fashion-mnist-save_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 200215 20:56:20 config:123] Using preprocessor: <kubeflow.fairing.preprocessors.converted_notebook.ConvertNotebookPreprocessor object at 0x7fd1dc3b10b8>\n",
      "[I 200215 20:56:20 config:125] Using builder: <kubeflow.fairing.builders.append.append.AppendBuilder object at 0x7fd1dc3a6198>\n",
      "[I 200215 20:56:20 config:127] Using deployer: <kubeflow.fairing.builders.append.append.AppendBuilder object at 0x7fd1dc3a6198>\n",
      "[W 200215 20:56:20 append:50] Building image using Append builder...\n",
      "[I 200215 20:56:20 base:105] Creating docker context: /tmp/fairing_context_68tn1odk\n",
      "[I 200215 20:56:20 converted_notebook:127] Converting fashion-mnist-save_model.ipynb to fashion-mnist-save_model.py\n",
      "[I 200215 20:56:20 docker_creds_:234] Loading Docker credentials for repository 'brightfly/kubeflow-jupyter-lab:tf2.0-gpu'\n",
      "[W 200215 20:56:22 append:54] Image successfully built in 2.239429968991317s.\n",
      "[W 200215 20:56:22 append:94] Pushing image kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E634C21D...\n",
      "[I 200215 20:56:22 docker_creds_:234] Loading Docker credentials for repository 'kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E634C21D'\n",
      "[W 200215 20:56:22 append:81] Uploading kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E634C21D\n",
      "[I 200215 20:56:22 docker_session_:280] Layer sha256:db108ae5c97b9d9b4a6ab30f08b9f00de1a383adb8b2fbdad530e801661c12c5 exists, skipping\n",
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      "[I 200215 20:56:22 docker_session_:284] Layer sha256:df715f2b6cc5ac0e3bc45cf288844c4eb6f53360639504ef8d46e835f63c9fe4 pushed.\n",
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      "[I 200215 20:56:22 docker_session_:334] Finished upload of: kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E634C21D\n",
      "[W 200215 20:56:22 append:99] Pushed image kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E634C21D in 0.2075631859479472s.\n",
      "[W 200215 20:56:22 job:90] The job fairing-job-sqdm5 launched.\n",
      "[W 200215 20:56:22 manager:227] Waiting for fairing-job-sqdm5-fhgmr to start...\n",
      "[W 200215 20:56:22 manager:227] Waiting for fairing-job-sqdm5-fhgmr to start...\n",
      "[W 200215 20:56:22 manager:227] Waiting for fairing-job-sqdm5-fhgmr to start...\n",
      "[W 200215 20:56:23 manager:227] Waiting for fairing-job-sqdm5-fhgmr to start...\n",
      "[I 200215 20:56:25 manager:233] Pod started running True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n",
      "2020-02-15 20:56:28.462303: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
      "2020-02-15 20:56:28.515396: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.516556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:\n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:04.0\n",
      "2020-02-15 20:56:28.516713: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.517660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties:\n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:05.0\n",
      "2020-02-15 20:56:28.518095: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
      "2020-02-15 20:56:28.520185: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
      "2020-02-15 20:56:28.521739: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
      "2020-02-15 20:56:28.522221: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
      "2020-02-15 20:56:28.524832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
      "2020-02-15 20:56:28.526791: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
      "2020-02-15 20:56:28.532663: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-02-15 20:56:28.532824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.533857: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.534983: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.536192: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.537199: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1\n",
      "2020-02-15 20:56:28.537659: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "2020-02-15 20:56:28.546405: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz\n",
      "2020-02-15 20:56:28.546957: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x41f9da0 executing computations on platform Host. Devices:\n",
      "2020-02-15 20:56:28.546986: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Host, Default Version\n",
      "2020-02-15 20:56:28.770520: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.772797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.773988: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x427c610 executing computations on platform CUDA. Devices:\n",
      "2020-02-15 20:56:28.774020: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
      "2020-02-15 20:56:28.774027: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (1): Tesla K80, Compute Capability 3.7\n",
      "2020-02-15 20:56:28.774656: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.775595: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:\n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:04.0\n",
