From e1553c4f2b6f5198930f4f0a97f5cf70204f2856 Mon Sep 17 00:00:00 2001 From: Sean Yang Date: Thu, 13 Jun 2024 17:57:33 -0700 Subject: [PATCH] add flare site to github pages (#2642) Co-authored-by: Chester Chen <512707+chesterxgchen@users.noreply.github.com> --- .github/workflows/deploy.yml | 39 + .nojekyll | 0 web/.gitignore | 13 + web/.npmrc | 1 + web/.prettierrc | 5 + web/README.md | 38 + web/astro.config.mjs | 8 + web/package-lock.json | 8991 +++++++++++++++++ web/package.json | 29 + web/public/.nojekyll | 0 web/public/favicon.ico | Bin 0 -> 25214 bytes web/src/components/code.astro | 1051 ++ web/src/components/footer.astro | 91 + web/src/components/gettingStarted.astro | 275 + web/src/components/hero.astro | 89 + web/src/components/learnMore.astro | 62 + web/src/components/overview.astro | 120 + web/src/components/research.astro | 206 + web/src/components/tutorials.astro | 706 ++ web/src/env.d.ts | 2 + web/src/images/google_colab.svg.png | Bin 0 -> 19888 bytes web/src/images/nvflare_graphic_animation.mp4 | Bin 0 -> 6438063 bytes web/src/images/nvflare_research.png | Bin 0 -> 1012356 bytes web/src/images/nvidia_eye.png | Bin 0 -> 249801 bytes web/src/images/research/exam.jpg | Bin 0 -> 175955 bytes web/src/images/research/mammography.jpg | Bin 0 -> 61641 bytes .../images/research/mammography_graphs.jpg | Bin 0 -> 84794 bytes web/src/images/research/pancreas.jpg | Bin 0 -> 136430 bytes web/src/images/research/research_works.jpg | Bin 0 -> 175962 bytes web/src/images/research/sun.jpg | Bin 0 -> 46881 bytes web/src/layouts/Layout.astro | 49 + web/src/pages/404.astro | 80 + web/src/pages/catalog.astro | 10 + web/src/pages/index.astro | 18 + web/src/pages/research.astro | 10 + web/tailwind.config.cjs | 20 + web/tsconfig.json | 16 + 37 files changed, 11929 insertions(+) create mode 100644 .github/workflows/deploy.yml create mode 100644 .nojekyll create mode 100644 web/.gitignore create mode 100644 web/.npmrc create mode 100644 web/.prettierrc create mode 100644 web/README.md create mode 100644 web/astro.config.mjs create mode 100644 web/package-lock.json create mode 100644 web/package.json create mode 100644 web/public/.nojekyll create mode 100644 web/public/favicon.ico create mode 100644 web/src/components/code.astro create mode 100644 web/src/components/footer.astro create mode 100644 web/src/components/gettingStarted.astro create mode 100644 web/src/components/hero.astro create mode 100644 web/src/components/learnMore.astro create mode 100644 web/src/components/overview.astro create mode 100644 web/src/components/research.astro create mode 100644 web/src/components/tutorials.astro create mode 100644 web/src/env.d.ts create mode 100644 web/src/images/google_colab.svg.png create mode 100644 web/src/images/nvflare_graphic_animation.mp4 create mode 100644 web/src/images/nvflare_research.png create mode 100644 web/src/images/nvidia_eye.png create mode 100644 web/src/images/research/exam.jpg create mode 100644 web/src/images/research/mammography.jpg create mode 100644 web/src/images/research/mammography_graphs.jpg create mode 100644 web/src/images/research/pancreas.jpg create mode 100644 web/src/images/research/research_works.jpg create mode 100644 web/src/images/research/sun.jpg create mode 100644 web/src/layouts/Layout.astro create mode 100644 web/src/pages/404.astro create mode 100644 web/src/pages/catalog.astro create mode 100644 web/src/pages/index.astro create mode 100644 web/src/pages/research.astro create mode 100644 web/tailwind.config.cjs create mode 100644 web/tsconfig.json diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000000..00a0b79d91 --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,39 @@ +name: Deploy to GitHub Pages + +on: + # Trigger the workflow every time you push to the `main` branch + # Using a different branch name? Replace `main` with your branch’s name + push: + branches: [ web ] + # Allows you to run this workflow manually from the Actions tab on GitHub. + workflow_dispatch: + +# Allow this job to clone the repo and create a page deployment +permissions: + contents: read + pages: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout your repository using git + uses: actions/checkout@v4 + - name: Install, build, and upload your site + uses: withastro/action@v2 + with: + path: ./web # The root location of your Astro project inside the repository. (optional) + # node-version: 20 # The specific version of Node that should be used to build your site. Defaults to 20. (optional) + # package-manager: pnpm@latest # The Node package manager that should be used to install dependencies and build your site. Automatically detected based on your lockfile. (optional) + + deploy: + needs: build + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000000..e69de29bb2 diff --git a/web/.gitignore b/web/.gitignore new file mode 100644 index 0000000000..ba277fd9d9 --- /dev/null +++ b/web/.gitignore @@ -0,0 +1,13 @@ +# build output +dist/ +.output/ +.astro + +# dependencies +node_modules/ + +# logs +npm-debug.log* + +# macOS-specific files +.DS_Store diff --git a/web/.npmrc b/web/.npmrc new file mode 100644 index 0000000000..4fd021952d --- /dev/null +++ b/web/.npmrc @@ -0,0 +1 @@ +engine-strict=true \ No newline at end of file diff --git a/web/.prettierrc b/web/.prettierrc new file mode 100644 index 0000000000..42f85a19ed --- /dev/null +++ b/web/.prettierrc @@ -0,0 +1,5 @@ +{ + "tabWidth": 2, + "useTabs": false, + "bracketSameLine": true +} \ No newline at end of file diff --git a/web/README.md b/web/README.md new file mode 100644 index 0000000000..f252b86427 --- /dev/null +++ b/web/README.md @@ -0,0 +1,38 @@ +# NVIDIA FLARE GitHub Pages Website + +## Installation + +### 1. Install Dependencies + +```bash +npm install +``` + +### 2. Start development Server + +```bash +npm run dev +``` + +### 3. Preview & Build + +```bash +npm run preview +npm run build +``` + +## Project Structure + +``` +/ +├── public/ +│ └── ... +├── src/ +│ ├── components/ +│ │ └── ... +│ ├── layouts/ +│ │ └── ... +│ └── pages/ +│ └── ... +└── package.json +``` \ No newline at end of file diff --git a/web/astro.config.mjs b/web/astro.config.mjs new file mode 100644 index 0000000000..32414d4b29 --- /dev/null +++ b/web/astro.config.mjs @@ -0,0 +1,8 @@ +import { defineConfig } from "astro/config"; +import tailwind from "@astrojs/tailwind"; + +export default defineConfig({ + site: "https://nvidia.github.io", + base: "/NVFlare", + integrations: [tailwind()], +}); diff --git a/web/package-lock.json b/web/package-lock.json new file mode 100644 index 0000000000..7a2292b2ed --- /dev/null +++ b/web/package-lock.json @@ -0,0 +1,8991 @@ +{ + "name": "NVIDIA Flare", + "version": "0.0.2", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "NVIDIA Flare", + "version": "0.0.2", + "dependencies": 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z`Bx70dCFJwq2J|v8T7}2a6F3bYrVhqd7ehjGhZ9v*z(MlXN*??mjfJwgMm>%4X_g6 zyH)D%1I`74^KBgu>T&?