From b68badb1176ba8aaff25469b706464a24bbbeb98 Mon Sep 17 00:00:00 2001 From: Charles Frye Date: Sun, 17 Nov 2024 23:19:25 -0800 Subject: [PATCH] remove old examples that are not referenced in the main site (#980) --- 06_gpu_and_ml/spam-detect/readme.md | 86 --- .../spam-detect/spam_detect/__init__.py | 0 06_gpu_and_ml/spam-detect/spam_detect/app.py | 27 - .../spam-detect/spam_detect/config.py | 41 -- .../spam-detect/spam_detect/dataset.py | 96 ---- .../spam-detect/spam_detect/model_registry.py | 106 ---- .../spam-detect/spam_detect/model_storage.py | 276 ---------- .../spam-detect/spam_detect/models.py | 518 ------------------ .../spam-detect/spam_detect/serving.py | 95 ---- .../spam-detect/spam_detect/train.py | 206 ------- .../spam-detect/tests/model_storage_test.py | 70 --- .../spam-detect/tests/models_test.py | 37 -- 06_gpu_and_ml/wandb-ui.png | Bin 590328 -> 0 bytes 07_web_endpoints/chatbot_spa.py | 106 ---- 07_web_endpoints/chatbot_spa/app.js | 121 ---- 07_web_endpoints/chatbot_spa/index.html | 13 - 10_integrations/pyjulia.py | 47 -- 17 files changed, 1845 deletions(-) delete mode 100644 06_gpu_and_ml/spam-detect/readme.md delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/__init__.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/app.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/config.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/dataset.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/model_registry.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/model_storage.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/models.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/serving.py delete mode 100644 06_gpu_and_ml/spam-detect/spam_detect/train.py delete mode 100644 06_gpu_and_ml/spam-detect/tests/model_storage_test.py delete mode 100644 06_gpu_and_ml/spam-detect/tests/models_test.py delete mode 100644 06_gpu_and_ml/wandb-ui.png delete mode 100644 07_web_endpoints/chatbot_spa.py delete mode 100644 07_web_endpoints/chatbot_spa/app.js delete mode 100644 07_web_endpoints/chatbot_spa/index.html delete mode 100644 10_integrations/pyjulia.py diff --git a/06_gpu_and_ml/spam-detect/readme.md b/06_gpu_and_ml/spam-detect/readme.md deleted file mode 100644 index 9ca38b8a0..000000000 --- a/06_gpu_and_ml/spam-detect/readme.md +++ /dev/null @@ -1,86 +0,0 @@ -## Spam detection with large-language models (LLMs) - -This demo application tackles the internet's old bΓͺte noire: spam. To focus of the demo -is fine-tuning the BERT LLM to acheive 99% precision on an email spam classification task, but -any self-respecting LLM should be able to beat a baseline model, so this demo also includes -other model architectures for comparision. - -The models are: - -- _Bidirectional Encoder Representations from Transformers_ (BERT) -- _Naive Bayes_ -- Blacklist-based hueristic model (`BadWords`) - -The inference API accepts email text and returns responses like: - -```json -curl -X POST "https://modal-labs--example-spam-detect-llm-web.modal.run/api/v1/classify" \ --H 'Content-Type: application/json' \ --d '{"text": "Subject: christmas tree farm pictures"}' - -{ - "spam": false, - "score": -0.10338655114173889, - "metadata": { - "model_name": "bert-base-cased", - "model_id": "sha256.4D4CA273952449C9D20E837F4425DC012C1BABF9AFD4D8E118BB50A596C72B87" - } -} -``` - -### ML engineering support - -The demo application showcases how to do all the major machine learning engineering processes: - -- training dataset preparation -- model training and evaluation loop -- model tracking and storage -- model deployment (promotion, rollback) -- model serving - -## Developing - -> Run all these commands in the project's directory: `cd "$(git rev-parse --show-toplevel)/ml/spam_detect"`. - -### Testing - -Unit-tests exist in `tests/` and can be run with: - -```bash -python3 -m pytest -``` - -### Training - -Any of the models can be trained using the `train.py` module. - -```bash -python3 -m spam_detect.train -``` - -### Serving - -```bash -cd "$(git rev-parse --show-toplevel)/06_gpu_and_ml/spam-detect" -python3 -m spam_detect.serving -``` - -Sending Ctrl+C will stop your app serving. - -## Deploy - -To deploy this application's spam detection API, run the following command: - -```bash -cd "$(git rev-parse --show-toplevel)/06_gpu_and_ml/spam-detect" -modal deploy spam_detect.serving -``` - -### Deploy models - -Update the `SERVING_MODEL_ID` string value in `config.py` to the -model ID you want to deploy, then redeploy the Modal app as shown -above. - -The updated model will be read from volume on container startup and -used in API inference requests. diff --git a/06_gpu_and_ml/spam-detect/spam_detect/__init__.py b/06_gpu_and_ml/spam-detect/spam_detect/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/06_gpu_and_ml/spam-detect/spam_detect/app.py b/06_gpu_and_ml/spam-detect/spam_detect/app.py deleted file mode 100644 index 470087a31..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/app.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Contains only definitions of Modal objects, to be imported -from other modules. -""" - -import modal - -image = modal.Image.debian_slim( - python_version="3.10" -).pip_install( - "datasets~=2.7.1", - "dill==0.3.4", # pinned b/c of https://github.com/uqfoundation/dill/issues/481 - "evaluate~=0.3.0", - "loguru~=0.6.0", - "pyarrow~=10.0.1", - "scikit-learn~=1.1.3", # Required by evaluate pkg. - "torch~=1.13.0", - "transformers~=4.24.0", - "fastapi[standard]==0.115.4", - "pydantic==2.9.1", -) - -app = modal.App(name="example-spam-detect-llm", image=image) -# Used to store datasets, trained models, model metadata, config. -volume = modal.Volume.from_name( - "example-spam-detect-vol", create_if_missing=True -) diff --git a/06_gpu_and_ml/spam-detect/spam_detect/config.py b/06_gpu_and_ml/spam-detect/spam_detect/config.py deleted file mode 100644 index 6776fdc34..