Himaprasoon P T himaprasoonpt

## convert_to_300kb.py
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'}

## zip_folder_python.py
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:
    """

## horovod_setup_mac.sh
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

## dynamic_init.py
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})

## horovod_tf_2.1_docker.MD

      
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                himaprasoonpt
                / horovod_tf_2.1_docker.MD
            
            
              Last active
              February 14, 2021 05:54
            
              
                Creating a horovod image for tensorflow 2.1
              
          
    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 -

  
## tensorboard_without_fit_or_summary.py
"""
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/"

## keras_tf_2.0_rnn_outputs.py
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"),
]

## tf_get_dependencies.py
# 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

## horovod_test.py
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())

## block_import_decorator.py
from functools import wraps


def block_imports(*imports):
    def real_decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            saved = {}
            import sys
            for i in imports:
	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'}
	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:
	"""
	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
	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})
	"""
	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/"
	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"),
	]
	# 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
	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())
	from functools import wraps


	def block_imports(*imports):
	def real_decorator(func):
	@wraps(func)
	def wrapper(args, *kwargs):
	saved = {}
	import sys
	for i in imports: