HEPML_HandsOn_install_check.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"HEPML_HandsOn_install_check_v2.ipynb","provenance":[],"collapsed_sections":[]},"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":"3.8.5"}},"cells":[{"cell_type":"markdown","metadata":{"id":"bu6L6Q23K6v1"},"source":["# SOS 2021 installation check"]},{"cell_type":"code","metadata":{"id":"v38BZnf9K6v3","executionInfo":{"status":"ok","timestamp":1611488240243,"user_tz":-60,"elapsed":501,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["COLAB=True # if running in COLAB\n","#COLAB=False # if not running on COLAB\n","GPU=True # if using GPU (should be True on COLAB and possibly Linux)\n","#GPU=False # no GPU on Mac and possibly Linux"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"FUmP_jR69TGW"},"source":["\n","\n","## Enabling and testing the GPU\n","\n","(largely copied from https://colab.research.google.com/notebooks/gpu.ipynb)\n","\n","On COLAB only, you'll need to enable GPUs for the notebook:\n","\n","- Navigate to Edit→Notebook Settings\n","- select GPU from the Hardware Accelerator drop-down\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ft269my28Q22","executionInfo":{"status":"ok","timestamp":1611488241132,"user_tz":-60,"elapsed":1378,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"8044ec93-4382-4adf-b3b2-ba4ab55043b5"},"source":["if COLAB: \n","    # Display available and used memory.\n","    !free -h\n","    print(\"-\"*70)\n","    # Display the CPU specification.\n","    !lscpu\n","    print(\"-\"*70)\n","    # Display the GPU specification (if available).\n","    !(nvidia-smi | grep -q \"has failed\") && echo \"No GPU found!\" || nvidia-smi\n"],"execution_count":24,"outputs":[{"output_type":"stream","text":["              total        used        free      shared  buff/cache   available\n","Mem:            12G        2.9G        3.8G         13M        6.0G         10G\n","Swap:            0B          0B          0B\n","----------------------------------------------------------------------\n","Architecture:        x86_64\n","CPU op-mode(s):      32-bit, 64-bit\n","Byte Order:          Little Endian\n","CPU(s):              2\n","On-line CPU(s) list: 0,1\n","Thread(s) per core:  2\n","Core(s) per socket:  1\n","Socket(s):           1\n","NUMA node(s):        1\n","Vendor ID:           GenuineIntel\n","CPU family:          6\n","Model:               79\n","Model name:          Intel(R) Xeon(R) CPU @ 2.20GHz\n","Stepping:            0\n","CPU MHz:             2199.998\n","BogoMIPS:            4399.99\n","Hypervisor vendor:   KVM\n","Virtualization type: full\n","L1d cache:           32K\n","L1i cache:           32K\n","L2 cache:            256K\n","L3 cache:            56320K\n","NUMA node0 CPU(s):   0,1\n","Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n","----------------------------------------------------------------------\n","Sun Jan 24 11:37:20 2021       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.32.03    Driver Version: 418.67       CUDA Version: 10.1     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   44C    P0    27W /  70W |   1705MiB / 15079MiB |      0%      Default |\n","|                               |                      |                 ERR! |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"7W10eOPy_-0P"},"source":["## Check XGBoost"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yn0oTPr5AVcN","executionInfo":{"status":"ok","timestamp":1611488243690,"user_tz":-60,"elapsed":3928,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"1f4606a4-c19e-430c-854d-5560553b658e"},"source":["import xgboost as xgb\n","print(xgb.__version__)  # preinstalled version 0.9.0 20210123\n","!pip install xgboost --upgrade # install 1.3.3 20210123 \n","print(xgb.