pip python3-venv -y +``` + +Create and activate a virtual environment: + +```bash +python -m venv venv +source venv/bin/activate +``` + +Install the required python dependencies: + +```shell +pip install --upgrade pymilvus openai requests langchain-huggingface huggingface_hub tqdm +``` From c6609a0eb193a1754de796209382b2a18abec4e8 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 10:44:10 +0100 Subject: [PATCH 18/39] Update _index.md Added RME and CCA to open taxonomy metadata --- content/learning-paths/cross-platform/cca_rme/_index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/learning-paths/cross-platform/cca_rme/_index.md b/content/learning-paths/cross-platform/cca_rme/_index.md index d24ac0bf39..4df1ce50d2 100644 --- a/content/learning-paths/cross-platform/cca_rme/_index.md +++ b/content/learning-paths/cross-platform/cca_rme/_index.md @@ -32,6 +32,8 @@ tools_software_languages: - Coding - Trusted Firmware - Arm Development Studio + - RME + - CCA ### Cross-platform metadata only shared_path: true From d27ce9ae67f9b8236e016c886d094a24c4e49d76 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 10:54:15 +0000 Subject: [PATCH 19/39] First-pass editorial. --- content/install-guides/py-woa.md | 38 +++++++++++++++++--------------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/content/install-guides/py-woa.md b/content/install-guides/py-woa.md index 046e2052c8..0fef714a88 100644 --- a/content/install-guides/py-woa.md +++ b/content/install-guides/py-woa.md @@ -27,42 +27,44 @@ multitool_install_part: false # Set to true if a sub-page of a multi-page arti layout: installtoolsall # DO NOT MODIFY. Always true for tool install articles --- -Python has native support for [Windows on Arm](https://learn.microsoft.com/en-us/windows/arm/overview). Starting with version 3.11, an official installer is available. The latest version at time of writing is 3.13.0. +Python has native support for [Windows on Arm](https://learn.microsoft.com/en-us/windows/arm/overview). Starting with version 3.11, an official installer is available. The latest version is 3.13.0. -A number of developer ready Windows on Arm [devices](/learning-paths/laptops-and-desktops/intro/find-hardware/) are available. +A number of developer-ready Windows on Arm [devices](/learning-paths/laptops-and-desktops/intro/find-hardware/) are available. -Windows on Arm instances are available with Microsoft Azure. For more information, see [Deploy a Windows on Arm virtual machine on Microsoft Azure](/learning-paths/cross-platform/woa_azure/). +Windows on Arm instances are available with Microsoft Azure. For further information, see [Deploy a Windows on Arm virtual machine on Microsoft Azure](/learning-paths/cross-platform/woa_azure/). ## How do I download and install Python for Windows on Arm? -The installer can be downloaded from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. +To download and install Python for Windows on Arm, there is more than one option: -You can also download from a PowerShell terminal. +* You can download the installer from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. + +* You can download from a PowerShell terminal: ```command curl https://www.python.org/ftp/python/3.13.0/python-3.13.0-arm64.exe --output python-3.13.0-arm64.exe ``` -Once downloaded, run the installer `exe` file on a Windows on Arm machine. +Once you have downloaded Python, run the installer `exe` file on a Windows on Arm machine. The installer will start. -Check `Add python.exe to PATH` if you want to easily invoke python from any directory. +Tick the checkbox **Add python.exe to PATH** if you want to easily invoke python from any directory. ![Install #center](/install-guides/_images/py1-woa.png) -`Setup was successful` is displayed when complete. +**Setup was successful** is displayed when complete. ![Complete #center](/install-guides/_images/py2-woa.png) ## How do I start Python on Windows? -At a Windows Command prompt or a PowerShell prompt use `python` or `py` to start the interpreter. +To start Python on Windows, at a Windows Command prompt or a PowerShell prompt, use `python` or `py` to start the interpreter: ```cmd py ``` -The interpreter starts with output similar to: +The interpreter starts with an output similar to: ```output Python 3.13.0 (tags/v3.13.0:60403a5, Oct 7 2024, 10:17:29) [MSC v.1941 64 bit (ARM64)] on win32 @@ -78,7 +80,7 @@ exit() ## How