Parasail's batch processing engine is a straightforward 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.
The first step is to create a Parasail API key: https://www.saas.parasail.io/keys. 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. Pasting directly in code isn't recommended as keys can get leaked.
Next, install openai-batch, the batch helper library that's compatible both with Parasail and OpenAI:
pipinstallopenai-batch
Now you're ready to run a batch job in as little as five lines of code. The batch endpoint supports most transformers on Hugging Face—all you have to do is put the Hugging Face ID in the request. There's no need for this model to be a dedicated or endpoint. In this example, Parasail uses NousResearch/DeepHermes-3-Mistral-24 B-Preview (https://huggingface.co/NousResearch/DeepHermes-3-Mistral-24B-Preview).
#test_batch.pyimport randomfrom openai_batch import Batch# Create a batch with random promptswithBatch()as batch: objects = ["cat","robot","coffee mug","spaceship","banana"]for i inrange(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)}"}] )#temporary workaround file_path = batch.submission_input_file.name batch.submission_input_file.close() batch.submission_input_file = file_path batch._should_close =False# 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 looks for PARASAIL_API_KEY in the environment, and passing it through the command line is straightforward. Running this code produces the following output:
The first two lines of batch-itvt3wmjs7-output.jsonl are the prompt responses:
Batch Submission Limitations
Both Parasail and OpenAI limit the maximum size of the batch input files:
Up to 50,000 requests (lines)
Up to 500 MB total input file size for Parasail
Up to 250 MB 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.
Workloads that exceed these limits must be split into multiple batches. The add_to_batch function in the Parasail Batch Helper Library will raise a ValueError exception when the file size or request count is exceeded for the provider.
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 submits the job and prints out the batch ID, then exits.
With a batch ID of batch-tclfzwczcd, you can now wait for the batch to finish and download it in a separate script:
Which outputs:
Broad Model and Parameter Support
Chat completions, embeddings, multimodal inputs, the full range of prompt parameters, and even OpenAI models are supported
OpenAI Models
Embedding Models
GritLM—developed by Contextual and hosted on Hugging Face by Parasail—and GTE-Qwen2-7 B-Instruct from Alibaba are two excellent open source embedding models that rival proprietary models. Embeddings like these can be easily run by changing messagesto input .
As seen below, Parasail strongly recommends using base64 encodingencoding_format="base64" to reduce the size of the output files for both Parasail and OpenAI.
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.
Metadata
You can add metadata to the batch. This is useful for passing information between separate submit and download processes, as well as tracking the results on the status UI page. Any metadata you add to the submission is visible in the Batch UI progress section. For detailed information about the metadata field, see the OpenAI Batch API Submit Specification.
Parasail Batch UI
The Parasail Batch UI can be used to 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 finished, you can download the input and the output file as well as view the total token usage:
Batch Submission
Batches can be submitted directly from the Batch section of the platform by clicking the Create Batch button. This brings up a dialog to upload a JSONL file.
Below is an example JSONL file, which follows the OpenAI format for batch submissions. Unlike the Parasail Batch Helper Library, this doesn't support OpenAI models and only supports open source Hugging Face transformers and embeddings.
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
{"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}
# Submit, wait for completion, and download results
batch_id = batch.submit()
print(f"Batch ID: {batch_id}")
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}")
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"}]
)
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}"
)
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"}]
)
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"})
button. This brings up a dialog to upload a JSONL file.