From 27f966e67218de43e8e243bf75c834a7e9018bdc Mon Sep 17 00:00:00 2001 From: Daniel Wu Date: Sun, 20 Aug 2023 15:07:38 -0500 Subject: [PATCH] Submit Trainspace (#847) * new tabular api * format * Make tabular call tabular-run instead of img-run * migrate tabular train code to backendCore * develop /tabular-run async request to /tabular * fix train /tabular in backendCore (works now)) * add training microservice with django ninja * abstract dataset, dl_model, optimizer * :art: Format Python code with psf/black * switch to redocly * add trainer, criterion handlers * generate output data * serverless framework + flask setup * recreate sls service * switch from serverless framework to sst * move files to serverless folder * add authentication, add additional dataset endpoints * use sst constructs, optimize code for bundling * delete flask app, move all training2 django files to training * reorganize django app directory, modify api routes, added cors * change python poetry github action directory from backend to training * add moto * uninstall flask * disable pytest for now * poetry + pyright configs * :art: Auto-generated directory tree for repository in Architecture.md --------- Co-authored-by: dwu359 --- .github/Architecture.md | 1 + .github/workflows/python-package-poetry.yml | 12 +- .gitignore | 3 +- .vscode/settings.json | 23 + README.md | 27 +- aws_write/common/ai_drive.py | 272 + aws_write/common/constants.py | 14 + aws_write/endpoints/trainspace.py | 35 + aws_write/poetry.lock | 479 +- aws_write/pyproject.toml | 2 + backend/__init__.py | 1 + .../dynamo_db_utils/dynamo_db_utils.py | 21 +- .../dynamo_db_utils/trainspace_db.py | 2 +- backend/endpoints/train_endpoints.py | 34 +- backend/poetry.lock | 710 ++- backend/pyproject.toml | 1 + backendCore/dl/__init__.py | 1 + backendCore/dl/detection.py | 104 + backendCore/dl/dl_eval.py | 39 + backendCore/dl/dl_model.py | 28 + backendCore/dl/dl_model_parser.py | 84 + backendCore/dl/dl_trainer.py | 414 ++ frontend/next.config.js | 12 +- .../features/Dashboard/redux/dashboardApi.ts | 2 +- .../components/TabularParametersStep.tsx | 4 +- .../features/Tabular/redux/tabularApi.ts | 38 +- .../src/features/Train/redux/trainspaceApi.ts | 27 +- package.json | 9 +- serverless/.gitignore | 15 + serverless/.nvmrc | 1 + serverless/.vscode/launch.json | 15 + serverless/.vscode/settings.json | 5 + serverless/package.json | 28 + serverless/packages/core/package.json | 14 + serverless/packages/core/src/parseJwt.ts | 3 + serverless/packages/core/sst-env.d.ts | 1 + serverless/packages/core/tsconfig.json | 7 + serverless/packages/functions/.nvmrc | 1 + serverless/packages/functions/package.json | 15 + .../src/datasets/user/all_of_type.ts | 37 + .../functions/src/datasets/user/columns.ts | 59 + .../functions/src/datasets/user/upload_url.ts | 35 + serverless/packages/functions/sst-env.d.ts | 1 + serverless/packages/functions/tsconfig.json | 11 + serverless/pnpm-workspace.yaml | 2 + serverless/sst.config.ts | 14 + serverless/stacks/AppStack.ts | 54 + serverless/tsconfig.json | 7 + serverless/yarn.lock | 5542 +++++++++++++++++ training/README.md | 0 training/cli.py | 67 + training/manage.py | 22 + training/poetry.lock | 2196 +++++++ training/poetry.toml | 2 + training/pyproject.toml | 35 + training/tests/__init__.py | 0 training/training/__init__.py | 0 training/training/asgi.py | 16 + training/training/core/__init__.py | 0 training/training/core/criterion.py | 76 + training/training/core/dataset.py | 100 + training/training/core/dl_model.py | 53 + training/training/core/optimizer.py | 19 + training/training/core/trainer.py | 293 + training/training/routes/__init__.py | 0 training/training/routes/datasets/__init__.py | 0 .../routes/datasets/default/__init__.py | 5 + .../routes/datasets/default/columns.py | 18 + .../routes/datasets/default/schemas.py | 6 + training/training/routes/schemas.py | 5 + training/training/routes/tabular/__init__.py | 5 + training/training/routes/tabular/schemas.py | 22 + training/training/routes/tabular/tabular.py | 64 + training/training/settings.py | 132 + training/training/urls.py | 39 + training/training/wsgi.py | 16 + yarn.lock | 2 + 77 files changed, 11204 insertions(+), 255 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 aws_write/common/ai_drive.py create mode 100644 backend/__init__.py create mode 100644 backendCore/dl/__init__.py create mode 100644 backendCore/dl/detection.py create mode 100644 backendCore/dl/dl_eval.py create mode 100644 backendCore/dl/dl_model.py create mode 100644 backendCore/dl/dl_model_parser.py create mode 100644 backendCore/dl/dl_trainer.py create mode 100644 serverless/.gitignore create mode 100644 serverless/.nvmrc create mode 100644 serverless/.vscode/launch.json create mode 100644 serverless/.vscode/settings.json create mode 100644 serverless/package.json create mode 100644 serverless/packages/core/package.json create mode 100644 serverless/packages/core/src/parseJwt.ts create mode 100644 serverless/packages/core/sst-env.d.ts create mode 100644 serverless/packages/core/tsconfig.json create mode 100644 serverless/packages/functions/.nvmrc create mode 100644 serverless/packages/functions/package.json create mode 100644 serverless/packages/functions/src/datasets/user/all_of_type.ts create mode 100644 serverless/packages/functions/src/datasets/user/columns.ts create mode 100644 serverless/packages/functions/src/datasets/user/upload_url.ts create mode 100644 serverless/packages/functions/sst-env.d.ts create mode 100644 serverless/packages/functions/tsconfig.json create mode 100644 serverless/pnpm-workspace.yaml create mode 100644 serverless/sst.config.ts create mode 100644 serverless/stacks/AppStack.ts create mode 100644 serverless/tsconfig.json create mode 100644 serverless/yarn.lock create mode 100644 training/README.md create mode 100644 training/cli.py create mode 100755 training/manage.py create mode 100644 training/poetry.lock create mode 100644 training/poetry.toml create mode 100644 training/pyproject.toml create mode 100644 training/tests/__init__.py create mode 100644 training/training/__init__.py create mode 100644 training/training/asgi.py create mode 100644 training/training/core/__init__.py create mode 100644 training/training/core/criterion.py create mode 100644 training/training/core/dataset.py create mode 100644 training/training/core/dl_model.py create mode 100644 training/training/core/optimizer.py create mode 100644 training/training/core/trainer.py create mode 100644 training/training/routes/__init__.py create mode 100644 training/training/routes/datasets/__init__.py create mode 100644 training/training/routes/datasets/default/__init__.py create mode 100644 training/training/routes/datasets/default/columns.py create mode 100644 training/training/routes/datasets/default/schemas.py create mode 100644 training/training/routes/schemas.py create mode 100644 training/training/routes/tabular/__init__.py create mode 100644 training/training/routes/tabular/schemas.py create mode 100644 training/training/routes/tabular/tabular.py create mode 100644 training/training/settings.py create mode 100644 training/training/urls.py create mode 100644 training/training/wsgi.py diff --git a/.github/Architecture.md b/.github/Architecture.md index 530643536..208e04236 100644 --- a/.github/Architecture.md +++ b/.github/Architecture.md @@ -40,6 +40,7 @@ | |- 📜 data.csv : data csv file for use in the playground | |- 📜 poetry.lock | |- 📜 middleware.py +| |- 📜 __init__.py | |- 📜 app.py : run the backend (entrypoint script) ``` diff --git a/.github/workflows/python-package-poetry.yml b/.github/workflows/python-package-poetry.yml index 5ebf02884..00b4577bc 100644 --- a/.github/workflows/python-package-poetry.yml +++ b/.github/workflows/python-package-poetry.yml @@ -7,7 +7,7 @@ on: - prod-deploy pull_request: paths: - - "backend/**" + - "training/**" - pyproject.toml jobs: @@ -41,23 +41,23 @@ jobs: id: cached-poetry-dependencies uses: actions/cache@v3 with: - path: backend/.venv - key: backend-venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/backend/poetry.lock') }} + path: training/.venv + key: training-venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/training/poetry.lock') }} #---------------------------------------------- # install dependencies if cache does not exist #---------------------------------------------- - name: Install dependencies if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - run: cd backend && poetry install --no-interaction --no-root + run: cd training && poetry install --no-interaction --no-root #---------------------------------------------- # run test suite with moto #---------------------------------------------- - name: Run tests with moto run: | - source backend/.venv/bin/activate + source training/.venv/bin/activate moto_server s3 -p 5000 & # Start moto S3 server in the background export AWS_ACCESS_KEY_ID=testing export AWS_SECRET_ACCESS_KEY=testing export AWS_DEFAULT_REGION=us-west-2 - cd backend && pytest tests + # cd training && pytest tests diff --git a/.gitignore b/.gitignore index 5bb28aa93..920c4b6ba 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,8 @@ # -------------------------------------------------------------------------- # VS Code -.vscode/ +.vscode/* +!.vscode/settings.json .github/ArchitectureTest.md # -------------------------------------------------------------------------- diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 000000000..2f3929c37 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,23 @@ +{ + "[python]": { + "editor.defaultFormatter": "ms-python.black-formatter" + }, + "[html]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[javascript]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[json]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[typescriptreact]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "[typescript]": { + "editor.defaultFormatter": "esbenp.prettier-vscode" + }, + "python.formatting.provider": "none", + "python.terminal.activateEnvironment": false, + "python.analysis.typeCheckingMode": "basic" +} diff --git a/README.md b/README.md index 7ae3ad38c..c6a095d80 100644 --- a/README.md +++ b/README.md @@ -22,14 +22,17 @@ Have the following installed first: ## To start on localhost (in order): -| Action | Command | -| --------------------------------------------- | ------------------------------ | -| Install (one-time) / Update Frontend Packages | `yarn run installf` | -| Install Backend Packages (one-time) | `yarn run installb` | -| Update Backend Packages | `cd backend && poetry install` | -| Get secrets | `yarn run secrets` | -| Running the Frontend | `yarn run startf` | -| Running the Backend | `yarn run startb` | +| Action | Command | +| --------------------------------------------- | ------------------- | +| Install (one-time) / Update Frontend Packages | `yarn run installf` | +| Install Backend Packages (one-time) | `yarn run installb` | +| Install AWS-Write Packages (one-time) | `yarn run installa` | +| Update Backend Packages | `yarn run updateb` | +| Update AWS-Write Packages | `yarn run updatea` | +| Get secrets | `yarn run secrets` | +| Running the Frontend | `yarn run startf` | +| Running the Backend | `yarn run startb` | +| Running the AWS-Write Microservice | `yarn run starta` | ## To run in `production` mode: @@ -67,6 +70,14 @@ If this command works properly, you will be redirected to an auth route in the G - If you encounter any error related to "ChefBuildError", downgrade your poetry version to 1.3.2 by running `pip install poetry==1.3.2` before doing `poetry install` (See related github issue [here](https://github.com/python-poetry/poetry/issues/7611)) +## Poetry env setup with VSCode + +- Access the VSCode command palette via `Ctrl+Shift+P`. Press `Python: Select Interpreter`. + +- To select aws_write's python interpreter, select the interpreter with the path `./aws_write/.venv/bin/python` (may need to manually enter interpreter path) + +- To select backend's python interpreter, select the interpreter with the path `./backend/.venv/bin/python` (may need to manually enter interpreter path) + ## pyenv setup ### Mac Instructions diff --git a/aws_write/common/ai_drive.py b/aws_write/common/ai_drive.py new file mode 100644 index 000000000..f25e642bd --- /dev/null +++ b/aws_write/common/ai_drive.py @@ -0,0 +1,272 @@ +import json +import traceback +import urllib.request as request +import csv +import numpy as np +import pandas as pd + +from sklearn.datasets import * +from sklearn.model_selection import train_test_split +import torch +from torch.autograd import Variable +from torch.utils.data import TensorDataset, DataLoader + +from backendCore.common.constants import CSV_FILE_PATH, DEFAULT_DATASETS, ONNX_MODEL +from backendCore.dl.dl_model import DLModel +from backendCore.dl.dl_model_parser import parse_deep_user_architecture +from backendCore.dl.dl_trainer import train_deep_model + + +def read_dataset(url): + """ + Given a url to a CSV dataset, read it and build temporary csv file + + Args: + url (str): URL to dataset + """ + try: + r = request.urlopen(url).read().decode("utf8").split("\n") + reader = csv.reader(r) + with open(CSV_FILE_PATH, mode="w", newline="") as f: + csvwriter = csv.writer(f) + for line in reader: + csvwriter.writerow(line) + except Exception as e: + traceback.print_exc() + raise Exception( + "Reading Dataset from URL failed. Might want to check the validity of the URL" + ) + + +def get_default_dataset(dataset, target=None, features=None): + """ + If user doesn't specify dataset + Args: + dataset (str): Which default dataset are you using (built in functions like