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/config.py +++ /dev/null @@ -1,41 +0,0 @@ -import enum -import logging -import pathlib -import sys - -VOLUME_DIR: str = "/cache" -MODEL_STORE_DIR = pathlib.Path(VOLUME_DIR, "models") -MODEL_REGISTRY_FILENAME: str = "registry.json" -DATA_DIR = pathlib.Path(VOLUME_DIR, "data") - -SERVING_MODEL_ID: str = ( - "sha256.12E5065BE4C3F7D2F79B7A0FD203380869F6E308DCBB4B8C9579FFAE6F32B837" -) - - -class ModelType(str, enum.Enum): - BAD_WORDS = "BAD_WORDS" - LLM = "LLM" - NAIVE_BAYES = "NAIVE_BAYES" - - -def get_logger(): - try: - from loguru import logger - - logger.remove() - logger.add(sys.stderr, colorize=True, level="INFO") - except ModuleNotFoundError: - logger = logging.getLogger() - handler = logging.StreamHandler() - handler.setFormatter( - logging.Formatter( - "%(levelname)s: %(asctime)s: %(name)s %(message)s" - ) - ) - logger.addHandler(handler) - logger.setLevel(logging.INFO) - logger.propagate = ( - False # Prevent the modal client from double-logging. - ) - return logger diff --git a/06_gpu_and_ml/spam-detect/spam_detect/dataset.py b/06_gpu_and_ml/spam-detect/spam_detect/dataset.py deleted file mode 100644 index 0316939c3..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/dataset.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -Module for the fetching, pre-processing, and loading of spam classification datasets. -Currently only provides access to the ENRON email dataset. -""" - -import csv -import json -import pathlib -import shutil -import tempfile -import urllib.request -import zipfile -from typing import NamedTuple - -# TODO: -# This dataset only produces ~50,000 examples. -# Other links to the dataset claim ~500,000 examples, eg. https://www.kaggle.com/wcukierski/enron-email-dataset -# which links to https://www.cs.cmu.edu/~./enron/. -enron_dataset_url = "https://github.com/MWiechmann/enron_spam_data/raw/master/enron_spam_data.zip" - - -class Example(NamedTuple): - email: str - spam: bool - - -RawEnronDataset = list[Example] -CleanEnronDataset = dict[str, Example] - - -def dataset_path(base: pathlib.Path) -> pathlib.Path: - return base / "raw" / "enron" / "all.json" - - -def deserialize_dataset(dataset_path: pathlib.Path) -> RawEnronDataset: - with open(dataset_path, "r") as f: - items = json.load(f) - return [Example(email=item[0], spam=bool(item[1])) for item in items] - - -def _download_and_extract_dataset(destination_root_path: pathlib.Path, logger): - logger.info("Downloading raw enron dataset.") - destination_path = destination_root_path / "enron.zip" - destination_path.parent.mkdir(parents=True, exist_ok=True) - req = urllib.request.Request( - enron_dataset_url, - headers={ - "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36" - }, - ) - with urllib.request.urlopen(req) as response, open( - destination_path, "wb" - ) as out_file: - shutil.copyfileobj(response, out_file) - - with zipfile.ZipFile(destination_path, "r") as zip_ref: - logger.info("Extracting zip with contents: ") - zip_ref.printdir() - zip_ref.extractall(destination_root_path) - - return destination_root_path / "enron_spam_data.csv" - - -def fix_nulls(f): - for line in f: - yield line.replace("\0", "") - - -def download(logger, base: pathlib.Path) -> None: - dest = dataset_path(base) - dest.parent.mkdir(exist_ok=True, parents=True) - tmp_path = pathlib.Path(tempfile.TemporaryDirectory().name) - dataset_csv_path = _download_and_extract_dataset( - destination_root_path=tmp_path, logger=logger - ) - ds: list[Example] = [] - spam_count = 0 - with open(dataset_csv_path, "r") as csvfile: - csv.field_size_limit(100_000_000) - reader = csv.DictReader(fix_nulls(csvfile), delimiter=",") - for row in reader: - is_spam = row["Spam/Ham"] == "spam" - if is_spam: - spam_count += 1 - ex = Example( - email=row["Subject"] + " " + row["Message"], - spam=is_spam, - ) - ds.append(ex) - - spam_percentage = round((spam_count / len(ds)) * 100, ndigits=4) - logger.info( - f"writing processed raw dataset to file. dataset contains {len(ds)} examples and is {spam_percentage}% spam" - ) - with open(dest, "w") as f: - json.dump(ds, f, indent=4) diff --git a/06_gpu_and_ml/spam-detect/spam_detect/model_registry.py b/06_gpu_and_ml/spam-detect/spam_detect/model_registry.py deleted file mode 100644 index 59db9bd08..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/model_registry.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -Defines minimal data structures and command-line interface (CLI) commands for a model registry. -The CLI commands are operationally useful, used to inspect prior trained models and promote the -most promising models to production serving. -""" - -import json -from typing import Callable, NamedTuple, Optional - -from . import config -from .app import app, volume - - -class Prediction(NamedTuple): - spam: bool - score: float - - -SpamClassifier = Callable[[str], Prediction] - - -class TrainMetrics(NamedTuple): - # human-readable identifier for the dataset used in training. - dataset_id: str - # How many examples in the evaluation subset. - eval_set_size: int - # (TP + TN) / (TP + TN + FP + FN) - accuracy: Optional[float] = None - # TP / (TP + FP) - precision: Optional[float] = None - # TP / (TP + FN) - recall: Optional[float] = None - - -class ModelMetadata(NamedTuple): - impl_name: str - save_date: str # UTC+ISO8601 formatted. - git_commit_hash: str - metrics: Optional[TrainMetrics] = None - - def serialize(self) -> dict: - d = self._asdict() - if d["metrics"]: - d["metrics"] = d["metrics"]._asdict() - return d - - @classmethod - def from_dict(cls, m: dict) -> "ModelMetadata": - if "metrics" not in m or m["metrics"] is None: - metrics = None - else: - metrics = TrainMetrics( - dataset_id=m["metrics"]["dataset_id"], - eval_set_size=m["metrics"]["eval_set_size"], - accuracy=m["metrics"]["accuracy"], - precision=m["metrics"]["precision"], - recall=m["metrics"]["recall"], - ) - return cls( - impl_name=m["impl_name"], - save_date=m["save_date"], - git_commit_hash=m["git_commit_hash"], - metrics=metrics, - ) - - -@app.function(volumes={config.VOLUME_DIR: volume}) -def _list_models() -> dict[str, ModelMetadata]: - registry_filepath = config.MODEL_STORE_DIR / config.MODEL_REGISTRY_FILENAME - with open(registry_filepath, "r") as f: - registry_data = json.load(f) - return { - m_id: ModelMetadata.from_dict(m) for m_id, m in registry_data.items() - } - - -@app.function(volumes={config.VOLUME_DIR: volume}) -def delete_model( - # sha256 hashtag of model. eg 'sha256.1234567890abcd' - model_id: str, - # Don't actually delete, just show deletion plan. - dry_run: bool = True, -) -> None: - """Remove a model from registry and storage.""" - pass - - -@app.local_entrypoint() -def list_models() -> None: - """Show all models in registry.""" - with app.run(): - models = _list_models.remote() - newest_to_oldest = sorted( - [(key, value) for key, value in models.items()], - key=lambda item: item[1].save_date, - reverse=True, - ) - for model_id, metadata in newest_to_oldest: - print( - f"\033[96m {model_id} \033[0m{metadata.impl_name}\033[93m {metadata.save_date} \033[0m" - ) - - -if __name__ == "__main__": - print("USAGE: modal run spam_detect.model_registry [FUNCTION]") - raise SystemExit(1) diff --git a/06_gpu_and_ml/spam-detect/spam_detect/model_storage.py b/06_gpu_and_ml/spam-detect/spam_detect/model_storage.py deleted file mode 100644 index 7606835e2..