diff --git a/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb b/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb
new file mode 100644
index 00000000..46b74655
--- /dev/null
+++ b/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "wFJPLbY7w7Vj"
+   },
+   "source": [
+    "# Allegro Trains Keras with Tensorboard example\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allegroai/trains/blob/master/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb)\n",
+    "\n",
+    "This tutorial introduce Trains with Keras and Tensorboard functionality. automatic logging model and Tensorboard outputs. You can find more frameworks examples [here](https://github.com/allegroai/trains/tree/master/examples/frameworks).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "K7HA0KcX3XBf"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install trains\n",
+    "!pip install tensorflow>=2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ZM0EIh-GqZuu"
+   },
+   "source": [
+    "### Create a new task.\n",
+    "Task object should be provided `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). A link to the newly generated task will be printed and the task will be updated real time in the Trains demo server.\n",
+    "\n",
+    "You can read about task in the docs [here](https://allegro.ai/docs/task.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "RYXhcm58uVGL"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import tempfile\n",
+    "\n",
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import utils as np_utils\n",
+    "from trains import Task\n",
+    "\n",
+    "# Start a new task\n",
+    "task = Task.init(project_name=\"Colab notebooks\", task_name=\"Keras with TensorBoard example\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GPLPiHQ1ygTg"
+   },
+   "source": [
+    "*Based on https://github.com/keras-team/keras/blob/master/examples/mnist_mlp.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6A36rDJ7s5Pb"
+   },
+   "outputs": [],
+   "source": [
+    "# Train a simple deep NN on the MNIST dataset.\n",
+    "# Gets to 98.40% test accuracy after 20 epochs\n",
+    "# (there is *a lot* of margin for parameter tuning).\n",
+    "# 2 seconds per epoch on a K520 GPU.\n",
+    "\n",
+    "\n",
+    "class TensorBoardImage(keras.callbacks.TensorBoard):\n",
+    "    @staticmethod\n",
+    "    def make_image(tensor):\n",
+    "        from PIL import Image\n",
+    "        import io\n",
+    "        tensor = np.stack((tensor, tensor, tensor), axis=2)\n",
+    "        height, width, channels = tensor.shape\n",
+    "        image = Image.fromarray(tensor)\n",
+    "        output = io.BytesIO()\n",
+    "        image.save(output, format='PNG')\n",
+    "        image_string = output.getvalue()\n",
+    "        output.close()\n",
+    "        return tf.Summary.Image(height=height,\n",
+    "                                width=width,\n",
+    "                                colorspace=channels,\n",
+    "                                encoded_image_string=image_string)\n",
+    "\n",
+    "    def on_epoch_end(self, epoch, logs=None):\n",
+    "        if logs is None:\n",
+    "            logs = {}\n",
+    "        super(TensorBoardImage, self).on_epoch_end(epoch, logs)\n",
+    "        images = self.validation_data[0]  # 0 - data; 1 - labels\n",
+    "        img = (255 * images[0].reshape(28, 28)).astype('uint8')\n",
+    "\n",
+    "        image = self.make_image(img)\n",
+    "        summary = tf.Summary(value=[tf.Summary.Value(tag='image', image=image)])\n",
+    "        self.writer.add_summary(summary, epoch)\n",
+    "\n",
+    "\n",
+    "# the data, shuffled and split between train and test sets\n",
+    "nb_classes = 10\n",
+    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "X_train = X_train.reshape(60000, 784).astype('float32') / 255.\n",
+    "X_test = X_test.reshape(10000, 784).astype('float32') / 255.\n",
+    "print(X_train.shape[0], 'train samples')\n",
+    "print(X_test.shape[0], 'test samples')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gFMDBxwN4nR2"
+   },
+   "outputs": [],
+   "source": [
+    "# convert class vectors to binary class matrices\n",
+    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+    "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+    "\n",
+    "model = keras.models.Sequential()\n",
+    "model.add(keras.layers.Dense(512, input_shape=(784,)))\n",
