forked from phoenix-oss/llama-stack-mirror

Add eval/scoring/datasetio API providers to distribution templates & UI developer guide (#564 )

# What does this PR do?

- add /eval, /scoring, /datasetio API providers to distribution
templates
- regenerate build.yaml / run.yaml files
- fix `template.py` to take in list of providers instead of only first
one
- override memory provider as faiss default for all distro (as only 1
memory provider is needed to start basic flow, chromadb/pgvector need
additional setup step).
```
python llama_stack/scripts/distro_codegen.py
```

- updated README to start UI via conda builds. 

## Test Plan

```
python llama_stack/scripts/distro_codegen.py
```

- Use newly generated `run.yaml` to start server
```
llama stack run ./llama_stack/templates/together/run.yaml
```
<img width="1191" alt="image"
src="https://github.com/user-attachments/assets/62f7d179-0cd0-427c-b6e8-e087d4648f09">


#### Registration
```
❯ llama-stack-client datasets register \
--dataset-id "mmlu" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
❯ llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata                                ┃ type    ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ mmlu       │ huggingface │ {'path': 'llamastack/evals', 'name':    │ dataset │
│            │             │ 'evals__mmlu__details', 'split':        │         │
│            │             │ 'train'}                                │         │
└────────────┴─────────────┴─────────────────────────────────────────┴─────────┘
```

```
❯ llama-stack-client datasets register \
--dataset-id "simpleqa" \
--provider-id "huggingface" \
--url "https://huggingface.co/datasets/llamastack/evals" \
--metadata '{"path": "llamastack/evals", "name": "evals__simpleqa", "split": "train"}' \
--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}'
❯ llama-stack-client datasets list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ identifier ┃ provider_id ┃ metadata                                                      ┃ type    ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ mmlu       │ huggingface │ {'path': 'llamastack/evals', 'name': 'evals__mmlu__details',  │ dataset │
│            │             │ 'split': 'train'}                                             │         │
│ simpleqa   │ huggingface │ {'path': 'llamastack/evals', 'name': 'evals__simpleqa',       │ dataset │
│            │             │ 'split': 'train'}                                             │         │
└────────────┴─────────────┴───────────────────────────────────────────────────────────────┴─────────┘
```

```
❯ llama-stack-client eval_tasks register \
> --eval-task-id meta-reference-mmlu \
> --provider-id meta-reference \
> --dataset-id mmlu \
> --scoring-functions basic::regex_parser_multiple_choice_answer
❯ llama-stack-client eval_tasks register \
--eval-task-id meta-reference-simpleqa \
--provider-id meta-reference \
--dataset-id simpleqa \
--scoring-functions llm-as-judge::405b-simpleqa
❯ llama-stack-client eval_tasks list
┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ dataset_id ┃ identifier       ┃ metadata ┃ provider_id    ┃ provider_resour… ┃ scoring_functio… ┃ type      ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ mmlu       │ meta-reference-… │ {}       │ meta-reference │ meta-reference-… │ ['basic::regex_… │ eval_task │
│ simpleqa   │ meta-reference-… │ {}       │ meta-reference │ meta-reference-… │ ['llm-as-judge:… │ eval_task │
└────────────┴──────────────────┴──────────┴────────────────┴──────────────────┴──────────────────┴───────────┘
```

#### Test with UI
```
streamlit run app.py
```

## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

2024-12-05 16:29:32 -08:00

2.3 KiB

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orphan
true

Fireworks Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-fireworks distribution consists of the following provider configurations.

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::fireworks`
memory	`inline::faiss`, `remote::chromadb`, `remote::pgvector`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`

Environment Variables

The following environment variables can be configured:

LLAMASTACK_PORT: Port for the Llama Stack distribution server (default: 5001)
FIREWORKS_API_KEY: Fireworks.AI API Key (default: ``)

Models

The following models are available by default:

meta-llama/Llama-3.1-8B-Instruct (fireworks/llama-v3p1-8b-instruct)
meta-llama/Llama-3.1-70B-Instruct (fireworks/llama-v3p1-70b-instruct)
meta-llama/Llama-3.1-405B-Instruct-FP8 (fireworks/llama-v3p1-405b-instruct)
meta-llama/Llama-3.2-1B-Instruct (fireworks/llama-v3p2-1b-instruct)
meta-llama/Llama-3.2-3B-Instruct (fireworks/llama-v3p2-3b-instruct)
meta-llama/Llama-3.2-11B-Vision-Instruct (fireworks/llama-v3p2-11b-vision-instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (fireworks/llama-v3p2-90b-vision-instruct)
meta-llama/Llama-Guard-3-8B (fireworks/llama-guard-3-8b)
meta-llama/Llama-Guard-3-11B-Vision (fireworks/llama-guard-3-11b-vision)

Prerequisite: API Keys

Make sure you have access to a Fireworks API Key. You can get one by visiting fireworks.ai.

Running Llama Stack with Fireworks

You can do this via Conda (build code) or Docker which has a pre-built image.

Via Docker

This method allows you to get started quickly without having to build the distribution code.

LLAMA_STACK_PORT=5001
docker run \
  -it \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  llamastack/distribution-fireworks \
  --port $LLAMA_STACK_PORT \
  --env FIREWORKS_API_KEY=$FIREWORKS_API_KEY

Via Conda

llama stack build --template fireworks --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env FIREWORKS_API_KEY=$FIREWORKS_API_KEY

2.3 KiB Raw Blame History