{J}$7Y$NLeqQJ(Q^0Qe4SH9#6;F&h{d{}(|!P<2(eKZrW{ z37$^}XX2i-6Y7kiIbRRkZw1}RE%Ei802;^bEkOTYtHJgu!*?4O0z5+<4O|D9@$Nx+ zFW?k_dW;R`+P>$+P-bjNdk&x;#?;LHrDiBW3-|vQsNV)qj`rBbm=P-geIBa)Gyasa ze?52&{a^gPFZF5cTR4WsKIhyFfVR?rvDFsxmDg`6yT+F3KgY??T7COsY@O|q;0Ch0N`d2>m+~hg3(LFFX_PLI&1?VgLai;GB$MP)T_dfmC$a(z* z-&66Nsi<=tsGEHHdj*{PdhQP2e;U($I|;d2*Vmw|cWa)*b>wK^hrk)Y=Yd0ks{wQE zKY_CT7QSAWZ%2J7+w?b>|0bAvOz%V+Y%%2*-uPTP~L)@c%Ia`n+MlXOCF@J~odNt52G(-dMfW<3_B!SQ}i~Z^MN#94j=7 z6>T18MeRzfa&**MDHE)o<{0>0YjYGjz-;skuu!P-oPsyH7+}sR_AA~wqU95sS`Hg8 z?MF2Z3-|ES->>aF{VQ0@J^+TkSKoHZqCx~NI)eM4Zvz_uzK>lD>;>!%aDT8KFnesh z7905+mvFE1bD#^@1NaVbm0y1ic?&QW*crGGco*mw%3u5q+RXyIFYgIF4^Z}gU={EM zfX^SVs4^Y)oCAOlmArmldmr^_z_Y-&fI|TK$bA~^?+(x|W5PX|zMs>+-d9lnbl{J` zfdJ+CosuZPd*gose9pKApk58i-<4v2Hv-hpdpG&7^UEh7?*YCFd=~f}P%O{>+_aQ- z&IQDBk&ebENAKi<+T=(AD^^8 nWL_H None: + self.info("Start FedAvg.") + + model = self.load_model() + model.start_round = self.start_round + model.total_rounds = self.num_rounds + + for self.current_round in range(self.start_round, self.start_round + self.num_rounds): + self.info(f"Round {self.current_round} started.") + model.current_round = self.current_round + + clients = self.sample_clients(self.min_clients) + + results = self.send_model_and_wait(targets=clients, data=model) + + aggregate_results = self.aggregate(results) + + model = self.update_model(model, aggregate_results) + + self.save_model(model) + + self.info("Finished FedAvg.") +`; + +const jobCode_pt = ` +from .cifar10_pt_fl import Net +from nvflare import FedAvg, FedJob, ScriptExecutor + +if __name__ == "__main__": + n_clients = 2 + num_rounds = 2 + train_script = "cifar10_pt_fl.py" + + job = FedJob(name="cifar10_pt_fedavg") + + # Define the controller workflow and send to server + controller = FedAvg( + min_clients=n_clients, + num_rounds=num_rounds, + ) + job.to(controller, "server") + + # Define the initial global model and send to server + job.to(Net(), "server") + + # Add clients + for i in range(n_clients): + executor = ScriptExecutor( + task_script_path=train_script, task_script_args="" + # f"--batch_size 32 --data_path /tmp/data/site-{i}" + ) + job.to(executor, f"site-{i}", gpu=0) + + # job.export_job("/tmp/nvflare/jobs/job_config") + job.simulator_run("/tmp/nvflare/jobs/workdir") +`; + +const runCode_pt = ` +python3 fedavg_cifar10_pt_job.py +`; + +// Lightning Code Sections -------------------------------------------------- + +const installCode_lt = ` +pip install nvflare pytorch_lightning +`; + +const clientCode_lt = ` +import torch +import torch.nn.functional as F +import torch.optim as optim +import torchvision +import torchvision.transforms as transforms +from pytorch_lightning import LightningDataModule, LightningModule Trainer, seed_everything +from torch.utils.data import DataLoader, random_split +from torchmetrics import Accuracy + + +class LitNet(LightningModule): + def __init__(self): + super().__init__() + self.save_hyperparameters() + self.model = Net() + self.train_acc = Accuracy(task="multiclass", num_classes=NUM_CLASSES) + self.valid_acc = Accuracy(task="multiclass", num_classes=NUM_CLASSES) + # (optional) pass additional information via self.__fl_meta__ + self.__fl_meta__ = {} + + def forward(self, x): + out = self.model(x) + return out + + def training_step(self, batch, batch_idx): + x, labels = batch + outputs = self(x) + loss = criterion(outputs, labels) + self.train_acc(outputs, labels) + self.log("train_loss", loss) + self.log("train_acc", self.train_acc, on_step=True, on_epoch=False) + return loss + + def evaluate(self, batch, stage=None): + x, labels = batch + outputs = self(x) + loss = criterion(outputs, labels) + self.valid_acc(outputs, labels) + + if stage: + self.log(f"{stage}_loss", loss) + self.log(f"{stage}_acc", self.valid_acc, on_step=True, on_epoch=True) + return outputs + + def validation_step(self, batch, batch_idx): + self.evaluate(batch, "val") + + def test_step(self, batch, batch_idx): + self.evaluate(batch, "test") + + def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any: + return self.evaluate(batch) + + def configure_optimizers(self): + optimizer = optim.SGD(self.parameters(), lr=0.001, momentum=0.9) + return {"optimizer": optimizer} + + +class CIFAR10DataModule(LightningDataModule): + def __init__(self, data_dir: str = DATASET_PATH, batch_size: int = BATCH_SIZE): + super().__init__() + self.data_dir = data_dir + self.batch_size = batch_size + + def prepare_data(self): + torchvision.datasets.CIFAR10(root=self.data_dir, train=True, download=True, transform=transform) + torchvision.datasets.CIFAR10(root=self.data_dir, train=False, download=True, transform=transform) + + def setup(self, stage: str): + # Assign train/val datasets for use in dataloaders + if stage == "fit" or stage == "validate": + cifar_full = torchvision.datasets.CIFAR10( + root=self.data_dir, train=True, download=False, transform=transform + ) + self.cifar_train, self.cifar_val = random_split(cifar_full, [0.8, 0.2]) + + # Assign test dataset for use in dataloader(s) + if stage == "test" or stage == "predict": + self.cifar_test = torchvision.datasets.CIFAR10( + root=self.data_dir, train=False, download=False, transform=transform + ) + + def train_dataloader(self): + return DataLoader(self.cifar_train, batch_size=self.batch_size) + + def val_dataloader(self): + return DataLoader(self.cifar_val, batch_size=self.batch_size) + + def test_dataloader(self): + return DataLoader(self.cifar_test, batch_size=self.batch_size) + + def predict_dataloader(self): + return DataLoader(self.cifar_test, batch_size=self.batch_size) + + +# (1) import nvflare lightning client API +import nvflare.client.lightning as flare + +seed_everything(7) + + +DATASET_PATH = "/tmp/nvflare/data" +BATCH_SIZE = 4 + +transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + +def main(): + model = LitNet() + cifar10_dm = CIFAR10DataModule() + if torch.cuda.is_available(): + trainer = Trainer(max_epochs=1, accelerator="gpu", devices=1 if torch.cuda.is_available() else None) + else: + trainer = Trainer(max_epochs=1, devices=None) + + # (2) patch the lightning trainer + flare.patch(trainer) + + while flare.is_running(): + # (3) receives FLModel from NVFlare + # Note that we don't need to pass this input_model to trainer + # because after