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Merge pull request #99 from Sharon-iguazio/gpu-demos-reorg
GPU demos reorganization and doc-review completion
2 parents b83a56a + cf3ba50 commit 8f0a04f

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demos/gpu/horovod-benchmark/running_horovod_benchmark.ipynb renamed to demos/gpu/horovod/benchmark-tf.ipynb

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"source": [
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"job.delete()"
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demos/gpu/horovod-image-classification/01-Load_data_cats_n_dogs.ipynb renamed to demos/gpu/horovod/image-classification/01-load-data-cats-n-dogs.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load cats & dogs images"
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"# Load Cats and Dogs Images"
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]
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"_uuid": "fe76d1d1ded592430e7548feacfa38dc42f085d9"
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"source": [
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"# Package installation"
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"## Install Packages"
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]
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},
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{
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"cell_type": "code",
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]
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"cell_type": "code",
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"execution_count": null,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
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"# note that after running the above pip command you should restart the Jupyter kernel\n",
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"# to do that click on the restart kernel button next to the \"Code\" button in the upper menu"
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"> **Note:** After running the pip command you should restart the Jupyter kernel.<br>\n",
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"> To restart the kernel, click on the kernel-restart button in the notebook menu toolbar (the refresh icon next to the **Code** button)."
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Import Library"
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"## Import Library"
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}
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],
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"source": [
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"# This Python 3 environment comes with many helpful analytics libraries installed\n",
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"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
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"# For example, here's several helpful packages to load in \n",
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"# This Python 3 environment comes with many helpful analytics libraries installed.\n",
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"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python.\n",
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"# For example, here are several helpful packages to load:\n",
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"\n",
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"import numpy as np # linear algebra\n",
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"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
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"\n",
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"# Input data files are available in the \"../input/\" directory.\n",
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"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
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"# For example, running the following (by selecting 'Run' or pressing Shift+Enter) will list the files in the input directory:\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import random\n",
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"cell_type": "markdown",
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"source": [
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"# Download the data"
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"## Download the Data"
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"outputs": [],
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"source": [
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"!mkdir cats_and_dogs_filtered\n",
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"# Download a sample stocks file from Iguazio demo bucket in S3\n",
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"# Download a sample stocks file from Iguazio demo bucket in AWS S3\n",
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"!curl -L \"iguazio-sample-data.s3.amazonaws.com/catsndogs.zip\" > ./cats_and_dogs_filtered/catsndogs.zip"
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]
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"_uuid": "7335a579cc0268fba5d34d6f7558f33c187eedb3"
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"source": [
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"# Prepare Traning Data"
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"## Prepare the Traning Data"
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"source": [
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"# create filenames list (jpg only)\n",
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"# Create a file-names list (JPG image-files only)\n",
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"filenames = [file for file in os.listdir(DATA_PATH+\"/cats_n_dogs\") if file.endswith('jpg')]\n",
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"categories = []\n",
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"\n",
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"# categories & prediction classes map\n",
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"# Categories and prediction-classes map\n",
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"categories_map = {\n",
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" 'dog': 1,\n",
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" 'cat': 0,\n",
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"with open(MODEL_PATH + 'prediction_classes_map.json', 'w') as f:\n",
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" json.dump(prediction_map, f)\n",
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"\n",
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"# Full samples DF\n",
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"# Create a pandas DataFrame for the full sample\n",
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"for filename in filenames:\n",
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" category = filename.split('.')[0]\n",
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" categories.append([categories_map[category]])\n",
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"_uuid": "a999484fc35b73373fafe2253ae9db7ff46fdb90"
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"### See Total In count"
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"## Check the Total Image Count\n",
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"\n",
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"Check the total image count for each category.<br>\n",
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"The data set has 12,000 cat images and 12,000 dog images."
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"df['category'].value_counts().plot.bar()"
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"_uuid": "3a08da58107777a1dd05c4a4bf5c484484923cac"
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},
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"From our data we have 12000 cats and 12000 dogs"
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]
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},
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"# See sample image"
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"## Display the Sample Image"
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"image = load_img(DATA_PATH+\"/cats_n_dogs/\"+sample)\n",
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demos/gpu/horovod-image-classification/02-training-with-horovod-cats-n-dogs.ipynb renamed to demos/gpu/horovod/image-classification/02-train-with-horovod-cats-n-dogs.ipynb

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"!rm -f /User/horovod/cats_dogs.hd5"
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"!rm -f /User/demos/gpu/horovod/image-classification/cats_dogs.hd5"
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" 'spec': {'replicas': 8,\n",
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" 'template': {'spec': {'containers': [{'command': ['mpirun',\n",
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" 'python',\n",
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" '/User/horovod/hvd_catsndogs.py',\n",
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" '/User/demos/image-classification/cats_and_dogs_filtered',\n",
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" '/User/horovod'],\n",
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" '/User/demos/gpu/horovod/image-classification/horovod_train_cats_n_dogs.py',\n",
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" '/User/demos/gpu/horovod/image-classification/cats_and_dogs_filtered',\n",
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" '/User/demos/gpu/horovod/image-classification'],\n",
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" 'image': 'iguaziodocker/horovod:0.1.1',\n",
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" 'name': 'horovod-cats-n-dogs',\n",
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" 'resources': {'limits': {'nvidia.com/gpu': 1}},\n",
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"from v3io_gputils.mpijob import MpiJob\n",
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"job = MpiJob(HOROVOD_JOB_NAME, 'iguaziodocker/horovod:0.1.1', ['/User/demos/gpu/image-classification-horovod/hvd_dogncat.py',\n",
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" '/User/demos/gpu/image-classification-horvod/cats_and_dogs_filtered',\n",
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" '/User/demos/gpu/image-classification-horovod/horovod'])\n",
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"job = MpiJob(HOROVOD_JOB_NAME, 'iguaziodocker/horovod:0.1.1', ['/User/demos/gpu/horovod/image-classification/horovod_train_cats_n_dogs.py',\n",
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" '/User/demos/gpu/horovod/image-classification/cats_and_dogs_filtered',\n",
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" '/User/demos/gpu/horovod/image-classification'])\n",
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"job.submit()"
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" - command:\n",
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" - mpirun\n",
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" - python\n",
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" - /User/horovod/hvd_catsndogs.py\n",
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" - /User/demos/image-classification/cats_and_dogs_filtered\n",
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" - /User/horovod\n",
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" - /User/demos/gpu/horovod/image-classification/horovod_train_cats_n_dogs.py\n",
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" - /User/demos/gpu/horovod/image-classification/cats_and_dogs_filtered\n",
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" - /User/demos/gpu/horovod/image-classification\n",
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" image: iguaziodocker/horovod:0.1.1\n",
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" name: horovod-cats-n-dogs\n",
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