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@cosmincatalin
cosmincatalin / mxnet-onnx-sagemaker-script.py
Created Dec 6, 2018
Training script used by SageMaker
View mxnet-onnx-sagemaker-script.py
import argparse
import logging
import os
from pickle import load
import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.contrib import onnx as onnx_mxnet
from mxnet.gluon.loss import L2Loss
@cosmincatalin
cosmincatalin / MXNet to ONNX.ipynb
Created Dec 6, 2018
Modeling of an MXNet regression problem
View MXNet to ONNX.ipynb
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View voice-recognition-sagmaker-notebook.ipynb
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@cosmincatalin
cosmincatalin / voice-recognition-sagemaker-script.py
Created May 31, 2018
Voice Recognition Sagemaker Script (Full)
View voice-recognition-sagemaker-script.py
import base64
import glob
import json
import logging
import subprocess
import sys
import tarfile
import traceback
import uuid
import wave
@cosmincatalin
cosmincatalin / voice-recognition-sagemaker-script.py
Created May 31, 2018
Voice Recognition Sagemaker Script (Part1)
View voice-recognition-sagemaker-script.py
import base64
import glob
import json
import logging
import subprocess
import sys
import tarfile
import traceback
import uuid
import wave
@cosmincatalin
cosmincatalin / notebook.ipynb
Last active Mar 21, 2018
The SageMaker attached notebook that allows building an MXNet model that counts shapes in an image
View notebook.ipynb
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@cosmincatalin
cosmincatalin / object-counting-sagemaker-script.py
Created Mar 21, 2018
The complete SageMaker script for building an MXNet model that counts shapes in an image.
View object-counting-sagemaker-script.py
import base64
import json
import logging
from pickle import load
import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.gluon import Trainer
from mxnet.gluon.loss import L2Loss
@cosmincatalin
cosmincatalin / object-counting-sagemaker-script.py
Created Mar 20, 2018
The first part of a SageMaker script for building an MXNet model that counts shapes in an image.
View object-counting-sagemaker-script.py
import logging
from pickle import load
import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.gluon import Trainer
from mxnet.gluon.loss import L2Loss
from mxnet.gluon.nn import Conv2D, MaxPool2D, Dropout, Flatten, Dense, Sequential
from mxnet.initializer import Xavier
@cosmincatalin
cosmincatalin / install-jupyter.sh
Last active Apr 16, 2018
AWS EMR bootstraps to install Jupyter (R, SparkR, Python 2, Python 3, PySpark)
View install-jupyter.sh
#!/bin/bash
MINICONDA_VERSION="4.3.21"
PANDAS_VERSION="0.20.3"
SCIKIT_VERSION="0.19.0"
while [[ $# > 1 ]]; do
key="$1"
case $key in
@cosmincatalin
cosmincatalin / readme.md
Last active Nov 5, 2019
AWS EMR bootstrap to install R packages from CRAN
View readme.md

AWS EMR bootstrap to install R packages from CRAN

This bootstrap is useful if you want to deploy SparkR applications that run arbitrary code on the EMR cluster's workers. The R code will need to have its dependencies already installed on each of the workers, and will fail otherwise. This is the case if you use functions such as gapply or dapply.

How to use the bootstrap

  1. You will first have to download the gist to a file and then upload it to S3 in a bucket of your choice.
  2. Using the AWS EMR Console create a cluster and choose advanced options.
  3. In Step 3 you can configure your bootstraps. Choose to Configure and add a Custom action
You can’t perform that action at this time.