flare.patch the trainer.fit/validate will get the + # global model internally + input_model = flare.receive() + print(f"\\n[Current Round={input_model.current_round}, Site = {flare.get_site_name()}]\\n") + + # (4) evaluate the current global model to allow server-side model selection + print("--- validate global model ---") + trainer.validate(model, datamodule=cifar10_dm) + + # perform local training starting with the received global model + print("--- train new model ---") + trainer.fit(model, datamodule=cifar10_dm) + + # test local model + print("--- test new model ---") + trainer.test(ckpt_path="best", datamodule=cifar10_dm) + + # get predictions + print("--- prediction with new best model ---") + trainer.predict(ckpt_path="best", datamodule=cifar10_dm) + + +if __name__ == "__main__": + main() +`; + +const serverCode_lt = ` +from .base_fedavg import BaseFedAvg + +class FedAvg(BaseFedAvg): + def run(self) -> None: + self.info("Start FedAvg.") + + model = self.load_model() + model.start_round = self.start_round + model.total_rounds = self.num_rounds + + for self.current_round in range(self.start_round, self.start_round + self.num_rounds): + self.info(f"Round {self.current_round} started.") + model.current_round = self.current_round + + clients = self.sample_clients(self.min_clients) + + results = self.send_model_and_wait(targets=clients, data=model) + + aggregate_results = self.aggregate(results) + + model = self.update_model(model, aggregate_results) + + self.save_model(model) + + self.info("Finished FedAvg.") +`; + +const jobCode_lt = ` +from .cifar10_lightning_fl import LitNet + +from nvflare import FedAvg, FedJob, ScriptExecutor + +if __name__ == "__main__": + n_clients = 2 + num_rounds = 2 + train_script = "cifar10_lightning_fl.py" + + job = FedJob(name="cifar10_lightning_fedavg") + + # Define the controller workflow and send to server + controller = FedAvg( + min_clients=n_clients, + num_rounds=num_rounds, + ) + job.to(controller, "server") + + # Define the initial global model and send to server + job.to(LitNet(), "server") + + # Add clients + for i in range(n_clients): + executor = ScriptExecutor( + task_script_path=train_script, task_script_args="" + # f"--batch_size 32 --data_path /tmp/data/site-{i}" + ) + job.to(executor, f"site-{i}", gpu=0) + + # job.export_job("/tmp/nvflare/jobs/job_config") + job.simulator_run("/tmp/nvflare/jobs/workdir") +`; + +const runCode_lt = ` +python3 fedavg_cifar10_lightning_job.py +`; + +// TensorFlow Code Sections -------------------------------------------------- + +const installCode_tf = ` +pip install nvflare tensorflow +`; + +const clientCode_tf = ` +from tensorflow.keras import datasets +from tensorflow.keras import Model, layers, losses + + +class TFNet(Model): + def __init__(self, input_shape): + super().__init__() + self._input_shape = input_shape # Required to get constructor arguments in job config + self.conv1 = layers.Conv2D(6, 5, activation="relu") + self.pool = layers.MaxPooling2D((2, 2), 2) + self.conv2 = layers.Conv2D(16, 5, activation="relu") + self.flatten = layers.Flatten() + self.fc1 = layers.Dense(120, activation="relu") + self.fc2 = layers.Dense(84, activation="relu") + self.fc3 = layers.Dense(10) + loss_fn = losses.SparseCategoricalCrossentropy(from_logits=True) + self.compile(optimizer="sgd", loss=loss_fn, metrics=["accuracy"]) + self.build(input_shape) + + def call(self, x): + x = self.pool(self.conv1(x)) + x = self.pool(self.conv2(x)) + x = self.flatten(x) + x = self.fc1(x) + x = self.fc2(x) + x = self.fc3(x) + return x + + +# (1) import nvflare client API +import nvflare.client as flare + +PATH = "./tf_model.ckpt" + + +def main(): + (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() + + # Normalize pixel values to be between 0 and 1 + train_images, test_images = train_images / 255.0, test_images / 255.0 + + model = TFNet(input_shape=(None, 32, 32, 3)) + model.summary() + + # (2) initializes NVFlare client API + flare.init() + + # (3) gets FLModel from NVFlare + while flare.is_running(): + input_model = flare.receive() + print(f"current_round={input_model.current_round}") + + # (optional) print system info + system_info = flare.system_info() + print(f"NVFlare system info: {system_info}") + + # (4) loads model from NVFlare + for k, v in input_model.params.items(): + model.get_layer(k).set_weights(v) + + # (5) evaluate aggregated/received model + _, test_global_acc = model.evaluate(test_images, test_labels, verbose=2) + print( + f"Accuracy of the received model on round {input_model.current_round} on the 10000 test images: + {test_global_acc * 100} %" + ) + + model.fit(train_images, train_labels, epochs=1, validation_data=(test_images, test_labels)) + + print("Finished Training") + + model.save_weights(PATH) + + _, test_acc = model.evaluate(test_images, test_labels, verbose=2) + print(f"Accuracy of the model on the 10000 test images: {test_acc * 100} %") + + # (6) construct trained FL model (A dict of {layer name: layer weights} from the keras model) + output_model = flare.FLModel( + params={layer.name: layer.get_weights() for layer in model.layers}, + metrics={"accuracy": test_global_acc} + ) + # (7) send model back to NVFlare + flare.send(output_model) + + +if __name__ == "__main__": + main() +`; + +const serverCode_tf = ` +from .base_fedavg import BaseFedAvg + +class FedAvg(BaseFedAvg): + def run(self) -> None: + self.info("Start FedAvg.") + + model = self.load_model() + model.start_round = self.start_round + model.total_rounds = self.num_rounds + + for self.current_round in range(self.start_round, self.start_round + self.num_rounds): + self.info(f"Round {self.current_round} started.") + model.current_round = self.current_round + + clients = self.sample_clients(self.min_clients) + + results = self.send_model_and_wait(targets=clients, data=model) + + aggregate_results = self.aggregate(results) + + model = self.update_model(model, aggregate_results) + + self.save_model(model) + + self.info("Finished FedAvg.") +`; + +const jobCode_tf = ` +from .cifar10_tf_fl import TFNet + +from nvflare import FedAvg, FedJob, ScriptExecutor + +if __name__ == "__main__": + n_clients = 2 + num_rounds = 2 + train_script = "cifar10_tf_fl.py" + + job = FedJob(name="cifar10_tf_fedavg") + + # Define the controller workflow and send to server + controller = FedAvg( + min_clients=n_clients, + num_rounds=num_rounds, + ) + job.to(controller, "server") + + # Define the initial global model and send to server + job.to(TFNet(input_shape=(None, 32, 32, 3)), "server") + + # Add clients + for i in range(n_clients): + executor = ScriptExecutor( + task_script_path=train_script, task_script_args="" + # f"--batch_size 32 --data_path /tmp/data/site-{i}" + ) + job.to(executor, f"site-{i}", gpu=0) + + # job.export_job("/tmp/nvflare/jobs/job_config") + job.simulator_run("/tmp/nvflare/jobs/workdir") +`; + +const runCode_tf = ` +python3 fedavg_cifar10_tf_job.py +`; + +const frameworks = [ + { + id: "pytorch", + colab_link: "https://colab.research.google.com/github/NVIDIA/NVFlare/blob/main/examples/getting_started/pt/nvflare_pt_getting_started.ipynb", + github_link: "https://github.com/NVIDIA/NVFlare/blob/main/examples/getting_started/pt/fedavg_script_executor_cifar10.py", + sections: [ + { + id: "install-pytorch", + type: "install", + framework: "pytorch", + title: "Installation", + description: + "Install the required PyTorch dependencies for this example.", + code: installCode_pt, + }, + { + id: "client-pytorch", + type: "client", + framework: "pytorch", + title: "Client Code (cifar10_pt_fl.py)", + description: + "We use the Client API to convert the centralized training PyTorch code into federated learning code with only a few lines of changes highlighted below. Essentially the client will receive a model from FLARE, perform local training and validation, and then send the model back to FLARE.", + code: clientCode_pt, + highlighted_lines: "29,58,61,63,139-143,145", + }, + { + id: "server-pytorch", + type: "server", + framework: "pytorch", + title: "Server Code (fedavg.py)", + description: + "The ModelController API is used to write a federated routine with mechanisms for distributing and receiving models from clients. Here we implement the basic FedAvg algorithm using some helper functions from BaseFedAvg.", + code: serverCode_pt, + highlighted_lines: "7,17,23", + }, + { + id: "job-pytorch", + type: "job", + framework: "pytorch", + title: "Job Code (fedavg_cifar10_pt_job.py)", + description: + "Lastly we construct our FLARE job with our 'cifar10_pt_fl.py' client script and 'FedAvg' server controller. We then run the job with the FL simulator.", + code: jobCode_pt, + }, + { + id: "run-pytorch", + type: "run", + framework: "pytorch", + title: "Run the Job", + description: + "To run the job with the simulator, copy the code and execute the job script, or run in Google Colab. Alternatively, export the job to a configuration and run the job in other modes.", + code: runCode_pt, + }, + ], + }, + { + id: "lightning", + colab_link: "https://colab.research.google.com/github/NVIDIA/NVFlare/blob/main/examples/getting_started/pt/nvflare_lightning_getting_started.ipynb", + github_link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/getting_started/pt/fedavg_script_executor_lightning_cifar10.py", + sections: [ + { + id: "install-lightning", + type: "install", + framework: "lightning", + title: "Installation", + description: + "Install the required PyTorch Lightning dependencies for this example.", + code: installCode_lt, + }, + { + id: "client-lightning", + type: "client", + framework: "lightning", + title: "Client Code (cifar10_lightning_fl.py)", + description: + "We use the Client API to convert the centralized training PyTorch Lightning code into federated learning code with only a few lines of changes highlighted below. Essentially the client will receive a model from FLARE, perform local training and validation, and then send the model back to FLARE.", + code: clientCode_lt, + highlighted_lines: "97,116,118,123", + }, + { + id: "server-lightning", + type: "server", + framework: "lightning", + title: "Server Code (fedavg.py)", + description: + "The ModelController API is used to write a federated routine with mechanisms for distributing and receiving models from clients. Here we implement the basic FedAvg algorithm using some helper functions from BaseFedAvg..", + code: serverCode_lt, + highlighted_lines: "7,17,23", + }, + { + id: "job-lightning", + type: "job", + framework: "lightning", + title: "Job Code (fedavg_cifar10_lightning_job.py)", + description: + "Lastly we construct our FLARE job with our 'cifar10_lightning_fl.py' client script and 'FedAvg' server controller. We then run the job with the FL simulator.", + code: jobCode_lt, + }, + { + id: "run-lightning", + type: "run", + framework: "lightning", + title: "Run the Job", + description: + "To run the job with the simulator, copy the code and execute the job script, or run in Google Colab. Alternatively, export the job to a configuration and run the job in other modes.", + code: runCode_lt, + }, + ], + }, + { + id: "tensorflow", + colab_link: "https://colab.research.google.com/github/NVIDIA/NVFlare/blob/main/examples/getting_started/tf/nvflare_tf_getting_started.ipynb", + github_link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/getting_started/tf/tf_fedavg_script_executor_cifar10.py", + sections: [ + { + id: "install-tensorflow", + type: "install", + framework: "tensorflow", + title: "Installation", + description: + "Install the required TensorFlow dependencies for this example.", + code: installCode_tf, + }, + { + id: "client-tensorflow", + type: "client", + framework: "tensorflow", + title: "Client Code (cifar10_tf_fl.py)", + description: + "We use the Client API to convert the centralized training TensorFlow code into federated learning code with only a few lines of changes highlighted below. Essentially the client will receive a model from FLARE, perform local training and validation, and then send the model back to FLARE.", + code: clientCode_tf, + highlighted_lines: "31,46,49,50,78-81,83", + }, + { + id: "server-tensorflow", + type: "server", + framework: "tensorflow", + title: "Server Code (fedavg.py)", + description: + "The ModelController API is used to write a federated routine with mechanisms for distributing and receiving models from clients. Here we implement the basic FedAvg algorithm using some helper functions from BaseFedAvg..", + code: serverCode_tf, + highlighted_lines: "7,17,23", + }, + { + id: "job-tensorflow", + type: "job", + framework: "tensorflow", + title: "Job Code (fedavg_cifar10_tf_job.py)", + description: + "Lastly we construct our FLARE job with our 'cifar10_tf_fl.py' client script and 'FedAvg' server controller. We then run the job with the FL simulator.", + code: jobCode_tf, + }, + { + id: "run-tensorflow", + type: "run", + framework: "tensorflow", + title: "Run the Job", + description: + "To run the job with the simulator, copy the code and execute the job script, or run in Google Colab. Alternatively, export the job to a configuration and run the job in other modes.", + code: runCode_tf, + }, + ], + } +] + +--- + +
+
+ +
+

