achimnol

description "CUDA initialization without X environments"

start on runlevel [2345]
stop on runlevel [!2345]

pre-start script
    DRIVER=nvidia
    HOLD_GPU=/usr/local/bin/hold_gpu.py

    [ -x "$HOLD_GPU" ] || { echo "$HOLD_GPU is missing or not executable!"; exit 1; }

stevenvo

# sort array with regards to nth column
arr = arr[arr[:,n].argsort()]

ramhiser

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

spro

import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

classilla

diff --git a/hw/ppc/pnv.c b/hw/ppc/pnv.c
index c9cb6fa3..c1f43310 100644
--- a/hw/ppc/pnv.c
+++ b/hw/ppc/pnv.c
@@ -23,16 +23,17 @@
 #include "qapi/error.h"
 #include "sysemu/sysemu.h"
 #include "sysemu/numa.h"
 #include "sysemu/reset.h"
 #include "sysemu/runstate.h"

lost-theory

import time

from flask import Flask, request, g, render_template

app = Flask(__name__)
app.config['DEBUG'] = True

@app.before_request
def before_request():
    g.request_start_time = time.time()

bridgeythegeek

My First PANDA

Introduction

Being someone who tries to play a lot with Windows memory, I really wanted to play with PANDA, but I was slightly scared because I'd never touched qemu before - all my experience had been with VirtualBox and VMware.

My goal was to install PANDA into a (relatively) clean install of Debian 8 'Jessie', capture a recording and successfully run a PANDA plugin.

1. Get PANDA

KodrAus

Profiling performance

Using perf:

$ perf record -g binary
$ perf script | stackcollapse-perf.pl | rust-unmangle | flamegraph.pl > flame.svg

NOTE: See @GabrielMajeri's comments below about the -g option.

Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

justinschuldt

hardware/software

Webcam parts:

• Raspberry Pi Zero W Rev 1.1
• Raspberry Pi Camera v2 (8-megapixel)
• Raspberry Pi High Quality Camera (12.3-megapixel)
• Raspbian Buster Lite 2020-02-13

Webcam works with:

• Windows 10
• Windows 10 "Camera" app