Installing recommended packages and update conda, sample code with performance test
This commit is contained in:
Christoph Schranz
parent
2990c25457
commit
e3a0bf2799
1243
extra/Getting_Started/ElasticsearchConnection.ipynb
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extra/Getting_Started/ElasticsearchConnection.ipynb
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extra/Getting_Started/GPU-processing.ipynb
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extra/Getting_Started/GPU-processing.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GPU-Jupyter\n",
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"\n",
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"This Jupyterlab Instance is connected to the GPU via CUDA drivers. In this notebook, we test the installation and perform some basic operations on the GPU."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test GPU connection\n",
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"\n",
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"#### Using the following command, your GPU type and its NVIDIA-SMI driver version should be listed:"
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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": 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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"Fri Dec 20 09:42:29 2019 \n",
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"+-----------------------------------------------------------------------------+\n",
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"| NVIDIA-SMI 440.26 Driver Version: 440.26 CUDA Version: 10.2 |\n",
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"|-------------------------------+----------------------+----------------------+\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
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"|===============================+======================+======================|\n",
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"| 0 GeForce RTX 207... Off | 00000000:01:00.0 Off | N/A |\n",
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"| 0% 54C P0 38W / 215W | 204MiB / 7974MiB | 0% Default |\n",
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"+-------------------------------+----------------------+----------------------+\n",
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" \n",
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"+-----------------------------------------------------------------------------+\n",
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"| Processes: GPU Memory |\n",
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"| GPU PID Type Process name Usage |\n",
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"|=============================================================================|\n",
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"+-----------------------------------------------------------------------------+\n"
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]
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}
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],
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"source": [
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Now, test if PyTorch can access the GPU via CUDA:"
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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": 2,
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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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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"torch.cuda.is_available()"
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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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{
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"data": {
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"text/plain": [
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"[name: \"/device:CPU:0\"\n",
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" device_type: \"CPU\"\n",
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" memory_limit: 268435456\n",
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" locality {\n",
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" }\n",
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" incarnation: 891330946073693377, name: \"/device:XLA_CPU:0\"\n",
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" device_type: \"XLA_CPU\"\n",
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" memory_limit: 17179869184\n",
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" locality {\n",
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" }\n",
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" incarnation: 9415777875944419380\n",
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" physical_device_desc: \"device: XLA_CPU device\"]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from tensorflow.python.client import device_lib\n",
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"device_lib.list_local_devices()"
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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": 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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"tensor([[0.8722, 0.5115, 0.9504],\n",
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" [0.7723, 0.2860, 0.5793],\n",
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" [0.5388, 0.5681, 0.4295],\n",
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" [0.5269, 0.5165, 0.7475],\n",
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" [0.4882, 0.8255, 0.6498]])"
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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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],
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"source": [
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"from __future__ import print_function\n",
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"import numpy as np\n",
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"import torch\n",
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"a = torch.rand(5, 3)\n",
