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Parent(s):
8773ff3
Upload 16 files
Browse files- defaults.py +22 -11
- pages/2_π©πΌβπ¬ Describe Domain.py +10 -2
- pages/3_π± Generate Dataset.py +65 -65
- pipeline.py +29 -5
- pipeline.yaml +6 -6
- project_config.json +1 -1
defaults.py
CHANGED
@@ -1,12 +1,14 @@
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import json
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SEED_DATA_PATH = "seed_data.json"
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PIPELINE_PATH = "pipeline.yaml"
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-
REMOTE_CODE_PATHS = ["defaults.py", "domain.py", "pipeline.py"]
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DIBT_PARENT_APP_URL = "https://argilla-domain-specific-datasets-welcome.hf.space/"
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N_PERSPECTIVES = 5
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N_TOPICS = 5
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N_EXAMPLES = 5
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################################################
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# DEFAULTS ON FARMING
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@@ -25,14 +27,23 @@ DEFAULT_SYSTEM_PROMPT = DEFAULT_DATA["domain_expert_prompt"]
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# PROJECT CONFIG FROM PARENT APP
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################################################
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-
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PROJECT_NAME = PROJECT_CONFIG["project_name"]
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ARGILLA_SPACE_REPO_ID = PROJECT_CONFIG["argilla_space_repo_id"]
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DATASET_REPO_ID = PROJECT_CONFIG["dataset_repo_id"]
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ARGILLA_SPACE_NAME = ARGILLA_SPACE_REPO_ID.replace("/", "-").replace("_", "-")
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ARGILLA_URL = f"https://{ARGILLA_SPACE_NAME}.hf.space"
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PROJECT_SPACE_REPO_ID = PROJECT_CONFIG["project_space_repo_id"]
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DATASET_URL = f"https://huggingface.co/datasets/{DATASET_REPO_ID}"
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HUB_USERNAME = DATASET_REPO_ID.split("/")[0]
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import os
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import json
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SEED_DATA_PATH = "seed_data.json"
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PIPELINE_PATH = "pipeline.yaml"
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+
REMOTE_CODE_PATHS = ["defaults.py", "domain.py", "pipeline.py", "requirements.txt"]
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DIBT_PARENT_APP_URL = "https://argilla-domain-specific-datasets-welcome.hf.space/"
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N_PERSPECTIVES = 5
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N_TOPICS = 5
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N_EXAMPLES = 5
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CODELESS_DISTILABEL = os.environ.get("CODELESS_DISTILABEL", True)
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################################################
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# DEFAULTS ON FARMING
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# PROJECT CONFIG FROM PARENT APP
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################################################
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try:
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with open("project_config.json") as f:
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PROJECT_CONFIG = json.load(f)
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PROJECT_NAME = PROJECT_CONFIG["project_name"]
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ARGILLA_SPACE_REPO_ID = PROJECT_CONFIG["argilla_space_repo_id"]
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DATASET_REPO_ID = PROJECT_CONFIG["dataset_repo_id"]
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ARGILLA_SPACE_NAME = ARGILLA_SPACE_REPO_ID.replace("/", "-").replace("_", "-")
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ARGILLA_URL = f"https://{ARGILLA_SPACE_NAME}.hf.space"
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PROJECT_SPACE_REPO_ID = PROJECT_CONFIG["project_space_repo_id"]
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DATASET_URL = f"https://huggingface.co/datasets/{DATASET_REPO_ID}"
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HUB_USERNAME = DATASET_REPO_ID.split("/")[0]
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except FileNotFoundError:
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PROJECT_NAME = "DEFAULT_DOMAIN"
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ARGILLA_SPACE_REPO_ID = ""
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DATASET_REPO_ID = ""
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ARGILLA_URL = ""
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PROJECT_SPACE_REPO_ID = ""
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DATASET_URL = ""
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HUB_USERNAME = ""
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pages/2_π©πΌβπ¬ Describe Domain.py
CHANGED
@@ -14,7 +14,6 @@ from defaults import (
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N_TOPICS,
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SEED_DATA_PATH,
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PIPELINE_PATH,
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PROJECT_NAME,
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DATASET_REPO_ID,
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)
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from utils import project_sidebar
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pipeline_path=PIPELINE_PATH,
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)
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st.
