Flova

// Parses the debug json message from the DSD and publishes the currently active action

import { Input } from "./types";

// Define message
type Output = {
  name: string;
};

interface DSDDebugDataElement {

Flova

# 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= []

Flova

# Get the process id of nginx
NGINX_PID=$(pgrep -o 'nginx')
echo Nginx server PID: $NGINX_PID

# Get the memory parts that are used by nginx. 
# That make gdb dump commands with it and dump the corresponding memory into files
cat /proc/$NGINX_PID/maps \
    | awk '
      $6 !~ "^/" {split ($1,address,"-");
      print "dump memory mem_" address[1] " 0x" address[1] " 0x" address[2] ;}

Flova