This topic describes how to work with the development environments of Data Science Workshop (DSW), including how to use user interfaces, run preset Notebook cases, upload data, and manage third-party libraries.
Prerequisites
A DSW instance is created. For more information, see Create an instance.
User interfaces
- Jupyterlab
No. Description 1 Top navigation bar 2 Left-side navigation pane 3 File browser 4 Main work area 5 Resource usage monitoring area - WebIDE
No. Description 1 Left-side navigation pane 2 File browser 3 Main work area 4 Resource usage monitoring area - Terminal
No. Description 1 Main work area 2 Resource usage monitoring area
Run preset Notebook cases
If you are a first-time user of DSW, we recommend that you use one of the preset cases to familiarize yourself with related features.
- Go to the development environment of DSW.
- Log on to the Machine Learning Platform for AI (PAI) console.
- On the Overview page, select a region in the top navigation bar.
- In the left-side navigation pane, click Workspaces. On the Workspace list page, click the name of the workspace that you want to manage.
- In the left-side navigation pane, choose .
- Optional:On the Interactive Modeling (DSW) page, enter the instance name or a keyword in the search box to search for an instance.
- Find the instance and click Launch DSW in the Actions column.
- Download a preset case.
- In the left-side navigation pane of the Data Science Workshop page, click the
icon.
- Find the case that you want to download, such as AutoML_HPO_101, and click the
icon next to the case.
- In the left-side navigation pane of the Data Science Workshop page, click the
- Open the model file of the downloaded case. In this example, the AutoML_HPO_101 case is used.
- In the AutoML_HPO_101.ipynb file, you can view the use principle of the case and perform tasks as instructed.
Upload files
- In the left-side navigation pane of the JupyterLab Notebook programming environment
of DSW, click the
icon.
- Click the
icon in the toolbar to upload files. Resumable upload is supported.
Manage third-party libraries
If you use a Python development environment, you can perform the following operations
to manage third-party libraries in the Terminal interface:
- Install a third-party library
You must replacepip install --user <yourLibraryName>
<yourLibraryName>
with the name of the third-party library that you want to install. For example, you can run thepip install --user sklearn
command to install a sklearn library. - View third-party libraries
You can view all third-party libraries that you have installed.pip list
- Remove a third-party library
You must replacepip uninstall <yourLibraryName>
<yourLibraryName>
with the name of the third-party library that you want to remove.Note You can remove only the third-party libraries that are installed by yourself.
tensoflow-gpu cannot be removed. You can only run the update command to install a specified version
of tensoflow-gpu. The specified version must be compatible with the Compute Unified Device Architecture
(CUDA) version of the DSW instance that you are using. Subscription DSW instances
use CUDA 10. Pay-as-you-go DSW instances use CUDA 9.
pip install --upgrade --user tensorflow-gpu=<versionNumber>
You must replace <versionNumber>
with the version number of tensoflow-gpu that you want to install.
Notice Do not upgrade pip. Otherwise, the installation may fail.
DSW provides the following development environments: Python2, Python3, PyTorch, and
TensorFlow2.0. By default, third-party libraries are installed in Python3. If you
want to install a third-party library in another development environment, you must
manually switch to the environment where you want to install the third-party library.
# Install a third-party library in Python2.
source activate python2
pip install --user <yourLibraryName>
# Install a third-party library in TensorFlow2.0.
source activate tf2
pip install --user <yourLibraryName>
You must replace <yourLibraryName>
with the name of the third-party library that you want to install.