The package is built upon a Python-based dashboard server, which leverages the Bokeh visualization library to display and update figures in real time. 1, NVDashboard enables Jupyter notebook users to visualize system hardware metrics within the same interactive environment they use for development. The GPU dashboards are shown along the right-hand side of the screen, while two dask-labextension dashboards are shown on the bottom left). 1 The NVDashboard Jupyter-Lab extension in action. While this can be accomplished with command-line tools like nvidia-smi, many professional data scientists prefer to use interactive Jupyter notebooks for day-to-day model and workflow development.įig. Although acceleration libraries (like cuDNN and RAPIDS) are specifically designed to do the heavy lifting in terms of performance optimization, it can be very useful for both developers and end users to verify that their software is actually leveraging GPU resources as intended. To achieve optimal performance, it is absolutely critical for the underlying software to utilize system resources effectively. Given the computational intensity of modern data-science algorithms, there are many cases in which GPUs can offer game-changing workflow acceleration. NVDashboard is a great way for all GPU users to monitor system resources, but it is especially valuable for users of RAPIDS, NVIDIA’s open-source suite of GPU-accelerated data-science software libraries. We are excited to announce NVDashboard, an open-source package for the real-time visualization of NVIDIA GPU metrics in interactive Jupyter environments. 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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