__version__)\n"],"execution_count":25,"outputs":[{"output_type":"stream","text":["1.3.3\n","Requirement already up-to-date: xgboost in /usr/local/lib/python3.6/dist-packages (1.3.3)\n","Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.19.5)\n","Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n","1.3.3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"H6BxlYHeBPSA","executionInfo":{"status":"ok","timestamp":1611488243691,"user_tz":-60,"elapsed":3920,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"fec8f09b-3d3b-48b5-ce4e-04654681abca"},"source":["from sklearn.datasets import load_boston\n","boston = load_boston()\n","import pandas as pd\n","\n","data = pd.DataFrame(boston.data)\n","data.columns = boston.feature_names\n","data.head()"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>B</th>\n","      <th>LSTAT</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0.0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1.0</td>\n","      <td>296.0</td>\n","      <td>15.3</td>\n","      <td>396.90</td>\n","      <td>4.98</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2.0</td>\n","      <td>242.0</td>\n","      <td>17.8</td>\n","      <td>396.90</td>\n","      <td>9.14</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.02729</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>7.185</td>\n","      <td>61.1</td>\n","      <td>4.9671</td>\n","      <td>2.0</td>\n","      <td>242.0</td>\n","      <td>17.8</td>\n","      <td>392.83</td>\n","      <td>4.03</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.03237</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.998</td>\n","      <td>45.8</td>\n","      <td>6.0622</td>\n","      <td>3.0</td>\n","      <td>222.0</td>\n","      <td>18.7</td>\n","      <td>394.63</td>\n","      <td>2.94</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.06905</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>7.147</td>\n","      <td>54.2</td>\n","      <td>6.0622</td>\n","      <td>3.0</td>\n","      <td>222.0</td>\n","      <td>18.7</td>\n","      <td>396.90</td>\n","      <td>5.33</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      CRIM    ZN  INDUS  CHAS    NOX  ...  RAD    TAX  PTRATIO       B  LSTAT\n","0  0.00632  18.0   2.31   0.0  0.538  ...  1.0  296.0     15.3  396.90   4.98\n","1  0.02731   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  396.90   9.14\n","2  0.02729   0.0   7.07   0.0  0.469  ...  2.0  242.0     17.8  392.83   4.03\n","3  0.03237   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  394.63   2.94\n","4  0.06905   0.0   2.18   0.0  0.458  ...  3.0  222.0     18.7  396.90   5.33\n","\n","[5 rows x 13 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"DAhK6VooCLEb","executionInfo":{"status":"ok","timestamp":1611488243692,"user_tz":-60,"elapsed":3920,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["X, y = data.iloc[:,:-1],data.iloc[:,-1]\n","data_dmatrix = xgb.DMatrix(data=X,label=y)\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n","xg_reg = xgb.XGBRegressor(objective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,\n","                max_depth = 5, alpha = 10, n_estimators = 10)\n","xg_reg.fit(X_train,y_train)\n","preds = xg_reg.predict(X_test)"],"execution_count":27,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8bp-IT4eEGzG","executionInfo":{"status":"ok","timestamp":1611488243692,"user_tz":-60,"elapsed":3914,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"ee093e64-c516-4f21-b378-00144894e7c1"},"source":["from sklearn import datasets\n","import xgboost as xgb\n","import numpy as np\n","import time\n","\n","iris = datasets.load_iris()\n","X = iris.data\n","y = iris.target\n","from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2)\n","D_train = xgb.DMatrix(X_train, label=Y_train)\n","D_test = xgb.DMatrix(X_test, label=Y_test)\n","param = {\n","    'eta': 0.3, \n","    'max_depth': 3,  \n","    'objective': 'multi:softprob',  \n","    'num_class': 3} \n","\n","steps = 20  # The number of training iterations\n","starting_time = time.time( )\n","model = xgb.train(param, D_train, steps)\n","\n","training_time = time.time( ) - starting_time\n","print(\"Training time:\",training_time)\n","from sklearn.metrics import accuracy_score\n","\n","preds = model.predict(D_test)\n","best_preds = np.asarray([np.argmax(line) for line in preds])\n","\n","print(\"Accuracy = {}\".format(accuracy_score(Y_test, best_preds)))\n"],"execution_count":28,"outputs":[{"output_type":"stream","text":["[11:37:23] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","Training time: 0.008787870407104492\n","Accuracy = 0.9666666666666667\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JBRUTl3GFbMo"},"source":["## Check lightgbm\n"]},{"cell_type":"code","metadata":{"id":"zDzjC_UvFo2X","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611488245739,"user_tz":-60,"elapsed":5959,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"ec54bef6-a8a3-4cd2-fb63-133ae0c6b3ec"},"source":["## Check lightgbm\n","import lightgbm as lgb\n","print (lgb.