+    "model.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.add(keras.layers.Dense(512))\n",
+    "model.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.add(keras.layers.Dense(10))\n",
+    "model.add(keras.layers.Activation('softmax'))\n",
+    "\n",
+    "model2 = keras.models.Sequential()\n",
+    "model2.add(keras.layers.Dense(512, input_shape=(784,)))\n",
+    "model2.add(keras.layers.Activation('relu'))\n",
+    "\n",
+    "model.summary()\n",
+    "\n",
+    "model.compile(loss='categorical_crossentropy',\n",
+    "              optimizer=keras.optimizers.RMSprop(),\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "40iQp_Wq4K28"
+   },
+   "outputs": [],
+   "source": [
+    "# Advanced: setting model class enumeration and set it for the task\n",
+    "labels = dict(('digit_%d' % i, i) for i in range(10))\n",
+    "task.set_model_label_enumeration(labels)\n",
+    "\n",
+    "output_folder = os.path.join(tempfile.gettempdir(), 'keras_example')\n",
+    "\n",
+    "board = keras.callbacks.TensorBoard(histogram_freq=1, log_dir=output_folder, write_images=False)\n",
+    "model_store = keras.callbacks.ModelCheckpoint(filepath=os.path.join(output_folder, 'weight.{epoch}.hdf5'))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "5FIKDIzy4YF6"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit and evaluate the model\n",
+    "\n",
+    "history = model.fit(X_train,\n",
+    "                    Y_train,\n",
+    "                    batch_size=128,\n",
+    "                    epochs=6,\n",
+    "                    callbacks=[board, model_store],\n",
+    "                    verbose=1,\n",
+    "                    validation_data=(X_test, Y_test))\n",
+    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
+    "print('Test score:', score[0])\n",
+    "print('Test accuracy:', score[1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Allegro Trains keras TB example.ipynb",
+   "provenance": []
+  },
+  "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb b/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb
new file mode 100644
index 00000000..7d9cef36
--- /dev/null
+++ b/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb
@@ -0,0 +1,232 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "Allegro Trains matplotlib example.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NKas2cYws8F6",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Allegro Trains matplotlib example\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allegroai/trains/blob/master/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb)\n",
+    "\n",
+    "This tutorial introduce Trains with matplotlib functionality. You can find more frameworks examples [here](https://github.com/allegroai/trains/tree/master/examples/frameworks)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "72lCj7MJmRkQ",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "!pip install trains\n",
+    "!pip install numpy\n",
+    "!pip install seaborn"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b8jtq0iSt3-U",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Create a new task.\n",
+    "Task object should be provided `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). A link to the newly generated task will be printed and the task will be updated real time in the Trains demo server.\n",
+    "\n",
+    "You can read about task in the docs [here](https://allegro.ai/docs/task.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ses67ulJkGPq",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from trains import Task\n",
+    "\n",
+    "# Start a new task\n",
+    "task = Task.init(project_name=\"Colab notebooks\", task_name=\"Matplotlib example\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "whxX3Xjmy1PI",
+    "colab_type": "text"
+   },
+   "source": [
+    "This example was generated based on [trains matplotlib example](https://github.com/allegroai/trains/blob/master/examples/frameworks/matplotlib/matplotlib_example.py)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "T2l-0WvJt_yo",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Matplotlib support\n",
+    "\n",
+    "Matplotlib plots are automatically logged. Data available in the task 'Results' section\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a-nOyg9xlxiR",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# create plot\n",
+    "N = 50\n",
+    "x = np.random.rand(N)\n",
+    "y = np.random.rand(N)\n",