can I run a Python example? -To confirm python is working, use a text editor to save the code below to a file named `uname.py`. +To run a Python example, and confirm that python is working, use a text editor to save the code below to a file named `uname.py`. ```python import platform @@ -92,7 +94,7 @@ Run the code: py uname.py ``` -Running on a Windows on Arm machine produces the output similar to: +Running on a Windows on Arm machine produces an output similar to: ```output Python version 3.13.0 @@ -101,7 +103,7 @@ Machine is Windows 11 ARM64 ## How do I install Python packages? -Python `pip` can be used to install packages. +To install Python packages, you can use Python `pip`. For example, to install [Flask](https://palletsprojects.com/p/flask/): ```console @@ -138,7 +140,7 @@ WARNING: This is a development server. Do not use it in a production deployment. * Running on http://10.8.0.10:5000 Press CTRL+C to quit ``` -Open a browser with the URL printed by the application. In this example: +Using the URL that the application prints, open a browser. Here is an example: ```url http://127.0.0.1:5000 ``` @@ -155,17 +157,17 @@ The accesses are reported in the command window: 10.8.0.10 - - [] "GET /favicon.ico HTTP/1.1" 404 - ``` -Use `Ctrl+C` to stop the application. +Use **Ctrl + C** to stop the application. ## Is Python IDLE available? -Python `IDLE` is included in the installation. IDLE is a simple IDE for python development. You can locate it in your start menu. +Python `IDLE` is included in the installation. IDLE is a simple IDE for Python development. You can locate it in the start menu. You can create and run Python applications in this environment. -For example, use `File` > `Open...` (`Ctrl+O`) to open the above `uname.py`. +For example, use **File**, then **Open...** (or **Ctrl + O**) to open the above `uname.py`. -Then select `Run` > `Run module` (`F5`) to execute. +Then select **Run** and **Run module** (or **F5**) to execute. ![IDLE uname #center](/install-guides/_images/py3-woa.png) From 30085d45d503b7bef35c425a3c29122424ab35c8 Mon Sep 17 00:00:00 2001 From: Przemyslaw Wirkus Date: Thu, 10 Oct 2024 13:34:50 +0100 Subject: [PATCH 20/39] docs: add Przemyslaw Wirkus to contributors.csv --- assets/contributors.csv | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/assets/contributors.csv b/assets/contributors.csv index 8c0bc22e11..c8f3b4bcfd 100644 --- a/assets/contributors.csv +++ b/assets/contributors.csv @@ -39,4 +39,5 @@ Gayathri Narayana Yegna Narayanan,Arm,,,, Alexandros Lamprineas,Arm,,,, Annie Tallund,Arm,annietllnd,annietallund,, Cyril Rohr,RunsOn,crohr,cyrilrohr,, -Rin Dobrescu,Arm,,,, \ No newline at end of file +Rin Dobrescu,Arm,,,, +Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,, \ No newline at end of file From 95aba3f49def6918cd406149a3c4d889aeda8808 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 12:35:17 +0000 Subject: [PATCH 21/39] Final checks --- content/install-guides/py-woa.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/install-guides/py-woa.md b/content/install-guides/py-woa.md index 0fef714a88..5de869d90b 100644 --- a/content/install-guides/py-woa.md +++ b/content/install-guides/py-woa.md @@ -37,9 +37,9 @@ Windows on Arm instances are available with Microsoft Azure. For further informa To download and install Python for Windows on Arm, there is more than one option: -* You can download the installer from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. +* Firstly, you can download the installer from the [Python website](https://www.python.org/downloads/windows/). Locate the `ARM64` installer. -* You can download from a PowerShell terminal: +* Or secondly, you can download from a PowerShell terminal, by running the following: ```command curl https://www.python.org/ftp/python/3.13.0/python-3.13.0-arm64.exe --output python-3.13.0-arm64.exe ``` @@ -48,7 +48,7 @@ Once you have downloaded Python, run the installer `exe` file on a Windows on Ar The installer will start. -Tick the checkbox **Add python.exe to PATH** if you want to easily invoke python from any directory. +Tick the checkbox **Add python.exe to PATH** to enable you to easily invoke Python from any directory. ![Install #center](/install-guides/_images/py1-woa.png) @@ -80,7 +80,7 @@ exit() ## How can I run a Python example? -To run a Python example, and confirm that python is working, use a text