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"a"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Performance test\n",
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"\n",
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"#### Now we want to know how much faster a typical operation is using GPU. Therefore we do the same operation in numpy, PyTorch and PyTorch with CUDA. The test operation is the calculation of the prediction matrix that is done in a linear regression."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1) Numpy"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.random.rand(10000, 256)"
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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": 6,
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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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"590 ms ± 41.6 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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"source": [
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"%%timeit\n",
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"H = x.dot(np.linalg.inv(x.transpose().dot(x))).dot(x.transpose())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2) PyTorch"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = torch.rand(10000, 256)"
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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": 8,
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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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"853 ms ± 16.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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}
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],
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"source": [
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"%%timeit\n",
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"# Calculate the projection matrix of x\n",
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"H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3) PyTorch on GPU via CUDA"
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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": 9,
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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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"tensor([[0.1054, 0.3291, 0.7729, 0.6005, 0.2372],\n",
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" [0.1022, 0.4534, 0.3964, 0.9174, 0.2610],\n",
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" [0.3969, 0.5472, 0.3876, 0.1979, 0.4063],\n",
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" [0.3630, 0.6374, 0.4176, 0.4804, 0.0396],\n",
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" [0.8256, 0.2289, 0.2265, 0.4388, 0.6070]], device='cuda:0')\n",
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"tensor([[0.1054, 0.3291, 0.7729, 0.6005, 0.2372],\n",
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" [0.1022, 0.4534, 0.3964, 0.9174, 0.2610],\n",
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" [0.3969, 0.5472, 0.3876, 0.1979, 0.4063],\n",
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" [0.3630, 0.6374, 0.4176, 0.4804, 0.0396],\n",
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" [0.8256, 0.2289, 0.2265, 0.4388, 0.6070]], dtype=torch.float64)\n"
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]
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}
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],
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"source": [
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"# let us run this cell only if CUDA is available\n",
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"# We will use ``torch.device`` objects to move tensors in and out of GPU\n",
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"if torch.cuda.is_available():\n",
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" device = torch.device(\"cuda\") # a CUDA device object\n",
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" x = torch.rand(10000, 256, device=device) # directly create a tensor on GPU\n",
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" y = x.to(device) # or just use strings ``.to(\"cuda\")``\n",
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" print(x[0:5, 0:5])\n",
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" print(y.to(\"cpu\", torch.double)[0:5, 0:5])"
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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": 10,
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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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"11.3 ms ± 60.3 µs 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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"source": [
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"%%timeit\n",
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"H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
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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": 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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exhaustive Testing on GPU"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"# let us run this cell only if CUDA is available\n",
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"# We will use ``torch.device`` objects to move tensors in and out of GPU\n",
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"import torch\n",
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"if torch.cuda.is_available():\n",
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" device = torch.device(\"cuda\") # a CUDA device object\n",
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" x = torch.rand(10000, 10, device=device) # directly create a tensor on GPU"
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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": 12,
