- miniogre: from source code to reproducible envionments, in seconds
pip install miniogre- Go to project folder
- Run
miniogre run
- freeze: generate images of code and terminal output
brew install charmbracelet/tap/freezefreeze --execute "ls -ltra"
- uv: An extremely fast Python package installer and resolver, written in Rust
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| ######################### Preamble ########################################### | |
| SHELL := bash | |
| .ONESHELL: | |
| .SHELLFLAGS := -eu -o pipefail -c | |
| .DELETE_ON_ERROR: | |
| .SECONDEXPANSION: | |
| .EXTRA_PREREQS := $(MAKEFILE_LIST) | |
| MAKEFLAGS += --warn-undefined-variables | |
| MAKEFLAGS += --no-builtin-rules | |
| MAKEFLAGS += -j$(shell nproc) |
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| # This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), | |
| # adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs. | |
| # | |
| # Usage: | |
| # - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate` | |
| # - Install deepspeed: `pip install deepspeed==0.9.5` | |
| # - Install TRL from main: pip install git+https://github.com/huggingface/trl.git | |
| # - Clone the repo: git clone github.com/huggingface/trl.git | |
| # - Copy this Gist into trl/examples/scripts | |
| # - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py |
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| On an Orin NX 16G the memory was too low to compile and the SWAP file had to be increased. | |
| /etc/systemd/nvzramconfig.sh | |
| change: | |
| ``` | |
| # Calculate memory to use for zram (1/2 of ram) | |
| totalmem=`LC_ALL=C free | grep -e "^Mem:" | sed -e 's/^Mem: *//' -e 's/ *.*//'` | |
| mem=$((("${totalmem}" / 2 / "${NRDEVICES}") * 1024)) | |
| ``` |
A quick guide on how to setup X11 forwarding on macOS when using docker containers requiring a DISPLAY. Works on both Intel and M1 macs!
This guide was tested on:
- macOS Catalina 10.15.4
- docker desktop 2.2.0.5 (43884) - stable release
- XQuartz 2.7.11 (xorg-server 1.18.4)
- Macbook Pro (Intel)
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| # Authors: Mathieu Blondel, Vlad Niculae | |
| # License: BSD 3 clause | |
| import numpy as np | |
| def _gen_pairs(gen, max_iter, max_inner, random_state, verbose): | |
| rng = np.random.RandomState(random_state) | |
| # if tuple, interpret as randn |
- Download & Install Sublime Text 3.2.2 Build 3211
- Visit https://hexed.it/
- Open file select sublime_text.exe
- Offset
0x8545: Original84->85 - Offset
0x08FF19: Original75->EB - Offset
0x1932C7: Original75->74(remove UNREGISTERED in title bar, so no need to use a license)
This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
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| ## Sublime Text 3 Serial key build is 3103 | |
| —– BEGIN LICENSE —– | |
| Michael Barnes | |
| Single User License | |
| EA7E-821385 | |
| 8A353C41 872A0D5C DF9B2950 AFF6F667 | |
| C458EA6D 8EA3C286 98D1D650 131A97AB | |
| AA919AEC EF20E143 B361B1E7 4C8B7F04 | |
| B085E65E 2F5F5360 8489D422 FB8FC1AA |