load_boston(), load_iris()) + Returns: + X: input (default dataset) + y: target (default dataset) + target_names: a list of strings representing category names (default dataset) + """ + try: + if dataset not in DEFAULT_DATASETS: + raise Exception( + f"The {dataset} file does not currently exist in our inventory. Please submit a request to the contributors of the repository" + ) + else: + raw_data = eval( + DEFAULT_DATASETS[dataset] + ) # get raw data from sklearn.datasets + target_names = [] + try: + target_names = list(raw_data["target_names"]) + except: + pass + default_dataset = pd.DataFrame( + data=np.c_[raw_data["data"], raw_data["target"]], + columns=raw_data["feature_names"] + ["target"], + ) + # remove any empty lines + default_dataset.dropna(how="all", inplace=True) + if features and target: + y = default_dataset[target] + X = default_dataset[features] + else: + y = default_dataset["target"] + X = default_dataset.drop("target", axis=1) + print(default_dataset.head()) + return X, y, target_names + except Exception: + raise Exception(f"Unable to load the {dataset} file into Pandas DataFrame") + + +def get_tensors(X_train, X_test, y_train, y_test): + """ + Helper function to convert X_train, X_test, y_train, y_test + into tensors for dataloader + Args: + X_train (pd.DataFrame): X_train (train set) + X_test (pd.DataFrame): X_test (test set) + y_train (pd.Series): label/value of target for each row of X_train + y_test (pd.Series): label/value of target for each row of X_test + + Return: + X_train, X_test, y_train, y_test in the form of tensor + """ + X_train_tensor = Variable(torch.Tensor(X_train.to_numpy())) + y_train_tensor = Variable(torch.Tensor(y_train.to_numpy())) + X_train_tensor = torch.reshape( + X_train_tensor, (X_train_tensor.size()[0], 1, X_train_tensor.size()[1]) + ) + y_train_tensor = torch.reshape(y_train_tensor, (y_train_tensor.size()[0], 1)) + + X_test_tensor = Variable(torch.Tensor(X_test.to_numpy())) + y_test_tensor = Variable(torch.Tensor(y_test.to_numpy())) + X_test_tensor = torch.reshape( + X_test_tensor, (X_test_tensor.size()[0], 1, X_test_tensor.size()[1]) + ) + y_test_tensor = torch.reshape(y_test_tensor, (y_test_tensor.size()[0], 1)) + + X_train_tensor.requires_grad_(True) + X_test_tensor.requires_grad_(True) + return X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor + + +def get_optimizer(model, optimizer_name, learning_rate): + """ + Given an optimizer name, instantiate the object + + Args: + model (nn.Module): pytorch model to train + optimizer_name (str): name of optimizer + learning_rate (float): learning rate + """ + if optimizer_name.upper() == "SGD": + return torch.optim.SGD(model.parameters(), lr=learning_rate) + elif optimizer_name.upper() == "ADAM": + return torch.optim.Adam(model.parameters(), lr=learning_rate) + + +def get_dataloaders( + X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor, batch_size +): + """ + Helper function to get Dataloaders for X_train, y_train, X_test, y_test + Args: + X_train_tensor (torch.Tensor) + y_train_tensor (torch.Tensor) + X_test_tensor (torch.Tensor) + y_test_tensor (torch.Tensor) + batch_size (int) + """ + train = TensorDataset(X_train_tensor, y_train_tensor) + test = TensorDataset(X_test_tensor, y_test_tensor) + train_loader = DataLoader( + train, batch_size=batch_size, shuffle=False, drop_last=True + ) + test_loader = DataLoader(test, batch_size=batch_size, shuffle=False, drop_last=True) + return train_loader, test_loader + + +def dl_tabular_drive( + user_arch: list, + fileURL: str, + params: dict, + json_csv_data_str: str = "", + customModelName: str = None, +): + """ + Driver function/entrypoint into backend for deep learning model. Onnx file is generated containing model architecture for user to visualize in netron.app + Args: + user_arch (list): list that contains user defined deep learning architecture + fileURL (str): URL of the dataset file, if provided by user + params (dict): dictionary containing all the parameters for the model, e.g. criterion and problem type + json_csv_data_str (str, optional): json string of the dataset, if provided by user. Defaults to "". + customModelName (str, optional): name of the custom model. Defaults to None. + :return: a dictionary containing the epochs, train and test accuracy and loss results, each in a list + + NOTE: + CSV_FILE_NAME is the data csv file for the torch model. Assumed that you have one dataset file + """ + + """ + Params: + criterion (str): What loss function to use + optimizer (str): What optimizer does the user wants to use (Adam or SGD for now, but more support in later iterations) + problem type (str): "classification" or "regression" problem + target (str): name of target column + features (list): list of columns in dataframe for the feature based on user selection + default (str, optional): the default dataset chosen by the user. Defaults to None. + test_size (float, optional): size of test set in train/test split (percentage). Defaults to 0.2. + epochs (int, optional): number of epochs/rounds to run model on + shuffle (bool, optional): should the dataset be shuffled prior to train/test split + """ + target = params.get("target", None) + features = params.get("features", None) + problem_type = params["problem_type"] + optimizer_name = params["optimizer_name"] + criterion = params["criterion"] + default = params.get("default", None) + epochs = params.get("epochs", 5) + shuffle = params.get("shuffle", True) + test_size = params.get("test_size", 0.2) + batch_size = params.get("batch_size", 20) + + category_list = [] + if not default: + if fileURL: + read_dataset(fileURL) + elif json_csv_data_str: + pass + else: + raise ValueError("Need a file input") + + if default and problem_type.upper() == "CLASSIFICATION": + X, y, category_list = get_default_dataset(default.upper(), target, features) + elif default and problem_type.upper() == "REGRESSION": + X, y, discard = get_default_dataset(default.upper(), target, features) + else: + if json_csv_data_str: + input_df = pd.read_json(json_csv_data_str, orient="records") + + y = input_df[target] + X = input_df[features] + + if len(y) * test_size < batch_size or len(y) * (1 - test_size) < batch_size: + raise ValueError("reduce batch size, not enough values in dataframe") + + if problem_type.upper() == "CLASSIFICATION": + # label encode the categorical values to numbers + y = y.astype("category") + y = y.cat.codes + print(y.head()) + + # Convert to tensor + if shuffle and problem_type.upper() == "CLASSIFICATION": + # using stratify only for classification problems to ensure correct AUC calculation + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, shuffle=True, stratify=y + ) + else: + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, shuffle=shuffle + ) + + X_train_tensor, y_train_tensor, X_test_tensor, y_test_tensor = get_tensors( + X_train, X_test, y_train, y_test + ) + # Build the Deep Learning model that the user wants + model = DLModel(parse_deep_user_architecture(user_arch)) + print(f"model: {model}") + + optimizer = get_optimizer(model, optimizer_name=optimizer_name, learning_rate=0.05) + + print(f"loss criterion: {criterion}") + train_loader, test_loader = get_dataloaders( + X_train_tensor, + y_train_tensor, + X_test_tensor, + y_test_tensor, + batch_size=batch_size, + ) + if problem_type.upper() == "CLASSIFICATION" and not default: + category_list = [] + json_data = json.loads(json_csv_data_str) + pandas_data = pd.DataFrame.from_dict(json_data) + target_categories = pandas_data[target] + for category in target_categories: + if category not in category_list: + category_list.append(category) + train_loss_results = train_deep_model( + model, + train_loader, + test_loader, + optimizer, + criterion, + epochs, + problem_type, + category_list, + ) + torch.onnx.export(model, X_train_tensor, ONNX_MODEL) + + return train_loss_results diff --git a/aws_write/common/constants.py b/aws_write/common/constants.py index d9740ad1f..d7006a711 100644 --- a/aws_write/common/constants.py +++ b/aws_write/common/constants.py @@ -1 +1,15 @@ +import os + + AWS_REGION = "us-west-2" +CSV_FILE_NAME = "data.csv" +CSV_FILE_PATH = f"{os.path.join(os.getcwd(), 'backend', CSV_FILE_NAME)}" +ONNX_MODEL = "./frontend/src/backend_outputs/my_deep_learning_model.onnx" +DEFAULT_DATASETS = { + "IRIS": "load_iris()", + "BREAST CANCER": "load_breast_cancer()", + "CALIFORNIAHOUSING": "fetch_california_housing()", + "DIABETES": "load_diabetes()", + "DIGITS": "load_digits()", + "WINE": "load_wine()", +} diff --git a/aws_write/endpoints/trainspace.py b/aws_write/endpoints/trainspace.py index 27c05b804..be55fc1d4 100644 --- a/aws_write/endpoints/trainspace.py +++ b/aws_write/endpoints/trainspace.py @@ -1,4 +1,5 @@ import traceback +import uuid from flask import Blueprint from flask import request @@ -148,3 +149,37 @@ def createTrainspaceDataInDb(): except Exception: print(traceback.format_exc()) return send_traceback_error() + + +@trainspace_bp.route("/tabular", methods=["POST"]) +def tabular(): + try: + request_data = json.loads(request.data) + print( + dl_tabular_drive( + request_data["user_arch"], + "", + { + "target": request_data["target"], + "features": request_data["features"], + "problem_type": request_data["problem_type"], + "optimizer_name": request_data["optimizer_name"], + "criterion": request_data["criterion"], + "default": request_data["default"], + "epochs": request_data["epochs"], + "shuffle": request_data["shuffle"], + "test_size": request_data["test_size"], + "batch_size": request_data["batch_size"], + }, + None, + request_data["name"], + ) + ) + return send_success( + { + "message": "Trainspace trained", + } + ) + except Exception: + print(traceback.format_exc()) + return send_traceback_error() diff --git a/aws_write/poetry.lock b/aws_write/poetry.lock index 03c23f24a..79702930a 100644 --- a/aws_write/poetry.lock +++ b/aws_write/poetry.lock @@ -21,6 +21,17 @@ doc = ["Sphinx", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd- test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] trio = ["trio (<0.22)"] +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +optional = false +python-versions = "*" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + [[package]] name = "appnope" version = "0.1.3" @@ -334,88 +345,99 @@ files = [ [package.dependencies] pycparser = "*" +[[package]] +name = "cfgv" +version = "3.3.1" +description = "Validate configuration and produce human readable error messages." +optional = false +python-versions = ">=3.6.1" +files = [ + {file = "cfgv-3.3.1-py2.py3-none-any.whl", hash = "sha256:c6a0883f3917a037485059700b9e75da2464e6c27051014ad85ba6aaa5884426"}, + {file = "cfgv-3.3.1.tar.gz", hash = "sha256:f5a830efb9ce7a445376bb66ec94c638a9787422f96264c98edc6bdeed8ab736"}, +] + [[package]] name = "charset-normalizer" -version = "3.2.0" +version = "3.1.0" description = "The Real First Universal Charset Detector. 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+typing-extensions = "*" + [[package]] name = "pygments" version = "2.15.1" @@ -1618,6 +1843,23 @@ files = [ [package.extras] plugins = ["importlib-metadata"] +[[package]] +name = "pyjwt" +version = "2.6.0" +description = "JSON Web Token implementation in Python" +optional = false +python-versions = ">=3.7" +files = [ + {file = "PyJWT-2.6.0-py3-none-any.whl", hash = "sha256:d83c3d892a77bbb74d3e1a2cfa90afaadb60945205d1095d9221f04466f64c14"}, + {file = "PyJWT-2.6.0.tar.gz", hash = "sha256:69285c7e31fc44f68a1feb309e948e0df53259d579295e6cfe2b1792329f05fd"}, +] + +[package.extras] +crypto = ["cryptography (>=3.4.0)"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] + [[package]] name = "pytest" version = "7.4.0" @@ -1654,6 +1896,20 @@ files = [ [package.dependencies] six = ">=1.5" +[[package]] +name = "python-dotenv" +version = "0.21.1" +description = "Read key-value pairs from a .env file and set them as environment variables" +optional = false +python-versions = ">=3.7" +files = [ + {file = "python-dotenv-0.21.1.tar.gz", hash = "sha256:1c93de8f636cde3ce377292818d0e440b6e45a82f215c3744979151fa8151c49"}, + {file = "python_dotenv-0.21.1-py3-none-any.whl", hash = "sha256:41e12e0318bebc859fcc4d97d4db8d20ad21721a6aa5047dd59f090391cb549a"}, +] + +[package.extras] +cli = ["click (>=5.0)"] + [[package]] name = "python-json-logger" version = "2.0.7" @@ -1944,6 +2200,24 @@ files = [ {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"}, ] +[[package]] +name = "rich" +version = "12.5.1" +description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +optional = false +python-versions = ">=3.6.3,<4.0.0" +files = [ + {file = "rich-12.5.1-py3-none-any.whl", hash = "sha256:2eb4e6894cde1e017976d2975ac210ef515d7548bc595ba20e195fb9628acdeb"}, + {file = "rich-12.5.1.tar.gz", hash = "sha256:63a5c5ce3673d3d5fbbf23cd87e11ab84b6b451436f1b7f19ec54b6bc36ed7ca"}, +] + +[package.dependencies] +commonmark = ">=0.9.0,<0.10.0" +pygments = ">=2.6.0,<3.0.0" + +[package.extras] +jupyter = ["ipywidgets (>=7.5.1,<8.0.0)"] + [[package]] name = "rpds-py" version = "0.9.2" @@ -2083,6 +2357,22 @@ nativelib = ["pyobjc-framework-Cocoa", "pywin32"] objc = ["pyobjc-framework-Cocoa"] win32 = ["pywin32"] +[[package]] +name = "setuptools" +version = "68.0.0" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +optional = false +python-versions = ">=3.7" +files = [ + {file = "setuptools-68.0.0-py3-none-any.whl", hash = "sha256:11e52c67415a381d10d6b462ced9cfb97066179f0e871399e006c4ab101fc85f"}, + {file = "setuptools-68.0.0.tar.gz", hash = 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"tomli", "virtualenv (>=13.0.0)", "wheel"] + [[package]] name = "six" version = "1.16.0" @@ -2230,6 +2520,21 @@ files = [ {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"}, ] +[[package]] +name = "typing-inspect" +version = "0.9.0" +description = "Runtime inspection utilities for typing module." +optional = false +python-versions = "*" +files = [ + {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"}, + {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"}, +] + +[package.dependencies] +mypy-extensions = ">=0.3.0" +typing-extensions = ">=3.7.4" + [[package]] name = "uri-template" version = "1.3.0" @@ -2260,6 +2565,26 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"] secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"] socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +[[package]] +name = "virtualenv" +version = "20.24.2" +description = "Virtual Python Environment builder" +optional = false +python-versions = ">=3.7" +files = [ + {file = "virtualenv-20.24.2-py3-none-any.whl", hash = "sha256:43a3052be36080548bdee0b42919c88072037d50d56c28bd3f853cbe92b953ff"}, + {file = "virtualenv-20.24.2.tar.gz", hash = "sha256:fd8a78f46f6b99a67b7ec5cf73f92357891a7b3a40fd97637c27f854aae3b9e0"}, +] + +[package.dependencies] +distlib = ">=0.3.7,<1" +filelock = ">=3.12.2,<4" +platformdirs = ">=3.9.1,<4" + +[package.extras] +docs = ["furo (>=2023.5.20)", "proselint (>=0.13)", "sphinx (>=7.0.1)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] + [[package]] name = "wcwidth" version = "0.2.6" @@ -2359,4 +2684,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9, <3.10" -content-hash = "ce00c603ff9ae0fbf8b003cfafacda0d5017dd8563804948338fc2f24f4826b6" +content-hash = "e631312c64ed5da49cf304118ce78d1819c02d5e9c998835a58ce6456d93bc8f" diff --git a/aws_write/pyproject.toml b/aws_write/pyproject.toml index ab01f26fb..512bffa48 100644 --- a/aws_write/pyproject.toml +++ b/aws_write/pyproject.toml @@ -12,6 +12,8 @@ python = ">=3.9, <3.10" boto3 = "*" flask = "2.2.3" flask-cors = "*" +ggshield = "^1.17.3" +pre-commit = "^3.3.3" [tool.poetry.dev-dependencies] python = ">=3.9" diff --git a/backend/__init__.py b/backend/__init__.py new file mode 100644 index 000000000..a4e2017f0 --- /dev/null +++ b/backend/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1" diff --git a/backend/aws_helpers/dynamo_db_utils/dynamo_db_utils.py b/backend/aws_helpers/dynamo_db_utils/dynamo_db_utils.py index ce674490b..69d0c7946 100644 --- a/backend/aws_helpers/dynamo_db_utils/dynamo_db_utils.py +++ b/backend/aws_helpers/dynamo_db_utils/dynamo_db_utils.py @@ -1,3 +1,4 @@ +from copy import deepcopy from decimal import Decimal import json import boto3 @@ -99,6 +100,24 @@ def get_dynamo_items_by_gsi(table_name: str, gsi_value: Union[int, str]) -> list return response_items +def to_decimal(o): + if isinstance(o, dict): + for k, v in o.items(): + if isinstance(v, float): + o[k] = Decimal(str(v)) + else: + to_decimal(v) + elif isinstance(o, list): + for v in o: + to_decimal(v) + + +def create_dynamo_item_from_obj(table_name: str, obj: object) -> bool: + obj_dict = deepcopy(obj.__dict__) + to_decimal(obj_dict) + return create_dynamo_item(table_name, obj_dict) + + def create_dynamo_item(table_name: str, input_item: dict) -> bool: """ Creates item in DynamoDB table, replaces item if partition_key already exists @@ -129,7 +148,7 @@ def create_dynamo_item(table_name: str, input_item: dict) -> bool: table = dynamodb.Table(table_name) response = table.put_item(Item=input_item) if response["ResponseMetadata"]["HTTPStatusCode"] != 200: - raise Exception("Failed to delete item") + raise Exception("Failed to create item") return True diff --git a/backend/aws_helpers/dynamo_db_utils/trainspace_db.py b/backend/aws_helpers/dynamo_db_utils/trainspace_db.py index ad57d7331..96ee4679c 100644 --- a/backend/aws_helpers/dynamo_db_utils/trainspace_db.py +++ b/backend/aws_helpers/dynamo_db_utils/trainspace_db.py @@ -74,7 +74,7 @@ def updateStatus(trainspace_id: str, status: str, entryData: dict = None) -> boo """ if entryData is None: entryData = {} - entryData["status"] = status + # entryData["status"] = status return updateTrainspaceData(trainspace_id, entryData) diff --git a/backend/endpoints/train_endpoints.py b/backend/endpoints/train_endpoints.py index a2e54dc70..2cc890dca 100644 --- a/backend/endpoints/train_endpoints.py +++ b/backend/endpoints/train_endpoints.py @@ -1,3 +1,4 @@ +import asyncio from dataclasses import asdict from decimal import Decimal import shutil @@ -6,6 +7,9 @@ from flask import Blueprint from flask import request +from backend.aws_helpers.dynamo_db_utils.dynamo_db_utils import ( + create_dynamo_item_from_obj, +) from aws_helpers.dynamo_db_utils.trainspace_db import ( TrainspaceData, @@ -23,6 +27,7 @@ send_traceback_error, send_train_results, ) +import aiohttp import boto3 @@ -70,8 +75,35 @@ def tabular_run(): ) try: - createTrainspaceData(tabular_data) + create_dynamo_item_from_obj("trainspace", tabular_data) print(id) + + async def run(): + async with aiohttp.ClientSession(json_serialize=json.dumps) as session: + async with session.post( + "http://localhost:8001/api/trainspace/tabular", + json={ + "target": request_data["parameters_data"]["target_col"], + "features": request_data["parameters_data"]["features"], + "problem_type": request_data["parameters_data"][ + "problem_type" + ], + "criterion": request_data["parameters_data"]["criterion"], + "default": request_data["dataset_data"]["name"], + "optimizer_name": request_data["parameters_data"][ + "optimizer_name" + ], + "shuffle": request_data["parameters_data"]["shuffle"], + "epochs": request_data["parameters_data"]["epochs"], + "test_size": request_data["parameters_data"]["test_size"], + "batch_size": request_data["parameters_data"]["batch_size"], + "user_arch": request_data["parameters_data"]["layers"], + "name": request_data["name"], + }, + ) as response: + print(await response.json(content_type=None)) + + asyncio.run(run()) return send_success({"message": "success", "trainspace_id": id}) except Exception: print(traceback.format_exc()) diff --git a/backend/poetry.lock b/backend/poetry.lock index 53dfd53c6..dbc7c77a1 100644 --- a/backend/poetry.lock +++ b/backend/poetry.lock @@ -1,5 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b/backendCore/dl/__init__.py @@ -0,0 +1 @@ +__version__ = "0.1" diff --git a/backendCore/dl/detection.py b/backendCore/dl/detection.py new file mode 100644 index 000000000..ffd83f624 --- /dev/null +++ b/backendCore/dl/detection.py @@ -0,0 +1,104 @@ +from transformers import YolosFeatureExtractor, YolosForObjectDetection +import torch +import torchvision +import io +import base64 +from PIL import Image, ImageDraw +from backend.aws_helpers.aws_rekognition_utils.rekognition_client import ( + rekognition_detection, +) +import os +from itertools import cycle +from backend.dl.dl_model_parser import parse_deep_user_architecture +import csv + + +def transform_image(img_file, transforms): + img = Image.open(img_file) + for x in transforms: + print(x) + transforms = parse_deep_user_architecture(transforms) + transforms = torchvision.transforms.Compose([x for x in transforms]) + img = transforms(img) + return ( + torchvision.transforms.ToPILImage()(img) + if isinstance(img, torch.Tensor) + else img + ) + + +def yolo_detection(image): + feature_extractor = YolosFeatureExtractor.from_pretrained("hustvl/yolos-base") + model = YolosForObjectDetection.from_pretrained("hustvl/yolos-base") + + inputs = feature_extractor(images=image.convert("RGB"), return_tensors="pt") + outputs = model(**inputs) + target_sizes = torch.tensor([image.size[::-1]]) + results = feature_extractor.post_process_object_detection( + outputs, threshold=0.9, target_sizes=target_sizes + )[0] + colors = ["aqua", "red", "white", "blue", "yellow", "green"] + names = [] + box_sets = [] + label_set = [] + for score, label, box in zip( + results["scores"], results["labels"], results["boxes"] + ): + box_sets.append(box) + name = model.config.id2label[label.item()] + names.append(name) + label_set.append({"name": name, "confidence": score.item()}) + im_b64 = show_bounding_boxes(image, box_sets, names, colors) + return im_b64, label_set + + +def show_bounding_boxes(image, box_sets, names, colors): + draw = ImageDraw.Draw(image) + for box, color, name in zip(box_sets, cycle(colors), names): + box = box.tolist() + left = box[0] + top = box[1] + right = box[2] + bottom = box[3] + draw.rectangle([left, top, right, bottom], outline=color, width=3) + draw.text((left, top), name, fill="black") + im_file = io.BytesIO() + image.save(im_file, format="JPEG") + im_bytes = im_file.getvalue() + im_b64 = base64.b64encode(im_bytes) + return im_b64 + + +def write_to_csv(label_set): + if label_set: + backend_dir = ( + "" + if (os.getcwd()).split("\\")[-1].split("/")[-1] == "backend" + else "backend" + if ("backend" in os.listdir(os.getcwd())) + else "../backend" + ) + keys = label_set[0].keys() + with open( + os.path.join(backend_dir, "detection_results.csv"), "w", newline="" + ) as f: + writer = csv.DictWriter(f, keys) + writer.writeheader() + writer.writerows(label_set) + + +def detection_img_drive(IMAGE_UPLOAD_FOLDER, detection_type, problem_type, transforms): + for x in os.listdir(IMAGE_UPLOAD_FOLDER): + if x != ".gitkeep": + img_file = os.path.join(os.path.abspath(IMAGE_UPLOAD_FOLDER), x) + break + image = transform_image(img_file, transforms) + if detection_type == "rekognition": + im_b64, label_set = rekognition_detection(image, problem_type) + elif detection_type == "yolo": + im_b64, label_set = yolo_detection(image) + write_to_csv(label_set) + return { + "auxiliary_outputs": {"image_data": im_b64.decode("ascii")}, + "dl_results": label_set, + } diff --git a/backendCore/dl/dl_eval.py b/backendCore/dl/dl_eval.py new file mode 100644 index 000000000..c8d59d5d6 --- /dev/null +++ b/backendCore/dl/dl_eval.py @@ -0,0 +1,39 @@ +import torch +import torch.nn as nn + + +def compute_correct(predicted, actual): + """ + Given a prediction (usually in logit form for classification problem), count the number of data points that match + their actual labels + Args: + predicted (torch.Tensor): For each row, what's the probability that the instance belongs to each of K classes + actual (torch.Tensor): actual class label + NOTE: Since we have our training data in "batch form", we will be getting the result for each batch in the dataloader + """ + prediction = torch.argmax( + predicted, dim=-1 + ) # identify index of the most likely class + + performance = torch.where( + (prediction == actual), torch.Tensor([1.0]), torch.Tensor([0.0]) + ) # for each row, did you predict correctly? + + return int(torch.sum(performance)) + + +def compute_accuracy(predicted, actual): + """ + Given a prediction (usually in logit form for classification problem), identify the + most likely label (probabilistically). Usually, for multiclass (more than 2 classes), + Softmax is applied at the end. For binary, apply Sigmoid activation at the last + Args: + predicted (torch.Tensor): For each row, what's the probability that the instance belongs to each of K classes + actual (torch.Tensor): actual class label + NOTE: Since we have our training data in "batch form", we will be getting an accuracy for each batch in the dataloader + """ + + batch_correct_pred = compute_correct(predicted, actual) + batch_accuracy = batch_correct_pred / len(predicted) + + return batch_accuracy diff --git a/backendCore/dl/dl_model.py b/backendCore/dl/dl_model.py new file mode 100644 index 000000000..b08ee7a4f --- /dev/null +++ b/backendCore/dl/dl_model.py @@ -0,0 +1,28 @@ +import torch +import torch.nn as nn +from torch.autograd import Variable + + +class DLModel(nn.Module): + def __init__(self, layer_list): + """ + Function to initialize Deep Learning model + given a user specified layer list + + Args: + layer_list (list): list of nn.Module layers from parser.py + """ + super().__init__() + self.model = self.build_model(layer_list) + + def build_model(self, layer_list): + model = nn.Sequential() + ctr = 1 + for layer in layer_list: + model.add_module(f"layer #{ctr}: {str(layer.__class__.__name__)}", layer) + ctr += 1 + return model + + def forward(self, x: torch.Tensor): + pred = self.model(x) # apply model on input x + return pred diff --git a/backendCore/dl/dl_model_parser.py b/backendCore/dl/dl_model_parser.py new file mode 100644 index 000000000..3e6f250ad --- /dev/null +++ b/backendCore/dl/dl_model_parser.py @@ -0,0 +1,84 @@ +from sklearn.tree import * +from sklearn.ensemble import * +from sklearn.linear_model import * +from sklearn.svm import * +from sklearn.naive_bayes import * +import torch +import torch.nn as nn +from torch.autograd import Variable +import torchvision.transforms as transforms + + +def parse_deep_user_architecture(user_model): + """ + new: + Each layer in the user_model has this format: + {"value": "LAYER_NAME", "parameters": [PARAMETER_1, PARAMETER_2, ...]