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/model_storage.py +++ /dev/null @@ -1,276 +0,0 @@ -""" -The model storage module contains functions for the serialization, and -disk-based storage of the email spam models defined within models.py. -""" - -import datetime -import hashlib -import io -import json -import pathlib -import pickle -import random -import string -import subprocess -from typing import ( - Any, - Callable, - Dict, - Iterable, - Optional, -) - -from . import config, dataset -from .model_registry import ModelMetadata, SpamClassifier, TrainMetrics - -logger = config.get_logger() - -Dataset = Iterable[dataset.Example] -TrainingFunc = Callable[[Dataset], Any] -ModelBuilder = Callable[[Dataset, Optional[TrainingFunc]], SpamClassifier] - - -Sha256Hash = str -ModelRegistryMetadata = Dict[Sha256Hash, ModelMetadata] - - -def get_git_revision_hash() -> str: - return ( - subprocess.check_output(["git", "rev-parse", "--verify", "HEAD"]) - .decode("ascii") - .strip() - ) - - -def serialize_model( - model_func: SpamClassifier, -) -> bytes: - try: - from datasets.utils.py_utils import Pickler - except ModuleNotFoundError: - from pickle import Pickler # type: ignore - - def dumps(obj, **kwds): - file = io.BytesIO() - Pickler(file, **kwds).dump(obj) - return file.getvalue() - - return dumps(model_func) - - -def create_hashtag_from_dir(dir: pathlib.Path) -> str: - dgst = hashlib.sha256() - for f in dir.glob("**/*"): - dgst.update(f.name.encode()) - dgst.update(f.read_bytes()) - return f"sha256.{dgst.hexdigest().upper()}" - - -def create_hashtag_from_bytes(b: bytes) -> str: - hash_base = hashlib.sha256(b).hexdigest().upper() - return f"sha256.{hash_base}" - - -def store_huggingface_model( - trainer: Any, - train_metrics: TrainMetrics, - model_name: str, - model_destination_root: pathlib.Path, - git_commit_hash: str, -) -> str: - """ - Accepts a Hugginface model that implements `save_model()` and stores it in model - registry and persistent filesystem. - """ - tmp_dirname = "".join( - random.choices(string.ascii_uppercase + string.digits, k=20) - ) - model_save_path = model_destination_root / tmp_dirname - trainer.save_model(output_dir=model_save_path) - model_hashtag = create_hashtag_from_dir(model_save_path) - model_save_path.rename(model_destination_root / model_hashtag) - - logger.info(f"serialized model's hash is {model_hashtag}") - - model_registry_metadata = load_model_registry_metadata( - model_registry_root=model_destination_root, - ) - - model_dest_path = model_destination_root / model_hashtag - if model_dest_path.is_file(): - logger.warning( - ( - f"model {model_hashtag} already exists. No need to save again. " - "consider caching model training to save compute cycles." - ) - ) - - logger.info( - f"updating models registry metadata to include information about {model_hashtag}" - ) - metadata = ModelMetadata( - impl_name=model_name, - save_date=datetime.datetime.now().strftime("%d-%m-%Y %H:%M:%S"), - git_commit_hash=git_commit_hash, - ) - store_model_registry_metadata( - model_registry_metadata=model_registry_metadata, - sha256_hash=model_hashtag, - metadata=metadata, - destination_root=model_destination_root, - ) - logger.info("πŸ“¦ done! Model stored.") - return model_hashtag - - -def store_pickleable_model( - *, - classifier_func: SpamClassifier, - metrics: TrainMetrics, - model_destination_root: pathlib.Path, - current_git_commit_hash: str, -) -> str: - """ - Stores a pickle-able model in registry and persistent filesystem. - The `pickle` process only works on a single classifier Python object, - and should only be used for simple, pure-Python classifiers. - """ - logger.info("storing spam model to model registry using pickling.") - - serialized_model = serialize_model(classifier_func) - ser_clssfr_hash = create_hashtag_from_bytes(serialized_model) - - logger.info(f"serialized model's hash is {ser_clssfr_hash}") - - model_registry_metadata = load_model_registry_metadata( - model_registry_root=model_destination_root, - ) - - model_dest_path = model_destination_root / ser_clssfr_hash - if model_dest_path.is_file(): - logger.warning( - ( - f"model {ser_clssfr_hash} already exists. No need to save again. " - "consider caching model training to save compute cycles." - ) - ) - else: - logger.info(f"saving model to file at '{model_dest_path}'") - model_dest_path.write_bytes(serialized_model) - - logger.info( - f"updating models registry metadata to include information about {ser_clssfr_hash}" - ) - metadata = ModelMetadata( - impl_name=model_name_from_function(classifier_func), - save_date=datetime.datetime.now().strftime("%d-%m-%Y %H:%M:%S"), - git_commit_hash=current_git_commit_hash, - metrics=metrics, - ) - store_model_registry_metadata( - model_registry_metadata=model_registry_metadata, - sha256_hash=ser_clssfr_hash, - metadata=metadata, - destination_root=model_destination_root, - ) - logger.info("πŸ“¦ done! Model stored.") - return ser_clssfr_hash - - -def model_name_from_function(model_func: SpamClassifier) -> str: - # NOTE: This may be buggy, and create name clashes or ambiguity. - return model_func.