LLMFlux: LLM Batch Processing Pipeline for HPC Systems

A streamlined solution for running Large Language Models (LLMs) in batch mode on HPC systems powered by Slurm. LLMFlux uses the OpenAI-compatible API format with a JSONL-first architecture, enabling your prompts to flow efficiently through LLM engines at scale.

Architecture

      JSONL Input                    Batch Processing                    Results
   (OpenAI Format)                 (Ollama/vLLM + Model)               (JSON Output)
         │                                 │                                 │
         │                                 │                                 │
         ▼                                 ▼                                 ▼
    ┌──────────┐                   ┌──────────────┐                   ┌──────────┐
    │  Batch   │                   │              │                   │  Output  │
    │ Requests │─────────────────▶ │   Model on   │─────────────────▶ │  Results │
    │  (JSONL) │                   │    GPU(s)    │                   │  (JSON)  │
    └──────────┘                   │              │                   └──────────┘
                                   └──────────────┘                    

LLMFlux processes JSONL files in a standardized OpenAI-compatible batch API format, enabling efficient processing of thousands of prompts on HPC systems with minimal overhead.

Documentation

• Configuration Guide - How to configure LLMFlux
• Models Guide - Supported models and requirements
• Multi-Node Inference - Serving a model that does not fit on one node
• Repository Structure - Codebase organization
• Testing Guide - How to run tests

Installation

Prerequisites: LLMFlux runs models inside Apptainer (formerly Singularity) containers on HPC systems. Apptainer must be available on your cluster — confirm with apptainer --version or contact your cluster admin.

pip install llmflux

Or for development:

1. Create and Activate Conda Environment:

   conda create -n llmflux python=3.11 -y
   conda activate llmflux

2. Install Package:

   pip install -e .

3. Environment Setup:

   cp .env.example .env
   # Edit .env with your SLURM account and model details

Confirm the installation by running a base command and ensuring your system gives the correct output:

$llmflux -h
usage: llmflux [-h] [--version] {run,serve,connect,benchmark,show-models,jobs,status,logs,cancel,clean,remove} ...

LLMFlux CLI

positional arguments:
  {run,serve,connect,benchmark,show-models,jobs,status,logs,cancel,clean,remove}
    run                 Submit a batch processing job
    serve               Start a model as a long-running service on a compute node
    connect             Show connection info for a running serve job
    benchmark           Run a benchmark job
    show-models         List all available model keys from models.yaml
    jobs                List LLMFlux tracked Slurm jobs
    status              Show detailed status for a job
    logs                Show last lines of stdout and stderr for a tracked job
    cancel              Cancel a tracked running/pending job
    clean               Delete LLMFlux scratch, logs and connection files, keeping model weights
    remove              Delete all LLMFlux data, including model weights and engine caches

options:
  -h, --help            show this help message and exit
  --version, -V         Show llmflux version and exit

Quick Start

Core Batch Processing on SLURM

The primary workflow for LLMFlux is submitting JSONL files for batch processing on SLURM:

from llmflux.slurm import SlurmRunner
from llmflux.core.config import Config

# Setup SLURM configuration
config = Config()
slurm_config = config.get_slurm_config()
slurm_config.account = "myaccount"

# Initialize runner
runner = SlurmRunner(config=slurm_config)

# Submit JSONL file directly for processing
job_id = runner.run(
    input_path="prompts.jsonl",
    output_path="results.json",
    model="Llama-3.2-3B-Instruct",
    batch_size=4
)
print(f"Job submitted with ID: {job_id}")

JSONL Input Format

JSONL input format follows the OpenAI Batch API specification:

{"custom_id":"request1","method":"POST","url":"/v1/chat/completions","body":{"messages":[{"role":"system","content":"You are a helpful assistant"},{"role":"user","content":"Explain quantum computing"}],"temperature":0.7,"max_tokens":500}}
{"custom_id":"request2","method":"POST","url":"/v1/chat/completions","body":{"messages":[{"role":"system","content":"You are a helpful assistant"},{"role":"user","content":"What is machine learning?"}],"temperature":0.7,"max_tokens":500}}

For advanced options like custom batch sizes, processing settings, or SLURM configuration, see the Configuration Guide.

For advanced model configuration, see the Models Guide.

Command-Line Interface

LLMFlux uses vLLM as its default engine. Model keys come from models.yaml (see llmflux show-models). --output is optional — if omitted, results are written to a timestamped file in your configured output directory.

