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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/shrey/Desktop/Kidney-Disease-Classifcation/research'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"os.chdir('../')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/shrey/Desktop/Kidney-Disease-Classifcation'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"MLFLOW_TRACKING_URI\"]=\"https://dagshub.com/Shrey-patel-07/Kidney-Disease-Classifcation.mlflow\"\n",
"os.environ[\"MLFLOW_TRACKING_USERNAME\"]=\"Shrey-patel-07\"\n",
"os.environ[\"MLFLOW_TRACKING_PASSWORD\"]=\"6a425a20bb5b645c5efa1ca49ab28097a92a76ee\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-04 12:24:18.257148: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-01-04 12:24:18.258826: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-01-04 12:24:18.285564: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-01-04 12:24:18.285595: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-01-04 12:24:18.286297: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-01-04 12:24:18.290364: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-01-04 12:24:18.290802: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-01-04 12:24:18.864121: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-04 12:24:20.808055: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
]
}
],
"source": [
"model = tf.keras.models.load_model('artifacts/training/model.h5')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"from pathlib import Path\n",
"\n",
"@dataclass(frozen=True)\n",
"class EvaluationConfig:\n",
" path_of_model: Path\n",
" training_data: Path\n",
" all_params: dict\n",
" mlflow_uri: str\n",
" params_image_size: list\n",
" params_batch_size: int"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"os.chdir('src/')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from kidney_classification.constants import *\n",
"from kidney_classification.utils.common import read_yaml, create_directories, save_json"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class ConfigurationManager:\n",
" def __init__(\n",
" self,\n",
" config_filepath=CONFIG_FILE_PATH,\n",
" param_filepath=PARAMS_FILE_PATH\n",
" ):\n",
" self.config = read_yaml(config_filepath)\n",
" self.params = read_yaml(param_filepath)\n",
" create_directories([self.config.artifacts_root])\n",
"\n",
" def get_evaluation_config(self) -> EvaluationConfig:\n",
" eval_config = EvaluationConfig(\n",
" path_of_model=\"artifacts/training/model.h5\",\n",
" training_data=\"artifacts/data_ingestion/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone\",\n",
" mlflow_uri=\"https://dagshub.com/Shrey-patel-07/Kidney-Disease-Classifcation.mlflow\",\n",
" all_params=self.params,\n",
" params_image_size=self.params.IMAGE_SIZE,\n",
" params_batch_size=self.params.BATCH_SIZE\n",
" )\n",
" return eval_config"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import mlflow\n",
"import mlflow.keras\n",
"from urllib.parse import urlparse"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"class Evaluation:\n",
" def __init__(self, config: EvaluationConfig):\n",
" self.config = config\n",
" self.valid_generator = None # Initialize to None\n",
"\n",
" def _valid_generator(self):\n",
" img_height, img_width = self.config.params_image_size[:-1]\n",
"\n",
" self.valid_generator = tf.keras.utils.image_dataset_from_directory(\n",
" self.config.training_data,\n",
" image_size=(img_height, img_width),\n",
" validation_split=0.30,\n",
" subset='validation',\n",
" seed=123\n",
" )\n",
"\n",
" self.valid_generator = self.valid_generator.map(lambda x, y: (x / 255, y))\n",
" AUTOTUNE = tf.data.AUTOTUNE\n",
" self.valid_generator = self.valid_generator.cache().prefetch(buffer_size=AUTOTUNE)\n",
"\n",
"\n",
" @staticmethod\n",
" def load_model(path: Path) -> tf.keras.Model:\n",
" return tf.keras.models.load_model(path)\n",
" \n",
"\n",
" def evaluation(self):\n",
" self.model = self.load_model(self.config.path_of_model)\n",
" self._valid_generator()\n",
" self.score = self.model.evaluate(self.valid_generator)\n",
" self.save_score()\n",
"\n",
" def save_score(self):\n",
" scores = {\"loss\": self.score[0], \"accuracy\": self.score[1]}\n",
" save_json(path=Path(\"scores.json\"), data=scores)\n",
"\n",
" def log_into_mlflow(self):\n",
" mlflow.set_registry_uri(self.config.mlflow_uri)\n",
" tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n",
" \n",
" with mlflow.start_run():\n",
" mlflow.log_params(self.config.all_params)\n",
" mlflow.log_metrics(\n",
" {\"loss\": self.score[0], \"accuracy\": self.score[1]}\n",
" )\n",
" # Model registry does not work with file store\n",
" if tracking_url_type_store != \"file\":\n",
"\n",
" # Register the model\n",
" mlflow.keras.log_model(self.model, \"model\", registered_model_name=\"VGG16Model\")\n",
" else:\n",
" mlflow.keras.log_model(self.model, \"model\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"os.chdir('../')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2024-01-04 12:24:51,908: INFO: common yaml file: config/config.yaml loaded successfully]\n",
"[2024-01-04 12:24:51,910: INFO: common yaml file: params.yaml loaded successfully]\n",
"Found 12446 files belonging to 1 classes.\n",
"Using 3733 files for validation.\n",
"117/117 [==============================] - 147s 1s/step - loss: 3.2269e-06 - accuracy: 1.0000\n",
"[2024-01-04 12:27:19,911: INFO: common json file saved at: scores.json]\n",
"[2024-01-04 12:27:19,912: INFO: common json file saved at: scores.json]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024/01/04 12:27:22 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /tmp/tmpdrlg6a4p/model/data/model/assets\n",
"[2024-01-04 12:27:23,234: INFO: builder_impl Assets written to: /tmp/tmpdrlg6a4p/model/data/model/assets]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/shrey/Desktop/Kidney-Disease-Classifcation/env/lib/python3.11/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n",
" warnings.warn(\"Setuptools is replacing distutils.\")\n",
"Registered model 'VGG16Model' already exists. Creating a new version of this model...\n",
"2024/01/04 12:27:47 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: VGG16Model, version 3\n",
"Created version '3' of model 'VGG16Model'.\n"
]
}
],
"source": [
"try:\n",
" config = ConfigurationManager()\n",
" eval_config = config.get_evaluation_config()\n",
" evaluation = Evaluation(eval_config)\n",
" evaluation.evaluation()\n",
" evaluation.log_into_mlflow()\n",
" \n",
"except Exception as e:\n",
" raise e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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