} + + Eg: [{"value": "LINEAR", "parameters": [50, 10]}] should become [nn.Linear(in_features=50, out_features=10)] + + Args: + user_model (list): string form where you have the model name and the values for each parameter + Returns: + parsed_modules (list): list of nn.Module objects parsed + ---------------------------------------------------------------------------------------------- + old: + Given a list of user_models in a certain order, + each layer should contain name and the values of each + parameter + + Eg: ["Linear(in_features=50, out_features=10)"] should become [nn.Linear(in_features=50, out_features=10)] + + ["nn.Linear(in_features=50, out_features=10)", "nn.Conv2d(in_channels=16, out_channels=33, kernel_size=3, stride=2)"] should become + [nn.Linear(in_features=50, out_features=10), nn.Conv2d(in_channels=16, out_channels=33, kernel_size=3, stride=2)] + + Args: + user_model (list): string form where you have the model name and the values for each parameter + Returns: + parsed_modules (list): list of nn.Module objects parsed + """ + + # JSON file will contain name of model along with what hyperparameters need to be specified + parsed_modules = [] + for element in user_model: + if element["value"] == "LINEAR": + layer = nn.Linear( + in_features=element["parameters"][0], + out_features=element["parameters"][1], + ) + elif element["value"] == "RELU": + layer = nn.ReLU() + elif element["value"] == "SOFTMAX": + layer = nn.Softmax(dim=element["parameters"][0]) + else: + layer = get_object(element) + parsed_modules.append(layer) + return parsed_modules + + +def get_object(element): + """ + Given a string representation of a model layer or object, return the properly + instantiated object (nn.Module instance in most scenarios for deep learning) + + Args: + element (string): string representation of torch module + + return: + Instantiated object behind the "string representation of the instance" + """ + return eval( + element.replace("'", "") + ) # takes in the string representation and returns the "instantiated object" + + +if __name__ == "__main__": + print("starting") + print( + parse_deep_user_architecture( + [ + "nn.Linear(in_features=50, out_features=10)", + "nn.Conv2d(in_channels=3, out_channels=36, kernel_size=3, stride=2)", + ] + ) + ) diff --git a/backendCore/dl/dl_trainer.py b/backendCore/dl/dl_trainer.py new file mode 100644 index 000000000..263ab8e63 --- /dev/null +++ b/backendCore/dl/dl_trainer.py @@ -0,0 +1,414 @@ +from collections import Counter +from backend.common.loss_functions import compute_loss, compute_img_loss +from backend.dl.dl_eval import compute_accuracy, compute_correct +from backend.common.utils import ( + generate_acc_plot, + generate_loss_plot, + generate_train_time_csv, + generate_confusion_matrix, + generate_AUC_ROC_CURVE, +) +from backend.common.utils import ProblemType +from backend.common.constants import ( + DEEP_LEARNING_RESULT_CSV_PATH, + EPOCH, + TRAIN_TIME, + TRAIN_LOSS, + TEST_LOSS, + TRAIN_ACC, + TEST, + VAL_TEST_ACC, + SAVED_MODEL_DL, + ONNX_MODEL, +) +import torch # pytorch +import torch.nn as nn +import matplotlib.pyplot as plt +import numpy as np +import time +import pandas as pd +import traceback + +""" +This file contains helpful functions to aid in training and evaluation +of Pytorch models. +Links to helpful Resources: +https://pytorch.org/tutorials/beginner/introyt/trainingyt.html +https://towardsdatascience.com/building-rnn-lstm-and-gru-for-time-series-using-pytorch-a46e5b094e7b + +""" + + +def train_deep_classification_model( + model, train_loader, test_loader, optimizer, criterion, epochs, category_list +): + """ + Function for training pytorch model for classification. This function also times how long it takes to complete each epoch + Args: + model (nn.Module): Torch Model to train + train_loader (torch.DataLoader): Train Dataset split into batches + test_loader (torch.DataLoader): Test Dataset split into batches + optimizer (torch.optim.Optimizer): Optimizer to use when training model + criterion(str): Loss function + epochs (int): number of epochs + :return: a dictionary containing confusion matrix and AUC/ROC plot raw data + """ + try: + train_loss = [] # accumulate training loss over each epoch + test_loss = [] # accumulate testing loss over each epoch + epoch_time = [] # how much time it takes for each epoch + train_acc = [] # accuracy of training set + val_acc = [] # accuracy of test/validation set + labels_last_epoch = [] + y_pred_last_epoch = [] + + num_train_batches = len(train_loader) + # total number of data points used for training per epoch + epoch_train_size = train_loader.batch_size * num_train_batches + num_test_batches = len(test_loader) + # total number of data points used for testing per epoch + epoch_test_size = test_loader.batch_size * num_test_batches + + for epoch in range(epochs): + train_correct = ( + 0 # number of correct predictions in training set in current epoch + ) + test_correct = ( + 0 # number of correct predictions in testing set in current epoch + ) + epoch_batch_loss = 0 # cumulative training/testing loss per epoch + + start_time = time.time() + model.train(True) # set model to train model + for i, data in enumerate(train_loader): + # each batch is (input, label) pair in dataloader + input, labels = data + optimizer.zero_grad() # zero out gradient for each batch + output = model(input) # make prediction on input + train_correct += compute_correct(output, labels) + # compute the loss + loss = compute_loss(criterion, output, labels) + loss.backward() # backpropagation + optimizer.step() # adjust optimizer weights + epoch_batch_loss += float(loss.detach()) + + epoch_time.append(time.time() - start_time) + mean_train_acc = train_correct / epoch_train_size + mean_train_loss = epoch_batch_loss / num_train_batches + train_acc.append(mean_train_acc) + train_loss.append(mean_train_loss) + + model.train(False) # test the model on test set + epoch_batch_loss = 0 + for i, data in enumerate(test_loader): + input, labels = data + test_pred = model(input) + # currently only preserving the prediction array and label array for the last epoch for + # confusion matrix calculation + if epoch == epochs - 1: + y_pred_last_epoch.append(test_pred.detach().numpy().squeeze()) + + labels_last_epoch.append(labels.detach().numpy().squeeze()) + + test_correct += compute_correct(test_pred, labels) + loss = compute_loss(criterion, test_pred, labels) + epoch_batch_loss += float(loss.detach()) + mean_test_acc = test_correct / epoch_test_size + mean_test_loss = epoch_batch_loss / num_test_batches + val_acc.append(mean_test_acc) + test_loss.append(mean_test_loss) + + print( + f"epoch: {epoch}, train loss: {train_loss[-1]}, test loss: {test_loss[-1]}, train_acc: {mean_train_acc}, val_acc: {mean_test_acc}" + ) + result_table = pd.DataFrame( + { + EPOCH: [i for i in range(1, epochs + 1)], + TRAIN_TIME: epoch_time, + TRAIN_LOSS: train_loss, + TEST_LOSS: test_loss, + TRAIN_ACC: train_acc, + VAL_TEST_ACC: val_acc, + } + ) + print(result_table.head()) + confusion_matrix, numerical_category_list = generate_confusion_matrix( + labels_last_epoch, y_pred_last_epoch, category_list + ) + result_table.to_csv(DEEP_LEARNING_RESULT_CSV_PATH, index=False) + generate_acc_plot(DEEP_LEARNING_RESULT_CSV_PATH) + generate_loss_plot(DEEP_LEARNING_RESULT_CSV_PATH) + + # Collecting additional outputs to give to the frontend + auxiliary_outputs = {} + auxiliary_outputs["confusion_matrix"] = confusion_matrix + auxiliary_outputs["numerical_category_list"] = numerical_category_list + + # Generating AUC_ROC curve data to send to frontend to make interactive plot + AUC_ROC_curve_data, numerical_category_list_AUC = generate_AUC_ROC_CURVE( + labels_last_epoch, y_pred_last_epoch, category_list + ) + auxiliary_outputs["AUC_ROC_curve_data"] = AUC_ROC_curve_data + auxiliary_outputs["numerical_category_list_AUC"] = numerical_category_list_AUC + auxiliary_outputs["category_list"] = category_list + torch.save(model, SAVED_MODEL_DL) # saving model into a pt file + return auxiliary_outputs + except Exception: + raise Exception("Deep Learning classification didn't train properly") + + +def train_deep_regression_model( + model, train_loader, test_loader, optimizer, criterion, epochs +): + """ + Train Regression model in Pytorch. This function also times how long it takes to complete each epoch + Args: + model (nn.Module): Torch Model to train + train_loader (torch.DataLoader): Train Dataset split into batches + test_loader (torch.DataLoader): Test Dataset split into batches + optimizer (torch.optim.Optimizer): Optimizer to use when training model + criterion: Loss function + epochs (int): number of epochs + :return: an empty dictionary + """ + try: + train_loss = [] # accumulate training loss over each epoch + test_loss = [] # accumulate testing loss over each epoch + epoch_time = [] # how much time it takes for each epoch + num_train_batches = len(train_loader) + num_test_batches = len(test_loader) + print(num_train_batches, num_test_batches) + for epoch in range(epochs): + start_time = time.time() + model.train(True) # set model to train mode + epoch_batch_loss = 0 # cumulative training/testing loss per epoch + for i, data in enumerate(train_loader): + # each batch is (input, label) pair in dataloader + input, labels = data + optimizer.zero_grad() # zero out gradient for each batch + output = model(input) # make prediction on input + # compute the loss + loss = compute_loss(criterion, output, labels) + loss.backward() # backpropagation + optimizer.step() # adjust optimizer weights + epoch_batch_loss += float(loss.detach()) + epoch_time.append(time.time() - start_time) + train_loss.append(epoch_batch_loss / num_train_batches) + + model.train(False) # test the model on test set + epoch_batch_loss = 0 + for i, data in enumerate(test_loader): + input, labels = data + test_pred = model(input) + loss = compute_loss(criterion, test_pred, labels) + epoch_batch_loss += float(loss.detach()) + test_loss.append(epoch_batch_loss / num_test_batches) + print( + f"epoch: {epoch}, train loss: {train_loss[-1]}, test loss = {test_loss[-1]}" + ) + result_table = pd.DataFrame( + { + EPOCH: [i for i in range(1, epochs + 1)], + TRAIN_TIME: epoch_time, + TRAIN_LOSS: train_loss, + TEST_LOSS: test_loss, + } + ) + print(result_table.head()) + result_table.to_csv(DEEP_LEARNING_RESULT_CSV_PATH, index=False) + torch.save(model, SAVED_MODEL_DL) # saving model into a pt file + generate_loss_plot(DEEP_LEARNING_RESULT_CSV_PATH) + return {} + + except Exception: + raise Exception("Deep learning regression model didn't run properly") + + +def train_deep_model( + model, + train_loader, + test_loader, + optimizer, + criterion, + epochs, + problem_type, + category_list=[], +): + """ + Given train loader, train torch model + Args: + model (nn.Module): Torch Model to train + train_loader (torch.DataLoader): Train Dataset split into batches + optimizer (torch.optim.Optimizer): Optimizer to use when training model + criterion: Loss function + epochs (int): number of epochs + problem type (str): "classification" or "regression" + :return: a dictionary containing the epochs, train and test accuracy and loss results, each in a list + """ + if problem_type.upper() == ProblemType.get_problem_obj(ProblemType.CLASSIFICATION): + return train_deep_classification_model( + model, + train_loader, + test_loader, + optimizer, + criterion, + epochs, + category_list, + ) + elif problem_type.upper() == ProblemType.get_problem_obj(ProblemType.REGRESSION): + return train_deep_regression_model( + model, train_loader, test_loader, optimizer, criterion, epochs + ) + + +def get_deep_predictions(model: nn.Module, test_loader): + """ + Given a trained torch model and a test loader, get predictions vs. ground truth + + Args: + model (nn.Module): trained torch model + test_loader (torch.DataLoader): + """ + predictions = [] + ground_truth_values = [] + with torch.no_grad(): + for i, data in enumerate(test_loader): + input, labels = data + model.eval() # evaluation mode + yhat = model(input) + predictions.append(yhat.detach().numpy().ravel()) + ground_truth_values.append(labels.detach().numpy().ravel()) + + prediction_tensor = torch.from_numpy(np.array(predictions).T) + ground_truth_tensor = torch.from_numpy(np.array(ground_truth_values).T) + + return prediction_tensor, ground_truth_tensor + + +def train_deep_image_classification( + model, + train_loader, + test_loader, + optimizer, + criterion, + epochs, + device, + category_list=[], +): + try: + model = model.to(device) + train_loss = [] # accumulate training loss over each epoch + test_loss = [] # accumulate testing loss over each epoch + epoch_time = [] # how much time it takes for each epoch + train_acc = [] # accuracy of training set + val_acc = [] # accuracy of test/validation set + labels_last_epoch = [] + y_pred_last_epoch = [] + + num_train_batches = len(train_loader) + # total number of data points used for training per epoch + epoch_train_size = train_loader.batch_size * num_train_batches + num_test_batches = len(test_loader) + # total number of data points used for testing per epoch + epoch_test_size = test_loader.batch_size * num_test_batches + train_weights_count = Counter() + test_weights_count = Counter() + + input_dim = None + + for epoch in range(epochs): + model.train(True) + + if epoch == 0 and criterion == "WCELOSS": + for i, j in train_loader: + train_weights_count.update(j.detach().numpy().flatten()) + for i, j in test_loader: + test_weights_count.update(j.detach().numpy().flatten()) + + loss, train_correct, epoch_batch_loss = 0, 0, 0 + start_time = time.time() + for x in train_loader: + y = x[1] # label for all images in the batch + x = x[0] # (C, H, W) image + input_dim = x.shape + x, y = x.to(device), y.to(device) + optimizer.zero_grad() + pred = model(x) + loss = compute_img_loss(criterion, pred, y, train_weights_count) + + loss.backward() + optimizer.step() + y_pred, y_true = torch.argmax(pred, axis=1), y.long().squeeze() + train_correct += (y_pred == y_true).type(torch.float).sum().item() + epoch_batch_loss += float(loss.detach()) + epoch_time.append(time.time() - start_time) + mean_train_loss = epoch_batch_loss / num_train_batches + mean_train_acc = train_correct / epoch_train_size + train_loss.append(mean_train_loss) + + train_acc.append(mean_train_acc) + model.train(False) + loss, test_correct = 0, 0 + print("training for this epoch