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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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"tensor([[0.3112, 0.7480, 0.1882, 0.8453, 0.8198],\n",
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" [0.5953, 0.8401, 0.3126, 0.6025, 0.5252],\n",
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" [0.1902, 0.5610, 0.7968, 0.1463, 0.7154],\n",
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" [0.7979, 0.2161, 0.6176, 0.2951, 0.1980],\n",
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" [0.6451, 0.3837, 0.5305, 0.2740, 0.3330]], device='cuda:0')\n"
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]
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}
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],
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"source": [
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"if torch.cuda.is_available():\n",
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" y = x.to(device) # or just use strings ``.to(\"cuda\")``\n",
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" print(x[0:5, 0:5])"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"if torch.cuda.is_available():\n",
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" # Here is the memory of the GPU a border. \n",
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" # A matrix with 100000 lines requires 37 GB, but only 8 GB are available.\n",
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" H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
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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": 14,
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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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"tensor([[ 1.2748e-03, 5.3656e-04, 1.7376e-04, 3.3888e-06, -1.7049e-04],\n",
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" [ 5.3656e-04, 6.3624e-04, 2.5957e-05, 3.3281e-04, -1.6239e-05],\n",
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" [ 1.7376e-04, 2.5957e-05, 7.6328e-04, 7.7603e-05, 1.8272e-04],\n",
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" [ 3.3888e-06, 3.3281e-04, 7.7603e-05, 9.6281e-04, 1.2375e-04],\n",
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" [-1.7049e-04, -1.6239e-05, 1.8272e-04, 1.2375e-04, 6.9231e-04]],\n",
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" device='cuda:0')\n"
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]
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}
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],
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"source": [
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"if torch.cuda.is_available():\n",
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" print(H[0:5, 0:5])"
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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": 15,
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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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"tensor([[ 1.2748e-03, 5.3656e-04, 1.7376e-04, 3.3888e-06, -1.7049e-04],\n",
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" [ 5.3656e-04, 6.3624e-04, 2.5957e-05, 3.3281e-04, -1.6239e-05],\n",
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" [ 1.7376e-04, 2.5957e-05, 7.6328e-04, 7.7603e-05, 1.8272e-04],\n",
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" [ 3.3888e-06, 3.3281e-04, 7.7603e-05, 9.6281e-04, 1.2375e-04],\n",
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" [-1.7049e-04, -1.6239e-05, 1.8272e-04, 1.2375e-04, 6.9231e-04]],\n",
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" dtype=torch.float64)\n"
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]
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}
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],
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"source": [
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"if torch.cuda.is_available():\n",
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" # This operation is difficult, as an symmetric matrix is transferred \n",
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" # back to the CPU. Is possible up to 30000 rows.\n",
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" print(H.to(\"cpu\", torch.double)[0:5, 0:5])"
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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": 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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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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5742
extra/Getting_Started/JuliaQuickstart.ipynb
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extra/Getting_Started/JuliaQuickstart.ipynb
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extra/Getting_Started/JupyterBasics.ipynb
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extra/Getting_Started/JupyterBasics.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Getting Started\n",
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"\n",
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"**Markdown** is a method to write documentations and even embed `HTML` and `Latex` formulas.\n",
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"\n",
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"### Jupyter tutorial\n",
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"\n",
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"[](https://www.youtube.com/watch?v=CwFq3YDU6_Y?rel=0&showinfo=0)\n",
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"\n",
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"\n",
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"### Bayesian Rule\n",
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"$$P(A \\mid B) = \\frac{P(B \\mid A)P(A)}{P(B)}$$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Coding and magic commands"
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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": 1,
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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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"[1, 8, 27, 64, 125, 216, 343]"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"[x**3 for x in range(1,8)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/json": {
