rodgtr1

#!/bin/bash
set -e

# specify your MFA_DEVICE_ARN
MFA_DEVICE_ARN=YOURMFAARN
PATH_TO_CREDENTIALS_FILE=/path/to/.aws/credentials
echo $PATH_TO_CREDENTIALS_FILE
#1H = 3600
#2H = 7200
#3H = 10800

jitsejan

FROM jupyter/pyspark-notebook
USER root
# Add essential packages
RUN apt-get update && apt-get install -y build-essential curl git gnupg2 nano apt-transport-https software-properties-common
# Set locale
RUN apt-get update && apt-get install -y locales \
    && echo "en_US.UTF-8 UTF-8" > /etc/locale.gen \
    && locale-gen
# Add config to Jupyter notebook
COPY jupyter/jupyter_notebook_config.py /home/jovyan/.jupyter/

iiSeymour

#!/usr/bin/env python

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

import sys
import torch
import numpy as np
import tensorflow as tf
from time import perf_counter

redknightlois

class Ralamb(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for ind in range(10)]
        super(Ralamb, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Ralamb, self).__setstate__(state)

CMCDragonkai

import numpy as np
import matplotlib.pyplot as plt

# ensure your arr is sorted from lowest to highest values first!
arr = np.array([1,4,6,9,100])

def gini(arr):
    count = arr.size
    coefficient = 2 / count
    indexes = np.arange(1, count + 1)

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

jeremyjordan

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

mattiasarro

Tensorflow 1.6 on macOS High Sierra 10.13.3 with GPU Acceleration (without disabling SIP)

This gist (based on a blog post at byai.io) documents how to set up TensorFlow 1.6 with (e)GPU support without the need to disable SIP. Following the original gist got me a saystem in which training TF on eGPU was successful, but there were various visual glitches due to the newer / less stable version of the driver.

As pointed out by ronchigram, many people are having issues with newer NVIDIA drivers, so it's worth using the nvidia-update script by Benjamin Dobell that installs the latest stable NVIDIA web driver, and if necessary patches it to run on your system. We also don't need to disable SIP when using nvidia-update.

I have als

ruslangrimov

import tensorflow as tf
from tensorflow.python.client import timeline
from keras import backend as K

run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()

model = ...  # A Keras model

fn = K.function(model.inputs, model.outputs, options=run_options, run_metadata=run_metadata)

MattKleinsmith

# https://gist.github.com/kevinzakka/d33bf8d6c7f06a9d8c76d97a7879f5cb#file-data_loader-py
# This is an example for the MNIST dataset (formerly CIFAR-10).
# There's a function for creating a train and validation iterator.
# There's also a function for creating a test iterator.
# Inspired by https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4

# Adapted for MNIST by github.com/MatthewKleinsmith

import numpy as np
import torch