+    "colors = np.random.rand(N)\n",
+    "area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
+    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
+    "plt.show()\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "iV4BtqRFmi0N",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# create another plot - with a name\n",
+    "x = np.linspace(0, 10, 30)\n",
+    "y = np.sin(x)\n",
+    "plt.plot(x, y, 'o', color='black')\n",
+    "plt.show()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yKT5UjDk6DGB",
+    "colab_type": "text"
+   },
+   "source": [
+    "Notice imshow will cause the plot images to apear as Images in the debug sample section"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "IVzUScalmio-",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# create unitlted image plot\n",
+    "m = np.eye(256, 256, dtype=np.uint8)\n",
+    "plt.imshow(m)\n",
+    "plt.show()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "mioKlXpimib1",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# create image plot - with a name\n",
+    "m = np.eye(256, 256, dtype=np.uint8)\n",
+    "plt.imshow(m)\n",
+    "plt.title('Image Title')\n",
+    "plt.show()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "AE7Gbm3GfvvK",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# create plot with savefig\n",
+    "N = 10\n",
+    "x = np.random.rand(N)\n",
+    "y = np.random.rand(N)\n",
+    "colors = np.random.rand(N)\n",
+    "area = (30 * np.random.rand(N))**2\n",
+    "plt.title('savefig Image')\n",
+    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
+    "plt.savefig(\"plot.png\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jcRWq9Xc56fX",
+    "colab_type": "text"
+   },
+   "source": [
+    "Seaborn example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "j-usk2d_mqS4",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "sns.set(style=\"darkgrid\")\n",
+    "# Load an example dataset with long-form data\n",
+    "fmri = sns.load_dataset(\"fmri\")\n",
+    "# Plot the responses for different events and regions\n",
+    "sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
+    "             hue=\"region\", style=\"event\",\n",
+    "             data=fmri)\n",
+    "plt.show()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  }
+ ]
+}
\ No newline at end of file
diff --git a/examples/pipeline/requirements.txt b/examples/pipeline/requirements.txt
new file mode 100644
index 00000000..7be35f6a
--- /dev/null
+++ b/examples/pipeline/requirements.txt
@@ -0,0 +1,4 @@
+joblib>=0.13.2
+matplotlib >= 3.1.1 ; python_version >= '3.6'
+matplotlib >= 2.2.4 ; python_version < '3.6'
+scikit-learn
\ No newline at end of file
diff --git a/examples/reporting/Allegro_Trains_logging_example.ipynb b/examples/reporting/Allegro_Trains_logging_example.ipynb
new file mode 100644
index 00000000..2780581c
--- /dev/null
+++ b/examples/reporting/Allegro_Trains_logging_example.ipynb
@@ -0,0 +1,382 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "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": "3.6.5"
+  },
+  "colab": {
+   "name": "Allegro Trains logging example.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RZiRah3QiR_G",
+    "colab_type": "text"
+   },
+   "source": [
+    "# Allegro Trains logging example\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allegroai/trains/blob/master/examples/reporting/Allegro_Trains_logging_example.ipynb)\n",
+    "\n",
+    "This tutorial introduce [Trains](https://github.com/allegroai/trains) logger functionality. You can find more reporting examples [here](https://github.com/allegroai/trains/tree/master/examples/reporting)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "RbESVEV0jl3c",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "!pip install trains\n",
+    "!pip install numpy"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8p9tkR5wue2x",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Create a new task\n",
+    "Create a new task and get the logger object for this task.\n",
+    "\n",
+    "Task object should be provided `project_name` (the project name for the experiment) and `task_name` (the name of the experiment).\n",
+    "A link to the newly generated task will be printed and the task will be updated real time in the Trains demo server.\n",
+    "\n",
+    "Logger is the Trains console log and metric interface.\n",
