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https://github.com/clearml/clearml
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Update examples to ClearML
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allegroai
238
examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb
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238
examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "NKas2cYws8F6"
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},
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"source": [
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"# Allegro ClearML matplotlib example\n",
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"\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb)\n",
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"\n",
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"This example introduces ClearML with matplotlib functionality. It also shows seaborn functionality. You can find more frameworks examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks).\n",
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"\n",
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"Note: This example is based on the ClearML [matplotlib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/matplotlib_example.py) example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "72lCj7MJmRkQ"
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},
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"outputs": [],
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"source": [
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"!pip install clearml\n",
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"!pip install numpy\n",
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"!pip install seaborn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "b8jtq0iSt3-U"
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},
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"source": [
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"## Create a new task.\n",
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"\n",
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"To create a new Task object, call the `Task.init` method providing it with `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). When `Task.init` executes, a link to the Web UI Results page for the newly generated Task will be printed, and the Task will be updated in real time in the ClearML demo server.\n",
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"\n",
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"You can read about the `Task` class in the docs [here](https://allegro.ai/docs/task.html)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "ses67ulJkGPq"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"\n",
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"from clearml import Task\n",
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"\n",
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"# Start a new task\n",
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"task = Task.init(project_name=\"Colab notebooks\", task_name=\"Matplotlib example\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "T2l-0WvJt_yo"
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},
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"source": [
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"## Matplotlib support\n",
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"\n",
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"ClearML automatically logs Matplotlib plots. They appear in the Web UI Results tab.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "a-nOyg9xlxiR"
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},
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"outputs": [],
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"source": [
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"# create plot\n",
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"N = 50\n",
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"x = np.random.rand(N)\n",
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"y = np.random.rand(N)\n",
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"colors = np.random.rand(N)\n",
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"area = (30 * np.random.rand(N))**2 # 0 to 15 point radii\n",
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"plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
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"plt.show()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "iV4BtqRFmi0N"
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},
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"outputs": [],
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"source": [
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"# create another plot - with a name\n",
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"x = np.linspace(0, 10, 30)\n",
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"y = np.sin(x)\n",
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"plt.plot(x, y, 'o', color='black')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "yKT5UjDk6DGB"
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},
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"source": [
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"By calling the `imshow` method, ClearML automatically reports plot images in Results tab."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "IVzUScalmio-"
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},
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"outputs": [],
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"source": [
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"# create unitlted image plot\n",
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"m = np.eye(256, 256, dtype=np.uint8)\n",
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"plt.imshow(m)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "mioKlXpimib1"
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},
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"outputs": [],
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"source": [
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"# create image plot - with a name\n",
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"m = np.eye(256, 256, dtype=np.uint8)\n",
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"plt.imshow(m)\n",
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"plt.title('Image Title')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "AE7Gbm3GfvvK"
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},
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"outputs": [],
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"source": [
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"# create plot with savefig\n",
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"N = 10\n",
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"x = np.random.rand(N)\n",
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"y = np.random.rand(N)\n",
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"colors = np.random.rand(N)\n",
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"area = (30 * np.random.rand(N))**2\n",
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"plt.title('savefig Image')\n",
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"plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
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"plt.savefig(\"plot.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "jcRWq9Xc56fX"
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},
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"source": [
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"## Seaborn support"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "j-usk2d_mqS4"
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},
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"outputs": [],
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"source": [
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"sns.set(style=\"darkgrid\")\n",
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"# Load an example dataset with long-form data\n",
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"fmri = sns.load_dataset(\"fmri\")\n",
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"# Plot the responses for different events and regions\n",
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"sns.lineplot(x=\"timepoint\", y=\"signal\",\n",
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" hue=\"region\", style=\"event\",\n",
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" data=fmri)\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Allegro ClearML matplotlib example.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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