Dreambooth_on_Blueprint.ipynb

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      "provenance": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jonbaer/googlecolab/blob/master/Dreambooth_on_Blueprint.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Installation"
      ],
      "metadata": {
        "id": "5JnpPhkPwsfd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YXA7Y_uyEI9h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e22b96c-b735-41c7-bc4c-b8ff62bbd71c"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: halo\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed Pillow-9.4.0 baseten-0.5.2 colorama-0.4.6 coolname-2.2.0 halo-0.0.31 log_symbols-0.0.14 msgpack-numpy-0.4.8 numpy-1.23.5 packaging-20.9 python-json-logger-2.0.7 python-on-whales-0.46.0 requests-toolbelt-0.9.1 semantic-version-2.10.0 single-source-0.3.0 spinners-0.0.24 truss-0.4.0 types-docutils-0.19.1.6 types-pillow-9.4.0.17 types-pytz-2022.7.1.2 types-pyyaml-6.0.12.8 types-requests-2.28.11.15 types-setuptools-65.7.0.4 types-urllib3-1.26.25.8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32mINFO\u001b[0m API key set.\n",
            "INFO:baseten:API key set.\n"
          ]
        }
      ],
      "source": [
        "# Install Baseten CLI \n",
        "! pip install baseten --upgrade\n",
        "import baseten\n",
        "# Get an API key from https://app.baseten.co/blueprint/keys\n",
        "baseten.login(\"xFSGHxk8.uMxt30FCJendRQoXvCZUpsYNd9P4i5DZ\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upload / setup dataset"
      ],
      "metadata": {
        "id": "_PYo0en1wnfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Relevant imports for Dreambooth fine-tuning\n",
        "from baseten.training import FinetuningRun, DreamboothConfig, LocalPath, PublicUrl"
      ],
      "metadata": {
        "id": "b9jLYcD4azwn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload your own dataset (docs here)\n",
        "# input_data = LocalPath(\"/path/to/my-dataset\")\n",
        "input_data = PublicUrl(\"https://cdn.baseten.co/docs/production/DreamboothSampleDataset.zip\")"
      ],
      "metadata": {
        "id": "1XiLL48xFuYR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Dreambooth fine-tuning config"
      ],
      "metadata": {
        "id": "xoKRNuvSBr2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "config = DreamboothConfig(\n",
        "    instance_prompt=\"photo of olliedog\",\n",
        "    input_dataset=input_data,\n",
        "    class_prompt=\"photo of dog\",\n",
        "    train_text_encoder=False,\n",
        "    max_train_steps=1300,\n",
        "    learning_rate=2e-6,\n",
        ")"
      ],
      "metadata": {
        "id": "K70bzUV2ZrJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Kick off fine-tuning"
      ],
      "metadata": {
        "id": "smVYSZiew2tL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Kick off your finetuning job \n",
        "fr = FinetuningRun.create(\"my-dog-model\", config)"
      ],
      "metadata": {
        "id": "jIeFyh1XZ6AP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "577fcc63-a00a-4380-9d3f-5136a072c630"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32mINFO\u001b[0m Starting fine-tuning of dreambooth\n",
            "INFO:baseten.training.finetuning:Starting fine-tuning of dreambooth\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ApiError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mApiError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-263edf2f2d36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Kick off your finetuning job\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFinetuningRun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"my-dog-model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/baseten/training/finetuning.