__version__) # Tested with 2.3.1\n","!pip install lightgbm --upgrade\n","print (lgb.__version__)"],"execution_count":29,"outputs":[{"output_type":"stream","text":["3.1.1\n","Requirement already up-to-date: lightgbm in /usr/local/lib/python3.6/dist-packages (3.1.1)\n","Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.19.5)\n","Requirement already satisfied, skipping upgrade: scikit-learn!=0.22.0 in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.22.2.post1)\n","Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.4.1)\n","Requirement already satisfied, skipping upgrade: wheel in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.36.2)\n","Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn!=0.22.0->lightgbm) (1.0.0)\n","3.1.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XvJAZU7MGWhF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611488245740,"user_tz":-60,"elapsed":5959,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"cee88654-02d8-4790-9283-cf0de4c977c4"},"source":["gbm = lgb.LGBMClassifier()\n","\n","\n","starting_time = time.time( )\n","\n","gbm.fit(X_train, Y_train)\n","\n","training_time = time.time( ) - starting_time\n","print(\"Training time:\",training_time)"],"execution_count":30,"outputs":[{"output_type":"stream","text":["Training time: 0.029570817947387695\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rx0exCnC_vy5"},"source":["# Check tensorflow "]},{"cell_type":"code","metadata":{"id":"CsBkvgIIK6v8","executionInfo":{"status":"ok","timestamp":1611488245741,"user_tz":-60,"elapsed":5959,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["if not COLAB:\n","    !pip install tensorflow"],"execution_count":31,"outputs":[]},{"cell_type":"code","metadata":{"id":"YDN7yNY57yAb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1611488245741,"user_tz":-60,"elapsed":5958,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"fed2e7f1-4afc-481b-e90b-07c8f96ce24e"},"source":["import tensorflow as tf\n","print (tf.__version__)  # COLAB default 20210123 2.4.0\n"],"execution_count":32,"outputs":[{"output_type":"stream","text":["2.4.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"szsUzB349TsO","executionInfo":{"status":"ok","timestamp":1611488249104,"user_tz":-60,"elapsed":9319,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"c815a492-d5df-4db1-9042-322df33d13ad"},"source":["import tensorflow as tf\n","import timeit\n","\n","device_name = tf.test.gpu_device_name()\n","if device_name != '/device:GPU:0':\n","  if GPU:\n","        raise SystemError('GPU device not found')\n","\n","def cpu():\n","  with tf.device('/cpu:0'):\n","    random_image_cpu = tf.random.normal((100, 100, 100, 3))\n","    net_cpu = tf.keras.layers.Conv2D(32, 7)(random_image_cpu)\n","    return tf.math.reduce_sum(net_cpu)\n","\n","def gpu():\n","  with tf.device('/device:GPU:0'):\n","    random_image_gpu = tf.random.normal((100, 100, 100, 3))\n","    net_gpu = tf.keras.layers.Conv2D(32, 7)(random_image_gpu)\n","    return tf.math.reduce_sum(net_gpu)\n","  \n","# We run each op once to warm up; see: https://stackoverflow.com/a/45067900\n","cpu()\n","if GPU:\n","    gpu()\n","\n","# Run the op several times.\n","from datetime import datetime\n","print (datetime.now())\n","print('Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images '\n","      '(batch x height x width x channel). Sum of ten runs.')\n","print('CPU (s):')\n","cpu_time = timeit.timeit('cpu()', number=10, setup=\"from __main__ import cpu\")\n","print(cpu_time)\n","print('GPU (s):')\n","gpu_time = timeit.timeit('gpu()', number=10, setup=\"from __main__ import gpu\")\n","print(gpu_time)\n","print('GPU speedup over CPU: {}x'.format(int(cpu_time/gpu_time)))"],"execution_count":33,"outputs":[{"output_type":"stream","text":["2021-01-24 11:37:25.795127\n","Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images (batch x height x width x channel). Sum of ten runs.\n","CPU (s):\n","2.862845119000042\n","GPU (s):\n","0.03868061600002193\n","GPU speedup over CPU: 74x\n"],"name":"stdout"}]}]}