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no env file, use list of pkgs to create env
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tutorial/README.md

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## Setting up your environment
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This HPAT tutorial makes use of hpat (latest), numpy, pandas, daal4py (latest) and their dependences.
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The easiest and most reliable way is to create a python environment is through using the provided environment configuration:
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The easiest and most reliable way is to create a python environment using conda:
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`conda env create -f environment.yml`
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`conda create -n hpattut -c ehsantn -c numba/label/dev -c defaults -c intel -c conda-forge hpat daal4py pandas blas=*=mkl jupyter notebook`
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Then activate the environment
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tutorial/environment.yml

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tutorial/hpat.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"93.9 ms ± 11.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import hpat\n",
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"import pandas as pd\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'daal4py.hpat'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-2-fa4b44e49c22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhpat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdaal4py\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0md4p\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdaal4py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhpat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mhpat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'daal4py.hpat'"
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]
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}
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],
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import hpat\n",
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"import daal4py as d4p\n",
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"import daal4py.hpat\n",
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"import pandas as pd\n",
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"\n",
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"@hpat.jit\n",
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"def train():\n",
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"# Intraday-stock-mean-reversion-trading-backtest\n",
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"A nice backtest from http://www.pythonforfinance.net/2017/02/20/intraday-stock-mean-reversion-trading-backtest-in-python/ works nicely with HPAT. The full code use many of the above features and is attached (intraday_mean.py) to the tutorial. Enjoy!\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import daal4py as d"
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]
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},
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'(2019, 3)'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
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"d.__version__"
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'daal4py.hpat'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-5-a435cc549af4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdaal4py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhpat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'daal4py.hpat'"
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]
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}
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],
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"source": [
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"import daal4py.hpat"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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