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guides/keras_nlp/upload.py

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+"""
+Title: Uploading Models with KerasNLP
+Author: [Samaneh Saadat](https://github.com/SamanehSaadat/), [Matthew Watson](https://github.com/mattdangerw/)
+Date created: 2024/04/29
+Last modified: 2024/04/29
+Description: An introduction on how to upload a fine-tuned KerasNLP model to model hubs.
+Accelerator: GPU
+"""
+
+"""
+# Introduction
+
+Fine-tuning a machine learning model can yield impressive results for specific tasks.
+Uploading your fine-tuned model to a model hub allows you to share it with the broader community.
+By sharing your models, you'll enhance accessibility for other researchers and developers,
+making your contributions an integral part of the machine learning landscape.
+This can also streamline the integration of your model into real-world applications.
+
+This guide walks you through how to upload your fine-tuned models to popular model hubs such as
+[Kaggle Models](https://www.kaggle.com/models) and [Hugging Face Hub](https://huggingface.co/models).
+"""
+
+"""
+# Setup
+
+Let's start by installing and importing all the libraries we need. We use KerasNLP for this guide.
+"""
+
+"""shell
+pip install -q --upgrade keras-nlp huggingface-hub kagglehub
+"""
+
+import os
+
+os.environ["KERAS_BACKEND"] = "jax"
+
+import keras_nlp
+
+
+"""
+# Data
+
+We can use the IMDB reviews dataset for this guide. Let's load the dataset from `tensorflow_dataset`.
+"""
+
+import tensorflow_datasets as tfds
+
+imdb_train, imdb_test = tfds.load(
+    "imdb_reviews",
+    split=["train", "test"],
+    as_supervised=True,
+    batch_size=4,
+)
+
+"""
+We only use a small subset of the training samples to make the guide run faster.
+However, if you need a higher quality model, consider using a larger number of training samples.
+"""
+
+imdb_train = imdb_train.take(100)
+
+"""
+# Task Upload
+
+A `keras_nlp.models.Task`, wraps a `keras_nlp.models.Backbone` and a `keras_nlp.models.Preprocessor` to create
+a model that can be directly used for training, fine-tuning, and prediction for a given text problem.
+In this section, we explain how to create a `Task`, fine-tune and upload it to a model hub.
+"""
+
+"""
+## Load Model
+
+If you want to build a Causal LM based on a base model, simply call `keras_nlp.models.CausalLM.from_preset`
+and pass a built-in preset identifier.
+"""
+
+causal_lm = keras_nlp.models.CausalLM.from_preset("gpt2_base_en")
+
+
+"""
+## Fine-tune Model
+
+After loading the model, you can call `.fit()` on the model to fine-tune it.
+Here, we fine-tune the model on the IMDB reviews which makes the model movie domain-specific.
+"""
+
+# Drop labels and keep the review text only for the Causal LM.
+imdb_train_reviews = imdb_train.map(lambda x, y: x)
+
+# Fine-tune the Causal LM.
+causal_lm.fit(imdb_train_reviews)
+
+"""
+## Save the Model Locally
+
+To upload a model, you need to first save the model locally using `save_to_preset`.
+"""
+
+preset_dir = "./gpt2_imdb"
+causal_lm.save_to_preset(preset_dir)
+
+"""
+Let's see the saved files.
+"""
+
+os.listdir(preset_dir)
+
+"""
+### Load a Locally Saved Model
+
+A model that is saved to a local preset can be loaded using `from_preset`.
+What you save in, is what you get back out.
+"""
+
+causal_lm = keras_nlp.models.CausalLM.from_preset(preset_dir)
+
+"""
+You can also load the `keras_nlp.models.Backbone` and `keras_nlp.models.Tokenizer` objects from this preset directory.
+Note that these objects are equivalent to `causal_lm.backbone` and `causal_lm.preprocessor.tokenizer` above.
+"""
+
+backbone = keras_nlp.models.Backbone.from_preset(preset_dir)
+tokenizer = keras_nlp.models.Tokenizer.from_preset(preset_dir)
+
+"""
+## Upload the Model to a Model Hub
+
+After saving a preset to a directory, this directory can be uploaded to a model hub such as Kaggle or Hugging Face directly from the KerasNLP library.