      "2020-02-15 20:56:28.775745: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.776865: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 1 with properties:\n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:05.0\n",
      "2020-02-15 20:56:28.776956: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
      "2020-02-15 20:56:28.777010: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
      "2020-02-15 20:56:28.777036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
      "2020-02-15 20:56:28.777055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
      "2020-02-15 20:56:28.777097: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
      "2020-02-15 20:56:28.777130: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
      "2020-02-15 20:56:28.777152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
      "2020-02-15 20:56:28.777283: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.778286: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.779458: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.780649: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.781661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1\n",
      "2020-02-15 20:56:28.781744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
      "2020-02-15 20:56:28.784729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2020-02-15 20:56:28.784770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 1\n",
      "2020-02-15 20:56:28.784784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N Y\n",
      "2020-02-15 20:56:28.784794: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 1:   Y N\n",
      "2020-02-15 20:56:28.785011: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.786196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.787292: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.788195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10804 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "2020-02-15 20:56:28.788867: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2020-02-15 20:56:28.789810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 10804 MB memory) -> physical GPU (device: 1, name: Tesla K80, pci bus id: 0000:00:05.0, compute capability: 3.7)\n",
      "2020-02-15 20:56:30.638359: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
      "2020-02-15 20:56:30.808803: I tensorflow/core/profiler/lib/profiler_session.cc:184] Profiler session started.\n",
      "2020-02-15 20:56:31.138641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcupti.so.10.0\n",
      "2020-02-15 20:56:31.349588: I tensorflow/core/platform/default/device_tracer.cc:588] Collecting 58 kernel records, 5 memcpy records.\n",
      "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.274383). Check your callbacks.\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #\n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0\n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               100480\n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0\n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290\n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "epoch 0:\n",
      "batch=0 accuracy=0.09375 loss=2.4206066\n",
      "batch=1 accuracy=0.15625 loss=2.1314042\n",
      "batch=2 accuracy=0.1875 loss=2.0834346\n",
      "batch=3 accuracy=0.2109375 loss=2.004715\n",
      "batch=4 accuracy=0.24375 loss=1.8008401\n",
      "batch=5 accuracy=0.296875 loss=1.7142336\n",
      "batch=6 accuracy=0.32142857 loss=1.8440833\n",
      "batch=7 accuracy=0.33984375 loss=1.4813299\n",
      "batch=8 accuracy=0.35416666 loss=1.6362844\n",
      "batch=9 accuracy=0.371875 loss=1.3692628\n",
      "batch=10 accuracy=0.38636363 loss=1.4657733\n",
      "batch=11 accuracy=0.4140625 loss=1.0869579\n",
      "batch=12 accuracy=0.42307693 loss=1.4086115\n",
      "batch=13 accuracy=0.43080357 loss=1.443257\n",
      "batch=14 accuracy=0.44375 loss=1.1000959\n",
      "batch=15 accuracy=0.453125 loss=1.1986897\n",
      "batch=16 accuracy=0.45036766 loss=1.8682535\n",
      "batch=17 accuracy=0.46180555 loss=1.2406802\n",
      "batch=18 accuracy=0.46875 loss=1.1488734\n",
      "batch=19 accuracy=0.4765625 loss=0.9436528\n",
      "batch=20 accuracy=0.47767857 loss=1.2280827\n",
      "batch=21 accuracy=0.48295453 loss=1.2321882\n",
      "batch=22 accuracy=0.48913044 loss=0.9751281\n",
      "batch=23 accuracy=0.4908854 loss=1.2569427\n",
      "batch=24 accuracy=0.49875 loss=0.91809076\n",
      "batch=25 accuracy=0.50961536 loss=0.94518375\n",
      "batch=26 accuracy=0.5162037 loss=0.85107875\n",
      "batch=27 accuracy=0.52678573 loss=0.73509514\n",
      "batch=28 accuracy=0.53125 loss=1.0446998\n",
      "batch=29 accuracy=0.5364583 loss=1.0629492\n",
      "batch=30 accuracy=0.5403226 loss=0.9531347\n",
      "batch=31 accuracy=0.5439453 loss=1.1378239\n",
      "batch=32 accuracy=0.5501894 loss=0.90402734\n",
      "batch=33 accuracy=0.55422795 loss=1.1035864\n",
      "batch=34 accuracy=0.55089283 loss=1.4037503\n",
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      "epoch 4:\n",
