fine_tuning_LLM_with_Comet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Eddiebee/AI-Craft/blob/main/fine_tuning_LLM_with_Comet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCgA7MOA-_hU"
      },
      "source": [
        "# Fine-tuning LLMs\n",
        "\n",
        "In this section, we demonstrate how to fine-tune LLMs. Note that you will need to use a GPU for this section. You can do so by clicking \"Runtime -> Change runtime type\" and selecting a GPU.\n",
        "\n",
        "Let's load all the necessary libraries:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install transformers[torch] comet-ml comet-llm datasets evaluate sentencepiece --quiet"
      ],
      "metadata": {
        "id": "WJ5ormVu_CYM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c7e57ef5-c301-46d1-f4da-64a67c04b0e7"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m648.9/648.9 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "3h39QrjT-_hW",
        "outputId": "2de224c8-ff0b-40c8-d20d-0be7b691a4aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "comet_ml is installed but `COMET_API_KEY` is not set.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "from datasets import load_dataset\n",
        "import evaluate\n",
        "from transformers import AutoModelForCausalLM\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "from transformers import Trainer, TrainingArguments\n",
        "import transformers\n",
        "transformers.set_seed(35)\n",
        "from datasets import Features, Value, Dataset, DatasetDict\n",
        "import comet_ml\n",
        "import comet_llm\n",
        "import os\n",
        "import numpy as np\n",
        "import pickle\n",
        "import json\n",
        "import pandas as pd\n",
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "COMET_API_KEY = userdata.get('COMET_API_KEY')"
      ],
      "metadata": {
        "id": "GfcCVyB_PbA0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxyKxm_i-_hX"
      },
      "source": [
        "### Dataset Preparation\n",
        "\n",
        "The code below loads the datasets and converts them into the proper format. We are also sampling the dataset. You can choose different sample sizes to run different experiments. More samples typically lead to a better performing model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "q4fQAVrW-_hX"
      },
      "outputs": [],
      "source": [
        "# loads the data from the jsonl files\n",
        "emotion_dataset_train = pd.read_json(path_or_buf=\"https://raw.githubusercontent.com/comet-ml/comet-llmops/main/data/merged_training_sample_prepared_train.jsonl\", lines=True)\n",
        "emotion_dataset_val_temp = pd.read_json(path_or_buf=\"https://raw.githubusercontent.com/comet-ml/comet-llmops/main/data/merged_training_sample_prepared_valid.jsonl\", lines=True)\n",
        "\n",
        "# takes first half of samples from emotion_dataset_val_temp and make emotion_dataset_val\n",
        "emotion_dataset_val = emotion_dataset_val_temp.iloc[:int(len(emotion_dataset_val_temp)/2)]\n",
        "\n",
        "# takes second half of samples from emotion_dataset_val_temp and make emotion_dataset_test\n",
        "emotion_dataset_test = emotion_dataset_val_temp.iloc[int(len(emotion_dataset_val_temp)/2):]\n",
        "\n",
        "sample = True\n",
        "\n",
        "if sample == True:\n",
        "    final_ds = DatasetDict({\n",
        "        \"train\": Dataset.from_pandas(emotion_dataset_train.sample(50)),\n",
        "        \"validation\": Dataset.from_pandas(emotion_dataset_val.sample(50)),\n",
        "        \"test\": Dataset.from_pandas(emotion_dataset_test.sample(50))\n",
        "    })\n",
        "else:\n",
        "    final_ds = DatasetDict({\n",
        "        \"train\": Dataset.from_pandas(emotion_dataset_train),\n",
        "        \"validation\": Dataset.from_pandas(emotion_dataset_val),\n",
        "        \"test\": Dataset.from_pandas(emotion_dataset_test)\n",
        "    })"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOeGTdbn-_hY"
      },
      "source": [
        "### Tokenize Dataset\n",
        "\n",
        "The code below defines a tokenizer and uses the Hugging Face tokenizer to tokenize the datasets. This is the format the model expects so this is an important step."