editor to save the code below to a file named `uname.py`. +To run a Python example, and confirm that Python is working, use a text editor to save the code below to a file named `uname.py`. ```python import platform From a69b106197d5058ff2508d484504c629e6ebfe88 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 13:57:31 +0100 Subject: [PATCH 22/39] Update java.md Further editorial revisions. --- content/install-guides/java.md | 44 +++++++++++++++++----------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/install-guides/java.md b/content/install-guides/java.md index d989227bbb..66795d4292 100644 --- a/content/install-guides/java.md +++ b/content/install-guides/java.md @@ -22,25 +22,25 @@ layout: installtoolsall Java is a high-level, object-oriented programming language first released by Sun Microsystems in 1995. -It is designed to have as few implementation dependencies as possible, making it a versatile and widely-used language. +Its aim is to have as few implementation dependencies as possible, making it a versatile and widely-used language. ## Is Java available for Arm Linux? Yes, there are numerous ways to install Java on Arm Linux distributions. -Below are some of the common methods to install Java. This includes both the Java runtime environment (JRE), which is used to run Java applications and the Java development kit (JDK) which is used to create Java applications. +Below are some of the common methods that you can use to install Java. This includes both the Java runtime environment (JRE), which is used to run Java applications, and the Java Development Kit (JDK), which is used to create Java applications. Pick the one that works best for you. {{% notice Note %}} -The Java Technology Compatibility Kit (TCK) is a test suite that verifies whether a Java implementation conforms to the Java SE Platform Specification. It's a crucial tool for ensuring that Java applications can run consistently across different platforms and implementations. +The Java Technology Compatibility Kit (TCK) is a test suite that you can use to verify whether a Java implementation conforms to the Java SE Platform Specification. It's a crucial tool for ensuring that Java applications can run consistently across different platforms and implementations. Check the [OCTLA Signatories List](https://openjdk.org/groups/conformance/JckAccess/jck-access.html) to see who has been granted access to the TCK. {{% /notice %}} ## Can I install Java using the Linux package manager? -For distributions using `apt` - including Debian and Ubuntu: +For distributions using `apt` - including Debian and Ubuntu, you can install Java using this code: ```console sudo apt update @@ -48,13 +48,13 @@ sudo apt install default-jre -y sudo apt install default-jdk -y ``` -For distributions using `dnf` - including Fedora and Red Hat: +For distributions using `dnf` - including Fedora and Red Hat, you can install Java using this code: ```console sudo dnf install java-latest-openjdk ``` -For distributions using `pacman` - including Arch and Manjaro: +For distributions using `pacman` - including Arch and Manjaro, you can install Java using this code: ```console sudo pacman -S jdk-openjdk @@ -63,7 +63,7 @@ sudo pacman -S jre-openjdk ## Can I install Java using Snap? -For Linux distributions with `snap` you can install Java using: +For Linux distributions with `snap` you can install Java using this code: ```console sudo snap install openjdk @@ -81,7 +81,7 @@ echo "deb [signed-by=/usr/share/keyrings/corretto-keyring.gpg] https://apt.corre sudo apt-get update; sudo apt-get install -y java-21-amazon-corretto-jdk ``` -More installation options for Corretto are available in the [Amazon Corretto 21 Guide for Linux](https://docs.aws.amazon.com/corretto/latest/corretto-21-ug/linux-info.html) +More installation options for Corretto are available in the [Amazon Corretto 21 Guide for Linux](https://docs.aws.amazon.com/corretto/latest/corretto-21-ug/linux-info.html). ## How do I install the Microsoft Build of OpenJDK? @@ -124,7 +124,7 @@ For more information about the available versions and supported platforms refer ## How do I install Eclipse Temurin from the Adoptium Working Group? -The Adoptium Working Group promotes and supports high-quality, TCK certified runtimes and associated technology for use across the Java ecosystem. +The Adoptium Working Group promotes and supports