__qualname__ - - -def load_model_registry_metadata( - *, - model_registry_root: pathlib.Path, -): - model_registry_metadata_filepath = ( - model_registry_root / config.MODEL_REGISTRY_FILENAME - ) - if not model_registry_metadata_filepath.exists(): - # Create registry metadata file on first save of a model. - model_registry_metadata_filepath.write_text("{}") - - with open(model_registry_metadata_filepath, "r") as model_registry_f: - data = json.load(model_registry_f) - model_registry_metadata: ModelRegistryMetadata = { - key: ModelMetadata( - impl_name=value["impl_name"], - save_date=value["save_date"], - git_commit_hash=value["git_commit_hash"], - ) - for key, value in data.items() - } - return model_registry_metadata - - -def retrieve_model_registry_metadata( - *, - model_registry_metadata: ModelRegistryMetadata, - sha256_hash: str, -) -> Optional[ModelMetadata]: - return model_registry_metadata.get(sha256_hash) - - -def store_model_registry_metadata( - *, - model_registry_metadata: ModelRegistryMetadata, - sha256_hash: str, - metadata: ModelMetadata, - destination_root: pathlib.Path, -) -> None: - existing_metadata = retrieve_model_registry_metadata( - model_registry_metadata=model_registry_metadata, - sha256_hash=sha256_hash, - ) - if existing_metadata is not None: - logger.debug("classifier with matching hash found in registry.") - # compare new metadata with old to detect registry corruption or - # strange renaming. - if metadata.impl_name != existing_metadata.impl_name: - raise RuntimeError( - "Existing classifier with identical sha256 hash to current classifier found " - "with conflicting metadata. " - "Something has gone wrong." - ) - model_registry_metadata_dict = { - key: value._asdict() for key, value in model_registry_metadata.items() - } - # NOTE: Potentially overwrites with new metadata. - model_registry_metadata_dict[sha256_hash] = metadata.serialize() - with open( - destination_root / config.MODEL_REGISTRY_FILENAME, "w" - ) as model_registry_f: - json.dump(model_registry_metadata_dict, model_registry_f, indent=4) - - -def load_pickle_serialized_model( - *, - sha256_hash: str, - destination_root: pathlib.Path, -) -> SpamClassifier: - def check_integrity(*, expected_hash: str, actual_hash: str) -> None: - if not expected_hash == actual_hash: - err_msg = f"Shasum integrity check failure. Expected '{expected_hash}' but got '{actual_hash}'" - raise ValueError(err_msg) - - expected_prefix = "sha256." - if not sha256_hash.startswith(expected_prefix): - raise ValueError( - f"model sha256 hashes are expected to start with the prefix '{expected_prefix}" - ) - - model_path = destination_root / sha256_hash - with open(model_path, "rb") as f: - model_bytes = f.read() - - hash_base = hashlib.sha256(model_bytes).hexdigest().upper() - filestored_model_hashtag = f"sha256.{hash_base}" - - check_integrity( - expected_hash=sha256_hash, - actual_hash=filestored_model_hashtag, - ) - return pickle.loads(model_bytes) diff --git a/06_gpu_and_ml/spam-detect/spam_detect/models.py b/06_gpu_and_ml/spam-detect/spam_detect/models.py deleted file mode 100644 index fbe80feba..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/models.py +++ /dev/null @@ -1,518 +0,0 @@ -""" - -The core model interface is `SpamModel`, which must be implemented by all -trainable and serveable spam-detection models in the module. - -Current model implementations are: - -* BadWords (a baseline heuristic classifier) -* LLM (a fine-tuned BERT language classifier) -* NaiveBayes -""" - -import json -import math -import pathlib -import re -from collections import defaultdict -from typing import ( - Optional, - Protocol, - cast, -) - -from . import config, model_storage -from .dataset import Example -from .model_registry import ( - ModelMetadata, - Prediction, - SpamClassifier, - TrainMetrics, -) - -Dataset = list[Example] - - -def load_model(model_id: str): - registry_filepath = config.MODEL_STORE_DIR / config.MODEL_REGISTRY_FILENAME - with open(registry_filepath, "r") as f: - registry_data = json.load(f) - if model_id not in registry_data: - raise ValueError(f"{model_id} not contained in registry.") - - metadata = ModelMetadata.from_dict(registry_data[model_id]) - m: SpamModel - if metadata.impl_name == "bert-base-cased": - m = LLM() - elif "NaiveBayes" in metadata.impl_name: - m = NaiveBayes() - else: - raise ValueError(f"Loading '{metadata.impl_name}' not yet supported.") - - classifier = m.load( - sha256_digest=model_id, - model_registry_root=config.MODEL_STORE_DIR, - ) - return classifier, metadata - - -def tokenize(text: str) -> set[str]: - text = text.lower() - all_words = re.findall("[a-z0-9]+", text) # extract the words - return set(all_words) - - -class SpamModel(Protocol): - """The training and storage interface that all spam-classification models must implement.""" - - def train(self, dataset: Dataset) -> tuple[SpamClassifier, TrainMetrics]: - ... - - def load( - self, sha256_digest: str, model_registry_root: pathlib.Path - ) -> SpamClassifier: - ... - - def save( - self, - fn: SpamClassifier, - metrics: TrainMetrics, - model_registry_root: pathlib.Path, - git_commit_hash: str, - ) -> str: - ... - - -def construct_huggingface_dataset(dataset: Dataset, label2id: dict[str, int]): - import datasets - import pyarrow as pa - - emails = pa.array((ex.email for ex in dataset), type=pa.string()) - labels = pa.array( - (label2id["SPAM"] if ex.spam else label2id["HAM"] for ex in dataset), - type=pa.uint8(), - ) - pa_table = pa.table([emails, labels], names=["text", "labels"]) - return datasets.Dataset(pa_table).train_test_split(test_size=0.1) - - -class LLMSpamClassifier: - """SpamClassifier that wraps a fine-tuned Huggingface BERT transformer model.""" - - def __init__(self, tokenizer, model) -> None: - self.tokenizer = tokenizer - self.model = model - - def __call__(self, email: str) -> Prediction: - """Ensures this class-based classifier can be used just like a function-based classifer.""" - import torch - - inputs = self.tokenizer(email, return_tensors="pt") - with torch.no_grad(): - logits = self.model(**inputs).logits - - predicted_class_id = logits.argmax().item() - spam_id = self.model.config.label2id["SPAM"] - spam_score = logits[0][spam_id] - predicted_label: str = self.model.config.id2label[predicted_class_id] - return Prediction( - spam=bool(predicted_label == "SPAM"), - score=spam_score, - ) - - -def train_llm_classifier( - dataset: Dataset, dry_run: bool = False -) -> tuple[LLMSpamClassifier, TrainMetrics]: - import evaluate - import numpy as np - from transformers import ( - AutoModelForSequenceClassification, - AutoTokenizer, - Trainer, - TrainingArguments, - ) - - logger = config.get_logger() - - id2label = {0: "HAM", 1: "SPAM"} - label2id = {"HAM": 0, "SPAM": 1} - huggingface_dataset = construct_huggingface_dataset(dataset, label2id) - - tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") - - def tokenize_function(examples): - return tokenizer( - examples["text"], padding="max_length", truncation=True - ) - - tokenized_datasets = huggingface_dataset.map( - tokenize_function, batched=True - ) - model = AutoModelForSequenceClassification.from_pretrained( - "bert-base-cased", - num_labels=len(label2id), - id2label=id2label, - label2id=label2id, - ) - - training_args = TrainingArguments(output_dir="test_trainer") - - metric = evaluate.load("accuracy") - - def compute_metrics(eval_pred): - logits, labels = eval_pred - predictions = np.argmax(logits, axis=-1) - return metric.compute(predictions=predictions, references=labels) - - training_args = TrainingArguments( - output_dir="test_trainer", evaluation_strategy="epoch" - ) - - small_train_dataset = ( - tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) - ) - small_eval_dataset = ( - tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) - ) - - trainer = Trainer( - model=model, - args=training_args, - train_dataset=small_train_dataset, - eval_dataset=small_eval_dataset, - compute_metrics=compute_metrics, - ) - - logger.opt(colors=True).info("training πŸ‹οΈ") - - if not dry_run: - trainer.train() - logger.opt(colors=True).info( - "βœ”οΈ training done!" - ) - else: - logger.info(f"{dry_run=}, so skipping training step.") - - metrics = TrainMetrics( - dataset_id="enron", - eval_set_size=-1, - accuracy=0.0, - ) - return trainer, metrics - - -class LLM(SpamModel): - """ - A large-language model (LLM) fine-tuned for the SPAM/HAM text classification problem. - - Uses huggingface/transformers library. - """ - - model_name = "bert-base-cased" - - def train(self, dataset: Dataset) -> tuple[SpamClassifier, TrainMetrics]: - from transformers import AutoTokenizer - - trainer, metrics = train_llm_classifier(dataset=dataset) - model = trainer.model - tokenizer = AutoTokenizer.from_pretrained(LLM.model_name) - return ( - LLMSpamClassifier( - tokenizer=tokenizer, - model=model, - ), - metrics, - ) - - def load( - self, sha256_digest: str, model_registry_root: pathlib.Path - ) -> SpamClassifier: - from transformers import ( - AutoModelForSequenceClassification, - AutoTokenizer, - ) - - # TODO: refactor to use model_storage module for loading. - model_path = model_registry_root / sha256_digest - model = AutoModelForSequenceClassification.from_pretrained(model_path) - tokenizer = AutoTokenizer.from_pretrained(LLM.model_name) - return LLMSpamClassifier( - tokenizer=tokenizer, - model=model, - ) - - def save( - self, - fn: SpamClassifier, - metrics: TrainMetrics, - model_registry_root: pathlib.Path, - git_commit_hash: str, - ) -> str: - from transformers import Trainer - - llm_fn = cast(LLMSpamClassifier, fn) - trainer = Trainer(model=llm_fn.model) - return model_storage.store_huggingface_model( - trainer=trainer, - train_metrics=metrics, - model_name=LLM.model_name, - model_destination_root=model_registry_root, - git_commit_hash=git_commit_hash, - ) - - -class BadWords(SpamModel): - """ - An extremely rudimentary heuritistic model. If a trained model - can't beat this something is very wrong. - """ - - def train(self, dataset: Dataset) -> tuple[SpamClassifier, TrainMetrics]: - def bad_words_spam_classifier(email: str) -> Prediction: - tokens = email.split(" ") - tokens_set = set(tokens) - # NB: using a set here makes pickle serialization non-deterministic. - bad_words = [ - "click", # http://www.paulgraham.com/spam.html - "sex", - "xxx", - "nigerian", - "teens", - ] - max_bad_words = 2 - bad_words_count = 0 - for word in bad_words: - if word in tokens_set: - bad_words_count += 1 - return ( - Prediction(score=1.0, spam=True) - if bad_words_count > max_bad_words - else Prediction(score=0.0, spam=False) - ) - - accuracy, precision = self._calc_metrics( - classifier=bad_words_spam_classifier, dataset=dataset - ) - metrics = TrainMetrics( - dataset_id="enron", - eval_set_size=0, - accuracy=accuracy, - precision=precision, - ) - return bad_words_spam_classifier, metrics - - def load( - self, sha256_digest: str, model_registry_root: pathlib.Path - ) -> SpamClassifier: - return model_storage.load_pickle_serialized_model( - sha256_hash=sha256_digest, - destination_root=model_registry_root, - ) - - def save( - self, - fn: SpamClassifier, - metrics: TrainMetrics, - model_registry_root: pathlib.Path, - git_commit_hash: str, - ) -> str: - return model_storage.store_pickleable_model( - classifier_func=fn, - metrics=metrics, - model_destination_root=model_registry_root, - current_git_commit_hash=git_commit_hash, - ) - - def _calc_metrics( - self, classifier: SpamClassifier, dataset: Dataset - ) -> tuple[float, float]: - if len(dataset) == 0: - raise ValueError("Evaluation dataset cannot be empty.") - tp, tn, fp, fn = 0, 0, 0, 0 - for example in dataset: - pred = classifier(example.email) - if pred.spam and example.spam: - tp += 1 - elif pred.spam and not example.spam: - fp += 1 - elif not pred.spam and not example.spam: - tn += 1 - else: - fn += 1 - accuracy = (tp + tn) / (tp + tn + fp + fn) - precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 - print(f"Summary: {tp=} {fp=} {tn=} {fn=}") - return accuracy, precision - - -class NaiveBayes(SpamModel): - """ - The classic Naive-Bayes classifier. Implementation drawn from the - *Data Science From Scratch* book: github.com/joelgrus/data-science-from-scratch. - """ - - def __init__( - self, - k: float = 0.5, - decision_boundary: Optional[float] = None, - test_set_size: float = 0.05, - ) -> None: - self.k = k - self.decision_boundary = decision_boundary - self.classify_fn: Optional[SpamClassifier] = None - self.test_set_size = test_set_size - - def train(self, dataset: Dataset) -> tuple[SpamClassifier, TrainMetrics]: - k = self.k - dataset_tokens: set[str] = set() - token_spam_counts: dict[str, int] = defaultdict(int) - token_ham_counts: dict[str, int] = defaultdict(int) - spam_messages = ham_messages = 0 - test_samples = int(len(dataset) * self.test_set_size) - if test_samples > 0: - train_set = dataset[:-test_samples] - test_set = dataset[-test_samples:] - else: - train_set = dataset - test_set = [] - - for example in train_set: - if example.spam: - spam_messages += 1 - else: - ham_messages += 1 - - # Increment word counts - for token in tokenize(example.email): - dataset_tokens.add(token) - if example.spam: - token_spam_counts[token] += 1 - else: - token_ham_counts[token] += 1 - - print("finished building word count dicts") - - def predict_prob(email: str) -> float: - email_tokens = tokenize(email) - log_prob_if_spam = log_prob_if_ham = 0.0 - - # Iterate through each word in our vocabulary - for token in dataset_tokens: - spam = token_spam_counts[token] - ham = token_ham_counts[token] - - prob_if_spam = (spam + k) / (spam_messages + 2 * k) - prob_if_ham = (ham + k) / (ham_messages + 2 * k) - # If *token* appears in the