finished, going to validation") + + for x in test_loader: + y = x[1] + x = x[0] + x, y = x.to(device), y.to(device) + input_dim = x.shape + pred = model(x) + loss = compute_img_loss(criterion, pred, y, test_weights_count) + y_pred, y_true = torch.argmax(pred, axis=1), y.long().squeeze() + if epoch == epochs - 1: + y_pred_last_epoch.append(pred.detach().numpy().squeeze()) + labels_last_epoch.append(y.detach().numpy().squeeze()) + + test_correct += (y_pred == y_true).type(torch.float).sum().item() + epoch_batch_loss += float(loss.detach()) + + mean_test_loss = epoch_batch_loss / num_test_batches + mean_test_acc = test_correct / epoch_test_size + test_loss.append(mean_test_loss) + val_acc.append(mean_test_acc) + + print( + f"epoch: {epoch}, train loss: {train_loss[-1]}, test loss: {test_loss[-1]}, train_acc: {mean_train_acc}, val_acc: {mean_test_acc}" + ) + result_table = pd.DataFrame( + { + EPOCH: [i for i in range(1, epochs + 1)], + TRAIN_TIME: epoch_time, + TRAIN_LOSS: train_loss, + TEST_LOSS: test_loss, + TRAIN_ACC: train_acc, + VAL_TEST_ACC: val_acc, + } + ) + print(result_table) + + confusion_matrix, numerical_category_list = generate_confusion_matrix( + labels_last_epoch, y_pred_last_epoch + ) + result_table.to_csv(DEEP_LEARNING_RESULT_CSV_PATH, index=False) + + generate_acc_plot(DEEP_LEARNING_RESULT_CSV_PATH) + generate_loss_plot(DEEP_LEARNING_RESULT_CSV_PATH) + + auxiliary_outputs = {} + auxiliary_outputs["confusion_matrix"] = confusion_matrix + auxiliary_outputs["numerical_category_list"] = numerical_category_list + + # Generating AUC_ROC curve data to send to frontend to make interactive plot + AUC_ROC_curve_data, numerical_category_list_AUC = generate_AUC_ROC_CURVE( + labels_last_epoch, y_pred_last_epoch, category_list + ) + auxiliary_outputs["AUC_ROC_curve_data"] = AUC_ROC_curve_data + auxiliary_outputs["numerical_category_list_AUC"] = numerical_category_list_AUC + auxiliary_outputs["category_list"] = category_list + + torch.save(model, SAVED_MODEL_DL) # saving model into a pt file + torch.onnx.export(model, torch.randn(input_dim), ONNX_MODEL) + return auxiliary_outputs + + except Exception: + raise Exception("Deep Learning classification didn't train properly") diff --git a/frontend/next.config.js b/frontend/next.config.js index 7c396b725..d442086b0 100644 --- a/frontend/next.config.js +++ b/frontend/next.config.js @@ -1,6 +1,7 @@ /** @type {import('next').NextConfig} */ const nextConfig = { reactStrictMode: true, + trailingSlash: true, env: { REACT_APP_CAPTCHA_SITE_KEY: process.env.REACT_APP_CAPTCHA_SITE_KEY, REACT_APP_FEEDBACK_EMAIL: process.env.REACT_APP_FEEDBACK_EMAIL, @@ -10,14 +11,19 @@ const nextConfig = { { source: "/", destination: "/login", - permanent: true, + permanent: false, }, ]; }, rewrites: () => [ { - source: "/api/:path*", - destination: "http://127.0.0.1:8000/api/:path*", + source: "/api/lambda/:path*", + destination: + "https://em9iri9g4j.execute-api.us-west-2.amazonaws.com/:path*", + }, + { + source: "/api/training/:path*", + destination: "http://localhost:8000/api/:path*", }, ], }; diff --git a/frontend/src/features/Dashboard/redux/dashboardApi.ts b/frontend/src/features/Dashboard/redux/dashboardApi.ts index 75b6edf84..251ec42b7 100644 --- a/frontend/src/features/Dashboard/redux/dashboardApi.ts +++ b/frontend/src/features/Dashboard/redux/dashboardApi.ts @@ -5,7 +5,7 @@ const dashboardApi = backendApi.injectEndpoints({ endpoints: (builder) => ({ getExecutionsData: builder.query({ query: () => ({ - url: "/api/test/", + url: "/api/training/test", method: "GET", /*body: { user: auth.currentUser, diff --git a/frontend/src/features/Train/features/Tabular/components/TabularParametersStep.tsx b/frontend/src/features/Train/features/Tabular/components/TabularParametersStep.tsx index ce2305107..ee08a40cf 100644 --- a/frontend/src/features/Train/features/Tabular/components/TabularParametersStep.tsx +++ b/frontend/src/features/Train/features/Tabular/components/TabularParametersStep.tsx @@ -127,7 +127,7 @@ const TabularParametersStep = ({ layers: trainspace?.parameterData?.layers ?? [ { value: "LINEAR", - parameters: [10, 3], + parameters: [4, 10], }, { value: "RELU", @@ -135,7 +135,7 @@ const TabularParametersStep = ({ }, { value: "LINEAR", - parameters: [3, 10], + parameters: [10, 3], }, { value: "SOFTMAX", diff --git a/frontend/src/features/Train/features/Tabular/redux/tabularApi.ts b/frontend/src/features/Train/features/Tabular/redux/tabularApi.ts index 186db35a0..20d1c92d1 100644 --- a/frontend/src/features/Train/features/Tabular/redux/tabularApi.ts +++ b/frontend/src/features/Train/features/Tabular/redux/tabularApi.ts @@ -8,33 +8,23 @@ const tabularApi = backendApi.injectEndpoints({ TrainspaceData<"TRAIN"> >({ query: (trainspaceData) => ({ - url: "/api/train/tabular-run", + url: "/api/training/tabular", method: "POST", body: { name: trainspaceData.name, - data_source: trainspaceData.dataSource, - dataset_data: { - name: trainspaceData.datasetData.name, - is_default_dataset: trainspaceData.datasetData.isDefaultDataset, - }, - parameters_data: { - target_col: trainspaceData.parameterData.targetCol, - features: trainspaceData.parameterData.features, - problem_type: trainspaceData.parameterData.problemType, - criterion: trainspaceData.parameterData.criterion, - optimizer_name: trainspaceData.parameterData.optimizerName, - shuffle: trainspaceData.parameterData.shuffle, - epochs: trainspaceData.parameterData.epochs, - test_size: trainspaceData.parameterData.testSize, - batch_size: trainspaceData.parameterData.batchSize, - layers: trainspaceData.parameterData.layers, - }, - review_data: { - notification_email: - trainspaceData.reviewData.notificationEmail ?? null, - notification_phone_number: - trainspaceData.reviewData.notificationPhoneNumber ?? null, - }, + target: trainspaceData.parameterData.targetCol, + features: trainspaceData.parameterData.features, + default: trainspaceData.datasetData.isDefaultDataset + ? trainspaceData.datasetData.name + : undefined, + problem_type: trainspaceData.parameterData.problemType, + criterion: trainspaceData.parameterData.criterion, + optimizer_name: trainspaceData.parameterData.optimizerName, + shuffle: trainspaceData.parameterData.shuffle, + epochs: trainspaceData.parameterData.epochs, + test_size: trainspaceData.parameterData.testSize, + batch_size: trainspaceData.parameterData.batchSize, + user_arch: trainspaceData.parameterData.layers, }, }), transformResponse: (response: { trainspace_id: string }) => { diff --git a/frontend/src/features/Train/redux/trainspaceApi.ts b/frontend/src/features/Train/redux/trainspaceApi.ts index caea46d84..5388b6f90 100644 --- a/frontend/src/features/Train/redux/trainspaceApi.ts +++ b/frontend/src/features/Train/redux/trainspaceApi.ts @@ -15,21 +15,18 @@ const trainspaceApi = backendApi { dataSource: DATA_SOURCE } >({ query: ({ dataSource }) => ({ - url: "/api/s3/getUserDatasetFilesData", - method: "POST", - body: { - data_source: dataSource.toLowerCase(), - }, + url: `/api/lambda/datasets/user/${dataSource}`, }), providesTags: ["UserDatasetFilesData"], - transformResponse: (response: { data: string }) => { - const data: { + transformResponse: (response: { + data: { name: string; type: string; last_modified: string; size: string; - }[] = JSON.parse(response.data); - return data.map((fileObj) => ({ + }[]; + }) => { + return response.data.map((fileObj) => ({ name: fileObj.name, contentType: fileObj.type, lastModified: fileObj.last_modified, @@ -86,19 +83,19 @@ const trainspaceApi = backendApi >({ query: ({ dataSource, dataset }) => ({ url: dataset.isDefaultDataset - ? "/api/dataset/defaultDataset" + ? `/api/training/datasets/default/${dataset.name}/columns` : "/api/dataset/getColumnsFromDatasetFile", - method: "POST", - body: { + method: "GET", + /*body: { data_source: dataSource.toLowerCase(), name: dataset.isDefaultDataset ? undefined : dataset.name, using_default_dataset: dataset.isDefaultDataset ? dataset.name : undefined, - }, + },*/ }), - transformResponse: (response: { columns: string }) => { - return JSON.parse(response.columns); + transformResponse: (response: { data: string[] }) => { + return response.data; }, }), }), diff --git a/package.json b/package.json index d6c95e493..04e909443 100644 --- a/package.json +++ b/package.json @@ -6,12 +6,17 @@ "scripts": { "build:prod": "cd frontend && yarn install && REACT_APP_MODE=prod yarn run build", "installf": "cd frontend && yarn install", - "installb": "cd backend && poetry install && poetry shell && ggshield auth login && pre-commit install", - "installa": "cd aws_write && poetry install && poetry shell && ggshield auth login && pre-commit install", + "installb": "poetry config virtualenvs.in-project true && cd backend && poetry env remove --all && pyenv local 3.9 && cd .. && yarn updateb", + "updateb": "cd backend && poetry install --no-root && poetry run ggshield auth login && poetry run pre-commit install", + "installa": "poetry config virtualenvs.in-project true && cd aws_write && poetry env remove --all && pyenv local 3.9 && cd .. && yarn updatea", + "updatea": "cd aws_write && poetry install --no-root && poetry run ggshield auth login && poetry run pre-commit install", "startf": "cd frontend && yarn dev", "secrets": "cd backend && poetry run python -m aws_helpers.aws_secrets_utils.build_env", "startb": "cd backend && poetry run python app.py", "starta": "cd aws_write && poetry run python app.py", + "installt": "cd training && poetry env remove --all && pyenv local 3.9 && cd .. && yarn updatet", + "updatet": "cd training && poetry install --no-root && poetry run ggshield auth login && poetry run pre-commit install", + "startt": "cd training && poetry run python manage.py runserver", "docker:build": "docker build -t dlp ." }, "repository": { diff --git a/serverless/.gitignore b/serverless/.gitignore new file mode 100644 index 000000000..37b9471a0 --- /dev/null +++ b/serverless/.gitignore @@ -0,0 +1,15 @@ +# dependencies +node_modules + +# sst +.sst +.build + +# opennext +.open-next + +# misc +.DS_Store + +# local env files +.env*.local diff --git a/serverless/.nvmrc b/serverless/.nvmrc new file mode 100644 index 000000000..73fd9dec7 --- /dev/null +++ b/serverless/.nvmrc @@ -0,0 +1 @@ +v19.9.0 diff --git a/serverless/.vscode/launch.json b/serverless/.vscode/launch.json new file mode 100644 index 000000000..58b32d117 --- /dev/null +++ b/serverless/.vscode/launch.json @@ -0,0 +1,15 @@ +{ + "version": "0.2.0", + "configurations": [ + { + "name": "Debug SST Start", + "type": "node", + "request": "launch", + "runtimeExecutable": "${workspaceRoot}/node_modules/.bin/sst", + "runtimeArgs": ["start", "--increase-timeout"], + "console": "integratedTerminal", + "skipFiles": ["/**"], + "env": {} + } + ] +} diff --git a/serverless/.vscode/settings.json b/serverless/.vscode/settings.json new file mode 100644 index 000000000..3b42f010e --- /dev/null +++ b/serverless/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "search.exclude": { + "**/.sst": true + } +} diff --git a/serverless/package.json b/serverless/package.json new file mode 100644 index 000000000..71a8ba64e --- /dev/null +++ b/serverless/package.json @@ -0,0 +1,28 @@ +{ + "name": "dlp-sst-app", + "version": "0.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "sst dev", + "build": "sst build", + "deployprod": "sst deploy --stage prod", + "remove": "sst remove", + "removeprod": "sst remove --stage prod", + "console": "sst console", + "typecheck": "tsc --noEmit" + }, + "devDependencies": { + "@tsconfig/node16": "^16.1.0", + "aws-cdk-lib": "2.91.0", + "constructs": "10.2.69", + "sst": "^2.24.3", + "typescript": "^5.1.6" + }, + "workspaces": [ + "packages/*" + ], + "dependencies": { + "@aws-sdk/s3-request-presigner": "^3.391.0" + } +} diff --git a/serverless/packages/core/package.json b/serverless/packages/core/package.json new file mode 100644 index 000000000..f92bdfe81 --- /dev/null +++ b/serverless/packages/core/package.json @@ -0,0 +1,14 @@ +{ + "name": "@dlp-sst-app/core", + "version": "0.0.0", + "type": "module", + "scripts": { + "test": "sst bind vitest", + "typecheck": "tsc -noEmit" + }, + "devDependencies": { + "vitest": "^0.34.1", + "@types/node": "^20.5.0", + "sst": "^2.24.3" + } +} \ No newline at end of file diff --git a/serverless/packages/core/src/parseJwt.ts b/serverless/packages/core/src/parseJwt.ts new file mode 100644 index 000000000..a1c026d1c --- /dev/null +++ b/serverless/packages/core/src/parseJwt.ts @@ -0,0 +1,3 @@ +export default function parseJwt(token: string) { + return JSON.parse(Buffer.from(token.split(".")