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Changing Storage Location

Under the hood, LineaPy stores artifacts as two data structures:

  • Serialized artifact values (i.e., pickle files)
  • Database that stores artifact metadata (e.g., timestamp, version, code, pointer to the serialized value)

By default, the serialized values and the metadata are stored in .lineapy/linea_pickles/ and .lineapy/db.sqlite, respectively, where .lineapy/ is created under the system's home directory.

These default locations can be overridden by modifying the configuration file:

    "artifact_storage_dir": [NEW-PATH-TO-STORE-SERIALIZED-VALUES],

or by making these updates in each interactive session:

lineapy.options.set('artifact_storage_dir', [NEW-PATH-TO-STORE-SERIALIZED-VALUES])
lineapy.options.set('database_url', [NEW-DATABASE-URL-FOR-STORING-METADATA])


The session should be reset when the storage location gets changed since we cannot retrieve previous information from the new database. Hence, when setting storage location in an interactive session, it should be done at the beginning.


When updating storage location, make sure that artifact_storage_dir and database_url get updated hand in hand. If not, artifact retrieval may suffer an error.

The best way to configure these filesystems is through the ways officially recommended by the cloud storage providers. For instance, if you want to configure your AWS credential to use an S3 bucket as your artifact storage directory, you should configure your AWS account using relevant official tools (e.g., AWS CLI, boto3), and LineaPy will use the default AWS credentials to access the S3 bucket just like pandas and fsspec.

Some filesystems might need extra configuration. In pandas, you can pass these configurations as storage_options in pandas.DataFrame.to_csv(storage_options={some storage options}), where the storage_options is a filesystem-specific dictionary pass into fsspec.filesystem. In LineaPy, you can use exactly the same storage_options to handle these extra configuration items, and you can set them with

lineapy.options.set('storage_options',{'same storage_options as you use in'})

or you can put them in the LineaPy configuration files.

Note that, LineaPy does not support configuring these items as environmental variables or CLI options, since passing a dictionary through these two methods are a little bit awkward. Instead, if you want ot use environmental variables, you should configure it through the official way from the storage provider and LineaPy should be able to handle these extra configuration items directly.

Note that, which storage_options items you can set are depends on the filesystem you are using.

Storing Artifact Metadata in PostgreSQL

By default, LineaPy uses SQLite to store artifact metadata (e.g., name, version, code), which keeps the package light and simple. Given the limitations of SQLite (e.g., single write access to a database at a time), however, we may want to use a more advanced database such as PostgreSQL.

To make LineaPy recognize and use a PostgreSQL database, we can export the database connection string into the relevant environmental variable, like so:

export LINEAPY_DATABASE_URL=postgresql://postgresuser:postgrespwd@localhost:15432/postgresdb

Note that this has to be done prior to using LineaPy so that the environment variable exists in runtime.


If you want to use PostgreSQL as the default backend, you can make the environment variable persist across sessions by defining it in .bashrc or .zshrc.

You can check the connection between LineaPy and PostgreSQL with:

from lineapy.db.db import RelationalLineaDB

which will print:


if successful. Otherwise, it will default back to SQLite and print:



If you are using PostgreSQL as your database, you might encounter the following error:

NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:postgres

This is caused by a change in SQLAlchemy where they dropped support for DB URLs of the form postgres://. Using postgresql:// instead should fix this error.

Storing Artifact Values in Amazon S3

To use S3 as LineaPy's serialized value location, you can run the following command in your notebook to change your storage backend:

lineapy.options.set('artifact_storage_dir', 's3://your-bucket/your-artifact-folder')

You should configure your AWS account just like you would for AWS CLI or boto3, and LineaPy will use the default AWS credentials to access the S3 bucket.

If you want to use other profiles available in your AWS configuration, you can set the profile name with:

lineapy.options.set('storage_options', {'profile': 'ANOTHER_AWS_PROFILE'})

which is equivalent to setting your environment variable AWS_PROFILE to the profile name.

If you really need to set your AWS credentials directly in the running session (strongly discouraged as it may result in accidentally saving these credentials in plain text), you can set them with:

lineapy.options.set('storage_options', {'key': 'AWS KEY', 'secret': 'AWS SECRET'})

which is equivalent to setting environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

To learn more about which S3 configuration items that you can set in storage_options, you can see the parameters of s3fs.S3FileSystem since fsspec is passing storage_options items to s3fs.S3FileSystem to access S3 under the hood.

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