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f"Dataset seed created and pushed to the Hub. Check it out [here](https://huggingface.co/datasets/{hub_username}/{project_name})"
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)
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else:
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st.info(
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"Please fill in all the required domain fields to push the dataset seed to the Hub"
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N_TOPICS,
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SEED_DATA_PATH,
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PIPELINE_PATH,
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DATASET_REPO_ID,
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)
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from utils import project_sidebar
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pipeline_path=PIPELINE_PATH,
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)
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st.success(
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f"Dataset seed created and pushed to the Hub. Check it out [here](https://huggingface.co/datasets/{hub_username}/{project_name})"
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)
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st.write("You can now move on to runnning your distilabel pipeline.")
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st.page_link(
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page="pages/3_π± Generate Dataset.py",
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label="Generate Dataset",
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icon="π±",
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)
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else:
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st.info(
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"Please fill in all the required domain fields to push the dataset seed to the Hub"
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pages/3_π± Generate Dataset.py
CHANGED
@@ -1,17 +1,13 @@
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import streamlit as st
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from streamlit.errors import EntryNotFoundError
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from hub import pull_seed_data_from_repo, push_pipeline_to_hub
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from defaults import (
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DEFAULT_SYSTEM_PROMPT,
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PIPELINE_PATH,
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PROJECT_NAME,
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ARGILLA_SPACE_REPO_ID,
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DATASET_REPO_ID,
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ARGILLA_SPACE_NAME,
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ARGILLA_URL,
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PROJECT_SPACE_REPO_ID,
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HUB_USERNAME,
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)
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from utils import project_sidebar
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st.markdown("### Run the pipeline")
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st.write(
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"Once you've defined the pipeline configuration, you can run the pipeline
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)
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if st.button("π» Run pipeline locally", key="run_pipeline_local"):
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hub_token=hub_token,
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pipeline_config_path=PIPELINE_PATH,
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argilla_dataset_name=argilla_dataset_name,
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)
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st.code(
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f"""
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pip install git+https://github.com/argilla-io/distilabel.git
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git clone https://huggingface.co/{hub_username}/{project_name}
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cd {project_name}
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""",
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language="bash",
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)
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###############################################################
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# SPACE
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###############################################################
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if st.button("π₯ Run pipeline right here, right now!"):
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try:
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seed_data = pull_seed_data_from_repo(
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repo_id=f"{hub_username}/{project_name}",
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hub_token=hub_token,
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)
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-
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)
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-
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argilla_api_key=argilla_api_key,
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argilla_dataset_name=argilla_dataset_name,
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argilla_api_url=argilla_url,
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topics=topics,
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perspectives=perspectives,
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pipeline_config_path=PIPELINE_PATH,
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domain_expert_prompt=domain_expert_prompt or DEFAULT_SYSTEM_PROMPT,
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hub_token=hub_token,
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endpoint_base_url=base_url,
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examples=examples,
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)
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with st.spinner("Starting the pipeline..."):
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logs = run_pipeline(PIPELINE_PATH)
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import streamlit as st
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from hub import pull_seed_data_from_repo, push_pipeline_to_hub
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from defaults import (
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DEFAULT_SYSTEM_PROMPT,
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PIPELINE_PATH,
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PROJECT_NAME,
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ARGILLA_URL,
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HUB_USERNAME,
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CODELESS_DISTILABEL,
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)
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from utils import project_sidebar
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st.markdown("### Run the pipeline")
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st.write(
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"Once you've defined the pipeline configuration, you can run the pipeline from your local machine."
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)
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if CODELESS_DISTILABEL:
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st.write(
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"""We recommend running the pipeline locally if you're planning on generating a large dataset. \
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But running the pipeline on this space is a handy way to get started quickly. Your synthetic
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samples will be pushed to Argilla and available for review.
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"""
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)
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st.write(
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"""If you're planning on running the pipeline on the space, be aware that it \
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will take some time to complete and you will need to maintain a \
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connection to the space."""