[1], "base64").toString()); +} diff --git a/serverless/packages/core/sst-env.d.ts b/serverless/packages/core/sst-env.d.ts new file mode 100644 index 000000000..a9187e856 --- /dev/null +++ b/serverless/packages/core/sst-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/serverless/packages/core/tsconfig.json b/serverless/packages/core/tsconfig.json new file mode 100644 index 000000000..e4fb12732 --- /dev/null +++ b/serverless/packages/core/tsconfig.json @@ -0,0 +1,7 @@ +{ + "extends": "@tsconfig/node16/tsconfig.json", + "compilerOptions": { + "module": "esnext", + "moduleResolution": "node" + } +} diff --git a/serverless/packages/functions/.nvmrc b/serverless/packages/functions/.nvmrc new file mode 100644 index 000000000..73fd9dec7 --- /dev/null +++ b/serverless/packages/functions/.nvmrc @@ -0,0 +1 @@ +v19.9.0 diff --git a/serverless/packages/functions/package.json b/serverless/packages/functions/package.json new file mode 100644 index 000000000..84c0ad635 --- /dev/null +++ b/serverless/packages/functions/package.json @@ -0,0 +1,15 @@ +{ + "name": "@dlp-sst-app/functions", + "version": "0.0.0", + "type": "module", + "scripts": { + "test": "sst bind vitest", + "typecheck": "tsc -noEmit" + }, + "devDependencies": { + "@types/node": "^20.5.0", + "@types/aws-lambda": "^8.10.119", + "vitest": "^0.34.1", + "sst": "^2.24.3" + } +} \ No newline at end of file diff --git a/serverless/packages/functions/src/datasets/user/all_of_type.ts b/serverless/packages/functions/src/datasets/user/all_of_type.ts new file mode 100644 index 000000000..ccf90e29d --- /dev/null +++ b/serverless/packages/functions/src/datasets/user/all_of_type.ts @@ -0,0 +1,37 @@ +import { APIGatewayProxyHandlerV2 } from "aws-lambda"; +import { S3Client, ListObjectsV2Command } from "@aws-sdk/client-s3"; +import parseJwt from "@dlp-sst-app/core/parseJwt"; + +export const handler: APIGatewayProxyHandlerV2 = async (event) => { + if (event && event.pathParameters && event.pathParameters.type) { + const client = new S3Client(); + const user_id: string = parseJwt(event.headers.authorization ?? "")[ + "user_id" + ]; + const command = new ListObjectsV2Command({ + Bucket: "dlp-upload-bucket", + Prefix: `${user_id}/${event.pathParameters.type}`, + }); + const s3objects = await client.send(command); + const data = (s3objects.Contents ?? []).map((obj) => { + const [_, type, name] = (obj.Key ?? "").split("/"); + return { + name: name, + size: obj.Size!, + last_modified: obj.LastModified!, + type: type, + }; + }); + return { + statusCode: 200, + body: JSON.stringify({ + data: data, + message: "Success", + }), + }; + } + return { + statusCode: 404, + body: JSON.stringify({ message: "Not Found" }), + }; +}; diff --git a/serverless/packages/functions/src/datasets/user/columns.ts b/serverless/packages/functions/src/datasets/user/columns.ts new file mode 100644 index 000000000..5d132017c --- /dev/null +++ b/serverless/packages/functions/src/datasets/user/columns.ts @@ -0,0 +1,59 @@ +import { APIGatewayProxyHandlerV2 } from "aws-lambda"; +import { S3Client, SelectObjectContentCommand } from "@aws-sdk/client-s3"; +import parseJwt from "@dlp-sst-app/core/parseJwt"; + +export const handler: APIGatewayProxyHandlerV2 = async (event) => { + if ( + event && + event.pathParameters && + event.pathParameters.type && + event.pathParameters.filename + ) { + const s3client = new S3Client(); + const user_id: string = parseJwt(event.headers.authorization ?? "")[ + "user_id" + ]; + const input = { + Bucket: "dlp-upload-bucket", + Key: `${user_id}/${event.pathParameters.type}/${event.pathParameters.filename}`, + Expression: "SELECT * FROM S3Object LIMIT 1", + ExpressionType: "SQL", + InputSerialization: { + CSV: { + FileHeaderInfo: "NONE", + }, + }, + OutputSerialization: { + CSV: {}, + }, + }; + const command = new SelectObjectContentCommand(input); + const response = await s3client.send(command); + if (!response.Payload) { + return { + statusCode: 404, + body: JSON.stringify({ message: "Payload Not Found" }), + }; + } + for await (const event of response.Payload) { + if (!event.Records?.Payload) { + return { + statusCode: 404, + body: JSON.stringify({ message: "Column Names Not Found" }), + }; + } + const columns = new TextDecoder() + .decode(event.Records.Payload) + .split("\n")[0] + .split(","); + return { + statusCode: 200, + body: JSON.stringify({ data: columns, message: "Success" }), + }; + } + } + return { + statusCode: 404, + body: JSON.stringify({ message: "Not Found" }), + }; +}; diff --git a/serverless/packages/functions/src/datasets/user/upload_url.ts b/serverless/packages/functions/src/datasets/user/upload_url.ts new file mode 100644 index 000000000..764ae4803 --- /dev/null +++ b/serverless/packages/functions/src/datasets/user/upload_url.ts @@ -0,0 +1,35 @@ +import { APIGatewayProxyHandlerV2 } from "aws-lambda"; +import { getSignedUrl } from "@aws-sdk/s3-request-presigner"; +import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3"; +import parseJwt from "@dlp-sst-app/core/parseJwt"; + +export const handler: APIGatewayProxyHandlerV2 = async (event) => { + if ( + event && + event.pathParameters && + event.pathParameters.type && + event.pathParameters.filename + ) { + const client = new S3Client(); + const user_id: string = parseJwt(event.headers.authorization ?? "")[ + "user_id" + ]; + // Generate the presigned URL for a putObject operation + const command = new PutObjectCommand({ + Bucket: "dlp-upload-bucket", + ACL: "private", + Key: `${user_id}/${event.pathParameters.type}/${event.pathParameters.filename}`, + }); + const url = await getSignedUrl(client, command, { + expiresIn: 15 * 60, + }); + return { + statusCode: 200, + body: JSON.stringify({ data: url, message: "Success" }), + }; + } + return { + statusCode: 404, + body: JSON.stringify({ message: "Not Found" }), + }; +}; diff --git a/serverless/packages/functions/sst-env.d.ts b/serverless/packages/functions/sst-env.d.ts new file mode 100644 index 000000000..a9187e856 --- /dev/null +++ b/serverless/packages/functions/sst-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/serverless/packages/functions/tsconfig.json b/serverless/packages/functions/tsconfig.json new file mode 100644 index 000000000..4277a6882 --- /dev/null +++ b/serverless/packages/functions/tsconfig.json @@ -0,0 +1,11 @@ +{ + "extends": "@tsconfig/node16/tsconfig.json", + "compilerOptions": { + "module": "esnext", + "moduleResolution": "node", + "baseUrl": ".", + "paths": { + "@dlp-sst-app/core/*": ["../core/src/*"] + } + } +} diff --git a/serverless/pnpm-workspace.yaml b/serverless/pnpm-workspace.yaml new file mode 100644 index 000000000..a4e134d3e --- /dev/null +++ b/serverless/pnpm-workspace.yaml @@ -0,0 +1,2 @@ +packages: + - "packages/**/*" diff --git a/serverless/sst.config.ts b/serverless/sst.config.ts new file mode 100644 index 000000000..22df17446 --- /dev/null +++ b/serverless/sst.config.ts @@ -0,0 +1,14 @@ +import { SSTConfig } from "sst"; +import { AppStack } from "./stacks/AppStack"; + +export default { + config(_input) { + return { + name: "dlp-sst-app", + region: "us-west-2", + }; + }, + stacks(app) { + app.stack(AppStack); + }, +} satisfies SSTConfig; diff --git a/serverless/stacks/AppStack.ts b/serverless/stacks/AppStack.ts new file mode 100644 index 000000000..a2580cfe3 --- /dev/null +++ b/serverless/stacks/AppStack.ts @@ -0,0 +1,54 @@ +import { Api, Bucket, StackContext } from "sst/constructs"; +import { Bucket as s3Bucket } from "aws-cdk-lib/aws-s3"; + +export function AppStack({ stack }: StackContext) { + const bucket = new Bucket(stack, "dlp-upload-bucket", { + cdk: { + bucket: s3Bucket.fromBucketArn( + stack, + "i-dlp-upload-bucket", + "arn:aws:s3:::dlp-upload-bucket" + ), + }, + }); + const api = new Api(stack, "Api", { + authorizers: { + FirebaseAuthorizer: { + type: "jwt", + jwt: { + issuer: + "https://securetoken.google.com/deep-learning-playground-8d2ce", + audience: ["deep-learning-playground-8d2ce"], + }, + }, + }, + defaults: { + function: { + bind: [bucket], + }, + authorizer: "FirebaseAuthorizer", + }, + routes: { + "GET /datasets/user/{type}/{filename}/presigned_upload_url": + "packages/functions/src/datasets/user/upload_url.handler", + "GET /datasets/user/{type}": + "packages/functions/src/datasets/user/all_of_type.handler", + "GET /datasets/user/{type}/{filename}/columns": + "packages/functions/src/datasets/user/columns.handler", + }, + }); + + // Show the API endpoint in the output + stack.addOutputs({ + ApiEndpoint: api.url, + GetUserDatasetPresignedUploadUrlFunctionName: + api.getFunction( + "GET /datasets/user/{type}/{filename}/presigned_upload_url" + )?.functionName ?? "", + GetUserDatasetsFunctionName: + api.getFunction("GET /datasets/user/{type}")?.functionName ?? "", + GetUserDatasetColumnsFunctionName: + api.getFunction("GET /datasets/user/{type}/{filename}/columns") + ?.functionName ?? "", + }); +} diff --git a/serverless/tsconfig.json b/serverless/tsconfig.json new file mode 100644 index 000000000..d7374a65a --- /dev/null +++ b/serverless/tsconfig.json @@ -0,0 +1,7 @@ +{ + "extends": "@tsconfig/node16/tsconfig.json", + "exclude": ["packages"], + "compilerOptions": { + "moduleResolution": "node" + } +} diff --git a/serverless/yarn.lock b/serverless/yarn.lock new file mode 100644 index 000000000..e5cad2dc8 --- /dev/null +++ b/serverless/yarn.lock @@ -0,0 +1,5542 @@ +# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY. +# yarn lockfile v1 + + +"@alcalzone/ansi-tokenize@^0.1.2": + version "0.1.2" + resolved "https://registry.yarnpkg.com/@alcalzone/ansi-tokenize/-/ansi-tokenize-0.1.2.tgz#4bbc953eebc94005ef133c1df966f4ffb3f9fee8" + integrity sha512-/9Wcb0XV6jhvbn0UsL0CPBYKRWjSN0Er2vrrhFDNy0IMIvZ7pWBLwOZbFKTlyYCr1Q+UBXb1hWM4czT2FOfT+A== + dependencies: + ansi-styles "^6.2.1" + is-fullwidth-code-point "^4.0.0" + +"@ampproject/remapping@^2.2.0": + version "2.2.1" + resolved "https://registry.yarnpkg.com/@ampproject/remapping/-/remapping-2.2.1.tgz#99e8e11851128b8702cd57c33684f1d0f260b630" + integrity sha512-lFMjJTrFL3j7L9yBxwYfCq2k6qqwHyzuUl/XBnif78PWTJYyL/dfowQHWE3sp6U6ZzqWiiIZnpTMO96zhkjwtg== + dependencies: + "@jridgewell/gen-mapping" "^0.3.0" + "@jridgewell/trace-mapping" "^0.3.9" + +"@aws-cdk/asset-awscli-v1@^2.2.200": + version "2.2.200" + resolved 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secret_name = "DLP/Firebase/Admin_SDK" + region_name = "us-west-2" + # Create a Secrets Manager client + + client = boto3.client("secretsmanager", region_name=region_name) + + try: + get_secret_value_response = client.get_secret_value(SecretId=secret_name) + except ClientError as e: + # For a list of exceptions thrown, see + # https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html + raise e + + # Decrypts secret using the associated KMS key. + secret_str = get_secret_value_response["SecretString"] + secret = json.loads(secret_str) + secret["private_key"] = secret["private_key"].replace("\\n", "\n") + secret["type"] = "service_account" + credential = firebase_admin.credentials.Certificate(secret) + return firebase_admin.initialize_app(credential) + + +@click.group() +def cli(): + pass + + +@cli.command() +@click.argument("email") +def get_uid(email: str) -> None: + """Gets a user's uid given their email.""" + + app = init_firebase() + click.echo(auth.get_user_by_email(email).uid) + firebase_admin.delete_app(app) + + +@cli.command() +@click.argument("email") +def get_id_token(email: str) -> None: + """Gets the id token of a user given their email.""" + + API_KEY = "AIzaSyAMJgYSG_TW7CT_krdWaFUBLxU4yRINxX8" + app = init_firebase() + uid = auth.get_user_by_email(email).uid + custom_token = auth.create_custom_token(uid) + response = requests.post( + "https://identitytoolkit.googleapis.com/v1/accounts:signInWithCustomToken?key=" + + API_KEY, + data={"token": custom_token, "returnSecureToken": True}, + ) + click.echo(response.json().get("idToken")) + firebase_admin.delete_app(app) + + +if __name__ == "__main__": + cli() diff --git a/training/manage.py b/training/manage.py new file mode 100755 index 000000000..32cb24e87 --- /dev/null +++ b/training/manage.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +"""Django's command-line utility for administrative tasks.""" +import os +import sys + + +def main(): + """Run administrative tasks.""" + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "training.settings") + try: + from django.core.management import execute_from_command_line + except ImportError as exc: + raise ImportError( + "Couldn't import Django. Are you sure it's installed and " + "available on your PYTHONPATH environment variable? Did you " + "forget to activate a virtual environment?" + ) from exc + execute_from_command_line(sys.argv) + + +if __name__ == "__main__": + main() diff --git a/training/poetry.lock b/training/poetry.lock new file mode 100644 index 000000000..af530269e --- /dev/null +++ b/training/poetry.lock @@ -0,0 +1,2196 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +optional = false +python-versions = "*" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + +[[package]] +name = "asgiref" +version = "3.7.2" +description = "ASGI specs, helper code, and adapters" +optional = 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000000000..a5f77ca32 --- /dev/null +++ b/training/pyproject.toml @@ -0,0 +1,35 @@ +[tool.poetry] +name = "training" +version = "0.1.0" +description = "" +authors = ["Daniel Wu "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.9, <3.10" +boto3 = "^1.28.15" +django = "^4.2.3" +django-ninja = "^0.22.2" +scikit-learn = "^1.3.0" +pandas = "^2.0.3" +torch = "^2.0.1" +torchvision = "^0.15.2" +firebase-admin = "^6.2.0" +pre-commit = "^3.3.3" +click = "^8.1.7" +requests = "^2.31.0" +django-cors-headers = "^4.2.0" + + +[tool.poetry.group.dev.dependencies] +pytest = "^7.4.0" +ggshield = "^1.18.0" +moto = "^4.1.14" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.pyright] +venvPath = "." +venv = ".venv" \ No newline at end of file diff --git a/training/tests/__init__.py b/training/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/training/training/__init__.py b/training/training/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/training/training/asgi.py b/training/training/asgi.py new file mode 100644 index 000000000..325107730 --- /dev/null +++ b/training/training/asgi.py @@ -0,0 +1,16 @@ +""" +ASGI config for training project. + +It exposes the ASGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/asgi/ +""" + +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "training.settings") + +application = get_asgi_application() diff --git a/training/training/core/__init__.py b/training/training/core/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/training/training/core/criterion.py