message, - # add the log probability of seeing it - if token in email_tokens: - log_prob_if_spam += math.log(prob_if_spam) - log_prob_if_ham += math.log(prob_if_ham) - # Otherwise add the log probability of _not_ seeing it, - # which is log(1 - probability of seeing it) - else: - log_prob_if_spam += math.log(1.0 - prob_if_spam) - log_prob_if_ham += math.log(1.0 - prob_if_ham) - prob_if_spam = math.exp(log_prob_if_spam) - prob_if_ham = math.exp(log_prob_if_ham) - score = ( - prob_if_spam / (prob_if_spam + prob_if_ham) - if prob_if_spam - else 0.0 - ) - return score - - def make_classifier( - prob_fn, decision_boundary: float - ) -> SpamClassifier: - def inner(email: str): - score = prob_fn(email) - return Prediction( - spam=score > decision_boundary, - score=score, - ) - - return inner - - if self.decision_boundary: - decision_boundary, precision, recall = ( - self.decision_boundary, - None, - None, - ) - else: - print("setting decision boundary for binary classifier") - decision_boundary, precision, recall = self._set_decision_boundary( - prob_fn=predict_prob, - test_dataset=test_set, - ) - - metrics = TrainMetrics( - dataset_id="enron", - eval_set_size=len(test_set), - accuracy=None, - precision=precision, - recall=recall, - ) - print("making classifier") - return make_classifier(predict_prob, decision_boundary), metrics - - def load( - self, sha256_digest: str, model_registry_root: pathlib.Path - ) -> SpamClassifier: - return model_storage.load_pickle_serialized_model( - sha256_hash=sha256_digest, - destination_root=model_registry_root, - ) - - def save( - self, - fn: SpamClassifier, - metrics: TrainMetrics, - model_registry_root: pathlib.Path, - git_commit_hash: str, - ) -> str: - return model_storage.store_pickleable_model( - classifier_func=fn, - metrics=metrics, - model_destination_root=model_registry_root, - current_git_commit_hash=git_commit_hash, - ) - - def _set_decision_boundary( - self, prob_fn, test_dataset - ) -> tuple[float, float, float]: - import numpy as np - from sklearn.metrics import precision_recall_curve - - print( - f"Using {len(test_dataset)} test dataset examples to set decision boundary.\n" - "Warning: this is a slow operation that takes ~15 minutes." - ) - - minimum_acceptable_precision = ( - 0.98 # ie. 2 in a 100 legit emails get marked as spam. - ) - y_true = np.array([1 if ex.spam else 0 for ex in test_dataset]) - # scores are rounded because curve calculation time scales quickly in dim U, where U is number of unique scores. - # NB: The precision-recall curve calculation is extremely slow on N ~10k+ - y_scores = np.array( - [round(prob_fn(ex.email), ndigits=2) for ex in test_dataset] - ) - # TODO: Optimize this very slow process. - precisions, recalls, thresholds = precision_recall_curve( - y_true, y_scores - ) - for p, r, thres in zip(precisions, recalls, thresholds): - print( - "Using threshold={} as decision boundary, we reach precision={} and recall={}".format( - thres, p, r - ) - ) - if p >= minimum_acceptable_precision: - print( - f"Reached {minimum_acceptable_precision=} at threshold {thres}. Setting that as boundary." - ) - break - return thres, p, r diff --git a/06_gpu_and_ml/spam-detect/spam_detect/serving.py b/06_gpu_and_ml/spam-detect/spam_detect/serving.py deleted file mode 100644 index 0890c38dd..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/serving.py +++ /dev/null @@ -1,95 +0,0 @@ -""" -Defines a serverless web API to expose trained models -""" - -from typing import Optional - -import modal -from fastapi import FastAPI, Header -from pydantic import BaseModel - -from . import config, models -from .app import app, volume - -web_app = FastAPI() - - -class ModelInput(BaseModel): - text: str - - -class ModelMetdata(BaseModel): - model_name: str - model_id: str - - -class ModelOutput(BaseModel): - spam: bool - score: float - metadata: ModelMetdata - - -# TODO(Jonathon): This will acquire a GPU even when `model_id` doesn't -# require it, which is inefficient. Find an elegant way to make the GPU optional. -@app.cls(gpu="A10G", volumes={config.VOLUME_DIR: volume}) -class Model: - model_id: str = modal.parameter() - - @modal.enter() - def load(self): - self.classifier, self.metadata = models.load_model( - model_id=self.model_id - ) - - @modal.method() - def generate(self, text: str) -> ModelOutput: - prediction = self.classifier(text) - return ModelOutput( - spam=prediction.spam, - score=prediction.score, - metadata=ModelMetdata( - model_name=self.metadata.impl_name, - model_id=self.model_id, - ), - ) - - -@web_app.get("/api/v1/models") -async def handle_list_models(): - """ - Show details of actively serving models. - """ - _, metadata = models.load_model(config.SERVING_MODEL_ID) - return {config.SERVING_MODEL_ID: metadata.serialize()} - - -@web_app.post("/api/v1/classify") -async def handle_classification( - input_: ModelInput, model_id: Optional[str] = Header(None) -): - r""" - Classify a body of text as spam or ham. - - eg. - - ```bash - curl -X POST https://modal-labs--example-spam-detect-llm-web.modal.run/api/v1/classify \ - -H 'Content-Type: application/json' \ - -H 'Model-Id: sha256.12E5065BE4C3F7D2F79B7A0FD203380869F6E308DCBB4B8C9579FFAE6F32B837' \ - -d '{"text": "hello world"}' - ``` - """ - model_id = model_id or config.SERVING_MODEL_ID - print(model_id) - model = Model(model_id) - return model.generate.remote(input_.text) - - -@app.function() -@modal.asgi_app() -def web(): - return web_app - - -if __name__ == "__main__": - app.serve() diff --git a/06_gpu_and_ml/spam-detect/spam_detect/train.py b/06_gpu_and_ml/spam-detect/spam_detect/train.py deleted file mode 100644 index 85ac87ad3..000000000 --- a/06_gpu_and_ml/spam-detect/spam_detect/train.py +++ /dev/null @@ -1,206 +0,0 @@ -# # A Plan for Spam, 20 Years On: LLM vs. Naive Bayes -# -# This example trains multiple models (LLM, Naive Bayes) to perform -# spam classification on the ENRON email dataset. This is a return to Paul Graham's -# well-known 2002 post, A Plan For Spam (http://www.paulgraham.com/spam.html). -# -# Graham's original post focused on the Naive Bayes model. Here we pit that model against -# a current state-of-the-art large-language-model (LLM). Both models are trained on the dataset -# and served via a model API (serving.py). -# -# This module, train.py, is the model training entrypoint, providing functions to do CPU/GPU training -# before saving to disk. The other significant modules are as