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Created May 12, 2023 12:40
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]
},
{
"cell_type": "markdown",
"source": [
"# MLFlow with Langchain"
],
"metadata": {
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}
},
{
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"source": [
"!pip -q install langchain huggingface_hub transformers sentence_transformers mlflow"
],
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},
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"\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"
]
}
]
},
{
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"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)"
],
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"07dfa78aa9944de5939a169514e889df",
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"5d575d61e2964d7c959642581437aaf8",
"507fcd8adf344d8a9c021378056a7293",
"088ef026de52490b8733cea19ab12fec",
"ed026c282a154c80848b002b018c6ed0",
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"68024760730e494aaa771bf77abc57b8",
"66f6092880d04d44854489e4d6f48988",
"c3f86cac34c147b4bc52bf9ec6256a69",
"7824985d2d8f4a298f28ac732678edeb",
"554b344369544a688d1dc8bdf6f52378",
"f7afd421fc3e4754adb9c14ada753459",
"f1d039afe7d5424f8a95bbe4f8ece566",
"7bda72cddb00475b82115a43e1a5676d",
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"54e220290aa54cfabf0eb043f33ef928",
"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<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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"metadata": {}
},
{
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"metadata": {}
},
{
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}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"template = \"\"\"Translate everything you see after this into French:{input}\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"input\"])\n",
"\n",
"llm_chain = LLMChain(prompt=prompt, \n",
" llm=local_llm\n",
" )\n",
"question = \"What is the capital of England?\"\n",
"print(llm_chain.run(question))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nwTdFaHZELQo",
"outputId": "d340fe28-7b9c-49f6-cb45-6a1646c220fc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Quel est le capital de l'Angleterre?\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"template2 = \"\"\"Answer the question comes after this {input}\"\"\"\n",
"prompt = PromptTemplate(template=template2, input_variables=[\"input\"])\n",
"\n",
"llm_chain = LLMChain(prompt=prompt, \n",
" llm=local_llm\n",
" )\n",
"\n",
"question = \"What is the capital of England?\"\n",
"print(llm_chain.run(question))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u0T9KseRG8sS",
"outputId": "7b1bcea7-6143-4671-8aaa-20fceeb763ef"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"london\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"template3 = \"\"\"Summerize the content: {input}\"\"\"\n",
"prompt = PromptTemplate(template=template3, input_variables=[\"input\"])\n",
"\n",
"llm_chain = LLMChain(prompt=prompt, \n",
" llm=local_llm\n",
" )\n",
"\n",
"content = \"The key innovation of the Transformer architecture is the self-attention mechanism, which allows the model to attend to different parts of the input sequence to learn relationships between words.\"\n",
"print(llm_chain.run(content))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "B_VpPDAfG3uy",
"outputId": "0411e31f-cd24-4cd9-a7a8-d3a498c90b0b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The self-attention mechanism is a key innovation of the Transformer architecture.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"mlflow.langchain.log_model(\n",
" lc_model=llm_chain,\n",
" artifact_path=\"model\",\n",
" registered_model_name=\"flan-t5\"\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fp7ApipR7RfX",
"outputId": "a6b3e3a3-58cf-4fee-e5dc-5189c8fdbda4"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"2023/05/09 16:51:05 WARNING mlflow: MLflow does not guarantee support for LLMChains outside of HuggingFaceHub and OpenAI, found <class 'langchain.llms.huggingface_pipeline.HuggingFacePipeline'>\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": [
"<mlflow.models.model.ModelInfo at 0x7fbb27503ac0>"
]
},
"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<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8549296dcb214dfb87b9cb69c865a93c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
" warnings.warn(\n",
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n",
"2023/05/09 16:55:48 WARNING mlflow.utils.requirements_utils: Found torch version (2.0.0+cu118) contains a local version label (+cu118). MLflow logged a pip requirement for this package as 'torch==2.0.0' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n",
"2023/05/09 16:55:48 WARNING mlflow.utils.requirements_utils: Found torchvision version (0.15.1+cu118) contains a local version label (+cu118). MLflow logged a pip requirement for this package as 'torchvision==0.15.1' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n",
"2023/05/09 16:56:33 WARNING mlflow.utils.requirements_utils: Found jaxlib version (0.4.7+cuda11.cudnn86) contains a local version label (+cuda11.cudnn86). MLflow logged a pip requirement for this package as 'jaxlib==0.4.7' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n",
"2023/05/09 16:56:33 WARNING mlflow.utils.requirements_utils: Found torch version (2.0.0+cu118) contains a local version label (+cu118). MLflow logged a pip requirement for this package as 'torch==2.0.0' without the local version label to make it installable from PyPI. To specify pip requirements containing local version labels, please use `conda_env` or `pip_requirements`.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Load as interactive pyfunc\n",
"chatbot = mlflow.pyfunc.load_model(model_info.model_uri)\n",
"chatbot.predict(\"What is the best books available on yoga?\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "qdRGFRJ5cPlE",
"outputId": "083d5923-0888-4417-94a4-ae2b304561e1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1219: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
" warnings.warn(\n",
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
"A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"I'm not sure, but I've heard good things about the book The Power of Yoga by Susan G. Komen.\""
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 40
}
]
}
]
}
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