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "813eedc64eb3466a972f4db57aedd060"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "model_id": "8e3888ee8bc245fcaafd7a0dbbe56e90"
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          "metadata": {}
        }
      ],
      "source": [
        "# model checkpoint\n",
        "model_checkpoint = \"google/flan-t5-base\"\n",
        "\n",
        "# We'll create a tokenizer from model checkpoint\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=False)\n",
        "\n",
        "# We'll need padding to have same length sequences in a batch\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# prefix\n",
        "prefix_instruction = \"Classify the provided piece of text into one of the following emotion labels.\\n\\nEmotion labels: ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\"\n",
        "\n",
        "# Define a tokenization function that first concatenates text and target\n",
        "def tokenize_function(example):\n",
        "    merged = prefix_instruction + \"\\n\\n\" + \"Text: \" + example[\"prompt\"].strip(\"\\n\\n###\\n\\n\") + \"\\n\\n\" + \"Emotion output:\" + example[\"completion\"].strip(\" \").strip(\"\\n\")\n",
        "    batch = tokenizer(merged, padding='max_length', truncation=True)\n",
        "    batch[\"labels\"] = batch[\"input_ids\"].copy()\n",
        "    return batch\n",
        "\n",
        "# Apply it on our dataset, and remove the text columns\n",
        "tokenized_datasets = final_ds.map(tokenize_function, remove_columns=[\"prompt\", \"completion\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KEKhD84-_hY"
      },
      "source": [
        "### Finetuning Model\n",
        "\n",
        "Once the datasets have been tokenized, it's time to finetune the model. We are using the HF Trainer to simplify the finetuning code. In the code below, it's also important to initialize a Comet project which allows tracking the experimental results to Comet. You can also set the `COMET_LOG_ASSETS` to `True` to store all artifacts to Comet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Q4jK612v-_hZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
          "referenced_widgets": [
            "8e755fe7ffa340e388af75143bb902f9",
            "0a60d4620c7a4bbaa3316a72f2b6e8c5",
            "fd65c03e488147f08b27b5aa387b5de8",
            "f7fde068b7b943728c2268b8c8498e25",
            "ca660e97e37b43fca93f7c5b2adaf08c",
            "68b3b6f158754c8d8edee013c7defd51",
            "79b4a806db754a94b07c6777ffdb022e",
            "1f4e964b375f4c9688c24c3591b088ee",
            "a8aa2b823a534e49b24718ef91e6a42c",
            "00906a90fa7246f382b930f2a553fad2",
            "53fbe3471bc54d489b8d058030a4e2ba",
            "d7e04d163c4746628c5ad42ed499e399",
            "92a6c9b7ddda4385b7825cdb2ebd5873",
            "fa68afef93be46cc91c41e16931292c9",
            "abc15a2772d9452ca55a1c521faa5768",
            "6068290d4be04fe4a4b50c472cd20e5f",
            "a6204cf7bbd14e8d82dc91b026d47ae0",
            "a092a46d04714d12bd234f956d18e4d4",
            "bee8e68ac0864eadbac8c13cae19d82d",
            "c85eb67e5c0a41b29ec59dccaa8aedb4",
            "72bc4a11a6244b9ab0cf10e47a6cc239",
            "2acc1e78df4e458e936e8ad8c4309ab2",
            "02db1daee3e24c76b44ddc75aee7359f",
            "e14cfdf2e7a84311ac2770d10d2fc254",
            "fd1c9b16c98a438fbdfe8ded670bf0d4",
            "ab0f2ca84a074f7595c295160b02300c",
            "92de4cfb71a141ba84fe94190138349f",
            "44caa5ab2c0c4411a6ae99d1d29b5f0c",
            "de4e4e7084e74fcbb50028b6f72f1576",
            "28f1073df60f48e1812e0216f889adc7",
            "4517a202dabd4183956c719e24e4e19b",
            "0def2f004e2349fe8028aa040e26c336",
            "a18ec2546f5040a7aa1d31eb0975de33"
          ]
        },
        "outputId": "2961642d-75be-4359-84de-00e39dc4df9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please paste your Comet API key from https://www.comet.com/api/my/settings/\n",
            "(api key may not show as you type)\n",
            "Comet API key: ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /root/.comet.config (set COMET_CONFIG to change where it is saved).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e755fe7ffa340e388af75143bb902f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7e04d163c4746628c5ad42ed499e399"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02db1daee3e24c76b44ddc75aee7359f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='13' max='13' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [13/13 00:38, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.027100</td>\n",
              "      <td>0.017744</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=13, training_loss=0.3605009632614943, metrics={'train_runtime': 39.8773, 'train_samples_per_second': 1.254, 'train_steps_per_second': 0.326, 'total_flos': 34237867622400.0, 'train_loss': 0.3605009632614943, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# initialize comet_ml\n",
