examples
aws-graviton-getting-started
examples | aws-graviton-getting-started | |
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23 | 62 | |
21,727 | 817 | |
0.6% | 1.1% | |
7.7 | 8.5 | |
11 days ago | 2 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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examples
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A Distributed File System in Go Cut Average Metadata Memory Usage to 100 Bytes
For “cloud-native” apps, JuiceFS is not needed.
S3 is not designed for intensive metadata operations, like listing, renaming etc. For these operations, you will need a somewhat POSIX-complaint system. For example, if you want to train on ImageNet dataset, the “canonical” way [1] is to extract the images and organize them into folders, class by class. The whole dataset is discovered by directory listing. This where JuiceFS shines.
Of course, if the dataset is really massive, you will mostly end-up with in-house solutions.
[1]: https://github.com/pytorch/examples/blob/main/imagenet/extra...
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Logistic Regression for Image Classification Using OpenCV
Pytorch includes a simple neural network example for the MNIST data: https://github.com/pytorch/examples/blob/main/mnist/main.py
It only takes a few minutes to train with default parameters and will have >99% accuracy on the MNIST test set.
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-examples
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I work at a non-tech company and have been asked to make software that is impossible. How do I explain it to my boss?
Pretty much just grab one of these, swap in your own database, go home early: https://pytorch.org/examples/
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MIT Course: Generative AI for Constructive Communication
[5] https://github.com/pytorch/examples/tree/main/word_language_...
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From a Dumb Student to a PyTorch Contributor: The Impact of Teachers on My Life⚡
The cherry on top of the cake I've added my father's name at the top of the code in the comments. I hope that for the next upcoming 200-300 years, someone will read modify and improve or perform experiments with my code.(Vivek V patel), My code can be found at official PyTorch's Website https://pytorch.org/examples/(Image Classification Using Forward-Forward Algorithm)
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What modifications can maximize the efficacy of the REINFORCE algorithm for a policy gradient task?
I am straying out of my domain knowledge to attempt a basic reinforcement learning task in a toy environment and have become fairly familiar with the REINFORCE algorithm for policy gradient agents, especially PyTorch’s implementation (found here). It is clear to me now that there are superior methods to train RL agents (PPO for instance), but as I read, these feel beyond my current intellectual or time resources. As such, I’d like to eek out as much power through modifications of REINFORCE as possible before determining how I might move on.
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How does Taichi differ from PyTorch? They are different in every sense!
import torch import torch.nn as nn import torch.nn.functional as F # Simplified version of https://github.com/pytorch/examples/blob/main/mnist/main.py#L21 class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) def forward(self, x): x = self.conv1(x) output = F.relu(x) return output
- Noob PyTorch Question
- Syntax Error, attempting to train neural network.
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?
self-driving-car - The Udacity open source self-driving car project
drupal-pi - Drupal on Docker on a Raspberry Pi. Pi Dramble's little brother.
fast-style-transfer - TensorFlow CNN for fast style transfer ⚡🖥🎨🖼
KasmVNC - Modern VNC Server and client, web based and secure
pytea - PyTea: PyTorch Tensor shape error analyzer
buildx - Docker CLI plugin for extended build capabilities with BuildKit
PyTorchProjectFramework - A basic framework for your PyTorch projects
examples - TensorFlow examples
raccoon_dataset - The dataset is used to train my own raccoon detector and I blogged about it on Medium
sysbench - Scriptable database and system performance benchmark
benchmarks_4090
flops - Tiny cpu benchmark