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"left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b2f68159db59402fad6b42a66c03aa82": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "source": [ "# MLFlow with Langchain" ], "metadata": { "id": "8j38-07me6GS" } }, { "cell_type": "code", "source": [ "!pip -q install langchain huggingface_hub transformers sentence_transformers mlflow" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sWsr68o-ElUJ", "outputId": "3d123366-2cba-4911-b513-535dada92df6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m66.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "code", "source": [ "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n", "from langchain.llms import HuggingFacePipeline\n", "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM\n", "\n", "\n", "import torch\n", "import mlflow\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-small\")\n", "\n", "model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-small\")\n", "pipe = pipeline(\n", " \"text2text-generation\",\n", " model=model, \n", " tokenizer=tokenizer, \n", " max_length=100\n", ")\n", "\n", "local_llm = HuggingFacePipeline(pipeline=pipe)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "c1bde6b8f3724f448abecc85b45cb242", "76b96370bb804d389585a761b66f93d4", "06a474301a8a46f88643ec5f44adbf04", "a7f3ff0aa21d41649a18a579922c8237", "889e4cd1a08342e08b16325e64a80d2c", "147db8739051418fbee23eb665e3dc32", "c4eeac56b73745f2ad6c09d9e039475a", "7031fc70b167460fad60d1fab9025871", "a3c27e14658f4492940a3f50e2fc2406", "dec89e56d56c4fa4a9c14705b3d2b4fb", "7069a3df24f0455d85164718ecee2579", "7a43e93969054d3cb08ae289cede8217", "7b642b6f0eae4778b6fd7425bd63ff21", "9d6a7d1f5d7d4e5892ea140516fa0ae9", "65d26e08aa254f7e8c6accb3302e2753", "3bc8834a43c2493dbffaf4d7a65861e3", "10fee1d0f260465f9327ff47def2a2c9", "0c0793eea5f14f7db80438d5e9b3b760", "36bba29d1cc14836865633ac701d3ee4", "0c0d25fc928f46e8b6ddd6f9821f86ea", "54b822fe70b244bbb68d1f7764aeb772", "e23df926fb9f4e9580227422b15623c9", 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"939acd9b99f141359817021fde5f24e6" ] }, "id": "BBoKgGd_DzA1", "outputId": "21d8a4cf-f8c0-4acc-fa95-4c3831c9d0d7" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)okenizer_config.json: 0%| | 0.00/2.54k [00:00\n", "Successfully registered model 'flan-t5'.\n", "2023/05/09 16:51:12 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation. Model name: flan-t5, version 1\n", "Created version '1' of model 'flan-t5'.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "code", "source": [ "get_ipython().system_raw(\"mlflow ui --port 5000 &\")# run tracking UI in the background" ], "metadata": { "id": "ep7CLQtFYUre" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#!pip install pyngrok --quiet\n", "from pyngrok import ngrok\n", "\n", "# Terminate open tunnels if exist\n", "ngrok.kill()\n", "\n", "# Setting the authtoken (optional)\n", "# Get your authtoken from https://dashboard.ngrok.com/auth\n", "NGROK_AUTH_TOKEN = \"2PZ34qJwLxJZXjFj4qzPUP20gqU_5ULAaaVrnvHcS9LXm93AA\"\n", "ngrok.set_auth_token(NGROK_AUTH_TOKEN)\n", "\n", "\n", "\n", "ngrok_tunnel = ngrok.connect(addr=\"5000\", proto=\"http\", bind_tls=True)\n", "print(\"MLflow Tracking UI:\", ngrok_tunnel.public_url)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VmNoRHKdYxkq", "outputId": "ec3ef01f-a29a-4987-ac99-86f4aca084c8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:pyngrok.process.ngrok:t=2023-05-09T16:46:49+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/root/.config/ngrok/ngrok.yml legacy_path=/root/.ngrok2/ngrok.yml\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "MLflow Tracking UI: https://07fd-35-194-202-111.ngrok-free.app\n" ] } ] }, { "cell_type": "code", "source": [ "mlflow.end_run()" ], "metadata": { "id": "Q9nEizQmcA_t" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## MLFlow with transformer model" ], "metadata": { "id": "dShXELCoesKH" } }, { "cell_type": "code", "source": [ "import transformers\n", "import mlflow\n", "\n", "chat_pipeline = transformers.pipeline(model=\"microsoft/DialoGPT-medium\")\n", "\n", "#Log your LLM: To log your LLM to MLflow, use the Python code snippet below:\n", "\n", "with mlflow.start_run():\n", " model_info = mlflow.transformers.log_model(\n", " transformers_model=chat_pipeline,\n", " artifact_path=\"chatbot\",\n", " input_example=\"Hi there!\"\n", " )\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 230, "referenced_widgets": [ "8549296dcb214dfb87b9cb69c865a93c", "9c9c182841a34db4a0460259ef609243", "4964b03072ec4891b80016dcff60edfe", "6dd785d0cdb8439f8d92d9a540e738af", "97b5ddff09bc4009aa32827e7d21559b", "12ba56b8321f4561b70a8b02e13c0ee5", "3c451086f32e4cfe81846c99f606c292", "6b6cd934a6da4cf8a96a402db36bc7c9", "83f3d2d3994040b193f7605cd0a82661", "c9bd2fd9ab1c42d0ba290f59ec44a616", "b2f68159db59402fad6b42a66c03aa82" ] }, "id": "39igSw-2GCU4", "outputId": "7056d559-b3a5-48e1-e4f1-8af4830dce0b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)solve/main/README.md: 0%| | 0.00/2.42k [00:00