Keep track of execution environment

[safaricd]

Change Description

With this change, we keep track of the environment that the user is running TabPFN on. More specifically, previously we were only recording:

• Whether user is running in interactive mode.
• Environments like Kaggle and Jupyter.

Now, we track whether TabPFN is ran on GCP, Azure, AWS or Databricks too.

We do this using a heuristic-based approach - with well-known and automatically set environmental variables.

[gemini-code-assist]

Summary of Changes

Hello @safaricd, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances TabPFN's telemetry by broadening the scope of detected execution environments. It introduces a more structured and comprehensive approach to identifying where the code is being run, moving beyond just interactive kernels to include major cloud providers and notebook platforms. This change provides more granular insights into user environments, which can be valuable for understanding usage patterns and improving the tool's performance and relevance across diverse deployment scenarios.

Highlights

• Expanded Environment Tracking: TabPFN now tracks execution environments beyond interactive kernels, including major cloud providers (GCP, AWS, Azure) and notebook platforms (Databricks, Colab), in addition to existing Kaggle and Jupyter environments.
• Unified Execution Context: A new ExecutionContext dataclass and get_execution_context function have been introduced to centralize and standardize the retrieval of runtime information, encompassing interactivity, kernel type, and the newly added environment types.
• Heuristic-Based Detection: A robust heuristic system has been implemented using predefined environment variables (ENV_TYPE_HINTS) to reliably identify various cloud and notebook platforms, making the detection process more comprehensive and adaptable.

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[gemini-code-assist]

Code Review

This pull request refactors the execution environment detection to be more structured and adds support for several new cloud environments like GCP, AWS, Azure, and Databricks. The changes are well-organized, separating kernel detection from environment detection. I've pointed out a potential issue in the environment variable check which might lead to incorrect detection in some edge cases. I've also recommended adding tests for the newly supported environments to ensure their detection logic is correct and robust against future changes.