        "comet_ml.init(project_name=\"emotion-classification\")\n",
        "\n",
        "# training an autoregressive language model from a pretrained checkpoint\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).to(device)\n",
        "\n",
        "# set this to log HF results and assets to Comet\n",
        "os.environ[\"COMET_LOG_ASSETS\"] = \"True\"\n",
        "\n",
        "# HF Trainer\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    num_train_epochs=1,\n",
        "    output_dir=\"./results\",\n",
        "    overwrite_output_dir=True,\n",
        "    logging_steps=1,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=1e-4,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=5,\n",
        "    save_steps=7,\n",
        "    auto_find_batch_size=True\n",
        ")\n",
        "\n",
        "# instantiate HF Trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "\n",
        "# run trainer\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kc7lwASN-_hZ"
      },
      "source": [
        "The code below stores the results locally:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "cJbcigg7-_hZ"
      },
      "outputs": [],
      "source": [
        "# save the model\n",
        "trainer.save_model(\"./results\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sl9KVZUI-_hZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8tDTdiQ1-_hZ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oDJ-aCyf-_ha"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cQX_7B6v-_ha"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HeCyvB17-_ha"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "af_UvgoQ-_ha"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_o_asQU-_ha"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sAn42Etg-_ha"
      },
      "source": [
        "### Register Model\n",
        "\n",
        "The code below registers the model to Comet."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "D5ycM0ZL-_ha",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2a7f43ac-387b-4126-82ef-a41796b04b0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: tensorboard, sklearn, tensorflow, keras, torch.\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Run will not be logged \n",
            "For more details, please refer to: https://www.comet.com/docs/v2/api-and-sdk/python-sdk/warnings-errors/\n",
            "\u001b[1;38;5;196mCOMET ERROR:\u001b[0m Error logging the model 'Emotion-T5-Base', no such file or directory: '/results/checkpoint-7'\n"
          ]
        }
      ],
      "source": [
        "# set existing experiment\n",
        "import os\n",
        "from comet_ml import ExistingExperiment\n",
        "\n",
        "# COMET_API_KEY = \"COMET_API_KEY\"\n",
        "\n",
        "experiment = ExistingExperiment(api_key=COMET_API_KEY, previous_experiment=\"097ab78e6e154f24b8090a1a7dd6abb8\")\n",
        "experiment.log_model(\"Emotion-T5-Base\", \"results/checkpoint-7\")\n",
        "experiment.register_model(\"Emotion-T5-Base\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from comet_ml import Experiment\n",
        "\n",
        "\n",
        "experiment = Experiment(\n",
        "  api_key=COMET_API_KEY,\n",
        "  project_name=\"llmops-course\",\n",
        "  workspace=\"eddiebee\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8qGOgwnZScr5",
        "outputId": "541323ee-814e-4d30-97c0-1cced440d8c7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m To get all data logged automatically, import comet_ml before the following modules: tensorboard, sklearn, tensorflow, keras, torch.\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/eddiebee/llmops-course/2c918ddf4d434ac2889691525662a454\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "experiment.log_model(\"Emotion-T5-Base\", \"results/checkpoint-7\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jwjysI-MTulL",
        "outputId": "d90e472b-0e09-4854-f63b-05f54147d3e4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('spiece.model',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=27beeca2bd374527bbf042e65267c613&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=27beeca2bd374527bbf042e65267c613&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '27beeca2bd374527bbf042e65267c613'}),\n",
              " ('config.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=2abb9c1bd0914f99b25ad2ea49c9adb0&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=2abb9c1bd0914f99b25ad2ea49c9adb0&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '2abb9c1bd0914f99b25ad2ea49c9adb0'}),\n",
