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Spark-on-HF-JupyterLab
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{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6fb06d81-1778-403c-b15b-d68200a5e6b5",
   "metadata": {},
   "source": [
    "# Spark on Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7399a5ed-aea8-45cf-866f-2decd7097456",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession.builder.appName(\"demo\").getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf07f63-6fed-4cf9-8fee-5f3a5fb6bed1",
   "metadata": {
    "tags": []
   },
   "source": [
    "Example:\n",
    "\n",
    "```python\n",
    "# Load the BAAI/Infinity-Instruct dataset\n",
    "df = read_parquet(\"hf://datasets/BAAI/Infinity-Instruct/7M/*.parquet\")\n",
    "\n",
    "# Load only one column\n",
    "df_langdetect_only = read_parquet(\"hf://datasets/BAAI/Infinity-Instruct/7M/*.parquet\", columns=[\"langdetect\"])\n",
    "\n",
    "# Load values within certain ranges\n",
    "criteria = [(\"langdetect\", \"=\", \"zh-cn\")]\n",
    "df_chinese_only = read_parquet(\"hf://datasets/BAAI/Infinity-Instruct/7M/*.parquet\", filters=criteria)\n",
    "\n",
    "# Save dataset\n",
    "write_parquet(df_chinese_only, \"hf://datasets/username/Infinity-Instruct-Chinese-Only\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca71b3ac-3291-4e4e-8fee-b3550b0426d6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from hf_spark_utils import read_parquet, write_parquet, set_session\n",
    "set_session(spark)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ea62a4-7549-4a75-8a12-9d830f6e3cde",
   "metadata": {},
   "source": [
    "#### (Optional) Login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "343b3a9a-2dce-492b-9384-703368ba3975",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login(new_session=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "332b7609-f0eb-4703-aea6-fec3d09f5870",
   "metadata": {},
   "source": [
    "#### Run your code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c0dfe01-9190-454c-9c52-216f74d339e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}