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In this step, we’re installing the `Simplifine` library, which provides tools to streamline the fine-tuning process for large language models. We’re also installing the `datasets` library, which allows us to easily access and manage datasets from Hugging Face.\n", + "\n", + "- The `Simplifine` library helps in making the fine-tuning process more efficient, whether you're working locally or in the cloud.\n", + "- The `datasets` library is essential for loading and processing the dataset we'll be using for this project.\n", + "\n", + "Running this cell will install both libraries quietly in the background.\n" + ], + "metadata": { + "id": "0SClYIzAQrpD" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lxDXEqYrw-gh", + "outputId": "b768c964-b87d-41da-f121-0e83373fbdac" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... 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This behaviour is the source of the following dependency conflicts.\n", + "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", + "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n", + "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install git+https://github.com/simplifine-llm/Simplifine.git -q\n", + "!pip install datasets -q" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 🛠️ Setting Up for Local Training\n", + "\n", + "In this section, we’re preparing to fine-tune our fake news detector model using Google Colab’s resources. The steps below outline how to configure and initiate the training process.\n", + "\n", + "1. **Importing Libraries:**\n", + " - We import `train_engine` from the `Simplifine` library, which provides the necessary functions to handle the fine-tuning process.\n", + " - We also import `SFTConfig` from the `trl` library, which allows us to configure the supervised fine-tuning parameters.\n", + "\n", + "2. **Dataset Selection:**\n", + " - We define the dataset name as `'community-datasets/fake_news_english'`. This dataset contains examples of fake news articles that we will use to fine-tune our model.\n", + "\n", + "3. **Prompt Configuration:**\n", + " - We create a `sftPromptConfig` object to specify how the training data is formatted.\n", + " - The `template` parameter defines the input format, and the `response_template` specifies how the model should generate outputs.\n", + " - The `use_chat_template` flag is set to `True` to format the inputs in a conversational style, which can be effective for chat-based models.\n", + "\n", + "4. **Training Configuration:**\n", + " - We define the training settings using `SFTConfig`. This includes parameters like batch size, learning rate, and the number of epochs.\n", + " - We also enable `fp16` (16-bit floating-point) training for faster computation and set `gradient_checkpointing` to save memory during training.\n", + "\n", + "5. **Model Selection:**\n", + " - The model we’re fine-tuning is `'TinyLlama/TinyLlama-1.1B-Chat-v1.0'`. This is a smaller, efficient model suitable for demonstration purposes on Colab.\n", + "\n", + "6. **Training the Model:**\n", + " - Finally, we call `sft_train` to start the fine-tuning process. This step will take a while to complete, as we’re training the model from scratch without any optimizations like quantization or LoRA.\n", + "\n", + "Running this cell will fine-tune the model locally on Colab, using the configurations we’ve set up. This is ideal for quick experiments or when cloud resources are not available." + ], + "metadata": { + "id": "C0dDwmg4Rb3N" + } + }, + { + "cell_type": "code", + "source": [ + "from simplifine_alpha import train_engine\n", + "from trl import SFTConfig\n", + "\n", + "dataset_name = 'community-datasets/fake_news_english'\n", + "\n", + "# defining prompt config\n", + "sft_prompt_config = train_engine.sftPromptConfig(\n", + " keys = ['url_of_article', 'fake_or_satire'],\n", + " template = \"###URL: {url_of_article}. \\n###CLS: {fake_or_satire}\",\n", + " response_template = '. \\n###CLS: ',\n", + " use_chat_template=True\n", + " )\n", + "\n", + "# defining training config\n", + "sft_config = SFTConfig(\n", + " output_dir='/content/fake_news_english_phi3',\n", + " per_device_train_batch_size=1,\n", + " gradient_accumulation_steps=4,\n", + " learning_rate=1e-5,\n", + " num_train_epochs=2,\n", + " report_to='none',\n", + " fp16=True,\n", + " gradient_checkpointing=True,\n", + ")\n", + "\n", + "model_name = 'TinyLlama/TinyLlama-1.1B-Chat-v1.0'\n", + "\n", + "# this is just for demo purposes, this will take a while here, no quantization, no lora...