|
||||
"cell": {
|
||||
"!": "OSMagics",
|
||||
"HTML": "Other",
|
||||
"SVG": "Other",
|
||||
"bash": "Other",
|
||||
"capture": "ExecutionMagics",
|
||||
"debug": "ExecutionMagics",
|
||||
"file": "Other",
|
||||
"html": "DisplayMagics",
|
||||
"javascript": "DisplayMagics",
|
||||
"js": "DisplayMagics",
|
||||
"latex": "DisplayMagics",
|
||||
"markdown": "DisplayMagics",
|
||||
"perl": "Other",
|
||||
"prun": "ExecutionMagics",
|
||||
"pypy": "Other",
|
||||
"python": "Other",
|
||||
"python2": "Other",
|
||||
"python3": "Other",
|
||||
"ruby": "Other",
|
||||
"script": "ScriptMagics",
|
||||
"sh": "Other",
|
||||
"svg": "DisplayMagics",
|
||||
"sx": "OSMagics",
|
||||
"system": "OSMagics",
|
||||
"time": "ExecutionMagics",
|
||||
"timeit": "ExecutionMagics",
|
||||
"writefile": "OSMagics"
|
||||
},
|
||||
"line": {
|
||||
"alias": "OSMagics",
|
||||
"alias_magic": "BasicMagics",
|
||||
"autoawait": "AsyncMagics",
|
||||
"autocall": "AutoMagics",
|
||||
"automagic": "AutoMagics",
|
||||
"autosave": "KernelMagics",
|
||||
"bookmark": "OSMagics",
|
||||
"cat": "Other",
|
||||
"cd": "OSMagics",
|
||||
"clear": "KernelMagics",
|
||||
"colors": "BasicMagics",
|
||||
"conda": "PackagingMagics",
|
||||
"config": "ConfigMagics",
|
||||
"connect_info": "KernelMagics",
|
||||
"cp": "Other",
|
||||
"debug": "ExecutionMagics",
|
||||
"dhist": "OSMagics",
|
||||
"dirs": "OSMagics",
|
||||
"doctest_mode": "BasicMagics",
|
||||
"ed": "Other",
|
||||
"edit": "KernelMagics",
|
||||
"env": "OSMagics",
|
||||
"gui": "BasicMagics",
|
||||
"hist": "Other",
|
||||
"history": "HistoryMagics",
|
||||
"killbgscripts": "ScriptMagics",
|
||||
"ldir": "Other",
|
||||
"less": "KernelMagics",
|
||||
"lf": "Other",
|
||||
"lk": "Other",
|
||||
"ll": "Other",
|
||||
"load": "CodeMagics",
|
||||
"load_ext": "ExtensionMagics",
|
||||
"loadpy": "CodeMagics",
|
||||
"logoff": "LoggingMagics",
|
||||
"logon": "LoggingMagics",
|
||||
"logstart": "LoggingMagics",
|
||||
"logstate": "LoggingMagics",
|
||||
"logstop": "LoggingMagics",
|
||||
"ls": "Other",
|
||||
"lsmagic": "BasicMagics",
|
||||
"lx": "Other",
|
||||
"macro": "ExecutionMagics",
|
||||
"magic": "BasicMagics",
|
||||
"man": "KernelMagics",
|
||||
"matplotlib": "PylabMagics",
|
||||
"mkdir": "Other",
|
||||
"more": "KernelMagics",
|
||||
"mv": "Other",
|
||||
"notebook": "BasicMagics",
|
||||
"page": "BasicMagics",
|
||||
"pastebin": "CodeMagics",
|
||||
"pdb": "ExecutionMagics",
|
||||
"pdef": "NamespaceMagics",
|
||||
"pdoc": "NamespaceMagics",
|
||||
"pfile": "NamespaceMagics",
|
||||
"pinfo": "NamespaceMagics",
|
||||
"pinfo2": "NamespaceMagics",
|
||||
"pip": "PackagingMagics",
|
||||
"popd": "OSMagics",
|
||||
"pprint": "BasicMagics",
|
||||
"precision": "BasicMagics",
|
||||
"prun": "ExecutionMagics",
|
||||
"psearch": "NamespaceMagics",
|
||||
"psource": "NamespaceMagics",
|
||||
"pushd": "OSMagics",
|
||||
"pwd": "OSMagics",
|
||||
"pycat": "OSMagics",
|
||||
"pylab": "PylabMagics",
|
||||
"qtconsole": "KernelMagics",
|
||||
"quickref": "BasicMagics",
|
||||
"recall": "HistoryMagics",
|
||||
"rehashx": "OSMagics",
|
||||
"reload_ext": "ExtensionMagics",
|
||||
"rep": "Other",
|
||||
"rerun": "HistoryMagics",
|
||||
"reset": "NamespaceMagics",
|
||||
"reset_selective": "NamespaceMagics",
|
||||
"rm": "Other",
|
||||
"rmdir": "Other",
|
||||
"run": "ExecutionMagics",
|
||||
"save": "CodeMagics",
|
||||
"sc": "OSMagics",
|
||||
"set_env": "OSMagics",
|
||||
"store": "StoreMagics",
|
||||
"sx": "OSMagics",
|
||||
"system": "OSMagics",
|
||||
"tb": "ExecutionMagics",
|
||||
"time": "ExecutionMagics",
|
||||
"timeit": "ExecutionMagics",
|
||||
"unalias": "OSMagics",
|
||||
"unload_ext": "ExtensionMagics",
|
||||
"who": "NamespaceMagics",
|
||||
"who_ls": "NamespaceMagics",
|
||||
"whos": "NamespaceMagics",
|
||||
"xdel": "NamespaceMagics",
|
||||
"xmode": "BasicMagics"
|
||||
}
|
||||
},
|
||||
"text/plain": [
|
||||
"Available line magics:\n",
|
||||
"%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
|
||||
"\n",
|
||||
"Available cell magics:\n",
|
||||
"%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
|
||||
"\n",
|
||||
"Automagic is ON, % prefix IS NOT needed for line magics."
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# List all magic commands\n",
|
||||
"%lsmagic"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np \n",
|
||||
"x = np.random.normal(0, 1, 1_000_000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"1.03 ms ± 4.93 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%timeit \n",
|
||||
"x.mean()**2 - (x**2).mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Send variables back and fourth in bash\n",
|
||||
"\n",
|
||||
"The strings will be saved as files in the working directory"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"string_0\n",
|
||||
"string_1\n",
|
||||
"string_2\n",
|
||||
"string_3\n",
|
||||
"string_4\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i in range(5):\n",
|
||||
" string = \"string_{}\".format(i)\n",
|
||||
" print(string)\n",
|
||||
" !touch $string"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_0\n",
|
||||
"-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_1\n",
|
||||
"-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_2\n",
|
||||
"-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_3\n",
|
||||
"-rw-r--r-- 1 jovyan users 0 Dec 19 08:44 string_4\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!ls -l | grep string_"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['string_0', 'string_1', 'string_2', 'string_3', 'string_4']"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"files = !ls -1 string_*\n",
|
||||
"!rm string_*\n",
|
||||
"files"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
142
extra/Getting_Started/MultiInterpreterNotebook.ipynb
Executable file
142
extra/Getting_Started/MultiInterpreterNotebook.ipynb
Executable file
File diff suppressed because one or more lines are too long
BIN
extra/Getting_Started/jupyterlab-overview.png
Executable file
BIN
extra/Getting_Started/jupyterlab-overview.png
Executable file
Binary file not shown.