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)
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if st.button("π» Run pipeline locally", key="run_pipeline_local"):
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hub_token=hub_token,
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pipeline_config_path=PIPELINE_PATH,
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argilla_dataset_name=argilla_dataset_name,
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argilla_api_key=argilla_api_key,
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argilla_api_url=argilla_url,
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)
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st.code(
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f"""
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pip install git+https://github.com/argilla-io/distilabel.git
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git clone https://huggingface.co/datasets/{hub_username}/{project_name}
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cd {project_name}
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pip install -r requirements.txt
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{' '.join(["python"] + command_to_run[1:])}
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""",
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language="bash",
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)
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###############################################################
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# SPACE
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###############################################################
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if CODELESS_DISTILABEL:
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if st.button("π₯ Run pipeline right here, right now!"):
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if all(
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[
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argilla_api_key,
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argilla_url,
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base_url,
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hub_username,
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project_name,
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hub_token,
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argilla_dataset_name,
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]
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):
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with st.spinner("Pulling seed data from the Hub..."):
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seed_data = pull_seed_data_from_repo(
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repo_id=f"{hub_username}/{project_name}",
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hub_token=hub_token,
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)
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domain = seed_data["domain"]
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perspectives = seed_data["perspectives"]
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topics = seed_data["topics"]
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examples = seed_data["examples"]
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domain_expert_prompt = seed_data["domain_expert_prompt"]
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with st.spinner("Serializing the pipeline configuration..."):
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serialize_pipeline(
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argilla_api_key=argilla_api_key,
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argilla_dataset_name=argilla_dataset_name,
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argilla_api_url=argilla_url,
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topics=topics,
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perspectives=perspectives,
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pipeline_config_path=PIPELINE_PATH,
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domain_expert_prompt=domain_expert_prompt or DEFAULT_SYSTEM_PROMPT,
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hub_token=hub_token,
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endpoint_base_url=base_url,
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examples=examples,
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)
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with st.spinner("Starting the pipeline..."):
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logs = run_pipeline(
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pipeline_config_path=PIPELINE_PATH,
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argilla_api_key=argilla_api_key,
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argilla_api_url=argilla_url,
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hub_token=hub_token,
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argilla_dataset_name=argilla_dataset_name,
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)
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st.success(f"Pipeline started successfully! π")
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with st.expander(label="View Logs", expanded=True):
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for out in logs:
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st.text(out)
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else:
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st.error("Please fill all the required fields.")
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pipeline.py
CHANGED
@@ -1,5 +1,5 @@
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-
import os
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import subprocess
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import time
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from typing import List
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@@ -82,10 +82,11 @@ def define_pipeline(
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input_batch_size=8,
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input_mappings={"instruction": "evolved_questions"},
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output_mappings={"generation": "domain_expert_answer"},
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_system_prompt=domain_expert_prompt,
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_template=template,
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)
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keep_columns = KeepColumns(
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name="keep_columns",
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columns=["model_name", "evolved_questions", "domain_expert_answer"],
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def create_pipelines_run_command(
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pipeline_config_path: str = "pipeline.yaml",
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argilla_dataset_name: str = "domain_specific_datasets",
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):
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"""Create the command to run the pipeline."""
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command_to_run = [
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-
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"-m",
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"distilabel",
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"pipeline",
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pipeline_config_path,
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"--param",
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f"text_generation_to_argilla.dataset_name={argilla_dataset_name}",
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]
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return command_to_run
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def run_pipeline(
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pipeline_config_path: str = "pipeline.yaml",
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argilla_dataset_name: str = "domain_specific_datasets",
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):
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"""Run the pipeline and yield the output as a generator of logs."""
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command_to_run = create_pipelines_run_command(
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pipeline_config_path=pipeline_config_path,
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argilla_dataset_name=argilla_dataset_name,
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)
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# Run the script file
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process = subprocess.Popen(
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command_to_run,
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)
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while process.stdout and process.stdout.readable():
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import subprocess
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import sys
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import time
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from typing import List
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input_batch_size=8,
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input_mappings={"instruction": "evolved_questions"},
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output_mappings={"generation": "domain_expert_answer"},
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)
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domain_expert._system_prompt = domain_expert_prompt
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domain_expert._template = template
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keep_columns = KeepColumns(
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name="keep_columns",
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columns=["model_name", "evolved_questions", "domain_expert_answer"],
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def create_pipelines_run_command(
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hub_token: str,
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argilla_api_key: str,
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argilla_api_url: str,
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pipeline_config_path: str = "pipeline.yaml",
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argilla_dataset_name: str = "domain_specific_datasets",
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):
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"""Create the command to run the pipeline."""
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command_to_run = [
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sys.executable,
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"-m",
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"distilabel",
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"pipeline",
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pipeline_config_path,
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"--param",
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f"text_generation_to_argilla.dataset_name={argilla_dataset_name}",
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"--param",
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f"text_generation_to_argilla.api_key={argilla_api_key}",
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"--param",
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f"text_generation_to_argilla.api_url={argilla_api_url}",
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+
"--param",
|
168 |
+
f"self-instruct.llm.api_key={hub_token}",
|
169 |
+
"--param",
|
170 |
+
f"evol_instruction_complexity.llm.api_key={hub_token}",
|
171 |
+
"--param",
|
172 |
+
f"domain_expert.llm.api_key={hub_token}",
|
173 |
+
"--ignore-cache",
|
174 |
]
|
175 |
return command_to_run
|
176 |
|
177 |
|
178 |
def run_pipeline(
|
179 |
+
hub_token: str,
|
180 |
+
argilla_api_key: str,
|
181 |
+
argilla_api_url: str,
|
182 |
pipeline_config_path: str = "pipeline.yaml",
|
183 |
argilla_dataset_name: str = "domain_specific_datasets",
|
184 |
):
|
185 |
"""Run the pipeline and yield the output as a generator of logs."""