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(trained_model_name, fine_tuning_config, auto_deploy, verbose)\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Starting fine-tuning of %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfine_tuning_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_truss_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         run_id = finetune_zoo_model(\n\u001b[0m\u001b[1;32m    543\u001b[0m             \u001b[0mtrained_model_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m             \u001b[0mfine_tuning_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_truss_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/baseten/common/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"api\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"api_key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mApiKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"api\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"api_key\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0msettings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_jwt_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/baseten/common/api.py\u001b[0m in \u001b[0;36mfinetune_zoo_model\u001b[0;34m(auth_token, trained_model_name, train_truss_name, encoded_variables, auto_deploy)\u001b[0m\n\u001b[1;32m    985\u001b[0m     \"\"\"\n\u001b[1;32m    986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m     \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_post_graphql_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauth_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    988\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"finetune_model_zoo_model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/baseten/common/api.py\u001b[0m in \u001b[0;36m_post_graphql_query\u001b[0;34m(auth_token, query_string)\u001b[0m\n\u001b[1;32m    674\u001b[0m     \u001b[0merrors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresp_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"errors\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mApiError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"message\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresp_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mApiError\u001b[0m: To proceed, please add a credit card.\n<Server response: {'errors': [{'message': 'To proceed, please add a credit card.', 'locations': [{'line': 3, 'column': 9}], 'path': ['finetune_model_zoo_model'], 'extensions': {'code': 'UNAUTHORIZED_ACCESS'}}], 'data': {'finetune_model_zoo_model': None}}>"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Stream logs from your finetuning job \n",
        "fr.stream_logs()"
      ],
      "metadata": {
        "id": "lRHDj28taRQe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a76b4f4-f3cf-496c-cfb4-dd07e3cfff5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rℹ Starting to stream logs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32mINFO\u001b[0m [['--adam_beta1', '0.9'], ['--adam_beta2', '0.999'], ['--adam_epsilon', '1e-08'], ['--adam_weight_decay', '0.01'], ['--center_crop', None], ['--class_data_dir', None], ['--class_prompt', 'photo of dog'], ['--gradient_accumulation_steps', '1'], ['--hf_access_token', None], ['--image_log_steps', '20'], ['--instance_prompt', 'photo of olliedog'], ['--learning_rate', '2e-06'], ['--lr_scheduler', 'constant'], ['--lr_warmup_steps', '500'], ['--max_grad_norm', '1.0'], ['--max_train_steps', '1300'], ['--mixed_precision', 'fp16'], ['--num_class_images', '100'], ['--num_train_epochs', '1'], ['--pretrained_model_name_or_path', 'CompVis/stable-diffusion-v1-4'], ['--prior_loss_weight', '1.0'], ['--resolution', '512'], ['--revision', None], ['--sample_batch_size', '1'], ['--seed', None], ['--tokenizer_name', None], ['--train_batch_size', '1'], ['--train_text_encoder', None], ['--wandb_api_key', None], ['--with_prior_preservation', None], ['--gradient_checkpointing', 'True'], ['--instance_data_dir', '/app/DreamboothSampleDataset/object'], ['--output_dir', '/output']]\n",
