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You now run the integration tests with these options:
```bash
Custom options:
--stack-config=STACK_CONFIG
a 'pointer' to the stack. this can be either be:
(a) a template name like `fireworks`, or
(b) a path to a run.yaml file, or
(c) an adhoc config spec, e.g.
`inference=fireworks,safety=llama-guard,agents=meta-
reference`
--env=ENV Set environment variables, e.g. --env KEY=value
--text-model=TEXT_MODEL
comma-separated list of text models. Fixture name:
text_model_id
--vision-model=VISION_MODEL
comma-separated list of vision models. Fixture name:
vision_model_id
--embedding-model=EMBEDDING_MODEL
comma-separated list of embedding models. Fixture name:
embedding_model_id
--safety-shield=SAFETY_SHIELD
comma-separated list of safety shields. Fixture name:
shield_id
--judge-model=JUDGE_MODEL
comma-separated list of judge models. Fixture name:
judge_model_id
--embedding-dimension=EMBEDDING_DIMENSION
Output dimensionality of the embedding model to use for
testing. Default: 384
--record-responses Record new API responses instead of using cached ones.
--report=REPORT Path where the test report should be written, e.g.
--report=/path/to/report.md
```
Importantly, if you don't specify any of the models (text-model,
vision-model, etc.) the relevant tests will get **skipped!**
This will make running tests somewhat more annoying since all options
will need to be specified. We will make this easier by adding some easy
wrapper yaml configs.
## Test Plan
Example:
```bash
ashwin@ashwin-mbp ~/local/llama-stack/tests/integration (unify_tests) $
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/test_text_inference.py \
--text-model meta-llama/Llama-3.2-3B-Instruct
```
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Llama Stack Integration Tests
We use pytest for parameterizing and running tests. You can see all options with:
cd tests/integration
# this will show a long list of options, look for "Custom options:"
pytest --help
Here are the most important options:
--stack-config: specify the stack config to use. You have three ways to point to a stack:- a URL which points to a Llama Stack distribution server
- a template (e.g.,
fireworks,together) or a path to a run.yaml file - a comma-separated list of api=provider pairs, e.g.
inference=fireworks,safety=llama-guard,agents=meta-reference. This is most useful for testing a single API surface.
--env: set environment variables, e.g. --env KEY=value. this is a utility option to set environment variables required by various providers.
Model parameters can be influenced by the following options:
--text-model: comma-separated list of text models.--vision-model: comma-separated list of vision models.--embedding-model: comma-separated list of embedding models.--safety-shield: comma-separated list of safety shields.--judge-model: comma-separated list of judge models.--embedding-dimension: output dimensionality of the embedding model to use for testing. Default: 384
Each of these are comma-separated lists and can be used to generate multiple parameter combinations.
Experimental, under development, options:
--record-responses: record new API responses instead of using cached ones--report: path where the test report should be written, e.g. --report=/path/to/report.md
Examples
Run all text inference tests with the together distribution:
pytest -s -v tests/api/inference/test_text_inference.py \
--stack-config=together \
--text-model=meta-llama/Llama-3.1-8B-Instruct
Run all text inference tests with the together distribution and meta-llama/Llama-3.1-8B-Instruct:
pytest -s -v tests/api/inference/test_text_inference.py \
--stack-config=together \
--text-model=meta-llama/Llama-3.1-8B-Instruct
Running all inference tests for a number of models:
TEXT_MODELS=meta-llama/Llama-3.1-8B-Instruct,meta-llama/Llama-3.1-70B-Instruct
VISION_MODELS=meta-llama/Llama-3.2-11B-Vision-Instruct
EMBEDDING_MODELS=all-MiniLM-L6-v2
TOGETHER_API_KEY=...
pytest -s -v tests/api/inference/ \
--stack-config=together \
--text-model=$TEXT_MODELS \
--vision-model=$VISION_MODELS \
--embedding-model=$EMBEDDING_MODELS
Same thing but instead of using the distribution, use an adhoc stack with just one provider (fireworks for inference):
FIREWORKS_API_KEY=...
pytest -s -v tests/api/inference/ \
--stack-config=inference=fireworks \
--text-model=$TEXT_MODELS \
--vision-model=$VISION_MODELS \
--embedding-model=$EMBEDDING_MODELS
Running Vector IO tests for a number of embedding models:
EMBEDDING_MODELS=all-MiniLM-L6-v2
pytest -s -v tests/api/vector_io/ \
--stack-config=inference=sentence-transformers,vector_io=sqlite-vec \
--embedding-model=$EMBEDDING_MODELS