MESA Runner

A stateless runner for deploying MESA registered models onto GSTT Infrastructure. It syncs models from S3, reads unprocessed documents from Snowflake, runs inference, and writes results back.

Requirements

Python 3.13+
uv package manager

Installation

Remote Inference (Default)

For remote inference via OpenAI-compatible endpoints:

uv sync

Offline Inference (Optional)

For local GPU inference with vLLM, install the optional dependency:

uv sync --group vllm-offline

Configuration

Create a config.yaml file (see example below):

Remote Inference Example

my_source:
  model_name: "your-model-name"

  inference:
    openai_endpoint: "http://localhost:5000/v1"

  storage:
    type: snowflake
    source_database: "str"
    source_schema: "str"
    source_table: "str"

    sink_database: "str"
    sink_schema: "str"
    sink_table: "str"

    connection_params:
      account: "str"
      user: "str"
      role: "str"
      password: "str"
      warehouse: "str"
      database: "str"

Offline Inference Example

my_source:
  model_s3_uri: "s3://aicentre-nlpteam-mesa-build/models/oncoqwen/oncoqwen_1/"

  inference:
    max_model_len: 18000

  storage:
    type: snowflake
    source_database: "str"
    source_schema: "str"
    source_table: "str"

    sink_database: "str"
    sink_schema: "str"
    sink_table: "str"

    connection_params:
      account: "str"
      user: "str"
      role: "str"
      password: "str"
      warehouse: "str"
      database: "str"

Usage

# Run with default config.yaml
mesa_runner

# Or specify a config file
mesa_runner --config /path/to/config.yaml

# Dry run mode (uses dummy data, does not read or write real data)
mesa_runner --dry-run

The dry run mode is useful for testing the runner without accessing real data sources or sinks. It generates 5 dummy documents by default and logs all write operations instead of executing them.

Docker

# Remote inference (default)
docker build -t mesa-runner .
docker run mesa-runner

# Offline inference (includes vLLM)
docker build --target offline -t mesa-runner:offline .
docker run --gpus all mesa-runner:offline

Development

# Run linting and tests
make test

# Auto-fix linting issues
make fix

# Run tests with coverage
make cov

Metadata

Release files for londonaicentre-mesa-runner 1.16.0

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Source distribution (sdist)

Source distribution for londonaicentre-mesa-runner 1.16.0
File	Size	Uploaded
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File	Interpreter	ABI	Platform	Reset
londonaicentre_mesa_runner-1.16.0-py3-none-any.whl 32.5 kB Oct 15, 2026	Python 3	none	any	Details

Total release size: 76.0 kB

Release files / londonaicentre_mesa_runner-1.16.0.tar.gz

Download URL	londonaicentre_mesa_runner-1.16.0.tar.gz
Size	43.5 kB
Tags	Source
SHA-256 checksum How to use checksums	`4ce95a43bab0c814d6d3c4ff3750b39f1b3cbb8675b0fadade11f277b35b339a`
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Upload date	Oct 15, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.13.1 {"installer":{"name":"uv","version":"0.13.1","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Amazon Linux","version":"2023","id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`

Release files / londonaicentre_mesa_runner-1.16.0-py3-none-any.whl

Download URL	londonaicentre_mesa_runner-1.16.0-py3-none-any.whl
Size	32.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b15084554de3de98aa79cf69734468671fffb910681afc093ab7e2c481c19368`
BLAKE2b-256 checksum How to use checksums	`e7be3f1e95d0713387ae51638c69b0817a320fd72afdf25df1c8e22612113dd6`
Upload date	Oct 15, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.13.1 {"installer":{"name":"uv","version":"0.13.1","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Amazon Linux","version":"2023","id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`