+ Example Code +

+

+ Try out these example code blocks below, where we showcase how simple it is to adapt + a popular machine learning framework to a federated learning scenario with FLARE. + For more details, refer to the getting started walkthrough guide above. +

+
+ +
+ + + + + + + + + + + NVIDIA logo + + Run in Google Colab + + + + + + + + View on Github + + +
+ + +
+ +
+ +
    + + + +
+ + +
+ + + + + +
+ + +
+ + +
+

+ To dive deeper into what FLARE has to offer, explore our catalog of various tutorials and examples showcasing everything from the core features of FLARE to advanced applications. +

+ Tutorial Catalog -> +
+
+
+ + + + + + + diff --git a/web/src/components/footer.astro b/web/src/components/footer.astro new file mode 100644 index 0000000000..a8d5400f57 --- /dev/null +++ b/web/src/components/footer.astro @@ -0,0 +1,91 @@ +--- +import NvidiaLogo from '../images/nvidia_eye.png'; +--- + diff --git a/web/src/components/gettingStarted.astro b/web/src/components/gettingStarted.astro new file mode 100644 index 0000000000..2f6555f8af --- /dev/null +++ b/web/src/components/gettingStarted.astro @@ -0,0 +1,275 @@ +--- +const walkthrough = [ + { + id: "step1", + step: "Step 1", + title: "Introduction to FLARE", + description: + "Learn about the core concepts and fundamentals of FLARE to help you get started.", + button_text: "Read More", + link: "https://nvflare.readthedocs.io/en/main/flare_overview.html", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=5e1b5b52-1bfc-49a2-995d-64cff4370c67&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step2", + step: "Step 2", + title: "Server Code", + description: + "Use the ModelController API to write a federated control flow for FedAvg.", + button_text: "View Source", + link: "https://github.com/NVIDIA/NVFlare/blob/main/nvflare/app_common/workflows/fedavg.py", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=893f599e-3068-4cb2-b22f-0cf8cf354186&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step3", + step: "Step 3", + title: "Client Code", + description: + "Use the Client API to write local training code for a PyTorch CIFAR-10 trainer.", + button_text: "View Source", + link: "https://github.com/NVIDIA/NVFlare/blob/main/examples/getting_started/pt/src/cifar10_fl.py", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=f75af046-c211-47ae-b682-a9d289dad42b&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step4", + step: "Step 4", + title: "FedJob and Simulator", + description: + "Formulate the FLARE job and simulate a federated run with the multi-process simulator.", + button_text: "View Notebook", + link: "https://colab.research.google.com/github/NVIDIA/NVFlare/blob/main/examples/getting_started/pt/nvflare_pt_getting_started.ipynb", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=09560ecb-f0b6-44b7-9913-033a853e890d&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step5", + step: "Step 5", + title: "Next Steps", + description: + "Learn more about FLARE and taking federated learning from simulation to the real world.", + button_text: "Tutorial Catalog", + link: "/NVFlare/catalog", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=bd0c2cbe-514c-4bb8-a37a-230bba95eae0&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, +]; + +const km = [ + { + id: "step1", + step: "Step 1", + title: "Federated Machine Learning", + description: + "Learn how to formulate a federated machine learning algorithm.", + button_text: "View Source", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/kaplan-meier-he", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=9ea8fe94-b607-49a7-bb7b-20edfde3b04d&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step2", + step: "Step 2", + title: "Kaplan-Meier Analysis", + description: + "What is Kaplan-Meier Analysis and how can we adapt it to a federated setting?", + button_text: "View Source", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/kaplan-meier-he", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=dedd74d4-5604-4aae-ad8f-7378a22643db&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, + { + id: "step3", + step: "Step 3", + title: "Implementation with FLARE", + description: + "Dive into an end-to-end implementation of federated Kaplan-Meier using FLARE.", + button_text: "View Source", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/kaplan-meier-he/src", + video: "https://nvidia-my.sharepoint.com/personal/seyang_nvidia_com/_layouts/15/embed.aspx?UniqueId=d4266248-b756-417a-8fbe-f0fe233f5d1c&embed=%7B%22hvm%22%3Atrue%2C%22ust%22%3Atrue%7D&referrer=StreamWebApp&referrerScenario=EmbedDialog.Create", + }, +]; + +const series = [ + walkthrough, + km, +] +--- + +
+
+
+