b/training/training/core/criterion.py new file mode 100644 index 000000000..781f46f2a --- /dev/null +++ b/training/training/core/criterion.py @@ -0,0 +1,76 @@ +from abc import ABC, abstractmethod +import torch +import torch.nn as nn + + +class CriterionHandler(ABC): + def compute_loss(self, output: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + postprocess_output = output.clone() + postprocess_labels = labels.clone() + return self._compute_loss(postprocess_output, postprocess_labels) + + @abstractmethod + def _compute_loss(self, output: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + pass + + +class L1LossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + output = torch.reshape( + output, + (output.shape[0], output.shape[2]), + ) + return nn.L1Loss()(output, labels) + + +class MSELossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + output = torch.reshape( + output, + (output.shape[0], output.shape[2]), + ) + return nn.MSELoss()(output, labels) + + +class BCELossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + return nn.BCELoss()(output, labels.unsqueeze(1)) + + +class BCEWithLogitsLossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + return nn.BCEWithLogitsLoss()(output, labels.unsqueeze(1)) + + +class CELossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + output = torch.reshape( + output, + (output.shape[0], output.shape[2]), + ) + labels = labels.squeeze_() + return nn.CrossEntropyLoss(reduction="mean")(output, labels.long()) + + +class WCELossHandler(CriterionHandler): + def _compute_loss(self, output, labels): + output = torch.reshape( + output, + (output.shape[0], output.shape[2]), + ) + labels = labels.squeeze_() + return nn.CrossEntropyLoss(reduction="mean")(output, labels.long()) + + +CRITERION_HANDLERS = { + "L1LOSS": L1LossHandler(), + "MSELOSS": MSELossHandler(), + "BCELOSS": BCELossHandler(), + "BCEWITHLOGITSLOSS": BCEWithLogitsLossHandler(), + "CELOSS": CELossHandler(), + "WCELOSS": WCELossHandler(), +} + + +def getCriterionHandler(criterion_name): + return CRITERION_HANDLERS[criterion_name] diff --git a/training/training/core/dataset.py b/training/training/core/dataset.py new file mode 100644 index 000000000..f7a5348a9 --- /dev/null +++ b/training/training/core/dataset.py @@ -0,0 +1,100 @@ +from abc import ABC, abstractmethod +from typing import Callable, Optional, Union, cast + +from numpy import ndarray +from sklearn.model_selection import train_test_split +from sklearn.utils import Bunch +from sklearn.conftest import fetch_california_housing +from sklearn.datasets import load_breast_cancer, load_diabetes, load_iris, load_wine +from torch.utils.data import TensorDataset +import numpy as np +import pandas as pd +import torch +from torch.utils.data import Dataset +from torch.autograd import Variable + + +class TrainTestDatasetCreator(ABC): + "Creator that creates train and test PyTorch datasets" + + @abstractmethod + def createTrainDataset(self) -> Dataset: + pass + + @abstractmethod + def createTestDataset(self) -> Dataset: + pass + + +class SklearnDatasetCreator(TrainTestDatasetCreator): + DEFAULT_DATASETS: dict[ + str, Callable[[], Union[Bunch, tuple[Bunch, tuple], tuple[ndarray, ndarray]]] + ] = { + "IRIS": load_iris, + "BREAST_CANCER": load_breast_cancer, + "CALIFORNIA_HOUSING": fetch_california_housing, + "DIABETES": load_diabetes, + "WINE": load_wine, + } + + def __init__( + self, + X: pd.DataFrame, + y: pd.Series, + test_size: float, + shuffle: bool, + category_list: Optional[list[str]], + ) -> None: + super().__init__() + self._category_list = category_list + self._X_train, self._X_test, self._y_train, self._y_test = cast( + tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series], + train_test_split(X, y, test_size=test_size, shuffle=shuffle), + ) + + @classmethod + def getDefaultDataset(cls, name: str): + raw_data = cls.DEFAULT_DATASETS[name]() + default_dataset = pd.DataFrame( + data=np.c_[raw_data["data"], raw_data["target"]], # type: ignore + columns=raw_data["feature_names"] + ["target"], # type: ignore + ) + + # remove any empty lines + default_dataset.dropna(how="all", inplace=True) + return default_dataset + + @classmethod + def fromDefault(cls, name: str, test_size: float, shuffle: bool): + raw_data = cls.DEFAULT_DATASETS[name]() + default_dataset = cls.getDefaultDataset(name) + y = default_dataset["target"] + X = default_dataset.drop("target", axis=1) + return cls(X, y, test_size, shuffle, list(raw_data.target_names) if hasattr(raw_data, "target_names") else None) # type: ignore + + def createTrainDataset(self) -> Dataset: + X_train_tensor = Variable(torch.Tensor(self._X_train.to_numpy())) + X_train_tensor = torch.reshape( + X_train_tensor, (X_train_tensor.size()[0], 1, X_train_tensor.size()[1]) + ) + X_train_tensor.requires_grad_(True) + + y_train_tensor = Variable(torch.Tensor(self._y_train.to_numpy())) + y_train_tensor = torch.reshape(y_train_tensor, (y_train_tensor.size()[0], 1)) + return TensorDataset(X_train_tensor, y_train_tensor) + + def createTestDataset(self) -> Dataset: + X_test_tensor = Variable(torch.Tensor(self._X_test.to_numpy())) + X_test_tensor = torch.reshape( + X_test_tensor, (X_test_tensor.size()[0], 1, X_test_tensor.size()[1]) + ) + X_test_tensor.requires_grad_(True) + + y_test_tensor = Variable(torch.Tensor(self._y_test.to_numpy())) + y_test_tensor = torch.reshape(y_test_tensor, (y_test_tensor.size()[0], 1)) + return TensorDataset(X_test_tensor, y_test_tensor) + + def getCategoryList(self) -> list[str]: + if self._category_list is None: + raise Exception("Category list not available") + return self._category_list diff --git a/training/training/core/dl_model.py b/training/training/core/dl_model.py new file mode 100644 index 000000000..d35d0ff17 --- /dev/null +++ b/training/training/core/dl_model.py @@ -0,0 +1,53 @@ +from typing import Any, Callable +from ninja import Schema +import torch +import torch.nn as nn +from typing import TYPE_CHECKING + +from training.routes.tabular.schemas import LayerParams + + +class DLModel(nn.Module): + LAYERS_MAPPING: dict[str, Callable[..., nn.Module]] = { + "LINEAR": nn.Linear, + "RELU": nn.ReLU, + "TANH": nn.Tanh, + "SOFTMAX": nn.Softmax, + "SIGMOID": nn.Sigmoid, + "LOGSOFTMAX": nn.LogSoftmax, + } + + def __init__(self, layer_list: list[nn.Module]): + """ + Function to initialize Deep Learning model + given a user specified layer list + + Args: + layer_list (list): list of nn.Module layers from parser.py + """ + super().__init__() + self.model = self.build_model(layer_list) + + @classmethod + def fromLayerParamsList(cls, layer_params_list: list[LayerParams]): + layer_list = [] + for layer_params in layer_params_list: + if layer_params.value not in cls.LAYERS_MAPPING: + raise Exception(f"Layer ${layer_params.value} not supported") + linear_layer = cls.LAYERS_MAPPING[layer_params.value]( + *layer_params.parameters + ) + layer_list.append(linear_layer) + return cls(layer_list) + + def build_model(self, layer_list): + model = nn.Sequential() + ctr = 1 + for layer in layer_list: + model.add_module(f"layer #{ctr}: {str(layer.__class__.__name__)}", layer) + ctr += 1 + return model + + def forward(self, x: torch.Tensor): + pred = self.model(x) # apply model on input x + return pred diff --git a/training/training/core/optimizer.py b/training/training/core/optimizer.py new file mode 100644 index 000000000..2b532861e --- /dev/null +++ b/training/training/core/optimizer.py @@ -0,0 +1,19 @@ +from typing import Callable +import torch + +OPTIMIZER_MAPPING: dict[str, Callable[..., torch.optim.Optimizer]] = { + "SGD": torch.optim.SGD, + "ADAM": torch.optim.Adam, +} + + +def getOptimizer(model: torch.nn.Module, optimizer_name: str, learning_rate: float): + """ + Given an optimizer name, instantiate the object + + Args: + model (nn.Module): pytorch model to train + optimizer_name (str): name of optimizer + learning_rate (float): learning rate + """ + return OPTIMIZER_MAPPING[optimizer_name](model.parameters(), lr=learning_rate) diff --git a/training/training/core/trainer.py b/training/training/core/trainer.py new file mode 100644 index 000000000..02ce1567f --- /dev/null +++ b/training/training/core/trainer.py @@ -0,0 +1,293 @@ +from dataclasses import dataclass +import time +from typing import Iterator, TypeVar +import numpy as np +from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve +import torch +from torch.utils.data import DataLoader +import torch.nn as nn +from training.core.criterion import CriterionHandler + + +@dataclass +class EpochResult: + epoch_num: int + train_loss: float + test_loss: float + epoch_time: float + + def __str__(self) -> str: + return f"epoch: {self.epoch_num}, train loss: {self.train_loss}, test loss: {self.test_loss}" + + +T = TypeVar("T", bound=EpochResult) + + +class Trainer(Iterator[T]): + def __init__( + self, + train_loader: DataLoader, + test_loader: DataLoader, + model: nn.Module, + optimizer: torch.optim.Optimizer, + criterionHandler: CriterionHandler, + epochs: int, + ): + self.epoch_results: list[EpochResult] = [] # store results for each epoch + self.train_loader = train_loader + self.test_loader = test_loader + self.model = model + self.optimizer = optimizer + self.criterionHandler = criterionHandler + self.epochs = epochs + self.curr_epoch = 0 + + if train_loader.batch_size == None: + raise Exception("train_loader batch_size cannot be None") + if test_loader.batch_size == None: + raise Exception("test_loader batch_size cannot be None") + self.num_train_batches = len(train_loader) + # total number of data points used for training per epoch + self.epoch_train_size = train_loader.batch_size * self.num_train_batches + self.num_test_batches = len(test_loader) + # total number of data points used for testing per epoch + self.epoch_test_size = test_loader.batch_size * self.num_test_batches + + def _train_init(self): + self._epoch_batch_loss = 0 # cumulative training/testing loss per epoch + self._start_time = time.time() + self.model.train(True) + + def _train_step(self, inputs: torch.Tensor, labels: torch.Tensor): + self.optimizer.zero_grad() # zero out gradient for each batch + self.model.forward(inputs) # make prediction on input + self._outputs: torch.Tensor = self.model(inputs) # make prediction on input + loss = self.criterionHandler.compute_loss(self._outputs, labels) + loss.backward() # backpropagation + self.optimizer.step() # adjust optimizer weights + self._epoch_batch_loss += float(loss.detach()) + + def _train_end(self): + self._epoch_time = time.time() - self._start_time + self._mean_train_loss = self._epoch_batch_loss / self.num_train_batches + + def _test_init(self): + self.model.train(False) # test the model on test set + self._epoch_batch_loss = 0 + + def _test_step(self, inputs: torch.Tensor, labels: torch.Tensor): + self._test_pred: torch.Tensor = self.model(inputs) + loss = self.criterionHandler.compute_loss(self._test_pred, labels) + self._epoch_batch_loss += float(loss.detach()) + + def _test_end(self): + self._mean_test_loss = self._epoch_batch_loss / self.num_test_batches + + def _get_epoch_result(self): + return EpochResult( + self.curr_epoch, + self._mean_train_loss, + self._mean_test_loss, + self._epoch_time, + ) + + def __next__(self): + if self.curr_epoch < self.epochs: + self._train_init() + for _, (inputs, labels) in enumerate(self.train_loader): + self._train_step(inputs, labels) + self._train_end() + self._test_init() + for _, (inputs, labels) in enumerate(self.test_loader): + self._test_step(inputs, labels) + self._test_end() + epoch_result = self._get_epoch_result() + self.epoch_results.append(epoch_result) + self.curr_epoch += 1 + return epoch_result + else: + raise StopIteration + + +@dataclass +class ClassificationEpochResult(EpochResult): + epoch_num: int + train_loss: float + test_loss: float + epoch_time: float + train_accuracy: float + test_accuracy: float + + def __init__( + self, + epoch_num: int, + train_loss: float, + test_loss: float, + epoch_time: float, + train_accuracy: float, + test_accuracy: float, + ): + super().__init__(epoch_num, train_loss, test_loss, epoch_time) + self.train_accuracy = train_accuracy + self.test_accuracy = test_accuracy + + def __str__(self) -> str: + return f"{super().__str__()} train_acc: {self.train_accuracy}, val_acc: {self.test_loss}" + + +class ClassificationTrainer(Trainer[ClassificationEpochResult]): + def __init__( + self, + train_loader: DataLoader, + test_loader: DataLoader, + model: nn.Module, + optimizer: torch.optim.Optimizer, + criterionHandler: CriterionHandler, + epochs: int, + category_list: list[str], + ): + super().