high-quality, TCK-certified runtimes and associated technology for use across the Java ecosystem. Eclipse Temurin is the name of the OpenJDK distribution from Adoptium. @@ -175,7 +175,7 @@ Yes, you can change the default version. For systems with `apt` use: sudo update-alternatives --config java ``` -You will be given the option to select a new version. The options are depend on the software currently installed on your computer. +You will be given the option to select a new version. The options are dependent on the software currently installed on your computer. ```output There are 3 choices for the alternative java (providing /usr/bin/java). @@ -190,11 +190,11 @@ There are 3 choices for the alternative java (providing /usr/bin/java). Press to keep the current choice[*], or type selection number: ``` -For this case, if you select option 1, Java 17 becomes the default. +In this example, if you select option 1, Java 17 becomes the default. ## How do I print the Java version? -Print the version of the Java runtime: +Print the version of the Java runtime, by running: ```console java -version @@ -222,13 +222,13 @@ javac 21.0.4 ## Which version of Java should I use for Arm Linux systems? -For performance and security, it’s important to ensure that your version of Java is at least 11.0.12. Earlier versions lack significant performance improvements. Java performance has steadily increased over time and newer versions will provide better performance. +For performance and security, it’s important to ensure that your version of Java is at least 11.0.12. Earlier versions lack significant performance improvements. Java performance has steadily increased over time and newer versions provide improved performance. ## Which flags are available for tuning the JVM? -The Java virtual machine (JVM) includes a number of flags which are available to tune performance and aid in debugging. Some of the flags are general purpose and some are Arm architecture specific. +The Java Virtual Machine (JVM) includes a number of flags which are available to tune performance and aid in debugging. Some of the flags are general-purpose and some are Arm architecture-specific. -To print the final values of the flags after the JVM has been initialized run: +To print the final values of the flags after the JVM has been initialized, run: ```console java -XX:+PrintFlagsFinal -version @@ -236,13 +236,13 @@ java -XX:+PrintFlagsFinal -version ## Are there other tools commonly used in Java projects? -There are a number of Java related tools you may want to install. +There are a number of Java-related tools you might like to install. ### Apache Maven Apache Maven is a powerful build automation tool primarily used for Java projects. -It simplifies the build process by providing a uniform build system, dependency management, and project management capabilities. +It simplifies the build process by providing a uniform build system, with dependency and project management capabilities. You can install it from the `apt` package manager: @@ -299,11 +299,11 @@ The output is similar to: Welcome to Gradle 8.10! Here are the highlights of this release: - - Support for Java 23 - - Faster configuration cache - - Better configuration cache reports + - Support for Java 23. + - Faster configuration cache. + - Improved configuration cache reports. -For more details see https://docs.gradle.org/8.10/release-notes.html +For further information, see https://docs.gradle.org/8.10/release-notes.html. ------------------------------------------------------------ @@ -387,4 +387,4 @@ INFO: Created user preferences directory. Copyright (c) 1999-2024 The Apache Software Foundation ``` -You are ready to use Java on your Arm Linux system. \ No newline at end of file +You are ready to use Java on your Arm Linux system. From f00444e70c76687b8f5a5dccf1fb5ad9954390c0 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 13:56:08 +0000 Subject: [PATCH 23/39] Final checks --- content/install-guides/java.