+ Getting Started with FLARE +

+

+ Begin your NVIDIA FLARE journey with these guides + designed to help you quickly grasp essential concepts. + Follow along with the videos below, and try it out yourself. +

+
+ + +
+
    + + +
+
+ + { + series.map((item, index) => ( +
+
+
+
    + { + item.map((card, card_index) => ( + + )) + } +
+ +
+
+ + + +
+ { + item.map((card, index) => ( + + )) + } +
+ + +
+
+ + +
+ { + item.map((card) => ( + + )) + } +
+
+ )) + } + +
+
+ + + + diff --git a/web/src/components/hero.astro b/web/src/components/hero.astro new file mode 100644 index 0000000000..64d6a2bb42 --- /dev/null +++ b/web/src/components/hero.astro @@ -0,0 +1,89 @@ +--- +import NvidiaLogo from '../images/nvidia_eye.png' +import NvflareAnimation from '../images/nvflare_graphic_animation.mp4' +--- +
+
+
+
+
+
+
+ + What's new + + + + Just shipped v2.4.1 + + + + +
+ NVIDIA logo +

+ NVIDIA FLARE +

+ +

+ NVIDIA FLARE™ (NVIDIA Federated Learning Application Runtime + Environment) is a domain-agnostic, open-source, and extensible SDK + for Federated Learning. It allows researchers and data scientists + to adapt existing ML/DL workflow to a federated paradigm and + enables platform developers to build a secure, privacy-preserving + offering for a distributed multi-party collaboration. +

+
+ Documentation + Tutorial Catalog + + + + GitHub + +
+
+
+
+
+ +
+
+
+
diff --git a/web/src/components/learnMore.astro b/web/src/components/learnMore.astro new file mode 100644 index 0000000000..aae205396b --- /dev/null +++ b/web/src/components/learnMore.astro @@ -0,0 +1,62 @@ +--- + +const learnMore = [ + { + title: "What is Federated Learning?", + description: + "Federated Learning is a distributed learning paradigm where training occurs across multiple clients, each with their own local datasets. This enables the creation of common robust models without sharing sensitive local data, helping solve issues of data privacy and security.", + link: "https://blogs.nvidia.com/blog/what-is-federated-learning/", + }, + { + title: "FLARE Design and Features", + description: + "At its core, FLARE serves as a federated computing framework, with applications such as Federated Learning and Federated Analytics built upon this foundation. Notably, it is agnostic to datasets, workloads, and domains. In contrast to centralized data lake solutions that necessitate copying data to a central location, FLARE brings computing capabilities directly to distributed datasets", + link: "https://nvflare.readthedocs.io/en/main/flare_overview.html", + }, + { + title: "Blogs & Articles", + description: + "NVIDIA Federated Learning Technical Blogs covering various applications and topics. ", + link: "https://developer.nvidia.com/blog/tag/federated-learning", + }, + { + title: "Publications & Talks", + description: + "List of NVIDIA FLARE related papers and publications along with videos from blogs and talks.", + link: "https://nvflare.readthedocs.io/en/main/publications_and_talks.html", + }, +]; +--- + +
+
+
+

+ Learn More +

+
+ +
+ { + learnMore.map((item) => ( + + )) + } +
+
+
\ No newline at end of file diff --git a/web/src/components/overview.astro b/web/src/components/overview.astro new file mode 100644 index 0000000000..063828a11f --- /dev/null +++ b/web/src/components/overview.astro @@ -0,0 +1,120 @@ +--- +const features = [ + { + id: "research", + title: "Research & Productivity", + icon: ` + `, + description: + "Provides user-friendly APIs for client and server programming. Use the Simulator and POC modes to quickly simulate a federated learning application.", + link:"/NVFlare/research", + }, + { + id: "framework-agnostic", + title: "Framework Agnostic", + icon: ` + `, + description: + "Designed as a federated computing platform agnostic to frameworks, workloads, datasets, and domains. Federated learning apps are built on this foundation.", + link: "/NVFlare/catalog", + }, + { + id: "open-arch", + title: "Open Architecture Design", + icon: ` + `, + description: + "Flexible open architecture allows for extensive customization, with a modular design that ensures each layer can be easily pluggable with custom components.", + link: "https://nvflare.readthedocs.io/en/main/flare_overview.html#high-level-system-architecture", + }, + { + id: "security", + title: "Security & Privacy", + icon: ` + `, + description: + "Prioritizes security with secure provisioning, event-based security plugins, authorization control, data filtering, audit logs, and advanced privacy-preserving algorithms.", + link: "https://nvflare.readthedocs.io/en/main/user_guide/nvflare_security.html", + }, + { + id: "integration", + title: "Easy Integration", + icon: ` + `, + description: + "Enables a seamless integration with 3rd-party systems with the FLARE agent, and supports deployment for both on-premise and cloud with various complementary tools.", + link: "https://nvflare.readthedocs.io/en/main/programming_guide/execution_api_type/3rd_party_integration.html#rd-party-integration", + }, + { + id: "production", + title: "Production Grade", + icon: ` + `, + description: + "Designed for robust, production-scale deployment in real-world federated learning scenarios. Successfully used in industries such as healthcare and financial services.", + link: "https://nvflare.readthedocs.io/en/main/real_world_fl.html", + }, +]; + +--- + +
+
+
+

