openai-batch 0.3.4

Make OpenAI batch easy to use.

openai-batch

Batch inferencing is an easy and inexpensive way to process thousands or millions of LLM inferences.

The process is:

1. Write inferencing requests to an input file
2. start a batch job
3. wait for it to finish
4. download the output

This library aims to make these steps easier. The OpenAI protocol is relatively easy to use, but it has a lot of boilerplate steps. This library automates those.

Supported Providers

• OpenAI - ChatGPT, GPT4o, etc.
• Parasail - Most transformer models on HuggingFace, such as LLama, Qwen, LLava, etc.

Direct Library Usage

You can also use the library directly in your Python code for more control over the batch processing workflow.

Basic Usage

import random
from openai_batch import Batch

# Create a batch with random prompts
with Batch() as batch:
    objects = ["cat", "robot", "coffee mug", "spaceship", "banana"]
    for i in range(100):
        batch.add_to_batch(
            model="meta-llama/Meta-Llama-3-8B-Instruct",
            temperature=0.7,
            max_completion_tokens=1000,
            messages=[{"role": "user", "content": f"Tell me a joke about a {random.choice(objects)}"}]
        )
    
    # Submit, wait for completion, and download results
    result, output_path, error_path = batch.submit_wait_download()
    print(f"Batch completed with status {result.status} and stored in {output_path}")

batch.add_to_batch accepts the same format as chat.completion.create in OpenAI's Python library, so any chat completion parameters can be included. Parasail supports most transformers on HuggingFace (TODO: link), while OpenAI supports all of their serverless models.

You can also create embedding batches in a similar way:

with Batch() as batch:
    documents = ["The quick brown fox jumps over the lazy dog", 
                 "Machine learning models can process natural language"]
    
    for doc in documents:
        batch.add_to_batch(
            model="text-embedding-3-small",  # OpenAI embedding model
            input=doc
        )
    
    result, output_path, error_path = batch.submit_wait_download()

Or analyze images:

from openai_batch import Batch, data_url

with Batch() as batch:
    images = (p for p in Path("/path/to/images").iterdir() if p.suffix.lower() in {".jpg", ".png", ".webp"})

    for image in images:
        batch.add_to_batch(
            model="Qwen/Qwen3-VL-8B-Instruct",
            max_completion_tokens=1000,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "image_url", "image_url": {"url": data_url(image)}},
                        {"type": "text", "text": "What is in the image?"},
                    ],
                }
            ],
        )

    result, output_path, error_path = batch.submit_wait_download()

See full example script: image_understanding.py

Step-by-Step Workflow

For more control, you can break down the process into individual steps:

from openai_batch import Batch
import time

# Create a batch object
batch = Batch(
    submission_input_file="batch_input.jsonl",
    output_file="batch_output.jsonl",
    error_file="batch_errors.jsonl"
)

# Add chat completion requests to the batch
objects = ["cat", "robot", "coffee mug", "spaceship", "banana"]
for i in range(5):
    batch.add_to_batch(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": f"Tell me a joke about a {objects[i]}"}]
    )

# Submit the batch
batch_id = batch.submit()
print(f"Batch submitted with ID: {batch_id}")

# Check status periodically
while True:
    status = batch.status()
    print(f"Batch status: {status.status}")
    
    if status.status in ["completed", "failed", "expired", "cancelled"]:
        break
        
    time.sleep(60)  # Check every minute

# Download results once completed
output_path, error_path = batch.download()
print(f"Output saved to: {output_path}")
print(f"Errors saved to: {error_path}")

Working with Different Providers

The library automatically selects the appropriate provider based on the model:

from openai_batch import Batch

# OpenAI models automatically use the OpenAI provider
openai_batch = Batch()
openai_batch.add_to_batch(
    model="gpt-4o-mini",  # OpenAI model
    messages=[{"role": "user", "content": "Hello, world!"}]
)

# Other models automatically use the Parasail provider
parasail_batch = Batch()
parasail_batch.add_to_batch(
    model="meta-llama/Meta-Llama-3-8B-Instruct",  # Non-OpenAI model
    messages=[{"role": "user", "content": "Hello, world!"}]
)

You can also explicitly specify a provider:

from openai_batch import Batch
from openai_batch.providers import get_provider_by_name

# Get a specific provider
provider = get_provider_by_name("parasail")

# Create a batch with this provider
batch = Batch(provider=provider)
batch.add_to_batch(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    messages=[{"role": "user", "content": "Hello, world!"}]
)

Resuming an Existing Batch

from openai_batch import Batch
import time

# Resume an existing batch
batch = Batch(batch_id="batch_abc123")

# Check status in a loop until completed
while True:
    status = batch.status()
    print(f"Batch status: {status.status}")
    
    if status.status == "completed":
        output_path, error_path = batch.download()
        print(f"Output saved to: {output_path}")
        break
    elif status.status in ["failed", "expired", "cancelled"]:
        print(f"Batch ended with status: {status.status}")
        break
        
    time.sleep(60)  # Check every minute

Command-Line Utilities

Use openai_batch.run to run a batch from an input file on disk:

python -m openai_batch.run input.jsonl

This will start the batch, wait for it to complete, then download the results.

Useful switches:

• -c Only create the batch, do not wait for it.
• --resume Attach to an existing batch job. Wait for it to finish then download results.
• --dry-run Confirm your configuration without making an actual request.
• Full list: python -m openai_batch.run --help

OpenAI Example

export OPENAI_API_KEY="<Your OpenAI API Key>"

# Create an example batch input file
python -m openai_batch.example_prompts | \
  python -m openai_batch.create_batch --model 'gpt-4o-mini' > input.jsonl

# Run this batch (resumable with `--resume <BATCH_ID>`)
python -m openai_batch.run input.jsonl

Parasail Example

export PARASAIL_API_KEY="<Your Parasail API Key>"

# Create an example batch input file
python -m openai_batch.example_prompts | \
  python -m openai_batch.create_batch --model 'meta-llama/Meta-Llama-3-8B-Instruct' > input.jsonl

# Run this batch (resumable with `--resume <BATCH_ID>`)
python -m openai_batch.run -p parasail input.jsonl

Resources

• OpenAI Batch Cookbook
• OpenAI Batch API reference
• OpenAI Files API reference