Experiment_Tracking_Session.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eib4Ks8sfarR"
   },
   "source": [
    "# Change the Runtime to GPU\n",
    "\n",
    "On the top nav-bar, choose `Runtme`, click on `change runtime type`, and under `Hardware accelerator` choose GPU.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RZx2_5zulSwE"
   },
   "source": [
    "# Create a Repository on DAGsHub 🏗 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Eu2l1fRSWOHq"
   },
   "source": [
    "- We will start by [creating a new repository](https://dagshub.com/repo/create) on DAGsHub.\n",
    "\n",
    "<center>\n",
    "<img src=\"https://dagshub.com/nirbarazida/images/raw/22ffadf74508f2f8626a528ea55f0fbf3d43f941/colab-mlflow/create-a-repo.png\" height=\"700\"/> \n",
    "</center>\n",
    "\n",
    "<center><b>Congratulations</b> - you created your first DAGsHub repository! 🥳 </center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "r5TnrfoBmvho"
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from getpass import getpass\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6r0RlC3mvho"
   },
   "source": [
    "**Set Environment Variables**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "S4yuBfAMmvhp"
   },
   "outputs": [],
   "source": [
    "#@title Enter the repository name for the project:\n",
    "REPO_NAME= \"test_dag\" #@param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "form",
    "id": "i_1jiOvFqPmH"
   },
   "outputs": [],
   "source": [
    "#@title Enter the repository name for the project:\n",
    "REPO_OWNER= \"aviad8040\" #@param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": "form",
    "id": "Gud7p2CImvhp"
   },
   "outputs": [],
   "source": [
    "#@title Enter the username of your DAGsHub account:\n",
    "USER_NAME = \"aviad8040\" #@param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "BZw-NNpjmvhr"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "zq7eXDWkTmKb"
   },
   "outputs": [],
   "source": [
    "# 776832adb6c692c4a2fe99c402f55a3adf431341\n",
    "os.environ['MLFLOW_TRACKING_USERNAME'] = 'aviad8040'\n",
    "os.environ['MLFLOW_TRACKING_PASSWORD'] = '776832adb6c692c4a2fe99c402f55a3adf431341'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yp2Jk-myRZ0t",
    "outputId": "dfcd8683-2eba-46a3-aed3-8663fe107600"
   },
   "outputs": [],
   "source": [
    "# ! pip install mlflow\n",
    "import mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "TqyV7A3VWIq2"
   },
   "outputs": [],
   "source": [
    "mlflow.set_tracking_uri('https://dagshub.com/aviad8040/test_dag.mlflow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BIPbycQ9KkTb"
   },
   "source": [
    "# Model 🪐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[33mWARNING: Error parsing requirements for pathspec: [Errno 2] No such file or directory: '/home/jrock/anaconda3/lib/python3.8/site-packages/pathspec-0.9.0.dist-info/METADATA'\u001b[0m\n",
      "Installing collected packages: pyasn1, six, rsa, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, tensorboard-plugin-wit, tensorboard-data-server, numpy, markdown, grpcio, google-auth-oauthlib, absl-py, typing-extensions, termcolor, tensorflow-estimator, tensorboard, opt-einsum, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, clang, astunparse, tensorflow\n",
      "  Attempting uninstall: six\n",
      "    Found existing installation: six 1.16.0\n",
      "    Uninstalling six-1.16.0:\n",
      "      Successfully uninstalled six-1.16.0\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.21.1\n",
      "    Uninstalling numpy-1.21.1:\n",
      "      Successfully uninstalled numpy-1.21.1\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing-extensions 3.10.0.0\n",
      "    Uninstalling typing-extensions-3.10.0.0:\n",
      "      Successfully uninstalled typing-extensions-3.10.0.0\n",
      "  Attempting uninstall: h5py\n",
      "    Found existing installation: h5py 3.3.0\n",
      "    Uninstalling h5py-3.3.0:\n",
      "      Successfully uninstalled h5py-3.3.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "conda-repo-cli 1.0.4 requires pathlib, which is not installed.\n",
      "visions 0.7.1 requires multimethod==1.4, but you have multimethod 1.5 which is incompatible.\u001b[0m\n",
      "Successfully installed absl-py-0.13.0 astunparse-1.6.3 cachetools-4.2.2 clang-5.0 flatbuffers-1.12 gast-0.4.0 google-auth-1.35.0 google-auth-oauthlib-0.4.5 google-pasta-0.2.0 grpcio-1.39.0 h5py-3.1.0 keras-2.6.0 keras-preprocessing-1.1.2 markdown-3.3.4 numpy-1.19.5 oauthlib-3.1.1 opt-einsum-3.3.0 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-oauthlib-1.3.0 rsa-4.7.2 six-1.15.0 tensorboard-2.6.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.0 tensorflow-2.6.0 tensorflow-estimator-2.6.0 termcolor-1.1.0 typing-extensions-3.7.4.3\n"
     ]
    }
   ],
   "source": [
