Custom DSP, Learn and Transformation blocks can have custom parameters. You use these to give the user an option to visually configure the block. For example to select the cutoff for a filter in a DSP block, or what data augmentation to apply in an ML algorithm.
Each parameter is defined as a JSON object. Here's a basic example:
This renders the following UI element:
Here "name" maps to the label of the element, "help" is shown under a tooltip, "value" is the default value of the element, and "type" is enforced if you enter an invalid value.
"param" is the parameter that'll be sent to your block. Depending on the type of block this is either:
DSP Blocks: Passed in an HTTP request to the block, and then (by default) passed as an argument to the Python function implementing the block. See Building custom processing blocks > Adding configuration options.
All other blocks: Passed in as command line arguments, prefixed with --. E.g. for the example above your block will be called with --scale-axes 1.
Parameters are described in a parameters.json file placed in the root of your block. This is then automatically picked up by the Studio when rendering your block.
For DSP blocks the parameters.json file also contains metadata for the block, and the parameters are listed in the parameters object. Additionally, DSP blocks can contain multiple groups. Each group has a header element that is rendered.
This is rendered as:
For learn blocks the parameters.json file looks very similar to the DSP block, but does not support parameter groups: the parameters field is just an array.
This is rendered as:
And passed into your script as:
If a learn block has no parameters (either no parameters.json file or one with an empty array) then the 'Number of training cycles' (maps to --epochs) and 'Learning rate' (maps to --learning-rate) elements are rendered.
For transformation blocks the parameters.json file looks very similar to the DSP block, but does not support parameter groups: the parameters field is just an array of items:
This is rendered as:
And passed into your script as:
Transformation blocks have two extra types: dataset and bucket. Both render a dropdown menu listing all datasets or all configured cloud buckets in your organization. The name of the dataset/bucket will be passed in to your script (e.g. in the configuration above ei-data-dev is passed in).
Synthetic data blocks follow the same pattern as transformation blocks.
The supported properties for the type attribute are:
These all render a text box.
This renders a dropdown menu and needs to be paired with a valid parameter listing all options. E.g.:
Renders as:
And:
To use a different label and value for the select element you can pass in an array of objects to valid. E.g.:
Here the UI renders the label ("gpu (NDP101)" and "log-bin (NDP120/200)") but the values ("gpu" or "log-bin") are actually passed to your blocks.
This renders a checkbox. E.g.:
Renders as:
Boolean values are passed into your transformation and learn blocks with either the value 0 (false) or 1 (true):
If you don't want a value (so just --do-log passed into the script) then use a flag (see the next section).
A flag renders exactly the same as a boolean, but for transformation and learn blocks the parameter is passed to your script differently.
If the value is true then the parameter is just passed in as an argument, e.g.:
If the value is false then the parameter is omitted.
This renders a dropdown menu with all your organizational datasets. E.g.:
Renders as:
The name of the dataset is passed into your script.
This renders a dropdown menu with all your organizational buckets. E.g.:{
Renders as:
The name of the bucket is passed into your script.
Parameters can be shown depending on the state of other parameters using the showIf property. E.g.:
Here the 'How much augmentation' property is shown only when the 'Apply augmentation' checkbox is checked.
Default state:
State when the checkbox is checked:
You can also use neq as the operator to show elements when a clause is not true. Other operators are currently not supported.
DSP Blocks can have multiple implementation versions. This is used heavily in our internal code to add new functionality to existing blocks without breaking earlier implementations. You set the latest implementation version in the info object of the parameters.json file (new blocks are created with this implementation version). E.g.:
Then to show certain parameters only for certain implementation versions, you use the showForImplementationVersion property:
Now the FFT parameter is only shown for implementation versions 3 and 4. You can find complete examples of this behavior in the spectral analysis DSP block.
