Feature/mlflow support

[aagumin]

#342 Implement Support Mlflow

Now we can:

modctl modelfile generate --model-url models://bert-base-cased-squad/1 

or

modctl modelfile generate --model-url bert-base-cased-squad/1 --provider mlflow

or autopull latest

modctl modelfile generate --model-url bert-base-cased-squad 

Two dependencies have been added:
• databricks-go-sdk — includes the MLflow client, with possible future support for their model registry.
• AWS SDK — used for downloading models. This dependency is relevant for many self-hosted solutions and cloud providers.

[gemini-code-assist]

Summary of Changes

Hello @aagumin, 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 extends the tool's capabilities by integrating with MLflow, a popular platform for managing the machine learning lifecycle. Users can now seamlessly pull and generate modelfiles from models stored in MLflow registries, including those with artifacts hosted on S3. This broadens the ecosystem of supported model sources, making the tool more versatile for MLOps workflows.

Highlights

• MLflow Integration: Introduced support for MLflow, enabling the generation of modelfiles directly from models registered in MLflow. This includes fetching model versions and downloading artifacts.
• New Dependencies: Added databricks-go-sdk for MLflow client interactions and AWS SDK for S3 model artifact downloading, expanding the tool's connectivity to cloud storage.
• Enhanced modctl Commands: The modctl modelfile generate command now supports specifying MLflow models using models://bert-base-cased-squad/1 or explicitly with --provider mlflow.
• Code Quality Tools: Updated .golangci.yml to include additional Go formatters and linters such as gofumpt, goimports, golines, and swaggo to maintain code consistency and quality.

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[gemini-code-assist]

Code Review

This pull request introduces support for downloading models from MLflow, which is a great addition. The implementation includes a new mlflow provider, logic to interact with the MLflow registry, and functionality to download model artifacts from S3. While the overall structure is good, I've identified several critical issues that must be addressed. These include a file descriptor leak due to improper use of defer in a loop, a potential panic from unsafe string slicing, and incorrect credential handling logic. Additionally, I've provided feedback on improving logging, making version selection deterministic, and enhancing test coverage to ensure the new provider is robust and reliable.

[aagumin]

/gemini review

[gemini-code-assist]

Code Review

This pull request adds support for downloading models from MLflow, which is a great feature. The implementation introduces a new MLflow provider, a downloader with S3 storage backend support, and associated tests. I've identified a few critical issues related to authentication and version handling that need to be addressed. Additionally, there are opportunities to improve efficiency by reusing clients and to enhance code quality by refactoring error handling and logging. Overall, this is a solid contribution with a few areas for refinement.

[aagumin]

hi! Look my pr pls