| # Based on https://discuss.pytorch.org/t/check-gradient-flow-in-network/15063/10 | |
| def plot_grad_flow(named_parameters): | |
| '''Plots the gradients flowing through different layers in the net during training. | |
| Can be used for checking for possible gradient vanishing / exploding problems. | |
| Usage: Plug this function in Trainer class after loss.backwards() as | |
| "plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow''' | |
| ave_grads = [] | |
| max_grads= [] |
| # script to decompose/recompose llama model in different number of shards | |
| # note that it loads the full model * 2 in cpu memory | |
| import os | |
| import json | |
| import sys | |
| import torch | |
| import glob | |
| if len(sys.argv) != 4: |
| # Based on younesbelkada/finetune_llama_v2.py | |
| # Install the following libraries: | |
| # pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy | |
| from dataclasses import dataclass, field | |
| from typing import Optional | |
| import torch | |
| from datasets import load_dataset | |
| from transformers import ( |
| [[source]] | |
| name = "pypi" | |
| url = "https://pypi.org/simple" | |
| verify_ssl = true | |
| [dev-packages] | |
| pylint = "*" | |
| [packages] | |
| sqlalchemy = "*" |
The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.
The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy
| gcc -fpic --shared $(python3-config --includes) greetmodule.c -o greet.abi3.so | |
| # can also use $(pkg-config --cflags python-3.5) | |
| # or | |
| # python3 setup.py install --record files.txt --user |
| gcc -fpic --shared $(python3-config --includes) greetmodule.c -o greet.abi3.so | |
| # can also use $(pkg-config --cflags python-3.5) | |
| # or | |
| # python3 setup.py install --record files.txt --user |
| root@ruo91:~# nano Dockerfile | |
| #------- Dockerfile start -------# | |
| # | |
| # Docker SSH Daemon | |
| # | |
| # 해당 Images를 선택 | |
| # docker pull images-name과 같다고 보면 됌. | |
| # 이미 이미지가 있다면 docker images 명령어로 해당 Image ID를 적으면 된다. | |
| # | |
| # 예제1) 이미지가 없을 경우 |
| import torch | |
| from torch.autograd import Variable | |
| import torch.nn.functional as F | |
| import torch.utils.data as Data | |
| import matplotlib.pyplot as plt | |
| %matplotlib inline | |
| import numpy as np | |
| import imageio |
