V0.5.16

[JadenFiotto-Kaufman]

No description provided.

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Greptile Summary

This PR implements v0.5.16 with major enhancements to remote execution on NDIF servers. The changes focus on improving cross-version compatibility, user experience, and robustness.

Key Changes:

• Source-based serialization: Replaced bytecode serialization with source code serialization to enable cross-Python-version compatibility between client and server (e.g., Python 3.10 client with Python 3.11 server)
• Compression upgrade: Migrated from zlib to zstandard for better compression ratios and performance in API requests/responses
• Environment comparison: Added compare() function to help users identify version mismatches between local and remote environments, highlighting critical packages like nnsight, transformers, and torch
• Enhanced error handling: Implemented ExceptionWrapper with rich traceback formatting and proper source code context for traced code
• Improved remote backend: Complete refactor with WebSocket support for real-time status updates, animated spinners, elapsed time tracking, and hybrid local/remote execution via streaming
• Better pickling support: Added __getstate__/__setstate__ methods to NNsight base class for proper serialization

Code Quality:

• Extensive documentation with detailed docstrings throughout
• Well-structured architecture with clear separation of concerns
• Proper error handling and graceful degradation

Confidence Score: 5/5

• This PR is safe to merge with high confidence
• The code is well-documented, follows best practices, and implements significant improvements to the remote execution infrastructure. The changes are substantial but well-structured, with proper error handling and graceful fallbacks. No logical errors, security issues, or breaking changes were identified.
• No files require special attention

Important Files Changed

Filename	Overview
src/nnsight/intervention/backends/remote.py	Major refactor implementing remote NDIF backend with WebSocket support, status display, and comprehensive error handling. Code is well-documented with detailed docstrings.
src/nnsight/intervention/serialization.py	Complete rewrite replacing bytecode-based serialization with source-based serialization for cross-Python-version compatibility. Well-documented implementation using cloudpickle.
src/nnsight/intervention/tracing/util.py	Enhanced exception handling with rich traceback formatting, better frame reconstruction logic, and support for displaying traced code context. Refactored ExceptionWrapper for cleaner output.
src/nnsight/ndif.py	Added environment comparison feature (NdifEnvComparison class and compare() function) to check local vs remote package versions. Improved status checking with better formatting.
src/nnsight/schema/request.py	Updated compression from zlib to zstandard, added source code attachment to interventions, improved deserialization to support persistent objects.
src/nnsight/init.py	Added linecache preloading for better traceback support, integrated custom exception hooks for both standard Python and IPython environments to display clean NNsight exceptions.

Sequence Diagram

sequenceDiagram
    participant Client as Client (Local)
    participant Tracer as Tracer
    participant Backend as RemoteBackend
    participant Serializer as CustomCloudPickler
    participant HTTP as NDIF HTTP API
    participant WS as WebSocket
    participant Server as NDIF Server
    participant LocalTracer as LocalTracer

    Client->>Tracer: trace(input, remote=True)
    Tracer->>Backend: __call__(tracer)
    Backend->>Serializer: dumps(RequestModel)
    Note over Serializer: Serializes functions<br/>by source code
    Serializer-->>Backend: serialized bytes
    Backend->>Backend: compress with zstandard
    Backend->>WS: connect()
    WS-->>Backend: session_id
    Backend->>HTTP: POST /request (data, headers)
    HTTP-->>Backend: job_id
    Backend->>Backend: JobStatusDisplay.update("RECEIVED")
    
    loop Status Updates
        Server->>WS: emit(ResponseModel)
        WS-->>Backend: status update
        Backend->>Backend: JobStatusDisplay.update(status)
        alt Status: STREAM
            Note over Backend: Server requests<br/>local execution
            Backend->>LocalTracer: execute(streamed_fn)
            LocalTracer->>LocalTracer: run locally
            LocalTracer-->>Backend: local_values
            Backend->>WS: emit("stream_upload", values)
        end
    end
    
    Server->>WS: emit("COMPLETED", result_url)
    Backend->>Backend: JobStatusDisplay.update("COMPLETED")
    Backend->>HTTP: GET result_url
    HTTP-->>Backend: compressed result bytes
    Backend->>Backend: decompress with zstandard
    Backend->>Backend: torch.load(result)
    Backend-->>Tracer: result
    Tracer-->>Client: traced values

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