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/install-guides/java.md b/content/install-guides/java.md index 66795d4292..22b4d19252 100644 --- a/content/install-guides/java.md +++ b/content/install-guides/java.md @@ -33,14 +33,14 @@ Below are some of the common methods that you can use to install Java. This incl Pick the one that works best for you. {{% notice Note %}} -The Java Technology Compatibility Kit (TCK) is a test suite that you can use to verify whether a Java implementation conforms to the Java SE Platform Specification. It's a crucial tool for ensuring that Java applications can run consistently across different platforms and implementations. +The Java Technology Compatibility Kit (TCK) is a test suite that you can use to verify whether a Java implementation conforms to the Java SE Platform Specification. It is a crucial tool for ensuring that Java applications can run consistently across different platforms and implementations. Check the [OCTLA Signatories List](https://openjdk.org/groups/conformance/JckAccess/jck-access.html) to see who has been granted access to the TCK. {{% /notice %}} ## Can I install Java using the Linux package manager? -For distributions using `apt` - including Debian and Ubuntu, you can install Java using this code: +For distributions using `apt`, including Debian and Ubuntu, you can install Java using this code: ```console sudo apt update @@ -48,13 +48,13 @@ sudo apt install default-jre -y sudo apt install default-jdk -y ``` -For distributions using `dnf` - including Fedora and Red Hat, you can install Java using this code: +For distributions using `dnf`, including Fedora and Red Hat, you can install Java using this code: ```console sudo dnf install java-latest-openjdk ``` -For distributions using `pacman` - including Arch and Manjaro, you can install Java using this code: +For distributions using `pacman`, including Arch and Manjaro, you can install Java using this code: ```console sudo pacman -S jdk-openjdk @@ -63,7 +63,7 @@ sudo pacman -S jre-openjdk ## Can I install Java using Snap? -For Linux distributions with `snap` you can install Java using this code: +For Linux distributions using `snap`, you can install Java using this code: ```console sudo snap install openjdk @@ -81,7 +81,7 @@ echo "deb [signed-by=/usr/share/keyrings/corretto-keyring.gpg] https://apt.corre sudo apt-get update; sudo apt-get install -y java-21-amazon-corretto-jdk ``` -More installation options for Corretto are available in the [Amazon Corretto 21 Guide for Linux](https://docs.aws.amazon.com/corretto/latest/corretto-21-ug/linux-info.html). +You can find out more about the installation options for Corretto in the [Amazon Corretto 21 Guide for Linux](https://docs.aws.amazon.com/corretto/latest/corretto-21-ug/linux-info.html). ## How do I install the Microsoft Build of OpenJDK? From 15cf581b128a9854ee29dd1e68ca57eca10f90e2 Mon Sep 17 00:00:00 2001 From: Madeline Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 10 Oct 2024 14:25:53 +0000 Subject: [PATCH 24/39] Editorial checks. --- content/install-guides/swift.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/install-guides/swift.md b/content/install-guides/swift.md index 0fbb859d27..3606e7ce82 100644 --- a/content/install-guides/swift.md +++ b/content/install-guides/swift.md @@ -18,7 +18,7 @@ tool_install: true weight: 1 --- -[Swift](https://swift.org/) is an open-source programming language developed by Apple. It was initially created for use with Apple's iOS, macOS, watchOS, and tvOS operating systems, but has since gained popularity as a general-purpose language suitable for a wide range of Linux and Windows applications. Swift is known for its safety, speed, and expressiveness, making it a great choice for both beginners and experienced programmers. +[Swift](https://swift.org/) is an open-source programming language developed by Apple. It was initially created for use with Apple's iOS, macOS, watchOS, and tvOS operating systems, but has since gained popularity as a general-purpose language suitable for a wide range of Linux and Windows applications. Swift is known for its safety, speed, and expressiveness, making it ideal for both beginners and experienced programmers. ## What do I need to install Swift? @@ -70,7 +70,7 @@ sudo apt-get -y install \ This guide uses Swift version 6.0.1 on Ubuntu 24.04. -You can get more information about other versions and platforms from [Download Swift](https://www.swift.org/download/). +You can get more information about other versions and platforms from [Download Swift](https://www.swift.org/download/). 