\n", + "train_engine.sft_train(model_name=model_name, dataset_name=dataset_name,\n", + " sft_config = sft_config, sft_prompt_config=sft_prompt_config,\n", + " use_zero=False, use_ddp=False\n", + " )" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "85b43ab19cda4f72a77fdfd5dc096496", + "121146c229204799b4d6c7defb3d6474", + "05fba94d8a2c4e1c9beda729c24511cf", + "b34a0424b9c54f20adfcf36fd31c20ec", + "0897b7e7dd4745338bb516eea9a91459", + "a70c9f8d1b0d4d46ac64f06e2edb867b", + "0082ad32163248f4a2012837a73d07de", + "42e2d8e7c622491d9c9acd8dd6d59493", + "e46fa81470a445d898f60b4afbc52e02", + "7fbd642afa624c3593a484da4e223f2e", + "826de6d75f7f4642999e32ad61441cc6", + "4d32eb49961e48f1958fc9bc6a494766", + "4f6e9afd59974e31b81b06e95ca77704", + 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"3adcdfad6da04e119f6301f6272d94b6", + "f8723f4b610d4b649f063de7b55bc284", + "b487318c01d44853890029fb496b4347", + "9aadd246448343b7af6f1c2506e072c7", + "7153c9902f874e87a02ee45d2bb5c3e2", + "d10241169ba44a18a7a25a7b7383c318", + "c2e992a64b7b4c6b9a2de645d8eda4ef", + "9d090fd9a91d48e6b1c4f7ef13dfe0de", + "ad06e1bdf44e46658315320afe17398e", + "c9abd4782c9e461b98c2982cac983c9e", + "212a753d65224d82b483572073416337", + "3cc4d8faa6084ff3b4f2725e3b69a3a2", + "0be5ab6599be4360b282aa558514fd0c", + "17287fdf1c4e4fd0a9117896b4761b6f", + "ecaa05f88907468696610a4f623e7517" + ] + }, + "id": "uKH1cxpkxFAr", + "outputId": "bae79adb-9ed2-49f6-c618-efdb66923cc3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[2024-08-06 17:41:44,218] [WARNING] [real_accelerator.py:162:get_accelerator] Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.\n", + "[2024-08-06 17:41:44,222] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cpu (auto detect)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/1.29k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'TinyLlama/TinyLlama-1.1B-Chat-v1.0'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m train_engine.sft_train(model_name=model_name, dataset_name=dataset_name,\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0msft_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msft_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msft_prompt_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msft_prompt_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0muse_zero\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_ddp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/simplifine_alpha/train_engine.py\u001b[0m in \u001b[0;36msft_train\u001b[0;34m(model_name, dataset_name, hf_token, dataset_config_name, data_from_hf, do_split, split_ratio, use_peft, lora_config, sft_config, data, wandb_config, use_ddp, use_zero, sft_prompt_config)\u001b[0m\n\u001b[1;32m 842\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_dir_final\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\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[1;32m 843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 844\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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[0m\u001b[1;32m 845\u001b[0m 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\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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 2152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2153\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 524\u001b[0m )\n\u001b[0;32m--> 525\u001b[0;31m torch.autograd.backward(\n\u001b[0m\u001b[1;32m 526\u001b[0m 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\u001b[0mt_outputs\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[0m\n\u001b[1;32m 746\u001b[0m ) # Calls into the C++ engine to run the backward pass\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 299\u001b[0m )\n\u001b[1;32m 300\u001b[0m \u001b[0muser_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvjp_fn\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvjp_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mFunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvjp\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mbackward_fn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m \u001b[0;32mreturn\u001b[0m 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\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs_with_grad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs_with_grad\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 321\u001b[0m grads = tuple(\n\u001b[1;32m 322\u001b[0m \u001b[0minp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m _engine_run_backward(\n\u001b[0m\u001b[1;32m 268\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[0mgrad_tensors_\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/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 743\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 745\u001b[0m \u001b[0mt_outputs\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[0m\n\u001b[1;32m 746\u001b[0m ) # Calls into the C++ engine to run the backward pass\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### ☁️ Training the Model on Cloud Servers\n", + "\n", + "In this section, we’re moving from local training to cloud-based training using Simplifine’s cloud infrastructure. This allows you to leverage powerful GPUs like the A100 for more intensive tasks, making it easier to handle larger models and datasets.