# Minimal: engine defaults to vllm, output path is auto-generated
llmflux run --model Llama-3.2-3B-Instruct --input data/prompts.jsonl

# Explicit engine and output path
llmflux run --model Llama-3.2-3B-Instruct --input data/prompts.jsonl --output results/output.json --engine vllm

# Use the Ollama engine instead
llmflux run --model Llama-3.2-3B-Instruct --input data/prompts.jsonl --engine ollama

# With SLURM account and partition
llmflux run \
   --account your-account \
   --partition gpu \
   --model Llama-3.2-3B-Instruct \
   --input data/prompts.jsonl \
   --output results/output.json

The --model value must match a key from models.yaml (e.g. MistralLite, Llama-3.2-3B-Instruct). LLMFlux resolves the correct HuggingFace repo name (hf_name) and Ollama tag (name) automatically from that key.

Note that in order to use some HuggingFace models, you will need a token from HF. Once you have a token, update your local copy of the .env file:

HUGGINGFACE_TOKEN=hf_XXXXXXXXXXXXXXX

For some gated repos, you will also need to visit the HuggingFace repository directly and accept the terms of access. The model cache defaults to ~/.cache/huggingface/hub. To change this, add to your .env:

HF_HOME=/path/to/dir

LLMFlux will automatically download the appropriate models for both Ollama and vLLM.

For detailed command options:

llmflux --help

Job Control Commands

LLMFlux tracks submitted jobs in a local registry (~/.llmflux/jobs.json) and combines that metadata with Slurm state.

# List tracked jobs (default: active states only)
llmflux jobs

# Include historical states
llmflux jobs --all

# Filter by one or more states
llmflux jobs --state RUNNING --state FAILED

# Show detailed merged status for one job
llmflux status <job-id>

# Tail logs (default: 100 lines)
llmflux logs <job-id>
llmflux logs <job-id> --tail 200
llmflux logs <job-id> -f

# Cancel a tracked job
llmflux cancel <job-id>
llmflux cancel <job-id> --force

# Cancel every active LLMFlux job at once
llmflux cancel --all

# Delete logs and scratch files, keeping model weights
llmflux clean

# Delete logs, scratch, model weights and engine caches
llmflux remove

Notes:

• jobs and status derive live state from Slurm JSON output.
• logs and cancel only operate on jobs present in the LLMFlux registry.
• clean empties logs/, tmp/, staged-input/ and ~/.llmflux/serve/, and deletes job.sh. remove also empties containers/, models/, .cache/, .ollama/, .vllm/ and ~/.llmflux/. The directories themselves stay — only their contents go. A configured directory outside the workspace (e.g. a site-wide LLMFLUX_CONTAINERS_DIR) is skipped, not emptied.
• Neither command touches data/input/ or data/output/, and neither wipes the workspace itself — on a source checkout that is where the repo lives.
• Both refuse to run while any LLMFlux job is running. Stop it with llmflux cancel <job-id> or llmflux cancel --all first, then re-run.

Output Format

Results are saved in the user's workspace:

[
  {
    "input": {
      "custom_id": "request1",
      "method": "POST",
      "url": "/v1/chat/completions",
      "body": {
        "messages": [
          {"role": "system", "content": "You are a helpful assistant"},
          {"role": "user", "content": "Original prompt text"}
        ],
        "temperature": 0.7,
        "max_tokens": 1024
      },
      "metadata": {
        "source_file": "example.txt"
      }
    },
    "output": {
      "id": "chat-cmpl-123",
      "object": "chat.completion",
      "created": 1699123456,
      "choices": [
        {
          "index": 0,
          "message": {
            "role": "assistant",
            "content": "Generated response text"
          },
          "finish_reason": "stop"
        }
      ]
    },
    "metadata": {
      "model": "llama3.2:3b",
      "timestamp": "2023-11-04T12:34:56.789Z",
      "processing_time": 1.23
    }
  }
]

Utility Converters

LLMFlux provides utility converters to help prepare JSONL files from various input formats. These are Python functions in llmflux.converters — there is no llmflux convert CLI command. Call them from a script, then pass the resulting JSONL file to llmflux run.

Supported source types:

• csv_to_jsonl — CSV files; use prompt_template with {column_name} placeholders
• directory_to_jsonl — directories of text files (.txt, .md, .json, .py, .yaml, .yml); use recursive=True to traverse subdirectories
• json_to_jsonl — existing JSON files already in batch or messages format

prompt_template syntax: Wrap column or field names in {braces} to interpolate values into the prompt. For example, "Summarize the following paper: {abstract}" pulls the abstract column from each CSV row.

from llmflux.converters import csv_to_jsonl, directory_to_jsonl, json_to_jsonl

# Convert CSV to JSONL using a prompt template
csv_to_jsonl(
    input_path="data/papers.csv",
    output_path="data/papers.jsonl",
    prompt_template="Summarize: {text}",
)

# Convert a directory of text files to JSONL
directory_to_jsonl(
    input_path="data/documents/",
    output_path="data/docs.jsonl",
    recursive=True,
)

# Convert an existing JSON file (batch or messages format) to JSONL
json_to_jsonl(
    input_path="data/prompts.json",
    output_path="data/prompts.jsonl",
)

See examples/csv_jsonl_example.py, examples/directory_jsonl_example.py, and examples/json_jsonl_example.py for complete, runnable versions of the above.

