himaprasoon

# resync -> hard reset a branch to origin 
git config --global alias.resync '!git fetch origin && git reset --hard origin/$(git rev-parse --abbrev-ref HEAD)'

#rebmain -> Fetches origin and rebase main
git config --global alias.rebmain '!git fetch origin && git rebase origin/main'

himaprasoon

import os
from flask import Flask, flash, request, redirect, url_for
from werkzeug.utils import secure_filename

import tempfile

tempdir = tempfile.mkdtemp(prefix="temp_image_upload")

UPLOAD_FOLDER = tempdir
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}

himaprasoon

import os
import zipfile

def zip_dir(folder_path, zip_name):
    """

    :param folder_path: Folder path which has to be zipped
    :param zip_name: the detsinatio zip name
    :return:
    """

himaprasoon

HOMEBREW_NO_AUTO_UPDATE=1 brew install openmpi

HOMEBREW_NO_AUTO_UPDATE=1 brew install cmake

HOMEBREW_NO_AUTO_UPDATE=1 brew install libuv

HOROVOD_WITH_MPI=1 pip3 install horovod

himaprasoon

from types import FunctionType


class BaseClass:

    def __new__(cls, *args, **kwargs):
        # Creating a custom __init__ function
        params_string = ",".join(cls.params) # For init function arguments
        params_kwargs = ','.join([f"{i}={i}" for i in cls.params]) # Creating 
        k=f"""def __init__(self,{params_string}):\n\tBaseClass.__init__(self, {params_kwargs})

himaprasoon

Get the lates tf gpu image

Get Tensorflow GPU Image

docker pull tensorflow/tensorflow:latest-gpu-py3

Install MPI

# apt-get wget
wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.3.tar.gz
gunzip -c openmpi-4.0.3.tar.gz | tar xf -

himaprasoon

"""
This example doesn't use summary writer . This is the easiest way to launch tensorboard without calling fit or running model with data
"""
from tensorflow.python import keras
from tensorflow.python.keras import layers
import tensorflow as tf
import shutil
from tensorboard import program

logdir = "/tmp/my_tensorboard/"

himaprasoon

from tensorflow.python import keras

output_dim = 3
time_steps = 10
input_dim = 4
cells = [
    keras.layers.LSTMCell(10, name="l1"),
    keras.layers.SimpleRNNCell(11, name="l2"),
    keras.layers.LSTMCell(12, name="l3"),
]

himaprasoon

# source : https://stackoverflow.com/a/47802308/3534616
def get_tensor_dependencies(tensor):

    # If a tensor is passed in, get its op
    try:
        tensor_op = tensor.op
    except:
        tensor_op = tensor

    # Recursively analyze inputs

himaprasoon

import horovod.tensorflow as hvd
import tensorflow as tf
hvd.init()
hvd_r=int(hvd.rank())
#each process compute a small part of something and then compute the average etc
#compute a small part
x= tf.random_uniform(shape=())
#compute the average for all processes
y=hvd.allreduce(x)
tf.set_random_seed(hvd.rank())