itsjhuang/watsonx-docs-document-type huggingface dataset:
Choosing compute resources for running tools in projects
Choosing compute resources for running tools in projects You use compute resources in projects when you run jobs and most tools. Depending on the tool, you might have a choice of compute resources for the runtime for the tool. Compute resources are known as either environment templates or hardware and software specifications. In general, compute resources with larger hardware configurations incur larger usage costs. These tools have multiple choices for configuring runtimes that you can choose from: * [Notebook editor](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/notebook-environments.html) * [Data Refinery](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/spark-dr-envs.html) * [SPSS Modeler](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/spss-envs.html) * [AutoAI](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/run-autoai.html) * [Decision Optimization experiment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/run-decisionopt.html) * [RStudio IDE](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/rstudio-envs.html) * [Synthetic Data Generator](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/synthetic-envs.html) * [Tuning Studio](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/run-fm-tuning.html) Prompt Lab does not consume compute resources. Prompt Lab usage is measured by the number of processed tokens. Learn more * [Monitoring account resource usage](https://dataplatform.cloud.ibm.com/docs/content/wsj/admin/monitor-resources.html) Parent topic:[Projects ](https://dataplatform.cloud.ibm.com/docs/content/wsj/manage-data/manage-projects.html)
Deploying scripts in Watson Machine Learning
Deploying scripts in Watson Machine Learning When a script is copied to a deployment space, you can deploy it for use. Supported script types are Python scripts. [Batch deployment](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.html) is the only supported deployment type for a script. * When the script is promoted from a project, your software specification is included. * When you create a deployment job for a script, you must manually override the default environment with the correct environment for your script. For more information, see [Creating a deployment job](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/create-jobs.html) Learn more * To learn more about supported input and output types and setting environment variables, see [Batch deployment details](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/deploy-batch-details.html). * To learn more about software specifications, see [Software specifications and hardware specifications for deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/pm_service_supported_frameworks.html). Parent topic:[Managing predictive deployments](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/ml-deploy-general.html)
Accessing the Expression Builder (SPSS Modeler)
Accessing the Expression Builder The Expression Builder is available in all nodes where CLEM expressions are used, including Select, Balance, Derive, Filler, Analysis, Report, and Table nodes. You can open it by double-clicking the node to open its properties, then click the calculator button by the formula field.
applyautoclusternode properties
applyautoclusternode properties You can use Auto Cluster modeling nodes to generate an Auto Cluster model nugget. The scripting name of this model nugget is applyautoclusternode. No other properties exist for this model nugget. For more information on scripting the modeling node itself, see [autoclusternode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/autoclusternodeslots.htmlautoclusternodeslots).
Statistics (SPSS Modeler)
Statistics node The Statistics node gives you basic summary information about numeric fields. You can get summary statistics for individual fields and correlations between fields.
Custom charts
Custom charts The custom charts option provides options for pasting or editing JSON code to create the wanted chart.
applyselflearningnode properties
applyselflearningnode properties You can use Self-Learning Response Model (SLRM) modeling nodes to generate a SLRM model nugget. The scripting name of this model nugget is applyselflearningnode. For more information on scripting the modeling node itself, see [slrmnode properties](https://dataplatform.cloud.ibm.com/docs/content/wsd/nodes/scripting_guide/clementine/selflearnnodeslots.htmlselflearnnodeslots). applyselflearningnode properties Table 1. applyselflearningnode properties applyselflearningnode Properties Values Property description max_predictions number randomization number scoring_random_seed number sort ascending <br>descending Specifies whether the offers with the highest or lowest scores will be displayed first. model_reliability flag Takes account of the model reliability option in the node settings. enable_sql_generation false <br>native When using data from a database, SQL code can be pushed back to the database for execution, providing superior performance for many operations.
Creating Synthetic data
Creating Synthetic data Use the graphical flow editor tool Synthetic Data Generator to generate synthetic tabular data based on production data or a custom data schema using visual flows and modeling algorithms. To create synthetic data, the first option is to use the Synthetic Data Generator graphical flow editor tool to mask and mimic production data, and then to load the result into a different location. The second option is to use the Synthetic Data Generator graphical flow editor to generate synthetic data from a custom data schema using visual flows and modeling algorithms. This image shows an overview of the Synthetic Data Generator graphical flow editor. ![Synthetic Data Generator overview](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-overview.png) Data format Learn more about [Creating synthetic data from imported data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/import_data_sd.html). Data size : The Synthetic Data Generator environment can import up to 2.5GB of data. Prerequisites Before you can create synthetic data, you need [to create a project](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/projects.html). Create synthetic data 1. Access the Synthetic Data Generator tool from within a project. To select a new asset, open a tool, and create an asset, click New asset. 2. Select All > Prepare Data > Generate synthetic tabular data from the What do you want to do? window. ![What do you want to do window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-what-do-you-want.png) 3. The Generate synthetic tabular data window opens. Add a name for the asset and a description (optional). Click Create. The flow will open and it might take a minute to create a new session for the flow. ![Generate synthetic tabular data flow asset](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-generate-synthetic-tabular-data-flow-asset.png) 4. The Welcome to Synthetic Data Generator wizard opens. You can choose to get started as a first time or experienced user. ![Synthetic Data Generator Get started wizard](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-wizard.png) 5. If you choose to get started as a first time user, the Generate synthetic tabular data flow window opens. ![Generate synthetic tabular data flow window](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/images/sd-mimic-mask-flow.png) Learn more * [Creating synthetic data from production data](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/mask_mimic_data_sd.html) * [Creating synthetic data from a custom data schema](https://dataplatform.cloud.ibm.com/docs/content/wsj/synthetic/generate_data_sd.html) * Try the [Generate synthetic tabular data tutorial](https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-generate-data.html)