Shreyas/finetuning client

examples/finetuning/finetuning_client.ipynb

@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up the finetuning job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from bespokelabs import curator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "finetuning_client = curator.Finetune(\n",
+    "    backend = \"bespoke\",\n",
+    "    backend_params = {\n",
+    "        \"base_url\": 'https://api-dev.bespokelabs.ai'\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset_id = \"41da7b9b3e384ae486a9945376d2fd9c\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "finetuning_client.create_job(\n",
+    "    model_name=\"Qwen/Qwen2.5-7B-Instruct\",\n",
+    "    job_name=\"bespoke-devrev-demo-live\",\n",
+    "    dataset_id=dataset_id,\n",
+    "    seed=42,\n",
+    "    suffix=\"ft\",\n",
+    "    method = {\n",
+    "        \"type\": \"supervised\",\n",
+    "        \"hyperparameters\": {\n",
+    "            ## Type\n",
+    "            \"finetuning_type\": \"lora\",\n",
+    "            \"lora_rank\": 16,\n",
+    "\n",
+    "            ## training dynamics\n",
+    "            \"learning_rate\": 0.0001,\n",
+    "            \"num_train_epochs\": 1,\n",
+    "            \"per_device_train_batch_size\": 4,\n",
+    "            \"gradient_accumulation_steps\": 1,\n",
+    "\n",
+    "            ## infra\n",
+    "            \"preprocessing_num_workers\": 32,\n",
+    "            \"dataloader_num_workers\": 16,\n",
+    "            \"logging_steps\": 1,\n",
+    "        }\n",
+    "    },\n",
+    "    num_gpus=8\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploying the finetuned model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "models_client = curator.Models(\n",
+    "    backend = \"bespoke\",\n",
+    "    backend_params = {\n",
+    "        \"base_url\": 'https://api-dev.bespokelabs.ai'\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "models_client.list_models(job_id='d3201c37255247c2825ff4ce52c110d6')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "models_client.deploy_model(model_id = '10426763-76be-483e-987b-07d2ee5adbd7')"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/bespokelabs/curator/__init__.py

@@ -1,10 +1,12 @@
 """BespokeLabs Curator."""
 
 from .code_executor.code_executor import CodeExecutor
+from .finetune.finetune import Finetune
 from .llm.llm import LLM
+from .models import Models
 from .types import prompt as types
 from .utils import load_dataset, push_to_viewer
 
-__all__ = ["LLM", "CodeExecutor", "types", "push_to_viewer", "load_dataset"]
+__all__ = ["LLM", "CodeExecutor", "types", "Finetune", "Models", "push_to_viewer", "load_dataset"]
 
 from .log import _CONSOLE  # noqa: F401

src/bespokelabs/curator/constants.py

@@ -3,8 +3,8 @@
 BATCH_REQUEST_ID_TAG = "custom_id"
 _CURATOR_DEFAULT_CACHE_DIR = "~/.cache/curator"
 _DEFAULT_CACHE_DIR = "~/.cache"
-BASE_CLIENT_URL = "https://api.bespokelabs.ai/v0/viewer"
-PUBLIC_CURATOR_VIEWER_HOME_URL = "https://curator.bespokelabs.ai"
+BASE_CLIENT_URL = "https://api-dev.bespokelabs.ai/v0/viewer"
+PUBLIC_CURATOR_VIEWER_HOME_URL = "https://curator-dev.bespokelabs.ai"
 PUBLIC_CURATOR_VIEWER_DATASET_URL = PUBLIC_CURATOR_VIEWER_HOME_URL + "/datasets"
 _INTERNAL_PROMPT_KEY = "__internal_prompt"
 _CACHE_MSG = (

src/bespokelabs/curator/datasets/__init__.py

@@ -0,0 +1,5 @@
+"""Dataset loading and processing utilities."""
+
+from .base import upload
+
+__all__ = ["upload"]

src/bespokelabs/curator/datasets/base.py

@@ -0,0 +1,54 @@
+# just upload files, a thin wrapper around push_to_viewer
+
+import logging
+from pathlib import Path
+
+from datasets import load_dataset
+
+from bespokelabs.curator.utils import push_to_viewer
+
+logger = logging.getLogger(__name__)
+
+
+def upload(path: str, split: str = "train"):
+    """Uploads a dataset file to the Hugging Face Hub.
+
+    Args:
+        path: The local path to the dataset file.
+        split: The name of the split to upload (e.g., "train", "test").
+
+    Raises:
+        FileNotFoundError: If the specified file does not exist.
+        ValueError: If the file type is not supported.
+    """
+    # load file into a huggingface dataset
+
+    # it could be a huggingface dataset or a local file
+    # first check if it is a huggingface dataset
+    try:
+        dataset = load_dataset(path, split=split)
+
+    except Exception:
+        path = Path(path)
+        if not path.exists():
+            raise FileNotFoundError(f"File {path} does not exist") from None
+
+        if path.suffix not in [".jsonl", ".json", ".csv", ".parquet"]:
+            raise ValueError("Only jsonl, json, csv, and parquet files are supported currently") from None
+
+        try:
+            if path.suffix == ".jsonl" or path.suffix == ".json":
+                format = "json"
+            elif path.suffix == ".csv":
+                format = "csv"
+            elif path.suffix == ".parquet":
+                format = "parquet"
+
+            dataset = load_dataset(format, data_files=str(path), split=split)
+
+        except Exception as e:
+            logger.error(f"Error loading dataset: {e}")
+            raise e
+
+    link = push_to_viewer(dataset)
+    return link

src/bespokelabs/curator/finetune/__init__.py

@@ -0,0 +1,5 @@
+"""Finetuning backend abstraction and implementations."""
+
+from .finetune import Finetune
+
+__all__ = ["Finetune"]

src/bespokelabs/curator/finetune/base_backend.py

@@ -0,0 +1,58 @@
+from abc import ABC, abstractmethod
+
+
+class BaseFinetuneBackend(ABC):
+    """Abstract base class for finetuning backends."""
+
+    def __init__(self, backend_params: dict):
+        """Initializes the finetuning backend.
+
+        Args:
+            backend_params: A dictionary containing backend-specific parameters.
+        """
+        self.backend_params = backend_params
+
+    @abstractmethod
+    def create_job(self, *args, **kwargs):
+        """Creates a new finetuning job."""
+        pass
+
+    @abstractmethod
+    def list_jobs(self, *args, **kwargs):
+        """Lists all finetuning jobs."""
+        pass
+
+    @abstractmethod
+    def list_job_events(self, job_id: str):
+        """Lists events for a specific finetuning job.
+
+        Args:
+            job_id: The ID of the finetuning job.
+        """
+        pass
+
+    # @abstractmethod
+    # def list_job_checkpoints(self, job_id: str):
+    #     pass
+
+    # @abstractmethod
+    # def list_job_metrics(self, job_id: str):
+    #     pass
+
+    @abstractmethod
+    def get_job_details(self, job_id: str):
+        """Retrieves details for a specific finetuning job.
+
+        Args:
+            job_id: The ID of the finetuning job.
+        """
+        pass
+
+    @abstractmethod
+    def cancel_job(self, job_id: str):
+        """Cancels a specific finetuning job.
+
+        Args:
+            job_id: The ID of the finetuning job.
+        """
+        pass