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Quick start

This page gives an introduction to Parasail's Batch Processing Library. For more detailed information, jump to:

Parasail's batch processing engine is a very easy and inexpensive way to process thousands or millions of LLM inferences. Batch inferencing is easy: create an input file, start a batch job, wait for it to finish, then download the output.

OpenAI compatibility, Parasail batch helper library

Getting Started with our Parasail Batch Helper Library

You can find the Parasail Batch Helper Library on Github. This library is 100% compatible with both Parasail and OpenAI

The first step is to create a Parasail API key: . This key should be stored in your environment through something like a .bashrc file or a) .env file, or passed through the command line invocation. It is not recommended to paste directly in code as this is a common way of keys getting leaked.

Next, install openai-batch, our batch helper library that is compatible both with Parasail and OpenAI:

pip install openai-batch

Now you're ready to run a batch job in as little as five lines of code! Our batch endpoint supports most transformers on HuggingFace, all you have to do is put the HuggingFace ID in the request. There is no need for this model to be a dedicated or serverless endpoint. In this example, we will use NousResearch/DeepHermes-3-Mistral-24B-Preview ().

#test_batch.py
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="NousResearch/DeepHermes-3-Mistral-24B-Preview",
            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}")

This code will look for PARASAIL_API_KEY in the environment, and an easy way to pass it is through the command line. Running this code will produce the following output:

PARASAIL_API_KEY=<INSERT API KEY> python3 test_batch.py
validating
in_progress
...
in_progress
in_progress
completed
Batch completed with status completed and stored in batch-itvt3wmjs7-output.jsonl

The first two lines of batch-itvt3wmjs7-output.jsonl are the prompt responses:

{"id":"vllm-51653b28a97e4e67a0d3587f959ffe3a","custom_id":"line-1","response":{"status_code":200,"request_id":"vllm-batch-1c7186c472cd49b1aa12a81760094540","body":{"id":"chatcmpl-01be171caf3b4dc69cfa9ef360d26a4e","object":"chat.completion","created":1743050291,"model":"NousResearch/DeepHermes-3-Mistral-24B-Preview","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"Why did the coffee mug get arrested? It had too much to behave!","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":41,"total_tokens":60,"completion_tokens":19,"prompt_tokens_details":null},"prompt_logprobs":null}},"error":null}
{"id":"vllm-58a8ac3f589d4380ad94e00b191da24e","custom_id":"line-3","response":{"status_code":200,"request_id":"vllm-batch-2dd59bd942fa4a25a8792f9b8ee28da1","body":{"id":"chatcmpl-a59bfccd7d134981bf5e1004e77c6854","object":"chat.completion","created":1743050291,"model":"NousResearch/DeepHermes-3-Mistral-24B-Preview","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"Why can't you trust an astronaut? Because they're always out of this world!","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":41,"total_tokens":59,"completion_tokens":18,"prompt_tokens_details":null},"prompt_logprobs":null}},"error":null}

Batch Submission Limitations

Both Parasail and OpenAI limit the maximum size of the batch input files:

Up to 50,000 requests (lines)
Up to 500MB total input file size for Parasail
Up to 250MB total input file size for OpenAI
Max completion tokens for Parasail defaults to 8,192, but can be overridden to 16,384 through the max_completion_tokens parameter.

Resuming Batch Jobs

A major convenience of batch processing is the ability to submit a job and resume the monitoring in a different process or flow. A developer can upload hundreds of batch jobs and millions of prompts without worrying about program crashes, errors, or resets - those prompts are processed on Parasail's servers until successful completion.

Changing the last two lines of the previous example will submit the job and print out the batch ID, then exit.

    # Submit, wait for completion, and download results
    batch_id = batch.submit()
    print(f"Batch ID: {batch_id}")

With a batch ID of batch-tclfzwczcd we can now wait for the batch to finish and download it in a separate script:

from openai_batch import Batch
import time

with Batch(batch_id="batch-tclfzwczcd",
           output_file="mybatch.jsonl") as batch:
    # 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}")

Which outputs:

Batch status: in_progress
...
Batch status: in_progress
Output saved to: mybatch.jsonl

Broad Model and Parameter Support

Chat completions, embeddings, multimodal inputs, the full range of prompt parameters, and even OpenAI models are supported

OpenAI Models

with Batch() as batch:
    for i in range(100):
        batch.add_to_batch(
            model="gpt-4o",
            messages=[{"role": "user", "content": f"Give me 10 dad jokes"}]
        )

Embedding Models

GritLM — developed by Contextual and hosted on HuggingFace by Parasail — and GTE-Qwen2-7B-Instruct from Alibaba are two excellent open source embeddding models that rival proprietary models. Embeddings like these can be easily run by changing messagesto input .

with Batch() as batch:
    for i in range(100):
        batch.add_to_batch(
            model="parasail-ai/GritLM-7B-vllm",
            encoding_format= "base64"
            input=f"This is input #{i}"
        )

Parameters

add_to_batch supports all of the parameters that client.chat.completions.create or client.embedding.create supports, though note that open source models on parasail may not always support every parameter.

with Batch() as batch:
    for i in range(100):
        batch.add_to_batch(
            model="NousResearch/DeepHermes-3-Mistral-24B-Preview",
            max_completion_tokens=1000,
            temperature=0.7,
            top_p=0.1,
            messages=[{"role": "user", "content": f"Give me 10 dad jokes"}]
        )

Metadata

with Batch() as batch:
    for i in range(100):
        batch.add_to_batch(
            model="NousResearch/DeepHermes-3-Mistral-24B-Preview",
            messages=[{"role": "user", "content": f"Give me 10 dad jokes"}]
        )
    batch.submit( metadata= {"Job Name": "Dad jokes"})

Parasail Batch UI

The Parasail Batch UI can be used upload new batches, track the status of batches, and download results when finished.

While a batch is queued or running, you can view its status, download the input, or cancel it:

When a batch job is done, you can download the input and the output file as well as view the total token usage:

Batch Submission

Below is an example JSONL file, which follows the OpenAI format for batch submissions. Unlike the Parsail Batch Helper Library, this does not support OpenAI models and only supports open source HuggingFace transformers and embeddings.

Further Reading

PreviousRate Limits and Limitations NextBatch Processing with Private Models

Last updated 1 month ago