|
After Width: | Height: | Size: 118 KiB |
57
extra/Getting_Started/scroller.py
Executable file
57
extra/Getting_Started/scroller.py
Executable file
@ -0,0 +1,57 @@
|
||||
def scroller(index, quantity, timerange=timedelta(days=0), startdt="", enddt=""):
|
||||
print("Starting to scroll", end='')
|
||||
# Retrieve the datetimes, note that timerange has a higher priority
|
||||
if timerange.total_seconds() > 0:
|
||||
now = datetime.utcnow().replace(tzinfo=pytz.UTC)
|
||||
startdt = (now - timerange).isoformat()
|
||||
enddt = now.isoformat()
|
||||
|
||||
# search the first page and write the result to data
|
||||
response = es.search(
|
||||
index=index,
|
||||
body={
|
||||
"query": {
|
||||
"bool": {
|
||||
"must": [
|
||||
{"range" : {
|
||||
"phenomenonTime" : {
|
||||
#"gte": "2018-02-20T09:08:34.230693+00:00",
|
||||
"gte": startdt,
|
||||
"lte": enddt,
|
||||
"time_zone": "+01:00"
|
||||
}
|
||||
}},
|
||||
{
|
||||
"match_phrase": {
|
||||
"Datastream.name.keyword": quantity
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
scroll='10m'
|
||||
)
|
||||
data = [[row["_source"]["phenomenonTime"], row["_source"]["result"]] for row in response['hits']['hits']]
|
||||
|
||||
# Append new pages until there aren't any left
|
||||
while len(response['hits']['hits']):
|
||||
print(".", end='')
|
||||
# process results
|
||||
# print([item["_id"] for item in response["hits"]["hits"]])
|
||||
response = es.scroll(scroll_id=response['_scroll_id'], scroll='10m')
|
||||
data += [[row["_source"]["phenomenonTime"], row["_source"]["result"]] for row in response['hits']['hits']]
|
||||
|
||||
# Convert data to a DataFrame and return it
|
||||
df = pd.DataFrame(data, columns=["phenomenonTime", quantity])
|
||||
# df.index = pd.to_datetime(df["phenomenonTime"].map(lambda t: t.split(".")[0]), utc=True)
|
||||
df.index = pd.to_datetime(df["phenomenonTime"].map(lambda t: roundto(t, 1)), utc=True)
|
||||
df = df.drop(["phenomenonTime"], axis=1)
|
||||
print("\nFetched {} tuples.".format(df.shape[0]))
|
||||
return df
|
||||
|
||||
def roundto(string, n):
|
||||
base = string.split(".")[0]
|
||||
if n > 0:
|
||||
base += "." + string.split(".")[1][:n]
|
||||
return base
|
||||
BIN
extra/performance-test.png
Normal file
BIN
extra/performance-test.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 346 KiB |
@ -417,6 +417,44 @@ RUN conda install -y -c pytorch \
|
||||
############################ Useful packages ###############################
|
||||
############################################################################
|
||||
|
||||
# Update conda
|
||||
RUN conda update -n base conda -y
|
||||
|
||||
# Install elasticsearch libs
|
||||
USER root
|
||||
RUN apt-get update \
|
||||
&& curl -sL http://central.maven.org/maven2/org/elasticsearch/elasticsearch-hadoop/6.8.1/elasticsearch-hadoop-6.8.1.jar
|
||||
RUN pip install --no-cache-dir elasticsearch==7.1.0
|
||||
|
||||
# Install rpy2 to share data between Python and R
|
||||
RUN conda install rpy2=2.9.4 plotly=4.4.1
|
||||
RUN conda install -c conda-forge ipyleaflet
|
||||
|
||||
# Install important packages and Graphviz
|
||||
RUN set -ex \
|
||||
&& buildDeps=' \
|
||||
graphviz==0.11 \
|
||||
' \
|
||||
&& apt-get update \
|
||||
&& apt-get -y install htop apt-utils graphviz libgraphviz-dev \
|
||||
&& pip install --no-cache-dir $buildDeps
|
||||
|
||||
# Install various extensions
|
||||
RUN jupyter labextension install @jupyterlab/github
|
||||
RUN jupyter labextension install jupyterlab-drawio
|
||||
RUN jupyter labextension install jupyter-leaflet
|
||||
RUN jupyter labextension install @jupyterlab/plotly-extension
|
||||
RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager
|
||||
RUN pip install --no-cache-dir jupyter-tabnine==1.0.2 && \
|
||||
jupyter nbextension install --py jupyter_tabnine && \
|
||||
jupyter nbextension enable --py jupyter_tabnine && \
|
||||
jupyter serverextension enable --py jupyter_tabnine
|
||||
RUN conda install -c conda-forge jupyter_contrib_nbextensions && \
|
||||
conda install -c conda-forge jupyter_nbextensions_configurator && \
|
||||
jupyter nbextension enable codefolding/main
|
||||
RUN jupyter labextension install @ijmbarr/jupyterlab_spellchecker
|
||||
|
||||
# Copying config and fix permissions
|
||||
COPY jupyter_notebook_config.json /etc/jupyter/
|
||||
RUN fix-permissions /home/$NB_USER
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user