+    "You can read about the logger in the [docs](https://allegro.ai/docs/logger.html)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "885DHN5SjsHy",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from trains import Task\n",
+    "\n",
+    "# Start a new task\n",
+    "task = Task.init(project_name=\"Colab notebooks\", task_name=\"Explicit Logging\")\n",
+    "\n",
+    "# Get the task logger,\n",
+    "# You can also call Task.current_task().get_logger() from anywhere in your code.\n",
+    "logger = task.get_logger()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SADmtLH8uwhw",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Explicit scalar logging\n",
+    "Explicit scalar logging. Data available in the task 'Results' section.\n",
+    "For more [usage examples](https://allegro.ai/docs/logger.html#trains.logger.Logger.report_scalar)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "E6YH4lNLjLs8",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# report two scalar series on the same graph\n",
+    "for i in range(10):\n",
+    "    logger.report_scalar(\"unified graph\", \"series A\", iteration=i, value=1./(i+1))\n",
+    "    logger.report_scalar(\"unified graph\", \"series B\", iteration=i, value=10./(i+1))\n",
+    "\n",
+    "# report two scalar series on two different graphs\n",
+    "for i in range(10):\n",
+    "    logger.report_scalar(\"graph A\", \"series A\", iteration=i, value=1./(i+1))\n",
+    "    logger.report_scalar(\"graph B\", \"series B\", iteration=i, value=10./(i+1))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8iqvizEzwRtk",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Explicit histogram logging\n",
+    "Explicit histogram logging. Data available in the task 'Results' section.\n",
+    "You can report histograms, matrix, 3D scatter diagrams and surface.\n",
+    "For more examples check [here](https://allegro.ai/docs/logger.html#trains.logger.Logger.report_histogram)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "_rWW7HTYjLtK",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "iteration = 100\n",
+    "\n",
+    "# report a single histogram\n",
+    "histogram = np.random.randint(10, size=10)\n",
+    "logger.report_histogram(\n",
+    "    \"single_histogram\",\n",
+    "    \"random histogram\",\n",
+    "    iteration=iteration,\n",
+    "    values=histogram,\n",
+    "    xaxis=\"title x\",\n",
+    "    yaxis=\"title y\",\n",
+    ")\n",
+    "\n",
+    "# report a two histograms on the same plot\n",
+    "histogram1 = np.random.randint(13, size=10)\n",
+    "histogram2 = histogram * 0.75\n",
+    "logger.report_histogram(\n",
+    "    \"two_histogram\",\n",
+    "    \"series 1\",\n",
+    "    iteration=iteration,\n",
+    "    values=histogram1,\n",
+    "    xaxis=\"title x\",\n",
+    "    yaxis=\"title y\",\n",
+    ")\n",
+    "logger.report_histogram(\n",
+    "    \"two_histogram\",\n",
+    "    \"series 2\",\n",
+    "    iteration=iteration,\n",
+    "    values=histogram2,\n",
+    "    xaxis=\"title x\",\n",
+    "    yaxis=\"title y\",\n",
+    ")\n",
+    "\n",
+    "# report confusion matrix\n",
+    "confusion = np.random.randint(10, size=(10, 10))\n",
+    "logger.report_matrix(\n",
+    "    \"example_confusion\",\n",
+    "    \"ignored\",\n",
+    "    iteration=iteration,\n",
+    "    matrix=confusion,\n",
+    "    xaxis=\"title X\",\n",
+    "    yaxis=\"title Y\",\n",
+    ")\n",
+    "\n",
+    "scatter2d = np.hstack(\n",
+    "    (np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1)))\n",
+    ")\n",
+    "# report 2d scatter plot with markers\n",
+    "logger.report_scatter2d(\n",
+    "    \"example_scatter\",\n",
+    "    \"series_lines+markers\",\n",
+    "    iteration=iteration,\n",
+    "    scatter=scatter2d,\n",
+    "    xaxis=\"title x\",\n",
+    "    yaxis=\"title y\",\n",
+    "    mode='lines+markers'\n",
+    ")\n",
+    "\n",
+    "# report 3d surface\n",
+    "surface = np.random.randint(10, size=(10, 10))\n",
+    "logger.report_surface(\n",
+    "    \"example_surface\",\n",
+    "    \"series1\",\n",
+    "    iteration=iteration,\n",
+    "    matrix=surface,\n",
+    "    xaxis=\"title X\",\n",
+    "    yaxis=\"title Y\",\n",
+    "    zaxis=\"title Z\",\n",
+    ")\n",
+    "\n",
+    "# report 3d scatter plot\n",
+    "scatter3d = np.random.randint(10, size=(10, 3))\n",
+    "logger.report_scatter3d(\n",
+    "    \"example_scatter_3d\",\n",
+    "    \"series_xyz\",\n",
+    "    iteration=iteration,\n",