              " ('generation_config.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=3840fa75e3e34372a3bc86c9b4550fd7&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=3840fa75e3e34372a3bc86c9b4550fd7&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '3840fa75e3e34372a3bc86c9b4550fd7'}),\n",
              " ('rng_state.pth',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=5a203db8710946d2a9fc2fa78cf19549&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=5a203db8710946d2a9fc2fa78cf19549&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '5a203db8710946d2a9fc2fa78cf19549'}),\n",
              " ('tokenizer_config.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=8c988f79b2a44562a182fd67ba4c36d7&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=8c988f79b2a44562a182fd67ba4c36d7&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '8c988f79b2a44562a182fd67ba4c36d7'}),\n",
              " ('training_args.bin',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=851ce6c1ed1b42af866185c8c76e9e45&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=851ce6c1ed1b42af866185c8c76e9e45&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '851ce6c1ed1b42af866185c8c76e9e45'}),\n",
              " ('trainer_state.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=e100cb57d6974d828257b52f982004a8&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=e100cb57d6974d828257b52f982004a8&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': 'e100cb57d6974d828257b52f982004a8'}),\n",
              " ('optimizer.pt',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=cf217ec3223c49d4bcb297b40af71f0b&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=cf217ec3223c49d4bcb297b40af71f0b&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': 'cf217ec3223c49d4bcb297b40af71f0b'}),\n",
              " ('model.safetensors',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=e8d08f2afe8d419d822ba0b033c32656&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=e8d08f2afe8d419d822ba0b033c32656&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': 'e8d08f2afe8d419d822ba0b033c32656'}),\n",
              " ('scheduler.pt',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=51eb3dfef1604bbb9cdca90f9414e055&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=51eb3dfef1604bbb9cdca90f9414e055&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '51eb3dfef1604bbb9cdca90f9414e055'}),\n",
              " ('added_tokens.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=1a8e7de0171e47a2bc015575c9893ffc&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=1a8e7de0171e47a2bc015575c9893ffc&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '1a8e7de0171e47a2bc015575c9893ffc'}),\n",
              " ('special_tokens_map.json',\n",
              "  {'web': 'https://www.comet.com/api/asset/download?assetId=2bced21f674a42d39454c93b2f70a4bd&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'api': 'https://www.comet.com/api/rest/v2/experiment/asset/get-asset?assetId=2bced21f674a42d39454c93b2f70a4bd&experimentKey=2c918ddf4d434ac2889691525662a454',\n",
              "   'assetId': '2bced21f674a42d39454c93b2f70a4bd'})]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# register the model\n",
        "experiment.register_model(\"Emotion-T5-Base\")"
      ],
      "metadata": {
        "id": "ybiKBJtbU2o_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l46sSt7I-_hb"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hHWoykLr-_hb"
      },
      "source": [
        "### Deploy Model\n",
        "\n",
        "The code below helps to download the model and specific version to whatever environment you are deploying from."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "fn0IoLmh-_hb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "7f5dda76-c61c-4731-d0dd-f21408d5aa8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'eddiebee/Emotion-T5-Base:1.0.0' download has been started asynchronously.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 12 file(s), remaining 2.77 GB/2.77 GB\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-3ce2ed83f1d5>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Download a Registry Model:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1.0.0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"./deploy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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      ],
      "source": [
        "from comet_ml import API\n",
        "\n",
        "api = API(api_key=COMET_API_KEY)\n",
        "COMET_WORKSPACE = \"eddiebee\"\n",
        "\n",
        "# model name\n",
        "model_name = \"Emotion-T5-Base\"\n",
        "\n",
        "#get the Model object\n",
        "model = api.get_model(workspace=COMET_WORKSPACE, model_name=model_name)\n",
        "\n",
        "# Download a Registry Model:\n",
        "model.download(\"1.0.0\", \"./deploy\", expand=True)"
      ]
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