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[PATCH 10/39] Update env-setup-5.md --- .../env-setup-5.md | 26 ++++++++++++------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index 2cb2fb72de..85b1b8581e 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -21,13 +21,25 @@ The Ubuntu version should be `20.04 or higher`. The `x86_64` architecture must b If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide](/install-guides/avh#corstone) provides setup instructions. +## Setup on Host Machine +1. Setup if you don't have access to the physical board: We would use the Corstone-300 FVP, it is pre-configured. +2. Setup if you have access to the board: Skip to "Compilers" Section + + +### Corstone-300 FVP {#fvp} Setup for ExecuTorch +For Arm Virtual Hardware users, the Corstone-300 FVP is pre-installed. + +To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html)). Follow this tutorial till "Install the TOSA reference model" Section. It should be the last thing you do from this tutorial. + +Since you already have the compiler installed from the above tutorial, skip to ## Install PyTorch. + ### Compilers The examples can be built with [Arm Compiler for Embedded](https://developer.arm.com/Tools%20and%20Software/Arm%20Compiler%20for%20Embedded) or [Arm GNU Toolchain](https://developer.arm.com/Tools%20and%20Software/GNU%20Toolchain). Both compilers are pre-installed in Arm Virtual Hardware. -Alternatively, if you are using Arch Linux or its derivatives, you can use Pacman to install GCC. +Alternatively, if you use Arch Linux or its derivatives, you can use Pacman to install GCC. Use the install guides to install the compilers on your computer: - [Arm Compiler for Embedded](/install-guides/armclang/) @@ -38,12 +50,6 @@ Use the install guides to install the compilers on your computer: pacman -S aarch64-linux-gnu-gcc ``` -### Corstone-300 FVP {#fvp} - -To install the Corstone-300 FVP on your computer refer to the [install guide for Arm Ecosystem FVPs](/install-guides/fm_fvp). - -The Corstone-300 FVP is pre-installed in Arm Virtual Hardware. - ## Install PyTorch The latest version needs Python 3.8 or later @@ -56,7 +62,7 @@ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.o 1. Follow the [Setting Up ExecuTorch guide](https://pytorch.org/executorch/stable/getting-started-setup.html ) to install it. -2. Activate the `executorch` virtual environment from the installation guide: +2. Activate the `executorch` virtual environment from the installation guide to ensure it is ready for use: ```console conda activate executorch @@ -86,7 +92,9 @@ Grove Vision V2 [Edge impulse Firmware](https://cdn.edgeimpulse.com/firmware/see ## Setup on Grove - Vision AI Module V2 -Due to its constrained environment, we'll focus on lightweight, optimized tools and models (will be introcuded in the next learning path). +**Kindly Note:** Only follow this part of the tutorial if you have the board. + +Due to its constrained environment, we'll focus on lightweight, optimized tools and models (which will be introduced in the next learning path). ![Hardware Overview #center](Overview.png) From 5c8da85d5ce3bf9772d16e47634bd62e71eab4e4 Mon Sep 17 00:00:00 2001 From: Abena Oforiwaa Amanfo <57913494+dominica-of@users.noreply.github.com> Date: Mon, 7 Oct 2024 22:29:23 +0200 Subject: [PATCH 11/39] Update env-setup-5.md --- .../env-setup-5.md | 104 +++++++++++++++++- 1 file changed, 98 insertions(+), 6 deletions(-) diff --git a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md index 85b1b8581e..dc48492010 100644 --- a/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md +++ b/content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md @@ -12,6 +12,7 @@ layout: "learningpathall" These instructions have been tested on: - A GCP Arm-based Tau T2A Virtual Machine instance Running Ubuntu 22.04 LTS. - Host machine with Ubuntu 24.04 on x86_64 architecture. +- Windows Subsystem for Linux (WSL): Windows x86_64 The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices. @@ -23,15 +24,15 @@ If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide] ## Setup on Host Machine 1. Setup if you don't have access to the physical board: We would use the Corstone-300 FVP, it is pre-configured. -2. Setup if you have access to the board: Skip to "Compilers" Section +2. Setup if you have access to the board: Skip to **"Compilers"** Section -### Corstone-300 FVP {#fvp} Setup for ExecuTorch +### Corstone-300 FVP Setup for ExecuTorch For Arm Virtual Hardware users, the Corstone-300 FVP is pre-installed. -To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html)). Follow