            "INFO:baseten.training.logs:[['--adam_beta1', '0.9'], ['--adam_beta2', '0.999'], ['--adam_epsilon', '1e-08'], ['--adam_weight_decay', '0.01'], ['--center_crop', None], ['--class_data_dir', None], ['--class_prompt', 'photo of dog'], ['--gradient_accumulation_steps', '1'], ['--hf_access_token', None], ['--image_log_steps', '20'], ['--instance_prompt', 'photo of olliedog'], ['--learning_rate', '2e-06'], ['--lr_scheduler', 'constant'], ['--lr_warmup_steps', '500'], ['--max_grad_norm', '1.0'], ['--max_train_steps', '1300'], ['--mixed_precision', 'fp16'], ['--num_class_images', '100'], ['--num_train_epochs', '1'], ['--pretrained_model_name_or_path', 'CompVis/stable-diffusion-v1-4'], ['--prior_loss_weight', '1.0'], ['--resolution', '512'], ['--revision', None], ['--sample_batch_size', '1'], ['--seed', None], ['--tokenizer_name', None], ['--train_batch_size', '1'], ['--train_text_encoder', None], ['--wandb_api_key', None], ['--with_prior_preservation', None], ['--gradient_checkpointing', 'True'], ['--instance_data_dir', '/app/DreamboothSampleDataset/object'], ['--output_dir', '/output']]\n",
            "\u001b[32mINFO\u001b[0m You need not use --class_prompt without --with_prior_preservation.\n",
            "INFO:baseten.training.logs:You need not use --class_prompt without --with_prior_preservation.\n",
            "\u001b[32mINFO\u001b[0m Namespace(pretrained_model_name_or_path='CompVis/stable-diffusion-v1-4', revision=None, hf_access_token=None, tokenizer_name=None, instance_data_dir='/app/DreamboothSampleDataset/object', class_data_dir=None, instance_prompt='photo of olliedog', class_prompt='photo of dog', with_prior_preservation=False, prior_loss_weight=1.0, num_class_images=100, output_dir='/output', seed=None, resolution=512, center_crop=False, train_text_encoder=False, train_batch_size=1, sample_batch_size=1, num_train_epochs=1, max_train_steps=1300, save_steps=500, gradient_accumulation_steps=1, gradient_checkpointing=True, learning_rate=2e-06, lr_scheduler='constant', lr_warmup_steps=500, adam_beta1=0.9, adam_beta2=0.999, adam_weight_decay=0.01, adam_epsilon=1e-08, max_grad_norm=1.0, logging_dir='logs', mixed_precision='fp16', local_rank=-1, report_to='tensorboard', wandb_api_key=None, image_log_steps=20)\n",
            "INFO:baseten.training.logs:Namespace(pretrained_model_name_or_path='CompVis/stable-diffusion-v1-4', revision=None, hf_access_token=None, tokenizer_name=None, instance_data_dir='/app/DreamboothSampleDataset/object', class_data_dir=None, instance_prompt='photo of olliedog', class_prompt='photo of dog', with_prior_preservation=False, prior_loss_weight=1.0, num_class_images=100, output_dir='/output', seed=None, resolution=512, center_crop=False, train_text_encoder=False, train_batch_size=1, sample_batch_size=1, num_train_epochs=1, max_train_steps=1300, save_steps=500, gradient_accumulation_steps=1, gradient_checkpointing=True, learning_rate=2e-06, lr_scheduler='constant', lr_warmup_steps=500, adam_beta1=0.9, adam_beta2=0.999, adam_weight_decay=0.01, adam_epsilon=1e-08, max_grad_norm=1.0, logging_dir='logs', mixed_precision='fp16', local_rank=-1, report_to='tensorboard', wandb_api_key=None, image_log_steps=20)\n",
            "\u001b[32mINFO\u001b[0m /usr/local/lib/python3.9/dist-packages/accelerate/accelerator.py:231: FutureWarning: `logging_dir` is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use `project_dir` instead.\n",
            "INFO:baseten.training.logs:/usr/local/lib/python3.9/dist-packages/accelerate/accelerator.py:231: FutureWarning: `logging_dir` is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use `project_dir` instead.\n",
            "\u001b[32mINFO\u001b[0m   warnings.warn(\n",
            "INFO:baseten.training.logs:  warnings.warn(\n",
            "Downloading (…)okenizer_config.json: 100%|██████████| 806/806 [00:00<00:00, 234kB/s]\n",
            "Downloading (…)okenizer_config.json: 100%|██████████| 806/806 [00:00<00:00, 234kB/s]\n",
            "Downloading (…)tokenizer/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 2.92MB/s]\n",
            "Downloading (…)tokenizer/vocab.json: 100%|██████████| 1.06M/1.06M [00:00<00:00, 2.92MB/s]\n",
            "Downloading (…)tokenizer/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 2.01MB/s]\n",
            "Downloading (…)tokenizer/merges.txt: 100%|██████████| 525k/525k [00:00<00:00, 2.01MB/s]\n",
            "Downloading (…)cial_tokens_map.json: 100%|██████████| 472/472 [00:00<00:00, 331kB/s]\n",
            "Downloading (…)cial_tokens_map.json: 100%|██████████| 472/472 [00:00<00:00, 331kB/s]\n",
            "Downloading (…)_encoder/config.json: 100%|██████████| 592/592 [00:00<00:00, 204kB/s]\n",