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      "2020-02-15 20:57:03.188397: W tensorflow/python/util/util.cc:299] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "batch=1705 accuracy=0.87298506 loss=0.2865295\n",
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      "batch=1874 accuracy=0.87326664 loss=0.105667174\n",
      "Validation-accuracy=0.875 Validation-loss=0.3553805700659752\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W 200215 20:57:04 job:162] Cleaning up job fairing-job-sqdm5...\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import argparse\n",
    "from tensorflow.python.keras.callbacks import Callback\n",
    "\n",
    "\n",
    "\n",
    "class MyFashionMnist(object):\n",
    "  def train(self):\n",
    "    \n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument('--learning_rate', required=False, type=float, default=0.001)\n",
    "    parser.add_argument('--dropout_rate', required=False, type=float, default=0.3)\n",
    "    parser.add_argument('--opt', required=False, type=int, default=1)    \n",
    "    parser.add_argument('--checkpoint_dir', required=False, default='/reuslt/training_checkpoints')\n",
    "    parser.add_argument('--saved_model_dir', required=False, default='/result/saved_model/001')        \n",
    "    parser.add_argument('--tensorboard_log', required=False, default='/result/log')     \n",
    "    args = parser.parse_args()    \n",
    "    \n",
    "    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
    "    x_train, x_test = x_train / 255.0, x_test / 255.023\n",
    "\n",
    "    model = tf.keras.models.Sequential([\n",
    "      tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "      tf.keras.layers.Dense(128, activation='relu'),\n",
    "      tf.keras.layers.Dropout(args.dropout_rate),\n",
    "      tf.keras.layers.Dense(10, activation='softmax')\n",
    "    ])\n",
    "    \n",
    "    model.summary()\n",
    "    \n",
    "    sgd = tf.keras.optimizers.SGD(lr=args.learning_rate)\n",
    "    adam = tf.keras.optimizers.Adam(lr=args.learning_rate)\n",
    "    \n",
    "    optimizers= [sgd, adam]\n",
    "    model.compile(optimizer=optimizers[args.opt],\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['acc'])\n",
    "    \n",
    "    # 체크포인트를 저장할 체크포인트 디렉터리를 지정합니다.\n",
    "    checkpoint_dir = args.checkpoint_dir\n",
    "    # 체크포인트 파일의 이름\n",
    "    checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")        \n",
    "\n",
    "    model.fit(x_train, y_train,\n",
    "              verbose=0,\n",
    "              validation_data=(x_test, y_test),\n",
    "              epochs=5,\n",
    "              callbacks=[KatibMetricLog(),\n",
    "                        tf.keras.callbacks.TensorBoard(log_dir=args.tensorboard_log),\n",
    "                        tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,\n",
    "                               save_weights_only=True)\n",
    "                        ])\n",
    "    path = args.saved_model_dir        \n",
    "    model.save(path, save_format='tf')\n",
    "\n",
    "class KatibMetricLog(Callback):\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        print(\"batch=\" + str(batch),\n",
    "              \"accuracy=\" + str(logs.get('acc')),\n",
    "              \"loss=\" + str(logs.get('loss')))\n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        print(\"epoch \" + str(epoch) + \":\")\n",
    "    \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        print(\"Validation-accuracy=\" + str(logs.get('val_acc')),\n",
    "              \"Validation-loss=\" + str(logs.get('val_loss')))\n",
    "        return\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    if os.getenv('FAIRING_RUNTIME', None) is None:\n",
    "        from kubeflow import fairing\n",
    "        from kubeflow.fairing.kubernetes import utils as k8s_utils\n",
    "\n",
    "        DOCKER_REGISTRY = 'kubeflow-registry.default.svc.cluster.local:30000'\n",
    "        fairing.config.set_builder(\n",
    "            'append',\n",
    "            image_name='fairing-job',\n",
    "            base_image='brightfly/kubeflow-jupyter-lab:tf2.0-gpu',\n",
    "            registry=DOCKER_REGISTRY, \n",
    "            push=True)\n",
    "        # cpu 2, memory 5GiB\n",
    "        fairing.config.set_deployer('job',\n",
    "                                    namespace='dudaji',\n",
    "                                    pod_spec_mutators=[\n",
    "                                        k8s_utils.mounting_pvc(pvc_name=\"fashion-mnist\", \n",
    "                                                              pvc_mount_path=\"/result\"),\n",
    "                                        k8s_utils.get_resource_mutator(cpu=2,\n",
    "                                                                       memory=5)]\n",
    "         \n",
    "                                   )\n",
    "        fairing.config.run()\n",
    "    else:\n",
    "        remote_train = MyFashionMnist()\n",
    "        remote_train.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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