Interactive Serving

In addition to batch processing, LLMFlux can start a model as a long-running OpenAI-compatible service on a compute node.

Start a serve job

llmflux serve \
    --model Llama-3.2-3B-Instruct \
    --email you@example.com \
    --time 02:00:00 \
    --engine vllm

• --model — model key from models.yaml (same as llmflux run)
• --email — you will receive an email when the model is ready (not just when the job starts)
• --time — how long to keep the service alive (e.g. 02:00:00, 08:00:00)
• --engine — vllm (default) or ollama

LLMFlux generates a unique API key for the session and prints the job ID:

Serve job submitted: 2301062
  Model:  Llama-3.2-3B-Instruct
  Engine: vllm
  Time:   02:00:00
  Email:  you@example.com

You will receive an email at you@example.com when the service is ready.
Then run: llmflux connect 2301062

Connect once the model is ready

After receiving the ready email, run:

llmflux connect 2301062

This pings the endpoint and prints everything you need:

Service is ready.

  Endpoint:  http://gpu-node-04:8031/v1
  API Key:   llmflux-57de4141f7d9b52a24481f05438c166c
  Model:     meta-llama/Llama-3.2-3B-Instruct
  Engine:    vllm

Example usage:

from openai import OpenAI
client = OpenAI(base_url="http://gpu-node-04:8031/v1", api_key="llmflux-57de4141f7d9b52a24481f05438c166c")
response = client.chat.completions.create(
    model="meta-llama/Llama-3.2-3B-Instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Use the endpoint from LLMFlux itself

The OpenAI SDK snippet above works from any tool. To use LLMFlux's own client instead, pass the API key that llmflux connect printed — a serve endpoint rejects unauthenticated requests with HTTP 401:

from llmflux.core.client import LLMClient

# Note: host omits the /v1 suffix — the client appends the path itself.
client = LLMClient(
    engine="vllm",
    host="http://gpu-node-04:8031",
    api_key="llmflux-57de4141f7d9b52a24481f05438c166c",
)
print(client.chat(
    model="meta-llama/Llama-3.2-3B-Instruct",
    engine="vllm",
    messages=[{"role": "user", "content": "Hello!"}],
))

The key is also read from LLMFLUX_API_KEY, so it can be left out of the code:

export VLLM_HOST=http://gpu-node-04:8031
export LLMFLUX_API_KEY=llmflux-57de4141f7d9b52a24481f05438c166c

BatchProcessor takes the same api_key argument and falls back to the same variable.

Check status and shut down

# See endpoint, API key, and email for a serve job
llmflux status 2301062

# Shut the service down early
llmflux cancel 2301062

llmflux jobs also shows a TYPE column (serve / batch) so you can distinguish long-running services from batch jobs at a glance.

Benchmarking

LLMFlux ships with a benchmarking workflow that can source prompts, submit the SLURM job, and collect results/metrics for you.

llmflux benchmark \
    --model Llama-3.2-3B-Instruct \
    --name nightly \
    --num-prompts 60 \
    --account ACCOUNT_NAME \
    --partition PARTITION_NAME \
    --nodes 1

• Prompt sources: omit --input to automatically download and cache LiveBench categories (benchmark_data/). Provide --input path/to/prompts.jsonl to reuse an existing JSONL file instead. Use --num-prompts, --temperature, and --max-tokens to control synthetic dataset generation.
• Outputs: results default to results/benchmarks/<name>_results.json and a metrics summary (<name>_metrics.txt) containing elapsed SLURM runtime and number of prompts processed.
• Batch tuning: adjust --batch-size for throughput. Pass model arguments such as --temperature and --max-tokens to forward them to the runner.
• SLURM overrides: forward scheduler settings with --account, --partition, --nodes, --gpus-per-node, --time, --mem, and --cpus-per-task. --nodes greater than 1 shards the model across nodes (vLLM only) — see Multi-Node Inference.
• Job controls: add --rebuild to force an Apptainer image rebuild or --debug to keep the generated job script for inspection.

For the complete option reference:

llmflux benchmark --help

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

MIT License