            "Downloading (…)_encoder/config.json: 100%|██████████| 592/592 [00:00<00:00, 204kB/s]\n",
            "\u001b[32mINFO\u001b[0m You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n",
            "INFO:baseten.training.logs:You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n",
            "Downloading (…)\"pytorch_model.bin\";: 100%|██████████| 492M/492M [00:03<00:00, 155MB/s]\n",
            "Downloading (…)\"pytorch_model.bin\";: 100%|██████████| 492M/492M [00:03<00:00, 155MB/s]\n",
            "Downloading (…)_pytorch_model.bin\";: 100%|██████████| 335M/335M [00:02<00:00, 144MB/s]\n",
            "Downloading (…)_pytorch_model.bin\";: 100%|██████████| 335M/335M [00:02<00:00, 144MB/s]\n",
            "Downloading (…)main/vae/config.json: 100%|██████████| 551/551 [00:00<00:00, 388kB/s]\n",
            "Downloading (…)main/vae/config.json: 100%|██████████| 551/551 [00:00<00:00, 388kB/s]\n",
            "Downloading (…)_pytorch_model.bin\";: 100%|██████████| 3.44G/3.44G [00:20<00:00, 167MB/s]\n",
            "Downloading (…)_pytorch_model.bin\";: 100%|██████████| 3.44G/3.44G [00:20<00:00, 167MB/s]\n",
            "Downloading (…)ain/unet/config.json: 100%|██████████| 743/743 [00:00<00:00, 254kB/s]\n",
            "Downloading (…)ain/unet/config.json: 100%|██████████| 743/743 [00:00<00:00, 254kB/s]\n",
            "Downloading (…)cheduler_config.json: 100%|██████████| 313/313 [00:00<00:00, 101kB/s]\n",
            "Downloading (…)cheduler_config.json: 100%|██████████| 313/313 [00:00<00:00, 101kB/s]\n",
            "S\n",
            "S\n",
            "Steps:  10%|█         | 134/130\n",
            "Steps:  10%|█         | 134/130\n",
            "Steps:  15%|█▌        | 198/1300 [01:53<10:22,  1.77it/s, train/epoch=1, train/loss=0.0259, train/lr=2e-\n",
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            "Steps:  20%|��\n",
            "Steps:  20%|��\n",
            "Steps:  25%|██▌       | 327/1300 [03:06<09:08,  1.77it/s, train/epoch=2, train/loss=0.0763, train/lr=2e-6, tra\n",
            "Steps:  25%|██▌       | 327/1300 [03:06<09:08,  1.77it/s, train/epoch=2, train/loss=0.0763, train/lr=2e-6, tra\n",
            "Steps:  30%|\n",
            "Steps:  30%|\n",
            "Steps:  35%|███▍      | 453/1300 [04:17<07:56,  1.78it/s, train/epoch=3, train/loss=0.00417, train/lr=2e-6, \n",
            "Steps:  35%|███▍      | 453/1300 [04:17<07:56,  1.78it/s, train/epoch=3, train/loss=0.00417, train/lr=2e-6, \n",
            "Steps:  40%|███▉\n",
            "Steps:  40%|███▉\n",
            "Steps:  44%|████▍  \n",
            "Steps:  44%|████▍  \n",
            "Steps:  49%|████▉     | 640/1300 [06:03<06:12,  1.77it/s, trai\n",
            "Steps:  49%|████▉     | 640/1300 [06:03<06:12,  1.77it/s, trai\n",
            "Steps:  54%|█████▍    | 701/1300 [06:38<05:39,  1.76it/s, train/epoch=6, train/loss=0.235, tr\n",
            "Steps:  54%|█████▍    | 701/1300 [06:38<05:39,  1.76it/s, train/epoch=6, train/loss=0.235, tr\n",
            "Steps:  59%|█████▊    | 762/1300 [07:12\n",
            "Steps:  59%|█████▊    | 762/1300 [07:12\n",
            "\u001b[31mERROR\u001b[0m GraphQL endpoint failed with error: b'upstream connect error or disconnect/reset before headers. reset reason: connection termination'\n",
            "ERROR:baseten.common.api:GraphQL endpoint failed with error: b'upstream connect error or disconnect/reset before headers. reset reason: connection termination'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rℹ Something went wrong with your connection to Baseten.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Deploy your fine-tuned model"
      ],
      "metadata": {
        "id": "kwbbn-06w5qj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# After completion, deploy your model \n",
        "model = fr.deploy()"
      ],
      "metadata": {
        "id": "o6_tGBSwMe43"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Invoke your model \n",
        "image, s3_url = model(\"photo of sks dog\")"
      ],
      "metadata": {
        "id": "VaPkFZd_aT6N"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}