This script requires a YAML file containing a list of all the libraries included in the default Python environment for Azure Synapse Spark. Run the following script to set up a local Python environment that's the same as the Azure Synapse Spark environment. If you're having trouble identifying required dependencies, follow these steps: Instead, upload all your dependencies as workspace libraries and install them to your Spark pool. Installing packages from a public repo is not supported within DEP-enabled workspaces. To learn more about these capabilities, see Manage Spark pool packages. You can also attach the workspace packages to your pools. This environment configuration file is used every time a Spark instance is created from that Spark pool. You can specify the pool-level Python libraries by providing a requirements.txt or environment.yml file. These libraries are installed on top of the base runtime.įor Python libraries, Azure Synapse Spark pools use Conda to install and manage Python package dependencies. This standardization can be useful if multiple people on your team commonly install the same packages.īy using the pool management capabilities of Azure Synapse Analytics, you can configure the default set of libraries to install on a serverless Apache Spark pool. In some cases, you might want to standardize the packages that are used on an Apache Spark pool. To learn more about how to manage workspace libraries, see Manage workspace packages. After you assign these workspace packages, they're installed automatically on all Spark pool sessions. You can upload these packages to your workspace and later assign them to a specific serverless Apache Spark pool. In Azure Synapse, workspace packages can be custom or private.
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