\n", + "\n", + "1. **Importing the `train_utils` Module:**\n", + " - We start by importing the `train_utils` module from the `Simplifine` library. This module provides utilities to interact with Simplifine's cloud servers.\n", + "\n", + "2. **Model and API Configuration:**\n", + " - We select a different model for this cloud training: `'microsoft/Phi-3-mini-4k-instruct'`. This model is more powerful and well-suited for deployment on cloud GPUs.\n", + " - The `simplifine_api_key` is your unique key to access Simplifine’s cloud services. Ensure you have it ready.\n", + " - The `gpu_type` is set to `'a100'`, which specifies the type of GPU to be used in the cloud. The A100 is a high-performance GPU ideal for deep learning tasks.\n", + "\n", + " ### 🔑 Need an API Key?\n", + " If you don't have an API key yet, you can [**request one here for free**](https://www.simplifine.com/api-key-interest). The turnaround time is just 24 hours, so you'll be up and running in no time!\n", + "\n", + "3. **Client Initialization:**\n", + " - We create a `Client` object using the API key and GPU type. This client will handle the communication with Simplifine’s cloud infrastructure, managing the training job on your behalf.\n", + "\n", + "4. **Defining the Training Job:**\n", + " - The `job_name` is set to `'fake_news_english_phi3'`, which uniquely identifies this training task.\n", + " - We then call the `sft_train_cloud` method on our `client` object. This method sends the training job to the cloud, using the model and configurations we’ve defined earlier.\n", + "\n", + "5. **Cloud Training Setup:**\n", + " - We enable `use_zero=True` to utilize DeepSpeed's ZeRO optimization, allowing the model to scale effectively across multiple GPUs.\n", + " - We disable Distributed Data Parallel (DDP) for this job, which is appropriate when ZeRO is handling the distribution of data.\n", + "\n", + "Running this cell will initiate the training process on Simplifine’s cloud servers, allowing you to offload the heavy lifting to a powerful cloud infrastructure. This is ideal when working with larger models or when your local resources are insufficient.\n" + ], + "metadata": { + "id": "oehMA7hwRky5" + } + }, + { + "cell_type": "code", + "source": [ + "from simplifine_alpha import train_utils\n", + "\n", + "# change name to phi 3\n", + "model_name = 'microsoft/Phi-3-mini-4k-instruct'\n", + "simplifine_api_key = 'PUT YOUR OWN API KEY PROVIDED BY SIMPLIFINE'\n", + "gpu_type = 'a100'\n", + "client = train_utils.Client(simplifine_api_key, gpu_type)\n", + "\n", + "job_name = 'fake_news_english_phi3'\n", + "\n", + "\n", + "client.sft_train_cloud(job_name=job_name, model_name=model_name, dataset_name=dataset_name,\n", + " sft_config = sft_config, sft_prompt_config=sft_prompt_config,\n", + " use_zero=True, use_ddp=False\n", + " )" + ], + "metadata": { + "id": "O1zdn8r85n-o", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d2510f4d-5246-4631-df37-8a741cf92240" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[2024-08-07 18:34:35,105] [WARNING] [real_accelerator.py:162:get_accelerator] Setting accelerator to CPU. If you have GPU or other accelerator, we were unable to detect it.\n", + "[2024-08-07 18:34:35,110] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cpu (auto detect)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 📝 Checking the Status of Your Training Jobs\n", + "\n", + "After submitting your training job to Simplifine’s cloud servers, it’s important to monitor its status to ensure everything is running smoothly. In this section, we’ll check the status of your most recent job.\n", + "\n", + "1. **Retrieving Job Status:**\n", + " - We call the `get_all_jobs` method on our `client` object. This method returns a list of all jobs associated with your API key, including their current statuses.\n", + "\n", + "2. **Displaying the Latest Job:**\n", + " - We loop through the latest job in the list and print its status. This gives you a quick overview of how your most recent training job is progressing.\n", + "\n", + "3. **Understanding Job Statuses:**\n", + " - Your job can have one of the following statuses:\n", + " - `pending`: The job has been submitted and is waiting to start.\n", + " - `in progress`: The job is currently running.\n", + " - `stopped`: The job was stopped before completion, either manually or due to an error.\n", + " - `completed`: The job has successfully finished.