Removes older versions
sudo rm -rf /usr/local/go* && sudo rm -rf /usr/local/go
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from peft import PeftModel | |
import torch | |
import os | |
import argparse | |
def get_args(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--base_model_name_or_path", type=str) |
Docker Image : pytorch/pytorch | |
Image Runtype : jupyter_direc ssh_direc ssh_proxy | |
Environment : [["JUPYTER_DIR", "/"], ["-p 41654:41654", "1"]] | |
pip install torch bitsandbytes sentencepiece "protobuf<=3.20.2" git+https://github.com/huggingface/transformers flask python-dotenv Flask-HTTPAuth accelerate | |
!mv /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda116.so /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so |
#!/bin/bash | |
### steps #### | |
# verify the system has a cuda-capable gpu | |
# download and install the nvidia cuda toolkit and cudnn | |
# setup environmental variables | |
# verify the installation | |
### | |
### to verify your gpu is cuda enable check |
def dot_product(x, kernel): | |
""" | |
Wrapper for dot product operation, in order to be compatible with both | |
Theano and Tensorflow | |
Args: | |
x (): input | |
kernel (): weights | |
Returns: | |
""" | |
if K.backend() == 'tensorflow': |
from keras import backend as K, initializers, regularizers, constraints | |
from keras.engine.topology import Layer | |
def dot_product(x, kernel): | |
""" | |
Wrapper for dot product operation, in order to be compatible with both | |
Theano and Tensorflow | |
Args: |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
/** @jsx React.DOM */ | |
var Graphic = React.createClass({ | |
componentDidMount: function() { | |
var context = this.getDOMNode().getContext('2d'); | |
this.paint(context); | |
}, | |
componentDidUpdate: function() { |
<!DOCTYPE HTML> | |
<html lang="en" ng-app="myApp"> | |
<head> | |
<meta charset="utf-8"> | |
<title>Dynamic Pagination w/ Filtering</title> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<meta name="description" content=""> | |
<meta name="author" content="Kim Maida"> | |
<!-- JS Libraries --> |
(function() { | |
// Do not use this library. This is just a fun example to prove a | |
// point. | |
var Bloop = window.Bloop = {}; | |
var mountId = 0; | |
function newMountId() { | |
return mountId++; | |
} |