In this post, we show you how to run your notebooks from your local JupyterLab environment as scheduled notebook jobs on SageMaker. SageMaker provides an open-source extension that can be installed on any JupyterLab environment and be used to run notebooks as ephemeral jobs and on a schedule. You can now use the same capability to run your Jupyter notebooks from any JupyterLab environment such as Amazon SageMaker notebook instances and JupyterLab running on your local machine. To help simplify the process of moving from interactive notebooks to batch jobs, in December 2022, Amazon SageMaker Studio and Studio Lab introduced the capability to run notebooks as scheduled jobs, using notebook-based workflows. To run this job repeatedly on a schedule, you had to set up, configure, and oversee cloud infrastructure to automate deployments, resulting in a diversion of valuable time away from core data science development activities. Migrating from interactive development on notebooks to batch jobs required you to copy code snippets from the notebook into a script, package the script with all its dependencies into a container, and schedule the container to run. Examples of such use cases include scaling up a feature engineering job that was previously tested on a small sample dataset on a small notebook instance, running nightly reports to gain insights into business metrics, and retraining ML models on a schedule as new data becomes available. However, there are scenarios in which data scientists may prefer to transition from interactive development on notebooks to batch jobs. Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. Figure ( data = data, layout = layout ) py. Surface ( x = x, y = y, z = z ) data = layout = go. ![]() cos ( tGrid ) # z = r*cos(t) surface = go. sin ( tGrid ) # y = r*sin(s)*sin(t) z = r * np. sin ( tGrid ) # x = r*cos(s)*sin(t) y = r * np. sin ( 7 * sGrid + 5 * tGrid ) # r = 2 + sin(7s+5t) x = r * np. Import chart_otly as py import aph_objects as go import numpy as np s = np. iplot ( fig, filename = 'jupyter-Nuclear Waste Sites on American Campuses' ) Layout ( title = 'Nuclear Waste Sites on Campus', autosize = True, hovermode = 'closest', showlegend = False, mapbox = dict ( accesstoken = mapbox_access_token, bearing = 0, center = dict ( lat = 38, lon =- 94 ), pitch = 0, zoom = 3, style = 'light' ), ) fig = dict ( data = data, layout = layout ) py. read_csv ( ' %20o n%20American%20Campuses.csv' ) site_lat = df. Import chart_otly as py import aph_objects as go import pandas as pd # mapbox_access_token = 'ADD YOUR TOKEN HERE' df = pd. See examples of statistic, scientific, 3D charts, and more here. Plotly: a graphing library for making interactive, publication-quality graphs.SciPy: a Python-based ecosystem of packages for math, science, and engineering.NumPy: a package for scientific computing with tools for algebra, random number generation, integrating with databases, and managing data. ![]() Pandas: import data via a url and create a dataframe to easily handle data for analysis and graphing. ![]() Some useful packages that we'll use in this tutorial include: You can reload all changed modules before executing a new line. IPython comes with automatic reloading magic. You may want to reload submodules if you've edited the code in one. When installing packages in Jupyter, you either need to install the package in your actual shell, or run the ! prefix, e.g.: !pip install packagename Skip down to the for more information on using IRkernel with Jupyter notebooks and graphing examples. You can also use Jupyter notebooks to execute R code. The bulk of this tutorial discusses executing python code in Jupyter notebooks.
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