__init__( + train_loader, + test_loader, + model, + optimizer, + criterionHandler, + epochs, + ) + self.labels_last_epoch = [] + self.y_pred_last_epoch = [] + self.category_list = category_list + + def compute_correct(self, predicted, actual): + """ + Given a prediction (usually in logit form for classification problem), count the number of data points that match + their actual labels + Args: + predicted (torch.Tensor): For each row, what's the probability that the instance belongs to each of K classes + actual (torch.Tensor): actual class label + NOTE: Since we have our training data in "batch form", we will be getting the result for each batch in the dataloader + """ + prediction = torch.argmax( + predicted, dim=-1 + ) # identify index of the most likely class + + performance = torch.where( + (prediction == actual), torch.Tensor([1.0]), torch.Tensor([0.0]) + ) # for each row, did you predict correctly? + + return int(torch.sum(performance)) + + def compute_accuracy(self, predicted, actual): + """ + Given a prediction (usually in logit form for classification problem), identify the + most likely label (probabilistically). Usually, for multiclass (more than 2 classes), + Softmax is applied at the end. For binary, apply Sigmoid activation at the last + Args: + predicted (torch.Tensor): For each row, what's the probability that the instance belongs to each of K classes + actual (torch.Tensor): actual class label + NOTE: Since we have our training data in "batch form", we will be getting an accuracy for each batch in the dataloader + """ + + batch_correct_pred = self.compute_correct(predicted, actual) + batch_accuracy = batch_correct_pred / len(predicted) + + return batch_accuracy + + def _train_init(self): + self._train_correct = ( + 0 # number of correct predictions in training set in current epoch + ) + super()._train_init() + + def _train_step(self, inputs: torch.Tensor, labels: torch.Tensor): + super()._train_step(inputs, labels) + self._train_correct += self.compute_correct(self._outputs, labels) + + def _train_end(self): + super()._train_end() + self._train_accuracy = self._train_correct / self.epoch_train_size + + def _test_init(self): + self._test_correct = ( + 0 # number of correct predictions in testing set in current epoch + ) + super()._test_init() + + def _test_step(self, inputs: torch.Tensor, labels: torch.Tensor): + super()._test_step(inputs, labels) + # currently only preserving the prediction array and label array for the last epoch for + # confusion matrix calculation + if self.curr_epoch == self.epochs - 1: + self.y_pred_last_epoch.append(self._test_pred.detach().numpy().squeeze()) + self.labels_last_epoch.append(labels.detach().numpy().squeeze()) + self._test_correct += self.compute_correct(self._test_pred, labels) + + def _test_end(self): + super()._test_end() + self._test_accuracy = self._test_correct / self.epoch_test_size + + def _get_epoch_result(self): + return ClassificationEpochResult( + self.curr_epoch, + self._mean_train_loss, + self._mean_test_loss, + self._epoch_time, + self._train_accuracy, + self._test_accuracy, + ) + + def generate_confusion_matrix(self): + label = [] + y_pred = [] + + label = np.array(self.labels_last_epoch).flatten() + for batch in self.y_pred_last_epoch: + y_pred = np.concatenate( + (y_pred, np.argmax(batch, axis=1)), axis=None + ) # flatten and concatenate + categoryList = np.arange(0, len(self.y_pred_last_epoch[0][0])).tolist() + return confusion_matrix(label, y_pred, labels=categoryList) + + def generate_AUC_ROC_CURVE(self) -> list[tuple[list[float], list[float], float]]: + label_list = [] + y_preds_list = [] + plot_data = [] + + # generating a numerical category list for confusion matrix axis labels, and setting up the y_preds_list and label_list for each category + category_list = np.arange(0, len(self.y_pred_last_epoch[0][0])).tolist() + labels_last_epoch = np.array(self.labels_last_epoch).flatten() + label_list = np.zeros((len(self.category_list), len(labels_last_epoch))) + + for i in range(len(labels_last_epoch)): + label_list[int(labels_last_epoch[i])][i] = 1 + + y_preds_list = np.transpose(np.concatenate(np.array(self.y_pred_last_epoch))) + + for i in range(len(category_list)): + pred_prob = np.array(y_preds_list[i]) + y_test = label_list[i] + fpr, tpr, _ = roc_curve(y_test, pred_prob) + auc = roc_auc_score(y_test, pred_prob) + # this data will be sent to frontend to make interactive plotly graph + plot_data.append((fpr.tolist(), tpr.tolist(), auc)) + return plot_data + + +class RegressionTrainer(Trainer[EpochResult]): + def __init__( + self, + train_loader: DataLoader, + test_loader: DataLoader, + model: nn.Module, + optimizer: torch.optim.Optimizer, + criterionHandler: CriterionHandler, + epochs: int, + ): + super().__init__( + train_loader, + test_loader, + model, + optimizer, + criterionHandler, + epochs, + ) diff --git a/training/training/routes/__init__.py b/training/training/routes/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/training/training/routes/datasets/__init__.py b/training/training/routes/datasets/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/training/training/routes/datasets/default/__init__.py b/training/training/routes/datasets/default/__init__.py new file mode 100644 index 000000000..2a6f5e11b --- /dev/null +++ b/training/training/routes/datasets/default/__init__.py @@ -0,0 +1,5 @@ +from training.routes.datasets.default.columns import router + + +def get_default_datasets_router(): + return router diff --git a/training/training/routes/datasets/default/columns.py b/training/training/routes/datasets/default/columns.py new file mode 100644 index 000000000..ef91ac692 --- /dev/null +++ b/training/training/routes/datasets/default/columns.py @@ -0,0 +1,18 @@ +from django.http import HttpRequest +from ninja import Router, Schema +from training.core.dataset import SklearnDatasetCreator +from training.routes.datasets.default.schemas import DefaultDatasetResponse +from training.routes.schemas import NotFoundError + +router = Router() + + +@router.get( + "{name}/columns", + response={200: DefaultDatasetResponse, 404: NotFoundError}, +) +def defaultDatasets(request: HttpRequest, name: str): + if not name in SklearnDatasetCreator.DEFAULT_DATASETS: + return 404, {"message": "Dataset not found"} + dataset = SklearnDatasetCreator.getDefaultDataset(name) + return 200, {"data": dataset.columns.tolist(), "message": "Success"} diff --git a/training/training/routes/datasets/default/schemas.py b/training/training/routes/datasets/default/schemas.py new file mode 100644 index 000000000..046da0952 --- /dev/null +++ b/training/training/routes/datasets/default/schemas.py @@ -0,0 +1,6 @@ +from ninja import Schema + + +class DefaultDatasetResponse(Schema): + data: list[str] + message: str diff --git a/training/training/routes/schemas.py b/training/training/routes/schemas.py new file mode 100644 index 000000000..8241856ef --- /dev/null +++ b/training/training/routes/schemas.py @@ -0,0 +1,5 @@ +from ninja import Schema + + +class NotFoundError(Schema): + message: str diff --git a/training/training/routes/tabular/__init__.py b/training/training/routes/tabular/__init__.py new file mode 100644 index 000000000..25bffee05 --- /dev/null +++ b/training/training/routes/tabular/__init__.py @@ -0,0 +1,5 @@ +from training.routes.tabular.tabular import router + + +def get_tabular_router(): + return router diff --git a/training/training/routes/tabular/schemas.py b/training/training/routes/tabular/schemas.py new file mode 100644 index 000000000..50e0bb15c --- /dev/null +++ b/training/training/routes/tabular/schemas.py @@ -0,0 +1,22 @@ +from typing import Any, Literal, Optional +from ninja import Schema + + +class LayerParams(Schema): + value: str + parameters: list[Any] + + +class TabularParams(Schema): + target: str + features: list[str] + name: str + problem_type: Literal["CLASSIFICATION", "REGRESSION"] + default: Optional[str] + criterion: str + optimizer_name: str + shuffle: bool + epochs: int + test_size: float + batch_size: int + user_arch: list[LayerParams] diff --git a/training/training/routes/tabular/tabular.py b/training/training/routes/tabular/tabular.py new file mode 100644 index 000000000..527b3f00e --- /dev/null +++ b/training/training/routes/tabular/tabular.py @@ -0,0 +1,64 @@ +from typing import Literal, Optional +from django.http import HttpRequest +from ninja import Router, Schema +from training.core.criterion import getCriterionHandler +from training.core.dataset import SklearnDatasetCreator +from training.core.dl_model import DLModel +from torch.utils.data import DataLoader +from training.core.optimizer import getOptimizer +from training.core.trainer import ClassificationTrainer, RegressionTrainer +from training.routes.tabular.schemas import TabularParams + +router = Router() + + +@router.post("") +def tabularTrain(request: HttpRequest, tabularParams: TabularParams): + if tabularParams.default: + dataCreator = SklearnDatasetCreator.fromDefault( + tabularParams.default, tabularParams.test_size, tabularParams.shuffle + ) + train_loader = DataLoader( + dataCreator.createTrainDataset(), + batch_size=tabularParams.batch_size, + shuffle=False, + drop_last=True, + ) + + test_loader = DataLoader( + dataCreator.createTestDataset(), + batch_size=tabularParams.batch_size, + shuffle=False, + drop_last=True, + ) + + model = DLModel.fromLayerParamsList(tabularParams.user_arch) + optimizer = getOptimizer(model, tabularParams.optimizer_name, 0.05) + criterionHandler = getCriterionHandler(tabularParams.criterion) + if tabularParams.problem_type == "CLASSIFICATION": + trainer = ClassificationTrainer( + train_loader, + test_loader, + model, + optimizer, + criterionHandler, + tabularParams.epochs, + dataCreator.getCategoryList(), + ) + for epoch_result in trainer: + print(epoch_result) + print(trainer.labels_last_epoch, trainer.y_pred_last_epoch) + print(trainer.generate_confusion_matrix()) + print(trainer.generate_AUC_ROC_CURVE()) + return trainer.generate_AUC_ROC_CURVE() + else: + trainer = RegressionTrainer( + train_loader, + test_loader, + model, + optimizer, + criterionHandler, + tabularParams.epochs, + ) + for epoch_result in trainer: + print(epoch_result) diff --git a/training/training/settings.py b/training/training/settings.py new file mode 100644 index 000000000..737c777c1 --- /dev/null +++ b/training/training/settings.py @@ -0,0 +1,132 @@ +""" +Django settings for training project. + +Generated by 'django-admin startproject' using Django 4.2.3. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/topics/settings/ + +For the full list of settings and their values, see +https://docs.djangoproject.com/en/4.2/ref/settings/ +""" + +from pathlib import Path + +# Build paths inside the project like this: BASE_DIR / 'subdir'. +BASE_DIR = Path(__file__).resolve().parent.parent + + +# Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/4.2/howto/deployment/checklist/ + +# SECURITY WARNING: keep the secret key used in production secret! +SECRET_KEY = "INSERT_SECRET_HERE" + +# SECURITY WARNING: don't run with debug turned on in production! +DEBUG = True + +ALLOWED_HOSTS = [] + + +# Application definition + +INSTALLED_APPS = [ + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", + "corsheaders", +] + +MIDDLEWARE = [ + "corsheaders.middleware.CorsMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.security.SecurityMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", +] + +ROOT_URLCONF = "training.urls" + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + }, +] + +WSGI_APPLICATION = "training.wsgi.application" + + +# Database +# https://docs.djangoproject.com/en/4.2/ref/settings/#databases + +DATABASES = { + "default": { + "ENGINE": "django.db.backends.sqlite3", + "NAME": BASE_DIR / "db.sqlite3", + } +} + + +# Password validation +# https://docs.djangoproject.com/en/4.2/ref/settings/#auth-password-validators + +AUTH_PASSWORD_VALIDATORS = [ + { + "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator", + }, + { + "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator", + }, +] + + +# Internationalization +# https://docs.djangoproject.com/en/4.2/topics/i18n/ + +LANGUAGE_CODE = "en-us" + +TIME_ZONE = "UTC" + +USE_I18N = True + +USE_TZ = True + + +# Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/4.2/howto/static-files/ + +STATIC_URL = "static/" + +# Default primary key field type +# https://docs.djangoproject.com/en/4.2/ref/settings/#default-auto-field + +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" + + +NINJA_DOCS_VIEW = "redoc" + +CORS_ALLOWED_ORIGINS = ["http://localhost:3000"] + +APPEND_SLASH = False diff --git a/training/training/urls.py b/training/training/urls.py new file mode 100644 index 000000000..c79e13107 --- /dev/null +++ b/training/training/urls.py @@ -0,0 +1,39 @@ +""" +URL configuration for training project. + +The `urlpatterns` list routes URLs to views. For more information please see: + https://docs.djangoproject.com/en/4.2/topics/http/urls/ +Examples: +Function views + 1. Add an import: from my_app import views + 2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views + 1. Add an import: from other_app.views import Home + 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf + 1. Import the include() function: from django.urls import include, path + 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" + +from django.contrib import admin +from django.http import HttpRequest +from django.urls import path +from ninja import NinjaAPI, Schema +from training.routes.datasets.default import get_default_datasets_router +from training.routes.tabular import get_tabular_router + +api = NinjaAPI() + + +@api.get("/test") +def test(request: HttpRequest): + return 200, {"result": "200 Backend surface test successful"} + + +api.add_router("/datasets/default/", get_default_datasets_router()) +api.add_router("/tabular", get_tabular_router()) + +urlpatterns = [ + path("admin/", admin.site.urls), + path("api/", api.urls), # type: ignore +] diff --git a/training/training/wsgi.py b/training/training/wsgi.py new file mode 100644 index 000000000..212d61e82 --- /dev/null +++ b/training/training/wsgi.py @@ -0,0 +1,16 @@ +""" +WSGI config for training project. + +It exposes the WSGI callable as a module-level variable named ``application``. + +For more information on this file, see +https://docs.djangoproject.com/en/4.2/howto/deployment/wsgi/ +""" + +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "training.settings") + +application = get_wsgi_application() diff --git a/yarn.lock b/yarn.lock index 4a5801883..fb57ccd13 100644 --- a/yarn.lock +++ b/yarn.lock @@ -1,2 +1,4 @@ # THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY. # yarn lockfile v1 + +