follows: -# -# * models.py β€” contains the core `SpamModel` interface and three implementing model classes, including `LLMSpamClassifier`. -# * serving.py β€” a minimal FastAPI model serving API, loading models by ID from a Modal persistent volume. -# * model_registry.py β€” defines minimal data structures and CLI commands for a model registry stored on Modal. -# * model_storage.py β€” functions concerned withn serializing and deserializing (ie. loading) the trained ML models. -# - -import pathlib -import random -import subprocess -from datetime import timedelta - -import modal - -from . import config, dataset, models -from .app import app, volume - - -def fetch_git_commit_hash(allow_dirty: bool) -> str: - # Ensure git state is clean so that the git commit hash accurately reflects - # the configuration of the training run. - # - # Ignoring dirty git state when kicking off a training run means accepting - # unreproducible model training outcomes. - if not allow_dirty: - if ( - subprocess.run( - ("git", "diff-index", "--quiet", "--cached", "HEAD", "--") - ).returncode - != 0 - ): - breakpoint() - raise RuntimeError( - "Dirty git status. Repository has staged but not yet committed changes.\n" - "Commit these changes or remove them to get a clean git state." - ) - elif subprocess.run(("git", "diff-files", "--quiet")).returncode != 0: - raise RuntimeError( - "Dirty git status. Repository has changes that could be staged.\n" - "Commit these changes or add them to .gitignore." - ) - res = subprocess.run( - ("git", "ls-files", "--exclude-standard", "--others"), - capture_output=True, - ) - if res.returncode != 0: - raise RuntimeError( - f"Could not check `git` for untracked files. {res.stderr.decode()}" - ) - if res.stdout: - raise RuntimeError( - "Dirty git status. Repository has untracked files.\n" - "Remove these files, commit them, or add them to .gitignore." - ) - result = subprocess.run( - ("git", "rev-parse", "HEAD"), - check=True, - capture_output=True, - ) - return result.stdout.decode().strip() - - -@app.function(volumes={config.VOLUME_DIR: volume}) -def init_volume(): - config.MODEL_STORE_DIR.mkdir(parents=True, exist_ok=True) - volume.commit() # Persist changes - - -@app.function( - timeout=int(timedelta(minutes=8).total_seconds()), - volumes={config.VOLUME_DIR: volume}, -) -def prep_dataset(): - logger = config.get_logger() - datasets_path = config.DATA_DIR - datasets_path.mkdir(parents=True, exist_ok=True) - dataset.download(base=datasets_path, logger=logger) - volume.commit() # Persist changes - - -@app.function( - volumes={config.VOLUME_DIR: volume}, - secrets=[modal.Secret.from_dict({"PYTHONHASHSEED": "10"})], - timeout=int(timedelta(minutes=30).total_seconds()), -) -def train( - model: models.SpamModel, dataset_path: pathlib.Path, git_commit_hash: str -): - logger = config.get_logger() - enron_dataset = dataset.deserialize_dataset(dataset_path) - random.shuffle(enron_dataset) - classifier, metrics = model.train(enron_dataset) - model_id = model.save( - fn=classifier, - metrics=metrics, - model_registry_root=config.MODEL_STORE_DIR, - git_commit_hash=git_commit_hash, - ) - volume.commit() # Persist changes - logger.info(f"saved model to model store. {model_id=}") - # Reload the model - logger.info("πŸ” testing reload of model") - classifier = model.load( - sha256_digest=model_id, - model_registry_root=config.MODEL_STORE_DIR, - ) - is_spam = classifier("fake email!") - print(f"classification: {is_spam=}") - - -@app.function( - volumes={config.VOLUME_DIR: volume}, - secrets=[modal.Secret.from_dict({"PYTHONHASHSEED": "10"})], - timeout=int(timedelta(minutes=30).total_seconds()), - gpu=modal.gpu.T4(), -) -def train_gpu( - model: models.SpamModel, dataset_path: pathlib.Path, git_commit_hash: str -): - logger = config.get_logger() - enron_dataset = dataset.deserialize_dataset(dataset_path) - random.shuffle(enron_dataset) - classifier, metrics = model.train(enron_dataset) - model_id = model.save( - fn=classifier, - metrics=metrics, - model_registry_root=config.MODEL_STORE_DIR, - git_commit_hash=git_commit_hash, - ) - volume.commit() # Persist changes - logger.info(f"saved model to model store. {model_id=}") - - -@app.function( - secrets=[modal.Secret.from_dict({"PYTHONHASHSEED": "10"})], - timeout=int(timedelta(minutes=30).total_seconds()), -) -def main(git_commit_hash: str, model_type=config.ModelType.BAD_WORDS): - logger = config.get_logger() - logger.opt(colors=True).info( - "Ready to detect SPAM from HAM?" - ) - dataset_path = dataset.dataset_path(config.DATA_DIR) - - logger.info( - f"πŸ’ͺ training a {model_type} model at git commit {git_commit_hash[:8]}" - ) - if model_type == config.ModelType.NAIVE_BAYES: - train.remote( - model=models.NaiveBayes(), - dataset_path=dataset_path, - git_commit_hash=git_commit_hash, - ) - elif model_type == config.ModelType.LLM: - train_gpu.remote( - model=models.LLM(), - dataset_path=dataset_path, - git_commit_hash=git_commit_hash, - ) - elif model_type == config.ModelType.BAD_WORDS: - train.remote( - model=models.BadWords(), - dataset_path=dataset_path, - git_commit_hash=git_commit_hash, - ) - else: - raise ValueError(f"Unknown model type '{model_type}'") - - -# Pass in the string representation of a supported `ModelType` to train -# a model of that type on the dataset. -# -# Example: -# -# ``` -# modal run spam_detect.train::app.train_model --model-type "BAD_WORDS" -# ``` - - -@app.local_entrypoint() -def train_model(model_type: str): - model_type_val = config.ModelType(model_type) - # All training runs are versioned against git repository state. - git_commit_hash: str = fetch_git_commit_hash(allow_dirty=False) - init_volume.remote() - main.remote( - git_commit_hash=git_commit_hash, - model_type=model_type_val, - ) - - -if __name__ == "__main__": - with app.run(): - train_model(model_type="NAIVE_BAYES") diff --git a/06_gpu_and_ml/spam-detect/tests/model_storage_test.py b/06_gpu_and_ml/spam-detect/tests/model_storage_test.py deleted file mode 100644 index 67af08852..000000000 --- a/06_gpu_and_ml/spam-detect/tests/model_storage_test.py +++ /dev/null @@ -1,70 +0,0 @@ -import pytest -from spam_detect import model_storage, models - - -def dummy_classifier(email: str) -> models.Prediction: - _ = email - return models.Prediction(spam=False, score=0.86) - - -def test_hashtag_from_bytes(): - b = b"1234" - expected = "sha256.03AC674216F3E15C761EE1A5E255F067953623C8B388B4459E13F978D7C846F4" - assert