    "! pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "4mXifSWopksr"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-17 13:28:47.056833: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2021-08-17 13:28:47.056867: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import IPython\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gcAedEf9nGmm"
   },
   "source": [
    "## Import the Data Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xgRwnXOQxYMO"
   },
   "source": [
    "![](https://camo.githubusercontent.com/01c057a753e92a9bc70b8c45d62b295431851c09cffadf53106fc0aea7e2843f/687474703a2f2f692e7974696d672e636f6d2f76692f3051493378675875422d512f687164656661756c742e6a7067)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ZIJLE6lrplAN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 6s 1us/step\n",
      "11501568/11490434 [==============================] - 6s 1us/step\n"
     ]
    }
   ],
   "source": [
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "X_train, X_test = X_train / 255.0, X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 605
    },
    "id": "rlZy5snKxe0I",
    "outputId": "b83cff0d-3c88-4246-9644-685e859df855"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21402/344507488.py:10: MatplotlibDeprecationWarning: The 's' parameter of annotate() has been renamed 'text' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n",
      "  ax.annotate(s=value, xy=(x, y), ha='center', va='center', color=color)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = np.reshape(X_train[1], [28, 28])\n",
    "image_array = np.asarray(image)\n",
    "fig, ax = plt.subplots(figsize=(10, 15))\n",
    "img = ax.imshow(image_array, cmap='gray')\n",
    "\n",
    "for x in range(28):\n",
    "    for y in range(28):\n",
    "        value = round(image[y][x], 2)\n",
    "        color = 'black' if value > 0.5 else 'white'\n",
    "        ax.annotate(s=value, xy=(x, y), ha='center', va='center', color=color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hDoMtJUfnLcT"
   },
   "source": [
    "## Configure a Tensorflow Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "mfgM7chBNMdQ"
   },
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "validation_split=0.2\n",
    "epochs=50\n",
    "optimizer='adam'\n",
    "metrics=['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "TKVAJU6Rpk99"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-17 13:29:18.900343: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2021-08-17 13:29:18.900396: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (jrock): /proc/driver/nvidia/version does not exist\n"
     ]
    }
   ],
   "source": [
    "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(0.2),\n",
    "  tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "0DLoBgKCpk7m"
   },
   "outputs": [],
   "source": [
    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss=loss_fn,\n",
    "              metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "GSL_XCvBSUNw"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021/08/17 13:29:52 WARNING mlflow.utils.autologging_utils: You are using an unsupported version of tensorflow. If you encounter errors during autologging, try upgrading / downgrading tensorflow to a supported version, or try upgrading MLflow.\n"
     ]
    }
   ],
   "source": [
    "# lets log before trainning\n",
    "mlflow.tensorflow.autolog()\n",
    "# mlflow.log_metric(\"task\",2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JGurX8PANiJT"
   },
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "ybcy1GiRuHp5",
    "outputId": "241dfd94-b78b-42c9-ea50-298feb9e4264"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-17 13:29:58.883570: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.\n",
      "2021-08-17 13:29:58.883614: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.\n",
      "2021-08-17 13:29:58.887094: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.\n",
      "2021-08-17 13:29:59.137260: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 150528000 exceeds 10% of free system memory.\n",
      "2021/08/17 13:30:13 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/jrock/anaconda3/lib/python3.8/site-packages/mlflow/tensorflow.py:781: UserWarning: Logging to MLflow failed: HTTPSConnectionPool(host='dagshub.com', port=443): Max retries exceeded with url: /aviad8040/test_dag.mlflow/api/2.0/mlflow/runs/log-parameter (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1131)')))\"\n",
      "2021-08-17 13:30:56.652341: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0024s vs `on_train_batch_end` time: 0.0053s). Check your callbacks.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-17 13:30:57.136598: I tensorflow/core/profiler/lib/profiler_session.cc:131] Profiler session initializing.\n",
      "2021-08-17 13:30:57.136632: I tensorflow/core/profiler/lib/profiler_session.cc:146] Profiler session started.\n",
