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[Frontend] add run batch to CLI (#18804)

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Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
This commit is contained in:
Reid
2025-05-28 22:08:57 +08:00
committed by GitHub
parent 4c2b38ce9e
commit 435fa95444
5 changed files with 110 additions and 22 deletions
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37
examples/offline_inference/openai_batch/README.md
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@@ -48,7 +48,19 @@ The batch running tool is designed to be used from the command line.
You can run the batch with the following command, which will write its results to a file called `results.jsonl`
```console
python -m vllm.entrypoints.openai.run_batch -i offline_inference/openai_batch/openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
python -m vllm.entrypoints.openai.run_batch \
-i offline_inference/openai_batch/openai_example_batch.jsonl \
-o results.jsonl \
--model meta-llama/Meta-Llama-3-8B-Instruct
```
or use command-line:
```console
vllm run-batch \
-i offline_inference/openai_batch/openai_example_batch.jsonl \
-o results.jsonl \
--model meta-llama/Meta-Llama-3-8B-Instruct
```
### Step 3: Check your results
@@ -68,7 +80,19 @@ The batch runner supports remote input and output urls that are accessible via h
For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl`, you can run
```console
python -m vllm.entrypoints.openai.run_batch -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl -o results.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
python -m vllm.entrypoints.openai.run_batch \
-i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
-o results.jsonl \
--model meta-llama/Meta-Llama-3-8B-Instruct
```
or use command-line:
```console
vllm run-batch \
-i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
-o results.jsonl \
--model meta-llama/Meta-Llama-3-8B-Instruct
```
## Example 3: Integrating with AWS S3
@@ -164,6 +188,15 @@ python -m vllm.entrypoints.openai.run_batch \
--model --model meta-llama/Meta-Llama-3-8B-Instruct
```
or use command-line:
```console
vllm run-batch \
-i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
-o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
--model --model meta-llama/Meta-Llama-3-8B-Instruct
```
### Step 4: View your results
Your results are now on S3. You can view them in your terminal by running