this tutorial till "Install the TOSA reference model" Section. It should be the last thing you do from this tutorial. +To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial. -Since you already have the compiler installed from the above tutorial, skip to ## Install PyTorch. +Since you already have the compiler installed from the above tutorial, skip to **"Install PyTorch"**. ### Compilers @@ -71,9 +72,9 @@ conda activate executorch ## Install Edge Impulse CLI 1. Create an [Edge Impulse Account](https://studio.edgeimpulse.com/signup) if you do not have one -2. Install the CLI tools +2. Install the CLI tools in your terminal -Ensure you have Nodejs install +Ensure you have Nodejs installed ```console node -v @@ -123,3 +124,94 @@ Follow the prompts to log in. If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse. + +## Build a Simple PyTorch Model +With our Environment ready, we will create a simple program to test our setup. This example will define a simple feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. Create a file called simple_nn.py with the following code: + +```python +import torch +from torch.export import export +from executorch.exir import to_edge + +# Define a simple Feedforward Neural Network +class SimpleNN(torch.nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super(SimpleNN, self).__init__() + self.fc1 = torch.nn.Linear(input_size, hidden_size) + self.relu = torch.nn.ReLU() + self.fc2 = torch.nn.Linear(hidden_size, output_size) + + def forward(self, x): + out = self.fc1(x) + out = self.relu(out) + out = self.fc2(out) + return out + +# Create the model instance +input_size = 10 # example input features size +hidden_size = 5 # hidden layer size +output_size = 2 # number of output classes + +model = SimpleNN(input_size, hidden_size, output_size) + +# Example input tensor (batch size 1, input size 10) +x = torch.randn(1, input_size) + +# torch.export: Defines the program with the ATen operator set for SimpleNN. +aten_dialect = export(model, (x,)) + +# to_edge: Make optimizations for edge devices. This ensures the model runs efficiently on constrained hardware. +edge_program = to_edge(aten_dialect) + +# to_executorch: Convert the graph to an ExecuTorch program +executorch_program = edge_program.to_executorch() + +# Save the compiled .pte program +with open("simple_nn.pte", "wb") as file: + file.write(executorch_program.buffer) + +print("Model successfully exported to simple_nn.pte") +``` + +Run it from your terminal: + +```console +python3 simple_nn.py +``` + +If everything runs successfully, the output will be: +```bash { output_lines = "1" } +Model successfully exported to simple_nn.pte +``` +Finally, the model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge. + +Now, we will run the ExecuTorch version, first run: + +```console +# Clean and configure the build system +rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake .. + +# Build the executor_runner target +cmake --build cmake-out --target executor_runner -j9 +``` + +You should see an output similar to: +```bash { output_lines = "1" } +[100%] Built target executor_runner +``` + +Now, run the executor_runner with the Model: +```console +./cmake-out/executor_runner --model_path simple_nn.pte +``` + +Expected Output: Since the model is a simple feedforward model, you can expect a tensor of shape [1, 2] + +```bash { output_lines = "1-3" } +Input tensor shape: [1, 10] +Output tensor shape: [1, 2] +Inference output: tensor([[0.5432, -0.3145]]) #will vary due to random initialization +``` + +If the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes. + From 5f1d5960ff4fd008f8502bd4363d8ef854432e13 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Tue, 8 Oct 2024 13:43:25 +0000 Subject: [PATCH 12/39] Swift install guide --- content/install-guides/swift.md | 143 ++++++++++++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 content/install-guides/swift.md diff --git a/content/install-guides/swift.md b/content/install-guides/swift.md new file mode 100644 index 0000000000..d7c9fc240c --- /dev/null +++ b/content/install-guides/swift.md @@ -0,0 +1,143 @@ +--- + +title: Swift +author_primary: Jason Andrews + +minutes_to_complete: 10 +additional_search_terms: +- swift + +layout: installtoolsall +multi_install: false +multitool_install_part: false +official_docs: https://www.swift.org/documentation/ +test_images: +- ubuntu:latest +test_maintenance: true +tool_install: true +weight: 1 +--- + +[Swift](https://swift.org/) is an open-source programming language developed by Apple. It was initially created for use with Apple's iOS, macOS, watchOS, and tvOS operating systems, but has since gained popularity as a general-purpose language suitable for a wide range of Linux and Windows applications. Swift is known for its safety, speed, and expressiveness, making it a great choice for both beginners and experienced programmers. + +## What do I need to install Swift? + +Swift is available for macOS, Windows, and Linux, including Arm Linux distributions. + +This guide provides a quick solution to install Swift on Ubuntu for Arm. + +Confirm you are using an Arm machine by running: + +```bash +uname -m +``` + +The output should be: + +```output +aarch64 +``` + +If you see a different result, you are not using an Arm computer running 64-bit Linux. + +### What software packages are needed? + +Before you install Swift on Ubuntu 24.04, install the following pacakges: + +```bash +sudo apt update +sudo apt-get -y install \ + binutils \ + git \ + gnupg2 \ + libc6-dev \ + libcurl4-openssl-dev \ + libedit2 \ + libgcc-13-dev \ + libncurses-dev \ + libpython3-dev \ + libsqlite3-0 \ + libstdc++-13-dev \ + libxml2-dev \ + libz3-dev \ + pkg-config \ + tzdata \ + unzip \ + zlib1g-dev +``` + +## How do I download and install Swift? + +This guide uses Swift version 6.0.1 on Ubuntu 24.04. + +You can get more information about other versions and platforms from [Download Swift](https://www.swift.org/download/). + +Download Swift for Arm Linux: + +```bash +wget https://download.swift.org/swift-6.0.1-release/ubuntu2404-aarch64/swift-6.0.1-RELEASE/swift-6.0.1-RELEASE-ubuntu24.04-aarch64.tar.gz +``` + +Extract the archive: + +```bash +sudo tar -xf swift-6.0.1-RELEASE-ubuntu24.04-aarch64.tar.gz -C /usr/local +``` + +Add the `bin/` directory to your search path: + +```bash +echo 'export PATH="$PATH:/usr/local/swift-6.0.1-RELEASE-ubuntu24.04-aarch64/usr/bin"' >> ~/.bashrc +source ~/.bashrc +``` + +## How can I confirm Swift is working? + +You can print the version with: + +```bash +swift --version +``` + +The expected output is: + +```output +Swift version 6.0.1 (swift-6.0.1-RELEASE) +Target: aarch64-unknown-linux-gnu +``` + +You can also create and run a simple example program. + +Use a text editor to create a new file named `hello.swift` and add the following code: + +```swift +print("Hello from Swift on Arm Linux!") +``` + +Compile and run the program: + +```bash +swift hello.swift +``` + +The output will be: + +```output +Hello from Swift on Arm Linux! +``` + +You can also compile and run the program using: + +```bash +swiftc hello.swift -o hello +./hello +``` + +The output will be the same: + +```output +Hello from Swift on Arm Linux! +``` + +You are ready to use the Swift programming language on your Arm Linux computer. + From 190498e037db583bd0449d5e930db17f5c8e7a6d Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Tue, 8 Oct 2024 13:59:34 -0400 Subject: [PATCH 13/39] Update swift.md --- content/install-guides/swift.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/install-guides/swift.md b/content/install-guides/swift.md index d7c9fc240c..0fbb859d27 100644 --- a/content/install-guides/swift.md +++ b/content/install-guides/swift.md @@ -42,7 +42,7 @@ If you see a different result, you are not using an Arm computer running 64-bit ### What software packages are needed? -Before you install Swift on Ubuntu 24.04, install the following pacakges: +Before you install Swift on Ubuntu 24.04, install the following packages: ```bash sudo apt update @@ -81,7 +81,7 @@ wget https://download.swift.org/swift-6.0.1-release/ubuntu2404-aarch64/swift-6.0 Extract the archive: ```bash -sudo tar -xf swift-6.0.1-RELEASE-ubuntu24.04-aarch64.tar.gz -C /usr/local +sudo tar -xf swift-6.0.1-RELEASE-ubuntu24.04-aarch64.tar.gz -C /usr/local ``` Add the `bin/` directory to your search path: From a3c779f113673b048c23d8a070a5fb1be71b2ff1 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Tue, 8 Oct 2024 20:19:55 +0000 Subject: [PATCH 14/39] Update Python for Windows on Arm install guide for version 3.13 --- content/install-guides/_images/idle.png | Bin 4019 -> 0 bytes content/install-guides/_images/py1-woa.png | Bin 168943 -> 105152 bytes 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