      "2021-08-17 13:30:57.142085: I tensorflow/core/profiler/lib/profiler_session.cc:66] Profiler session collecting data.\n",
      "2021-08-17 13:30:57.146395: I tensorflow/core/profiler/lib/profiler_session.cc:164] Profiler session tear down.\n",
      "2021-08-17 13:30:57.149476: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57\n",
      "\n",
      "2021-08-17 13:30:57.150540: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for trace.json.gz to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.trace.json.gz\n",
      "2021-08-17 13:30:57.156167: I tensorflow/core/profiler/rpc/client/save_profile.cc:136] Creating directory: /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57\n",
      "\n",
      "2021-08-17 13:30:57.156414: I tensorflow/core/profiler/rpc/client/save_profile.cc:142] Dumped gzipped tool data for memory_profile.json.gz to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.memory_profile.json.gz\n",
      "2021-08-17 13:30:57.156907: I tensorflow/core/profiler/rpc/client/capture_profile.cc:251] Creating directory: /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57\n",
      "Dumped tool data for xplane.pb to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.xplane.pb\n",
      "Dumped tool data for overview_page.pb to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.overview_page.pb\n",
      "Dumped tool data for input_pipeline.pb to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.input_pipeline.pb\n",
      "Dumped tool data for tensorflow_stats.pb to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.tensorflow_stats.pb\n",
      "Dumped tool data for kernel_stats.pb to /tmp/tmpcgdfrw6x/train/plugins/profile/2021_08_17_13_30_57/jrock.kernel_stats.pb\n",
      "\n",
      "2021-08-17 13:30:59.699780: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 37632000 exceeds 10% of free system memory.\n",
      "2021/08/17 13:31:28 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: \"/home/jrock/anaconda3/lib/python3.8/site-packages/mlflow/utils/autologging_utils/__init__.py:226: UserWarning: Logging to MLflow failed: HTTPSConnectionPool(host='dagshub.com', port=443): Max retries exceeded with url: /aviad8040/test_dag.mlflow/api/2.0/mlflow/runs/log-batch (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1131)')))\"\n",
      "2021-08-17 13:33:53.565500: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /tmp/tmpa6dlzdga/model/data/model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021/08/17 13:34:08 WARNING mlflow.models.model: Logging model metadata to the tracking server has failed, possibly due older server version. The model artifacts have been logged successfully under ./artifacts/259/7e5aef3159f745deb03243b2bddfbff4/artifacts. In addition to exporting model artifacts, MLflow clients 1.7.0 and above attempt to record model metadata to the  tracking store. If logging to a mlflow server via REST, consider  upgrading the server version to MLflow 1.7.0 or above.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f4a0bb87910>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size= batch_size, validation_split=validation_split, epochs=epochs,verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XcU2y1F_Hyub"
   },
   "source": [
    "# Finish Line 🏁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TzKu-M0dMix-"
   },
   "source": [
    "**Congratulations**  - You made it to the finish line! 🥳\n",
    "\n",
    "In this section, we covered how to track experiments with MLflow to DAGsHub servers. If you want to start fast with DAGsHub, this [notebook](https://colab.research.google.com/drive/1JJIwAH0TBSY49um5s2FD0GEA6bw3SKrd#scrollTo=XcU2y1F_Hyub) is for you.  <br><br>\n",
    "\n",
    "More resources that can interest you:\n",
    "- [DAGsHub Docs](https://dagshub.com/docs/).\n",
    "- [Get Started Tutorial](https://dagshub.com/docs/getting-started/overview/).\n",
    "- [DAGsHub Blog](https://dagshub.com/blog/).\n",
    "- [FAQ](https://dagshub.com/docs/faq/).\n",
    "\n",
    "<br>\n",
    "\n",
    "We hope that this Tutorial was helpful and made the onboarding process easier for you. If you found an issue in the notebook, please [let us know](https://dagshub.com/DAGsHub-Official/DAGsHub-Issues/issues/). If you have any questions feel free to join our [Discord channel](https://discord.com/invite/9gU36Y6) and ask there. We can't wait to see what remarkable project you will create and share with the Data Science community!\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-dnKOTI9WSyJ"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
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   "name": "Copy of Experiment Tracking Session.ipynb",
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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