Code Snippet
Ideal for relatively straightforward scripts that primarily utilize basic Python packages, along with Pandas and Numpy. FCP automatically provides access to your cohort data as a Pandas Dataframe named df. Additionally, it generates the output cohort of your model from this same Dataframe. This setup facilitates elementary Pandas-based operations on your raw input data, such as feature extraction and value normalization.
An Example, consider this Python code snippet:
normalized_df = (df-df.mean())/df.std() df = normalized_df
Executing this code snippet yields an output cohort with z-normalized numerical features, all without requiring any additional code.
Standalone File
This option grants you the freedom to execute varied Python code with custom dependencies, extending beyond Pandas and Numpy. The Standalone File option lets you specify your code's prerequisites, which will be automatically installed within the image generated by FCP. Moreover, you're required to define the Container Base Image for your container, a crucial step if your code necessitates a specific environment – for instance, to support GPU operations.
Once you have provided all the necessary inputs, click "Create New Model" at the bottom of the dialog box. The FCP will now create a container image, in the background, based on your specifications, push it to your workgroup's ECR repository, and create a Model which you can then run.
Note: This can take significant time in some cases, depending on your requirements and selected container base image,
If you have received an error or run into any issues throughout the process, please check out our community forums, or reach out to support@rhinohealth.com for more assistance.