Snowpark_HuggingFace.ipynb

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    "# Snowpark Hugging Face\n",
    "Let's try out Hugging Face libraries\n",
    "\n",
    "The process has improved for Transformers and Sentence Transformers, there is native support for Snowpark.\n",
    "Here is an example of [Sentence Transformers using the Model Registry](https://medium.com/@michaelgorkow/custom-embedding-models-from-hugging-face-in-snowflake-fd9cc79e25c8)."
   ]
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   "source": [
    "### 1. Setup Environment"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "# Snowflake connector\n",
    "from snowflake import connector\n",
    "#from snowflake.ml.utils import connection_params\n",
    "\n",
    "# Snowpark for Python\n",
    "from snowflake.snowpark.session import Session\n",
    "from snowflake.snowpark.types import Variant\n",
    "from snowflake.snowpark.version import VERSION\n",
    "\n",
    "# Misc\n",
    "import json\n",
    "import logging \n",
    "logger = logging.getLogger(\"snowflake.snowpark.session\")\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "953f6906",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('creds.json') as f:\n",
    "    data = json.load(f)\n",
    "    USERNAME = data['user']\n",
    "    PASSWORD = data['password']\n",
    "    SF_ACCOUNT = data['account']\n",
    "    SF_WH = data['warehouse']\n",
    "\n",
    "CONNECTION_PARAMETERS = {\n",
    "   \"account\": SF_ACCOUNT,\n",
    "   \"user\": USERNAME,\n",
    "   \"password\": PASSWORD,\n",
    "}\n",
    "\n",
    "session = Session.builder.configs(CONNECTION_PARAMETERS).create()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65109d11",
   "metadata": {},
   "source": [
    "Let's grab the FinBERT text classification model, use it, save it to the registry, and then run prediction jobs. You can also add the parameter `token` to add your Hugging Face token for models that are restricted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da3e7472",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "\n",
    "finbert_model = transformers.pipeline(\n",
    "    task=\"text-classification\",\n",
    "    model=\"ProsusAI/finbert\",\n",
    "    top_k=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8aa01e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'label': 'neutral', 'score': 0.7302333116531372},\n",
       "  {'label': 'positive', 'score': 0.18303203582763672}]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finbert_model.predict(\"I am bullish on $AAPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce71f7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snowflake.ml.registry import Registry\n",
    "\n",
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")\n",
    "\n",
    "# Log the model\n",
    "mv = reg.log_model(\n",
    "    finbert_model,\n",
    "    model_name=\"finbert\",\n",
    "    version_name=\"v1\",\n",
    "    conda_dependencies=[\"transformers==4.32.1\",\"tokenizers==0.13.3\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ce14a415",
   "metadata": {},
   "outputs": [],
   "source": [
    "mv = reg.get_model(\"finbert\").version(\"v1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c349a55e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>outputs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[{\"label\": \"negative\", \"score\": 0.810623705387...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[{\"label\": \"neutral\", \"score\": 0.9263970851898...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                                             outputs\n",
       "0  [{\"label\": \"negative\", \"score\": 0.810623705387...\n",
       "1  [{\"label\": \"neutral\", \"score\": 0.9263970851898..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "import pandas as pd \n",
    "# Use the model\n",
    "mv.run(pd.DataFrame(\n",
    "        [\n",
    "            [\"I have a problem with my Snowflake that needs to be resolved asap!!\", \"\"],\n",
    "            [\"I would like to have udon for today's dinner.\", \"\"],\n",
    "        ]\n",
    "    )\n",
    ")\n"
   ]
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   "id": "8d88df13",
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   "source": [
    "Let's do an embedding model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3db71073",
   "metadata": {},
   "outputs": [],
   "source": [
    "import transformers\n",
    "\n",
    "embedding_model = transformers.pipeline(\n",
    "    task=\"feature-extraction\",\n",
    "    model=\"sentence-transformers/all-mpnet-base-v2\",type=\"text\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5d9ae290",
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       "[-0.15248064696788788,\n",
       " -0.04956672713160515,\n",
       " -0.10847000032663345,\n",
       " -0.09166843444108963,\n",
       " -0.04182581976056099]"
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     "execution_count": 26,
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   ],
   "source": [
    "result = embedding_model.predict(\"I am bullish on $AAPL\")\n",
    "result[0][0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ed097206",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = embedding_model.predict(\n",
    "            [\"I have a problem with my Snowflake that needs to be resolved asap!!\" ,\n",
    "            \"I would like to have udon for today's dinner.\"]\n",
    "    )"
   ]
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   "execution_count": 29,
   "id": "68f6005e",
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       "[-0.009068042039871216,\n",
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       " 0.2925925552845001,\n",
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   "source": [
    "result[0][0][0][:5]"
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   "cell_type": "code",
   "execution_count": null,
   "id": "b7e7e5c9",
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       "[0.07064098119735718,\n",
       " 0.17909322679042816,\n",
       " -0.04938044026494026,\n",
       " 0.06628119200468063,\n",
       " 0.05977983400225639]"
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     "execution_count": 61,
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    "result[1][0][0][:5]"
   ]
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   "id": "a6242263",
   "metadata": {},
   "source": [
    "The conda dependencies are because my local environment was ahead of snowpark. This is production data science, so you want to pin what version you want to use for your model. The feature extraction pipeline has many ways to provide outputs, so I specified to Snowflake what I am expecting for the model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69fa1e71",
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    {
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     "text": [
      "/Users/rajishah/anaconda3/envs/working38/lib/python3.8/site-packages/snowflake/ml/model/_packager/model_env/model_env.py:127: UserWarning: Package requirement torch==2.0.1 specified, while version 2.2.0 is installed. Local version will be ignored to conform to package requirement.\n",
      "  req_to_add = env_utils.get_local_installed_version_of_pip_package(req_to_check)\n"
     ]
    }
   ],
   "source": [
    "reg = Registry(session=session, database_name=\"RAJIV\", schema_name=\"PUBLIC\")\n",
    "\n",
    "# Log the model\n",
    "from snowflake.ml.model.model_signature import ModelSignature, DataType, FeatureSpec, infer_signature\n",
    "\n",
    "mv = reg.log_model(\n",
    "    embedding_model,\n",
    "    model_name=\"mpnet_base\",\n",
    "    version_name=\"v3\",\n",
    "    conda_dependencies=[\"transformers==4.32.1\",\"tokenizers==0.13.3\"],\n",
    "    signatures={\n",
    "        \"predict\": ModelSignature(\n",
    "            inputs=[FeatureSpec(name='\"review\"', dtype=DataType.STRING)],\n",
    "            outputs=[FeatureSpec(name=\"embedding\", dtype=DataType.FLOAT, shape=(768,))]\n",
    "        )}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f4f0b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "mv = reg.get_model(\"mpnet_base\").version(\"v3\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23cf2140",
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       "                                           embedding\n",
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    "mv.run(pd.DataFrame(\n",
    "        [\n",
    "            [\"I have a problem with my Snowflake that needs to be resolved asap!!\"],\n",
    "            [\"I would like to have udon for today's dinner.\"],\n",
    "        ]\n",
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    ")"
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