examples
aws-graviton-getting-started
examples | aws-graviton-getting-started | |
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143 | 62 | |
7,763 | 819 | |
0.8% | 1.7% | |
5.3 | 8.5 | |
10 days ago | 6 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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examples
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
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Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
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MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
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🔥14 Excellent Open-source Projects for Developers😎
10. TensorFlow - Make Machine Learning Work for You 🤖
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GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
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🔥🚀 Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot 🤖💬
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
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Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
aws-graviton-getting-started
- AWS Graviton Technical Guide
- Cómo comenzar a trabajar con AWS Graviton: La pregunta del Millón
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What infra did you deploy for Iceberg/Hudi/Delta?
EMR serverless + Athena + Glue works for us. We are evaluating Graviton instance to further optimize stuff. AWS link if you are interested
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Slash CAPEX, OPEX, and Carbon Emissions with T408
Now we turn our attention to carbon emissions which are presented in Table 8. In the table, the AMD – CPU only and AMD – T408 server watts/hour are actual measurements on the test system during operation. To estimate the AWS server watts/hour, we reduced the CPU-only AMD number by 60%, which is the savings that Amazon claims that Graviton3 CPUs provide over other CPUs. In all three cases, we multiplied this by the number of servers, then hours, days, and years, to compute the three-year power consumption total.
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Framework ARM
https://aws.amazon.com/ec2/graviton/ https://cloud.google.com/compute/docs/instances/arm-on-compute
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Google Has Developed Its Own Data Center Server Chips
From the relevant product page [0]:
"AWS Graviton3 processors feature always-on memory encryption, dedicated caches for every vCPU, and support for pointer authentication."
Further reading on pointer authentication [1].
[0] https://aws.amazon.com/ec2/graviton/
[1] https://www.qualcomm.com/content/dam/qcomm-martech/dm-assets...
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can i repurpose a server and make it a computer
Amazon makes their own Arm CPUs, like the Graviton3: https://aws.amazon.com/ec2/graviton/
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Cost Cutting AWS strategies
Read More about Graviton Processors
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Blackberry Partnership Panning Out!
According to BlackBerry, both QNX and IVY can run on EC2 instances powered by AWS’ Graviton2 processor. Graviton2 is an internally-developed processor that AWS debuted at re:Invent last year. It promises to provide up to 40% better price performance than comparable chips. "
- AWS Graviton
What are some alternatives?
cppflow - Run TensorFlow models in C++ without installation and without Bazel
drupal-pi - Drupal on Docker on a Raspberry Pi. Pi Dramble's little brother.
mlpack - mlpack: a fast, header-only C++ machine learning library
KasmVNC - Modern VNC Server and client, web based and secure
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
buildx - Docker CLI plugin for extended build capabilities with BuildKit
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
sysbench - Scriptable database and system performance benchmark
Selenium WebDriver - A browser automation framework and ecosystem.
examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
flops - Tiny cpu benchmark