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* huggingface: log some parameters * huggingface: Add `log_model`. - If `None` (default) will not log any artifact. - If `all` will call log_artifact with `output_dir` at each `on_save` call. - If `last` will save the model `on_train_end` and call `log_artifact` with type=model and copy=True. * examples: Add DVCLive-HuggingFace notebook * Don't cherry-pick args * huggingface: Conditional model name based on load_best_model_at_end * huggingface: Keep model_file behavior * Use `True` instead of `last`.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,167 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install accelerate datasets dvclive evaluate 'transformers[torch]' --upgrade" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!git init -q\n", | ||
| "!git config --local user.email \"you@example.com\"\n", | ||
| "!git config --local user.name \"Your Name\"\n", | ||
| "!dvc init -q\n", | ||
| "!git commit -m \"DVC init\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Dataset" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from datasets import load_dataset\n", | ||
| "from transformers import AutoTokenizer\n", | ||
| "\n", | ||
| "dataset = load_dataset(\"imdb\")\n", | ||
| "\n", | ||
| "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n", | ||
| "\n", | ||
| "def tokenize_function(examples):\n", | ||
| " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)\n", | ||
| "\n", | ||
| "small_train_dataset = dataset[\"train\"].shuffle(seed=42).select(range(2000)).map(tokenize_function, batched=True)\n", | ||
| "small_eval_dataset = dataset[\"test\"].shuffle(seed=42).select(range(200)).map(tokenize_function, batched=True)\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import evaluate\n", | ||
| "\n", | ||
| "metric = evaluate.load(\"f1\")\n", | ||
| "\n", | ||
| "def compute_metrics(eval_pred):\n", | ||
| " logits, labels = eval_pred\n", | ||
| " predictions = np.argmax(logits, axis=-1)\n", | ||
| " return metric.compute(predictions=predictions, references=labels)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Tracking experiments with DVCLive" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from dvclive.huggingface import DVCLiveCallback\n", | ||
| "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n", | ||
| "\n", | ||
| "for epochs in (5, 10, 15):\n", | ||
| " model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-cased\", num_labels=2)\n", | ||
| " for param in model.base_model.parameters():\n", | ||
| " param.requires_grad = False\n", | ||
| "\n", | ||
| " training_args = TrainingArguments(\n", | ||
| " evaluation_strategy=\"epoch\", \n", | ||
| " learning_rate=3e-4,\n", | ||
| " logging_strategy=\"epoch\",\n", | ||
| " num_train_epochs=epochs,\n", | ||
| " output_dir=\"output\", \n", | ||
| " overwrite_output_dir=True,\n", | ||
| " load_best_model_at_end=True,\n", | ||
| " report_to=\"none\",\n", | ||
| " save_strategy=\"epoch\",\n", | ||
| " weight_decay=0.01,\n", | ||
| " )\n", | ||
| "\n", | ||
| " trainer = Trainer(\n", | ||
| " model=model,\n", | ||
| " args=training_args,\n", | ||
| " train_dataset=small_train_dataset,\n", | ||
| " eval_dataset=small_eval_dataset,\n", | ||
| " compute_metrics=compute_metrics,\n", | ||
| " callbacks=[DVCLiveCallback(report=\"notebook\", save_dvc_exp=True, log_model=\"last\")],\n", | ||
| " )\n", | ||
| " trainer.train()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Comparing" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import dvc.api\n", | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "columns = [\"Experiment\", \"epoch\", \"eval.f1\"]\n", | ||
| "\n", | ||
| "df = pd.DataFrame(dvc.api.exp_show(), columns=columns)\n", | ||
| "\n", | ||
| "df.dropna(inplace=True)\n", | ||
| "df.reset_index(drop=True, inplace=True)\n", | ||
| "df\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!dvc plots diff $(dvc exp list --names-only)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from IPython.display import HTML\n", | ||
| "HTML(filename='./dvc_plots/index.html')" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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