\n", + "\n", + "Running this cell will display the status of your most recent job, helping you keep track of your training tasks on Simplifine’s cloud servers.\n" + ], + "metadata": { + "id": "W88J_Ef7yaYG" + } + }, + { + "cell_type": "code", + "source": [ + "status = client.get_all_jobs()\n", + "for num,i in enumerate(status[-1:]):\n", + " print(f'Job {num}: {i}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l70vZyPV6_AC", + "outputId": "b32db3fe-e353-4105-e8b7-63a772d7ccde" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Job 0: {'job_id': '183c65ad-2b4e-4d11-b2a5-d66232d5b15b', 'job_name': 'fake_news_english_phi3', 'status': 'completed'}\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 📊 Retrieving and Viewing Training Logs\n", + "\n", + "After checking the status of your training job, you might want to dive deeper into the details by viewing the training logs. These logs provide insights into the training process, including any issues or updates on the progress.\n", + "\n", + "1. **Getting the `job_id`:**\n", + " - We start by extracting the `job_id` of the last job from the status list. The `job_id` is a unique identifier for each training job, which we’ll use to retrieve its logs.\n", + "\n", + "2. **Retrieving Logs:**\n", + " - We call the `get_train_logs` method on our `client` object, passing in the `job_id`. This method fetches the detailed logs for the specified job, giving you access to the complete training history.\n", + "\n", + "3. **Viewing the Logs:**\n", + " - Finally, we print the `response` from the logs, which contains detailed information about the training process. This includes updates, errors, and any other relevant messages from the training run.\n", + "\n", + "Running this cell will display the logs for your most recent job, allowing you to monitor and troubleshoot the training process effectively.\n" + ], + "metadata": { + "id": "BDe93gbayl_n" + } + }, + { + "cell_type": "code", + "source": [ + "# getting the job_id of the last job\n", + "job_id = status[-1]['job_id']\n", + "\n", + "logs = client.get_train_logs(job_id)\n", + "print(logs['response'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jt35FPNn8ADK", + "outputId": "1de668ed-718e-452d-eb85-0632d7652008" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "W0806 18:14:41.510000 129132731527296 torch/distributed/run.py:779] \n", + "W0806 18:14:41.510000 129132731527296 torch/distributed/run.py:779] *****************************************\n", + "W0806 18:14:41.510000 129132731527296 torch/distributed/run.py:779] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \n", + "W0806 18:14:41.510000 129132731527296 torch/distributed/run.py:779] *****************************************\n", + "[2024-08-06 18:14:46,878] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-08-06 18:14:46,910] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "[2024-08-06 18:14:46,961] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "[2024-08-06 18:14:47,065] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "[2024-08-06 18:14:47,135] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "[2024-08-06 18:14:47,158] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-08-06 18:14:47,172] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "[2024-08-06 18:14:47,194] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n", + "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n", + "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.4\n", + "\u001b[93m [WARNING] \u001b[0m using untested triton version (3.0.0), only 1.0.0 is known to be compatible\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:47: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_fwd\n", + "/home/ubuntu/mlenv/lib/python3.12/site-packages/deepspeed/runtime/zero/linear.py:66: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.\n", + " @autocast_custom_bwd\n", + "[2024-08-06 18:14:48,688] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,695] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,785] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,850] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,890] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "Destroying existing process group\n", + "Destroying existing process group\n", + "[2024-08-06 18:14:48,922] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,946] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "[2024-08-06 18:14:48,947] [INFO] [comm.py:637:init_distributed] cdb=None\n", + "Destroying existing process group\n", + "Destroying existing process group\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Destroying existing process group\n", + "Destroying existing process group\n", + "Destroying existing process group\n", + "Destroying existing process group\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "\n", + "Map: 0%| | 0/393 [00:00