Skip to content
GitLab
Projects Groups Snippets
  • /
  • Help
    • Help
    • Support
    • Community forum
    • Submit feedback
    • Contribute to GitLab
  • Sign in / Register
  • A AutoIntent
  • Project information
    • Project information
    • Activity
    • Labels
    • Members
  • Repository
    • Repository
    • Files
    • Commits
    • Branches
    • Tags
    • Contributors
    • Graph
    • Compare
  • Issues 10
    • Issues 10
    • List
    • Boards
    • Service Desk
    • Milestones
  • Merge requests 2
    • Merge requests 2
  • CI/CD
    • CI/CD
    • Pipelines
    • Jobs
    • Schedules
  • Deployments
    • Deployments
    • Environments
    • Releases
  • Packages and registries
    • Packages and registries
    • Package Registry
    • Infrastructure Registry
  • Monitor
    • Monitor
    • Incidents
  • Analytics
    • Analytics
    • Value stream
    • CI/CD
    • Repository
  • Wiki
    • Wiki
  • Snippets
    • Snippets
  • Activity
  • Graph
  • Create a new issue
  • Jobs
  • Commits
  • Issue Boards
Collapse sidebar
  • DeepPavlov
  • AutoIntent
  • Merge requests
  • !36

Feat/pipeline simpler fitting

  • Review changes

  • Download
  • Email patches
  • Plain diff
Merged Roman Zlobin requested to merge feat/pipeline-simpler-fitting into dev Nov 05, 2024
  • Overview 25
  • Commits 30
  • Pipelines 0
  • Changes 60

Created by: voorhs

Лаконичный пример работы с новым апи для оптимизации пайплайна.

# load data
from autointent.context.data_handler import Dataset
from autointent.context.utils import load_data

train_dataset = load_data("./data/train_data.json")
val_dataset = load_data("./data/test_data.json")

# define search space
from autointent.pipeline.optimization import PipelineOptimizer

config = {
    "nodes": [
        {
            "node_type": "scoring",
            "metric": "scoring_roc_auc",
            "search_space": [
                {"module_type": "knn", "k": [5, 10], "weights": ["uniform", "distance", "closest"], "model_name": ["avsolatorio/GIST-small-Embedding-v0"]},
                {"module_type": "linear", "model_name": ["avsolatorio/GIST-small-Embedding-v0"]},
            ],
        },
        {
            "node_type": "prediction",
            "metric": "prediction_accuracy",
            "search_space": [
                {"module_type": "threshold", "thresh": [0.5]},
                {"module_type": "tunable"},
            ],
        },
    ]
}

pipeline_optimizer = PipelineOptimizer.from_dict_config(config)

# optionally, configure your run
from autointent.configs.optimization_cli import LoggingConfig, VectorIndexConfig, EmbedderConfig
from pathlib import Path

pipeline_optimizer.set_config(LoggingConfig(run_name="sweet_cucumber", dirpath=Path(".").resolve(), dump_modules=False))
pipeline_optimizer.set_config(VectorIndexConfig(db_dir=Path("./my_vector_db").resolve(), device="cuda"))
pipeline_optimizer.set_config(EmbedderConfig(batch_size=16, max_length=32))

# run optimization
context = pipeline_optimizer.optimize_from_dataset(train_dataset, val_dataset)

# dump logs
context.dump()

Еще из фич:

  • инициализация Context теперь не такая громоздкая
  • модули можно не дампить, если указать logs.dump_modules=False в конфиге

TODO:

  • опция очищать ли модули из RAM (т.е. убрать gc.collect() и проч по запросу пользователя)
  • очистка db_dir по запросу пользователя
  • fix unintended runs directory creation
Assignee
Assign to
Reviewers
Request review from
Time tracking
Source branch: feat/pipeline-simpler-fitting