Settings

mlop encourages higher resolution logging and does not block any of your existing code.

Performance

mlop strives to be THE FASTEST logger. When using mlop, you should experience a noticeable performance improvement. This is because mlop never artificially blocks your code in any way, unlike other platforms. We work to reduce your training time and overhead wherever possible while ensuring data integrity and experiment reproducibility.

If you notice any performance issues, drop us an email at founders@mlop.ai and you can be sure we will respond immediately.

List of Preferences

mlop logger officially supports customizing the following settings:

Parameter	Type	Description
`host`	`str`	Set the host address for the logger for self-hosted instances.
`disable_iface`	`bool`	Disable the network interface exposed to the logger.
`disable_store`	`bool`	Disable the local data store.
`x_sys_label`	`str`	Set the log group label for system metrics.
`x_grad_label`	`str`	Set the log group label for model gradients.
`x_param_label`	`str`	Set the log group label for model parameters.
`x_sys_sampling_interval`	`int`	Set the sampling interval for the system metrics.
`x_log_level`	`int`	Set the log level for the logger.
`x_file_stream_max_conn`	`int`	Set the maximum number of connections allowed for API calls.
`x_file_stream_max_size`	`int`	Set the maximum size of individual connections.
`x_file_stream_timeout_seconds`	`int`	Set the timeout in seconds for individual connections.
`x_file_stream_retry_max`	`int`	Set the maximum number of retries for REST API calls.
`x_file_stream_retry_wait_min_seconds`	`int`	Set the minimum wait time in seconds between retries.
`x_file_stream_retry_wait_max_seconds`	`int`	Set the maximum wait time in seconds between retries.

Source

See the Python code for more details.

Customizing Preferences

When initializing the logger, you can pass in logger settings either as a Settings object or a dictionary.

Examples

mlop.init(
    project="example",
    settings={"host": "127.0.0.1"}
)

settings = mlop.Settings()
settings.host = "127.0.0.1"
 
mlop.init(
    project="example",
    settings=settings
)

Pre-Authentication

To pre-authenticate the logger instead of logging in interactively, you can manually obtain an API key and pass it to the logger settings.

settings = mlop.Settings()
settings._auth = "mlpi_example"
mlop.login(
    project="example",
    settings=settings
)

Settings

List of Preferences

Customizing Preferences

Examples

Pre-Authentication

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