+    "    scatter=scatter3d,\n",
+    "    xaxis=\"title x\",\n",
+    "    yaxis=\"title y\",\n",
+    "    zaxis=\"title z\",\n",
+    ")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OZqPEaFRwcVY",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Explicit debug samples reporting\n",
+    "Explicit debug samples reporting. Data available in the task 'Results' section\n",
+    "\n",
+    "We are using StorageManager to download a local copy of the files. \n",
+    "You can use the StorageManager immediately, you only need to provide the url. \n",
+    "Cache is enabled by default for all downloaded remote urls/files.\n",
+    "\n",
+    "For more information, you can read about the storage manager [here](https://allegro.ai/docs/storage.html#trains.storage.manager.StorageManager)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "s4rf0ap0jLtb",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "from trains.storage import StorageManager\n",
+    "image_local_copy = StorageManager.get_local_copy(\n",
+    "    remote_url=\"https://pytorch.org/tutorials/_static/img/neural-style/picasso.jpg\",\n",
+    "    name=\"picasso.jpg\"\n",
+    ")\n",
+    "\n",
+    "print(\"Image location: {}\".format(image_local_copy))\n",
+    "\n",
+    "video_local_copy = StorageManager.get_local_copy(\n",
+    "    remote_url=\"https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_1MB.mp4\",\n",
+    "    name=\"Big_Buck_Bunny_720_10s_1MB.mp4\"\n",
+    ")\n",
+    "\n",
+    "print(\"Video location: {}\".format(video_local_copy))\n",
+    "\n",
+    "audio_local_copy = StorageManager.get_local_copy(\n",
+    "    remote_url=\"https://www2.cs.uic.edu/~i101/SoundFiles/PinkPanther30.wav\",\n",
+    "    name=\"PinkPanther30.wav\"\n",
+    ")\n",
+    "\n",
+    "print(\"Audio location: {}\".format(audio_local_copy))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vydmi7HWw0gS",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Report images and media\n",
+    "Reporting the downloaded samples. Data available in the task 'Results' section.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "MtEhbE4S_P66",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "logger.report_image(\"image\", \"image from url\", iteration=100, local_path=image_local_copy)\n",
+    "\n",
+    "# Image can be uploaded via 'report_media' too\n",
+    "# report video, an already uploaded video media (url)\n",
+    "logger.report_media(\n",
+    "    'video', 'big bunny', iteration=1, local_path=video_local_copy)\n",
+    "\n",
+    "\n",
+    "# This will actually use the cache and will not download the file again.\n",
+    "audio_local_copy_cache = StorageManager.get_local_copy(\n",
+    "    remote_url=\"https://www2.cs.uic.edu/~i101/SoundFiles/PinkPanther30.wav\",\n",
+    "    name=\"PinkPanther30.wav\"\n",
+    ")\n",
+    "\n",
+    "# report audio, report an already uploaded audio media (url)\n",
+    "logger.report_media(\n",
+    "    'audio', 'pink panther', iteration=1, local_path=audio_local_copy)\n",
+    "\n",
+    "# reporting html from url to debug samples section\n",
+    "logger.report_media(\"html\", \"url_html\", iteration=1, url=\"https://allegro.ai/docs/index.html\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uDrcRCJxxCQP",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Explicit text logging\n",
+    "Explicit text logging. Data available in the task 'Results' section.\n",
+    "For more examples check [here](https://allegro.ai/docs/logger.html?highlight=report_text#trains.logger.Logger.report_text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a2UlIETAjLtk",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "# report text\n",
+    "logger.report_text(\"hello, this is plain text\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aNFDbjZ7xNco",
+    "colab_type": "text"
+   },
+   "source": [
+    "### Flushing the reports\n",
+    "If flush is not called, reports are flushed in the background every couple of seconds, \n",
+    "and at the end of the process execution.\n",
+    "\n",
+    "More information can be found [here](https://allegro.ai/docs/logger.html?highlight=report_text#trains.logger.Logger.flush)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "SSyGUskJjLtr",
+    "colab_type": "code",
+    "colab": {}
+   },
+   "source": [
+    "logger.flush()"
+   ],
+   "execution_count": null,
+   "outputs": []
+  }
+ ]
+}
\ No newline at end of file