Using a Python Code Snippet or Standalone File

• 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.

As 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. If your code necessitates a specific environment – for instance, to support GPU operations - you can define the Container Base Image that supports it. Use this option for more complex code, which can still be run as a single file. Unlike the Code Snippet, no additional "hidden" functionality is included here.

• Upload File(s) - This option is similar to "Standalone File", but allows you to provide your code in more than one file, e.g., multiple Python files and/or shell scripts. Files can include non-Python files of any format, such as configuration files, model parameter files, and so forth. You just have to upload the files you need, or a folder(s), and select the entry point in the text box that appears when this option is selected. The entry point is the file that you'd like your container to run. Be sure to specify code requirements and the container environment as needed.