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ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 70C P8 11W / 70W | 0MiB / 15109MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "source": [ "!wget -O prompts.csv https://raw.githubusercontent.com/krea-ai/open-prompts/main/data/1k.csv" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "37DIDwda7V3l", "outputId": "69b8d1a5-5da1-4676-c256-d50227d9f533" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2022-11-16 13:22:11-- https://raw.githubusercontent.com/krea-ai/open-prompts/main/data/1k.csv\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1027887 (1004K) [text/plain]\n", "Saving to: ‘prompts.csv’\n", "\n", "prompts.csv 100%[===================>] 1004K --.-KB/s in 0.004s \n", "\n", "2022-11-16 13:22:11 (226 MB/s) - ‘prompts.csv’ saved [1027887/1027887]\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd" ], "metadata": { "id": "T1m2EijGvEQh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "prompts = pd.read_csv(\"prompts.csv\")\n", "prompts.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "eylc41a27mXU", "outputId": "5b0ba00f-08e2-4d07-9bca-fbbf9733325d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " prompt \\\n", "0 A portrait photo of a kangaroo wearing an oran... \n", "1 inmates with cow heads inside a jailcell \n", "2 daguerrotype of a corgi astronaut on the moon,... \n", "3 totem animal tribal chaman vodoo mask feather ... \n", "4 p. cubensis \n", "\n", " raw_data \n", "0 {\"image_uri\": \"PENDING\", \"modifiers\": [\"portra... \n", "1 {\"image_uri\": \"PENDING\", \"modifiers\": [\"inmate... \n", "2 {\"image_uri\": \"PENDING\", \"modifiers\": [\"daguer... \n", "3 {\"image_uri\": \"PENDING\", \"modifiers\": [\"totem ... \n", "4 {\"image_uri\": \"PENDING\", \"modifiers\": [\"p cube... 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promptraw_data
0A portrait photo of a kangaroo wearing an oran...{\"image_uri\": \"PENDING\", \"modifiers\": [\"portra...
1inmates with cow heads inside a jailcell{\"image_uri\": \"PENDING\", \"modifiers\": [\"inmate...
2daguerrotype of a corgi astronaut on the moon,...{\"image_uri\": \"PENDING\", \"modifiers\": [\"daguer...
3totem animal tribal chaman vodoo mask feather ...{\"image_uri\": \"PENDING\", \"modifiers\": [\"totem ...
4Illustration of a sugar skull day of the dead ...{\"image_uri\": \"PENDING\", \"modifiers\": [\"illust...
.........
889an abstract, simplified icon depicting a pengu...{\"image_uri\": \"PENDING\", \"modifiers\": [\"abstra...
890nematode worms find a plant cell, microscopy, ...{\"image_uri\": \"PENDING\", \"modifiers\": [\"nemato...
891hyperrealism close-up portrait of beautiful me...{\"image_uri\": \"PENDING\", \"modifiers\": [\"hyperr...
892an extremely detailed masterpiece of a mouse w...{\"image_uri\": \"PENDING\", \"modifiers\": [\"extrem...
893a cinematic headshot portrait of a young woman...{\"image_uri\": \"PENDING\", \"modifiers\": [\"cinema...
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894 rows × 2 columns

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|████████████████████████████████| 7.6 MB 59.7 MB/s \n", "\u001b[?25h Building wheel for aitextgen (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "code", "source": [ "from aitextgen import aitextgen\n", "from aitextgen.TokenDataset import TokenDataset" ], "metadata": { "id": "QOMK0U6nj3IV" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "data = TokenDataset('/content/cleaned_prompts.csv', line_by_line=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "c2478eef4223483f99587762293baa8d", "3db63f3dfda6453d805fe557323966a3", "aa783f10a9544c789ca254f5e4400269", "86609a1de76445559859a60c68d2106f", "c84d320e3f254865ba048ca77fe1d91b", "12660f15938744bf97ae568913602699", "40906845e14c47658bcc8c55365deda2", "ae59aa03440145de80bccb10980b1474", "ffb1196ee84d4da78fce63dca90b838a", "91485b735e924fe2921fdf4c4d4523c2", "eed40bc8a4c342b1b5636147fa077420" ] }, "id": "KrK4rHS4j6bl", "outputId": "fe631d14-8d60-4a22-e606-c0b5ae0ab77b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/893 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m ai.train(data\n\u001b[0m\u001b[1;32m 2\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/aitextgen/aitextgen.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, train_data, output_dir, fp16, fp16_opt_level, n_gpu, tpu_cores, max_grad_norm, gradient_accumulation_steps, seed, learning_rate, weight_decay, adam_epsilon, warmup_steps, num_steps, save_every, generate_every, n_generate, loggers, batch_size, num_workers, benchmark, avg_loss_smoothing, save_gdrive, run_id, progress_bar_refresh_rate, freeze_layers, num_layers_freeze, use_deepspeed, **kwargs)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 674\u001b[0m \u001b[0;31m# Wrap the model in a pytorch-lightning module\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m \u001b[0mtrain_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mATGTransformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizer\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 676\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;31m# Begin training\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/python3.7/dist-packages/aitextgen/train.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, dataset, hparams, tokenizer)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mhparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mtokenizer\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 26\u001b[0m )\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 1253\u001b[0m \u001b[0mbuffers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\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 1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1257\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__delattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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;31mAttributeError\u001b[0m: can't set attribute" ] } ] }, { "cell_type": "code", "source": [ "ai.save()" ], "metadata": { "id": "b5peJOHIo6uK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "prompt_ai = aitextgen(model_folder = '.', to_gpu=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 304 }, "id": "0Zenb5Cio9PC", "outputId": "edf56628-cd45-47ea-bc92-0d31b758fb16" }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "AssertionError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprompt_ai\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maitextgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_folder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_gpu\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", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/aitextgen/aitextgen.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, model_folder, config, vocab_file, merges_file, tokenizer_file, schema_tokens, schema_return, cache_dir, tf_gpt2, to_gpu, to_fp16, verbose, gradient_checkpointing, bos_token, eos_token, unk_token, **kwargs)\u001b[0m\n\u001b[1;32m 170\u001b[0m assert os.path.exists(\n\u001b[1;32m 171\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pytorch_model.bin\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m ), f\"There is no pytorch_model.bin in /{model_folder}.\"\n\u001b[0m\u001b[1;32m 173\u001b[0m assert os.path.exists(\n\u001b[1;32m 174\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_folder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: There is no pytorch_model.bin in /.." ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "vzJIoP9VpA47" }, "execution_count": null, "outputs": [] } ] }