gsomoza

#!/usr/bin/env python

# Usage:
#     s3-presign-url path/to/object
#
# 1) Install boto3:
#     pip install boto3
# 2) Configure aws:
#     aws configure
# 3) Change the bucket name in this script (below)

floer32

I started out pasting Python code into the AWS Lambda web GUI, right in that textarea. Got it working, making a call to a Slack API (whee!). So I had a working test case and I could test a refactor.

With the same code, I wanted to take advantage of the cool zipfile/ virtualenv upload support ("Create a Deployment Package") so I could use third-party libraries. Keeping the code itself the same (same as what I'd pasted into the textarea), the test should work again, as long as I packaged it right.

I kept trying to upload a zip with the same code, I thought, to AWS Lambda ... but I kept getting an error:

auxten

Golang Elliptic Curve benchmark

Benchmark Code: https://github.com/CovenantSQL/CovenantSQL/blob/develop/crypto/asymmetric/signature_test.go#L204

BenchmarkSign/Secp256k1-12                   20000         66413 ns/op
BenchmarkSign/Secp256k1-25%-12               20000         64881 ns/op
BenchmarkSign/C-Secp256k1-12                 20000         77700 ns/op
BenchmarkSign/P224-12                         2000        627489 ns/op
BenchmarkSign/P256-12                       100000         22207 ns/op
BenchmarkSign/P384-12                          300       3394950 ns/op

MattUV

extends TextEdit

export var expand = true
export var min_line = 3
export var max_line = 6

var scroll_bar

var font = get_font("")
var line_spacing

eladroz

I'm now working on big data processing with Pandas at scale, as a lightweight alternative to Spark. Fortunately, the Apache Arrow project brings with it an excellent and very fast Parquet reader and writer.

With the current push to ARM in both personal computers and the data center, I was curious to check the performance of my code on ARM - running on AWS' homegrown Graviton2 processor. Their c6g instance types are 20% cheaper than the equivalent Intel-based c5's, while promising faster performance. If that's the future, why not start getting ready now?

While there are already Python wheels for NumPy and Pandas, there is no official build yet for PyArrow. There's a pull request in the works,

ulises-jeremias

Markdown License badges

Collection of License badges for your Project's README file. Easily copy and paste the code under the badges into your Markdown files.

Notes

• Badges are made with Shields.io.
• This badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Want to add a License?

Comment this gist or write me an E-Mail (lukas@himsel.me)

alexcasalboni

How to use Python3 Type Hints in AWS Lambda

TL;DR

Static Type Checkers help you find simple (but subtle) bugs in your Python code. Check out lambda_types.py and incrementally improve your code base and development/debugging experience with type hints.

Your Lambda Function code will go from this:

Birch-san

Fine-tuning LLaMA-7B on ~12GB VRAM with QLoRA, 4-bit quantization

nvidia-smi said this required 11181MiB, at least to train on the sequence lengths of prompt that occurred initially in the alpaca dataset (~337 token long prompts).
You can get this down to about 10.9GB if (by modifying qlora.py) you run torch.cuda.empty_cache() after PEFT has been applied to your loaded model and before you begin training.

Setup

All instructions are written assuming your command-line shell is bash.

Clone repository:

peterhurford

Using py.test is great and the support for test fixtures is pretty awesome. However, in order to share your fixtures across your entire module, py.test suggests you define all your fixtures within one single conftest.py file. This is impractical if you have a large quantity of fixtures -- for better organization and readibility, you would much rather define your fixtures across multiple, well-named files. But how do you do that? ...No one on the internet seemed to know.

Turns out, however, you can define fixtures in individual files like this:

tests/fixtures/add.py

import pytest

@pytest.fixture

jo

JavaScript Crypto Libraries

List some crypto libraries for JavaScript out there. Might be a bit out dated. Scroll to the bottom.

WebCryptoAPI

http://www.w3.org/TR/WebCryptoAPI/

This specification describes a JavaScript API for performing basic cryptographic operations in web applications, such as hashing, signature generation and verification, and encryption and decryption. Additionally, it describes an API for applications to generate and/or manage the keying material necessary to perform these operations. Uses for this API range from user or service authentication, document or code signing, and the confidentiality and integrity of communications.