model_storage.create_hashtag_from_bytes(b) == expected - - -def test_hashtag_from_dir(tmp_path): - dir = tmp_path / "dir1" - dir.mkdir() - contents = { - "one": b"1234", - "two": b"5678", - "three": b"hello world", - } - for filename, b in contents.items(): - p = dir / filename - p.write_bytes(b) - - hashtag = model_storage.create_hashtag_from_dir(dir) - # If we add file, the hash changes - p = dir / "four" - p.write_bytes(b"I change the hash") - hashtag_2 = model_storage.create_hashtag_from_dir(dir) - assert hashtag != hashtag_2 - # Renaming a file changes the hash - p = dir / "one" - p.rename(dir / "one.2") - hashtag_3 = model_storage.create_hashtag_from_dir(dir) - assert hashtag != hashtag_3 - assert hashtag_2 != hashtag_3 - - -def test_load_model_success(tmp_path): - tmp_classifier_digest = model_storage.store_pickleable_model( - classifier_func=dummy_classifier, - metrics=None, - model_destination_root=tmp_path, - current_git_commit_hash="TEST-NOT-REALLY-A-COMMIT-HASH", - ) - - loaded_dummy_classifier = model_storage.load_pickle_serialized_model( - sha256_hash=tmp_classifier_digest, - destination_root=tmp_path, - ) - test_email = "test email: doesn't matter what contents" - assert dummy_classifier(test_email) == loaded_dummy_classifier(test_email) - - -def test_load_model_corrupted_data(tmp_path): - dummy_classifier_b: bytes = model_storage.serialize_model(dummy_classifier) - # intentionally write model to non-content-addressed location. bogus path. - bogus_hash = "bogus_pocus" - bogus_path = tmp_path / bogus_hash - with open(bogus_path, "wb") as f: - f.write(dummy_classifier_b) - - with pytest.raises(ValueError): - _ = model_storage.load_pickle_serialized_model( - sha256_hash=bogus_hash, - destination_root=tmp_path, - ) diff --git a/06_gpu_and_ml/spam-detect/tests/models_test.py b/06_gpu_and_ml/spam-detect/tests/models_test.py deleted file mode 100644 index 5d3e6d78c..000000000 --- a/06_gpu_and_ml/spam-detect/tests/models_test.py +++ /dev/null @@ -1,37 +0,0 @@ -import math - -from spam_detect import models -from spam_detect.dataset import Example - - -def test_prob_calculation(): - dataset = [ - Example(email="spam rules", spam=True), - Example(email="ham rules", spam=False), - Example(email="hello ham", spam=False), - ] - - classify_func, _ = models.NaiveBayes( - decision_boundary=0.5, test_set_size=0.0 - ).train(dataset) - email = "hello spam" - probs_if_ham = [ - (1 + 0.5) / (2 + 2 * 0.5), # "hello" (present) - (0 + 0.5) / (2 + 2 * 0.5), # "spam" (present) - 1 - (2 + 0.5) / (2 + 2 * 0.5), # "ham" (not present) - 1 - (1 + 0.5) / (2 + 2 * 0.5), # "rules" (not present) - ] - probs_if_spam = [ - (0 + 0.5) / (1 + 2 * 0.5), # "hello" (present) - (1 + 0.5) / (1 + 2 * 0.5), # "spam" (present) - 1 - (0 + 0.5) / (1 + 2 * 0.5), # "ham" (not present) - 1 - (1 + 0.5) / (1 + 2 * 0.5), # "rules" (not present) - ] - p_if_spam = math.exp(sum(math.log(p) for p in probs_if_spam)) - p_if_ham = math.exp(sum(math.log(p) for p in probs_if_ham)) - - # Should be about 0.83 - actual = classify_func(email).score - expected = p_if_spam / (p_if_spam + p_if_ham) - residual = abs(actual - expected) - assert residual <= 0.001 diff --git a/06_gpu_and_ml/wandb-ui.png b/06_gpu_and_ml/wandb-ui.png deleted file mode 100644 index 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-
-
-

- Talk to${" "} - Modal${" "} - Transformer -

- ${c} -
-
-
- `; -} - -function App() { - const [id, setId] = createSignal(null); - const [message, setMessage] = createSignal(""); - const [sending, setSending] = createSignal(false); - const [chat, setChat] = createSignal(["Hello! I'm a bot running on Modal."]); - - let chatEl; - - // Scroll to bottom when new messages appear. - createEffect(() => { - chat(); - chatEl.scrollTop = chatEl.scrollHeight; - }); - - async function handleSubmit(event) { - event.preventDefault(); - setSending(true); - try { - setChat([...chat(), message()]); - const resp = await sendMessage(message(), id()); - setChat([...chat(), resp.response]); - setId(resp.id); - setMessage(""); - } catch (error) { - alert("An error occurred: " + error.message); - setChat(chat().slice(0, -1)); - } finally { - setSending(false); - } - } - - return html` - <${Layout}> -
(chatEl = el)} - class="h-[320px] overflow-x-hidden overflow-y-scroll space-y-2 my-4" - > - <${For} each=${chat}> - ${(msg, i) => html` -
-
- ${msg} -
-
- `} - - - <${Show} when=${sending}> -
-
- β€’β€’β€’ -
-
- -
- -
- setMessage(e.target.value)} - class="w-full rounded-[16px] border px-3 py-1.5 disabled:opacity-50" - placeholder="Aa" - autofocus - required - disabled=${sending} - /> -
- - `; -} - -render(App, document.body); diff --git a/07_web_endpoints/chatbot_spa/index.html b/07_web_endpoints/chatbot_spa/index.html deleted file mode 100644 index a036df6fc..000000000 --- a/07_web_endpoints/chatbot_spa/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - Talk to Modal Transformer - - - - - - - diff --git a/10_integrations/pyjulia.py b/10_integrations/pyjulia.py deleted file mode 100644 index df7cca4f7..000000000 --- a/10_integrations/pyjulia.py +++ /dev/null @@ -1,47 +0,0 @@ -# # Run Julia code from Python using PyJulia - -# This example shows how you can run Julia code from Python using the PyJulia package. - -import modal - -image = image = ( - modal.Image.debian_slim() - # Install Julia 1.10 - .apt_install("wget", "ca-certificates") - .run_commands( - "wget -nv https://julialang-s3.julialang.org/bin/linux/x64/1.10/julia-1.10.0-linux-x86_64.tar.gz", - "tar -xf julia-1.10.0-linux-x86_64.tar.gz", - "cp -r julia-1.10.0 /opt/", - "ln -s /opt/julia-1.10.0/bin/julia /usr/local/bin/julia", - ) - # Install PyJulia bindings - .pip_install("julia==0.6.1") - .run_commands('python -c "import julia; julia.install()"') -) -app = modal.App("example-pyjulia", image=image) - - -@app.function() -def julia_subprocess(): - """Run the Julia interpreter as a subprocess.""" - import subprocess - - print("-> Calling Julia as a subprocess...") - subprocess.run('julia -e "println(2 + 3)"', shell=True) - - -@app.function() -def julia_matrix_determinant(): - """Compute the determinant of a random matrix with PyJulia.""" - from julia.Base import rand - from julia.LinearAlgebra import det - - print("-> Calling Julia using PyJulia...") - print(det(rand(5, 5))) - print(det(rand(10, 10))) - - -@app.local_entrypoint() -def run(): - julia_subprocess.remote() - julia_matrix_determinant.remote()