+To upload the model to Kaggle, the URI must start with `kaggle://` and to upload to Hugging Face, it should start with `hf://`.
+"""
+"""
+### Upload to Kaggle
+"""
+
+"""
+To upload a model to Kaggle, first, we need to authenticate with Kaggle.
+This can in one of the following ways:
+1. Set environment variables `KAGGLE_USERNAME` and `KAGGLE_KEY`.
+2. Provide a local `~/.kaggle/kaggle.json`.
+3. Call `kagglehub.login()`.
+
+Let's make sure we are logged in before continuing.
+"""
+
+import kagglehub
+
+if "KAGGLE_USERNAME" not in os.environ or "KAGGLE_KEY" not in os.environ:
+    kagglehub.login()
+
+
+"""
+
+To upload a model we can use `keras_nlp.upload_preset(uri, preset_dir)` API where `uri` has the format of
+`kaggle://<KAGGLE_USERNAME>/<MODEL>/Keras/<VARIATION>` for uploading to Kaggle and `preset_dir` is the directory that the model is saved in.
+
+Running the following uploads the model that is saved in `preset_dir` to Kaggle:
+"""
+kaggle_username = kagglehub.whoami()["username"]
+kaggle_uri = f"kaggle://{kaggle_username}/gpt2/keras/gpt2_imdb"
+keras_nlp.upload_preset(kaggle_uri, preset_dir)
+
+"""
+### Upload to Hugging Face
+"""
+
+"""
+To upload a model to Hugging Face, first, we need to authenticate with Hugging Face.
+This can in one of the following ways:
+1. Set environment variables `HF_USERNAME` and `HF_TOKEN`.
+2. Call `huggingface_hub.notebook_login()`.
+
+Let's make sure we are logged in before coninuing.
+"""
+
+import huggingface_hub
+
+if "HF_USERNAME" not in os.environ or "HF_TOKEN" not in os.environ:
+    huggingface_hub.notebook_login()
+
+"""
+
+`keras_nlp.upload_preset(uri, preset_dir)` can be used to upload a model to Hugging Face if `uri` has the format of
+`kaggle://<HF_USERNAME>/<MODEL>`.
+
+Running the following uploads the model that is saved in `preset_dir` to Hugging Face:
+"""
+
+hf_username = huggingface_hub.whoami()["name"]
+hf_uri = f"hf://{hf_username}/gpt2_imdb"
+keras_nlp.upload_preset(hf_uri, preset_dir)
+
+
+"""
+## Load a User Uploaded Model
+
+After verifying that the model is uploaded to Kaggle, we can load the model by calling `from_preset`.
+
+```python
+causal_lm = keras_nlp.models.CausalLM.from_preset(
+    f"kaggle://{kaggle_username}/gpt2/keras/gpt2_imdb"
+)
+```
+
+We can also load the model uploaded to Hugging Face by calling `from_preset`.
+
+```python
+causal_lm = keras_nlp.models.CausalLM.from_preset(f"hf://{hf_username}/gpt2_imdb")
+```
+"""
+
+
+"""
+# Classifier Upload
+
+Uploading a classifier model is similar to Causal LM upload.
+To upload the fine-tuned model, first, the model should be saved to a local directory using `save_to_preset`
+API and then it can be uploaded via `keras_nlp.upload_preset`.
+"""
+
+# Load the base model.
+classifier = keras_nlp.models.Classifier.from_preset(
+    "bert_tiny_en_uncased", num_classes=2
+)
+
+# Fine-tune the classifier.
+classifier.fit(imdb_train)
+
+# Save the model to a local preset directory.
+preset_dir = "./bert_tiny_imdb"
+classifier.save_to_preset(preset_dir)
+
+# Upload to Kaggle.
+keras_nlp.upload_preset(
+    f"kaggle://{kaggle_username}/bert/keras/bert_tiny_imdb", preset_dir
+)
+
+"""
+After verifying that the model is uploaded to Kaggle, we can load the model by calling `from_preset`.
+
+```python
+classifier = keras_nlp.models.Classifier.from_preset(
+    f"kaggle://{kaggle_username}/bert/keras/bert_tiny_imdb"
+)
+```
+"""