|
186 |
|
187 |
command_to_run = create_pipelines_run_command(
|
188 |
+
hub_token=hub_token,
|
189 |
pipeline_config_path=pipeline_config_path,
|
190 |
argilla_dataset_name=argilla_dataset_name,
|
191 |
+
argilla_api_key=argilla_api_key,
|
192 |
+
argilla_api_url=argilla_api_url,
|
193 |
)
|
194 |
|
195 |
# Run the script file
|
196 |
process = subprocess.Popen(
|
197 |
+
args=command_to_run,
|
198 |
+
stdout=subprocess.PIPE,
|
199 |
+
stderr=subprocess.PIPE,
|
200 |
+
env={"HF_TOKEN": hub_token},
|
201 |
)
|
202 |
|
203 |
while process.stdout and process.stdout.readable():
|
pipeline.yaml
CHANGED
@@ -1,5 +1,5 @@
|
|
1 |
distilabel:
|
2 |
-
version: 1.0.
|
3 |
pipeline:
|
4 |
name: farming
|
5 |
description: null
|
@@ -54,7 +54,7 @@ pipeline:
|
|
54 |
model_id: null
|
55 |
endpoint_name: null
|
56 |
endpoint_namespace: null
|
57 |
-
base_url: https://
|
58 |
tokenizer_id: null
|
59 |
model_display_name: null
|
60 |
use_openai_client: false
|
@@ -163,7 +163,7 @@ pipeline:
|
|
163 |
model_id: null
|
164 |
endpoint_name: null
|
165 |
endpoint_namespace: null
|
166 |
-
base_url: https://
|
167 |
tokenizer_id: null
|
168 |
model_display_name: null
|
169 |
use_openai_client: false
|
@@ -390,7 +390,7 @@ pipeline:
|
|
390 |
model_id: null
|
391 |
endpoint_name: null
|
392 |
endpoint_namespace: null
|
393 |
-
base_url: https://
|
394 |
tokenizer_id: null
|
395 |
model_display_name: null
|
396 |
use_openai_client: false
|
@@ -489,9 +489,9 @@ pipeline:
|
|
489 |
generation: domain_expert_answer
|
490 |
output_mappings: {}
|
491 |
input_batch_size: 50
|
492 |
-
dataset_name:
|
493 |
dataset_workspace: admin
|
494 |
-
api_url: https://argilla-
|
495 |
runtime_parameters_info:
|
496 |
- name: input_batch_size
|
497 |
optional: true
|
|
|
1 |
distilabel:
|
2 |
+
version: 1.0.1
|
3 |
pipeline:
|
4 |
name: farming
|
5 |
description: null
|
|
|
54 |
model_id: null
|
55 |
endpoint_name: null
|
56 |
endpoint_namespace: null
|
57 |
+
base_url: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2
|
58 |
tokenizer_id: null
|
59 |
model_display_name: null
|
60 |
use_openai_client: false
|
|
|
163 |
model_id: null
|
164 |
endpoint_name: null
|
165 |
endpoint_namespace: null
|
166 |
+
base_url: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2
|
167 |
tokenizer_id: null
|
168 |
model_display_name: null
|
169 |
use_openai_client: false
|
|
|
390 |
model_id: null
|
391 |
endpoint_name: null
|
392 |
endpoint_namespace: null
|
393 |
+
base_url: https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2
|
394 |
tokenizer_id: null
|
395 |
model_display_name: null
|
396 |
use_openai_client: false
|
|
|
489 |
generation: domain_expert_answer
|
490 |
output_mappings: {}
|
491 |
input_batch_size: 50
|
492 |
+
dataset_name: test_3
|
493 |
dataset_workspace: admin
|
494 |
+
api_url: https://burtenshaw-test-3-argilla-space.hf.space
|
495 |
runtime_parameters_info:
|
496 |
- name: input_batch_size
|
497 |
optional: true
|
project_config.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"project_name": "
|
|
|
1 |
+
{"project_name": "test_3", "argilla_space_repo_id": "burtenshaw/test_3_argilla_space", "project_space_repo_id": "burtenshaw/test_3_config_space", "dataset_repo_id": "burtenshaw/test_3"}
|