Get the lates tf gpu image
docker pull tensorflow/tensorflow:latest-gpu-py3
# 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 -
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: |