+ Why FLARE? +

+
+ +
+ { + features.map((item) => ( +
+
+
+
+

+ {item.title} +

+
+

+ {item.description} +

+
+ + Read More + +
+
+
+ )) + } +
+
+
+ + \ No newline at end of file diff --git a/web/src/components/research.astro b/web/src/components/research.astro new file mode 100644 index 0000000000..b64f824fad --- /dev/null +++ b/web/src/components/research.astro @@ -0,0 +1,206 @@ +--- +import NvflareResearch from '../images/nvflare_research.png' +import ExamFL from '../images/research/exam.jpg' +import Mammography from '../images/research/mammography.jpg' +import MammographyGraphs from '../images/research/mammography_graphs.jpg' +import Pancreas from '../images/research/pancreas.jpg' +import Sun from '../images/research/sun.jpg' + + +const case_studies = [ + { + id: "exam_ai", + title: "EXAM AI Model for Predicting Oxygen Requirements in COVID Patients", + subheader: "AI model to predict oxygen requirements", + team: "NVIDIA researchers, Massachusetts General Brigham Hospital", + problem: "During COVID-19, it was challenging to determine which patients would need a higher level of care in the near future, despite perhaps presenting with minimal symptoms. Therefore the goal of this study was to train a previously developed AI model that determines whether a person with COVID-19 symptoms will need supplemental oxygen hours or even days after an initial exam.", + approach: "The approach included a separate server hosted on AWS, which held the global deep neural network. Each client-site received a copy of the model to train on its own dataset and FLARE was used to aggregate to form a global model.", + result: "Training was completed in two weeks and produced a global model with .94 Area Under the Curve (AUC), resulting in excellent prediction for the level of oxygen required by incoming patients.", + reference: "Federated learning for predicting clinical outcomes in patients with COVID-19", + link: "https://doi.org/10.1038/s41591-021-01506-3", + image: ExamFL.src, + }, + { + id: "adops", + title: "ADOPS (ACR DASA OSU Partners HealthCare Stanford)", + subheader: "Breast Mammography AI Model. Early detection: breast density classification Improvement", + team: "The American College of Radiology (ACR), Diagnosticos da America (DASA), Ohio State University (OSU), Partners HealthCare (PHS), and Stanford University", + problem: "Breast Mammography AI Model Early detection through mammography is critical when it comes to reducing breast cancer deaths, but breast density can make it harder to detect the disease.", + approach: "The team used a 2D mammography classification model provided by PHS, which was trained using NVIDIA Clara Train on NVIDIA GPUs. The model was then retrained using NVIDIA Clara Federated Learning at PHS, as well as the client-sites, without any data being transferred.", + result: "Each institution obtained a better performing model that had an overall superior predictive power on their own local dataset. In doing so, Federated Learning enabled improved breast density classification from mammograms, which could lead to better breast cancer risk assessment.", + image: Mammography.src, + image2: MammographyGraphs.src, + reference: "Federated Learning for Breast Density Classification: A Real-World Implementation", + link: "https://arxiv.org/pdf/2009.01871.pdf", + }, + { + id: "um_fairview", + title: "University of Minnesota and Fairview X-Ray COVID AI Model", + subheader: "Improve AI models for COVID-19 diagnosis based on chest X-rays", + team: "University of Minnesota and Fairview Mhealth, Indiana University (Indiana, USA), and Emory University (Georgia, USA)", + problem: "The goal of this study was to improve real-world AI models for COVID-19 diagnosis based on chest X-rays.", + approach: "This study leveraged a three-phase pipeline composed of U-Net lung segmentation, a conditional Generative Adversarial Network (cGAN) for outlier detection, and a DenseNet121 COVID-19 Classification model. The aggregate multi-institutional dataset consisted of approximately 80,000 labeled images with a 30/70% positive/negative COVID classification. This classification model was trained with a federation of Federated Learning server and Federated Learning clients at University of Minnesota and Fairview (Minnesota, USA), with additional participant clients at Indiana University (Indiana, USA) and Emory University (Georgia, USA) using a mix of cloud (AWS/Azure) and local servers.", + result: "Initial results showed an improvement in performance of the global model of 5% AUROC and 8% AUPRC on the UMN local dataset as compared to the UMN local model.", + image: "", + }, + { + id: "sun_prostate", + title: "SUN Initiative Prostate Cancer AI Model", + subheader: "Federated segmentation model", + team: "SUNY, UCLA, NIH", + problem: "Prostate cancer is a common cancer of the prostate gland in men. Accurate segmentation of the prostate gland is useful for developing AI models to help in detection of Prostate cancer.", + approach: "In this initiative, we tested the hypothesis that Federated Learning can be used to train a segmentation model comparable to one trained from a pooled data (PD) set.", + result: "The results showed equivalent performance from both the experimental Federated Learning and benchmark PD models, showing the feasibility of training an AI model in a Federated Learning approach.", + image: Sun.src, + }, + { + id: "ct_pancreas", + title: "CT Pancreas Segmentation AI Model", + subheader: "Automated segmentation model of the pancreas and pancreatic tumors in abdominal CT", + team: "National Taiwan University, Taiwan, and Nagoya University, Japan", + problem: "The aim of this experiment was to build models for the automated segmentation of the pancreas and pancreatic tumors in abdominal CT.", + approach: "A 3D segmentation model based on neural architecture search developed by NVIDIA's Applied Research team was collaboratively trained using Federated Learning.", + result: "The global Federated Learning model achieved a segmentation performance of 82.3% Dice score on healthy pancreatic patients on average.", + image: Pancreas.src, + }, +]; + +--- + +
+
+
+ + + Back to home + +
+
+

+ Research with FLARE +

+

+ Learn how FLARE faciliates research from simulation tools to real-world case studies. +

+ +
+

+ Research Tools +

+ +

+ FLARE is an excellent research tool, offering robust simulation capabilities and extensive support for experimentation in federated learning. + Many researchers need to simulate federated learning scenarios without setting up an actual federated learning system. + FLARE allows for repeated experimentation with different parameters, facilitating quick evaluations and monitoring of results. +

+ +

+ FLARE Simulation Tools +

+ +

+ Simulator: + The Simulator is a multi-threaded/process simulation tool that offers both a Command Line Interface (CLI) and a Python API. + It enables the simulation of different numbers of clients and the execution of various federated learning jobs. + Once a simulation is complete, users can deploy the same code in production without any changes. + Additionally, users can utilize an Integrated Development Environment (IDE) debugger to step through the code for easier debugging. +

+ +

+ Proof of Concept (POC) Mode: + POC mode simulates real-world deployment on a local host. + Clients and servers can be deployed in different directories and launched using separate terminals, each representing a different client or server startup. + This mode allows for job submissions to the server as would occur in a real production environment. +

+ +

+ Interaction Methods with FL Server +

+

+ Admin Console: + Issue interactive commands such as submitting jobs, listing jobs, and aborting jobs. +

+

+ Job CLI: + Command-line interface is used for job submission. +

+

+ FLARE API: + Allows submission and listing of jobs through Python code. +

+

+ Experiment Tracking Tools Integration +

+ +

+ Experiment tracking: + FLARE supports logging metrics using FLARE's metrics tracking log writers. Users can choose from TensorBoard, MLflow, or W&B syntax. + Metrics can be streamed to either the FL server or FL client, and changing the metric system does not require any code changes. +

+ +
+ +
+

+ Research Works +

+ +

+ FLARE offers a lot of state of art research work, here is a quick view of the recent work. + Learn more in the research directory in our GitHub and view our list of Publications. +

+ + NVFLARE research + +
+ +
+

+ Case Studies +

+ +

+ NVIDIA has worked with several institutions to test and validate the utility of federated learning. We hope this will help to inspire or relate to your research cases. +

+ Here are five real life implementations in healthcare, pushing the envelope for training robust, generalizable AI models: +

+ +
    + { + case_studies.map((item) => ( +
  • {item.title}
    • + )) + } +
+ +
+ { + case_studies.map((item) => ( +
+

+ {item.title} +

+

+ {item.subheader}
+ {item.team}
+

+ +
+ {item.problem}{" "+item.approach} +

+ {item.result} +

+ {item.reference} +

+
+ )) + } +
+ +
+ +
+ +
+
diff --git a/web/src/components/tutorials.astro b/web/src/components/tutorials.astro new file mode 100644 index 0000000000..da51aeb308 --- /dev/null +++ b/web/src/components/tutorials.astro @@ -0,0 +1,706 @@ +--- +const highlights = [ + { + title: "LLM Tuning via HuggingFace SFT Trainer", + tags: ["advanced", "algorithm", "huggingface", "llm"], + description: "Example for using FLARE with a HuggingFace trainer for LLM tuning tasks.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/llm_hf" + }, + { + title: "Survival Analysis with Federated Kaplan-Meier", + tags: ["intermediate", "algorithm", "healthcare", "client-api", "model-controller-api", "homomorphic-encryption", "analytics"], + description: + "Kaplan-Meier survival analysis in federated setting without and with secure features via time-binning and Homomorphic Encryption (HE).", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/kaplan-meier-he", + }, + { + title: "Real-world Federated Learning with CIFAR-10", + tags: ["intermediate", "algorithm", "poc", "pytorch", "learner", "deep-learning", "homomorphic-encryption"], + description: "Provisioning secure workspace and running FedAvg with streaming of TensorBoard metrics to the server during training and homomorphic encryption.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10/cifar10-real-world/README.md" + }, + { + title: "Financial Application with Federated XGBoost Methods", + tags: ["advanced", "algorithm", "xgboost", "finance"], + description: "Example using XGBoost in vertical and horizontal approaches to train a federated model to perform fraud detection with a finance dataset.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/finance" + }, + { + title: "Swarm Learning", + tags: ["intermediate", "algorithm", "pytorch", "learner", "deep-learning"], + description: + "Example using Swarm Learning and Client-Controlled Cross-site Evaluation workflows.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/swarm_learning", + } +] + +const tutorials = [ + { + title: "Intro to the FL Simulator", + tags: ["beginner", "tools"], + description: "Use the FLARE Simulator to run a local simulation with multi-process settings within a single computer, offering quick response and debugging.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/tutorials/flare_simulator.ipynb" + }, + { + title: "NVFLARE in POC Mode", + tags: ["beginner", "tools"], + description: "Proof-of-concept mode to simulate real-world deployment on a local host with different processes represent server, clients, and an admin console.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/tutorials/setup_poc.ipynb" + }, + { + title: "Running FLARE in Docker", + tags: ["beginner", "deployment"], + description: + "Launch FLARE server and cleints with docker compose using the DockerBuilder.", + link: "https://nvflare.readthedocs.io/en/main/user_guide/docker_compose.html", + }, + { + title: "FLARE API", + tags: ["beginner", "tools"], + description: "Manage system and jobs programmatically with the python FLARE API.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/tutorials/flare_api.ipynb" + }, + { + title: "Job CLI", + tags: ["beginner", "tools"], + description: "Job CLI for options to create and submit jobs from a command line interface in POC or production environments.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/tutorials/job_cli.ipynb" + }, + { + title: "Operating FLARE: Admin Client, Commands, FLARE API", + tags: ["intermediate", "tools"], + description: + "Operate FLARE with the commands and functionalities of the admin client, which allow you to manage your FLARE system.", + link: "https://nvflare.readthedocs.io/en/main/real_world_fl/operation.html", + }, + { + title: "FL Experiment Tracking with TensorBoard Streaming", + tags: ["intermediate", "tools", "pytorch"], + description: "Example integrating FLARE with TensorBoard streaming capability from clients to the server.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/experiment-tracking/tensorboard/README.md" + }, + { + title: "FL Experiment Tracking with MLflow", + tags: ["intermediate", "tools", "pytorch"], + description: "Example integrating FLARE with MLflow streaming capability from clients to the server.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/experiment-tracking/mlflow/README.md" + }, + { + title: "FL Experiment Tracking with Weights and Biases", + tags: ["intermediate", "tools", "pytorch"], + description: "Example integrating FLARE with Weights and Biases streaming capability from clients to the server.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/experiment-tracking/wandb/README.md" + }, + { + title: "Cloud Deployment", + tags: ["advanced", "deployment"], + description: + "Deploy the FLARE dashboard, server, and clients in the cloud. Learn to use the provided scripts for AWS and Azure.", + link: "https://nvflare.readthedocs.io/en/main/real_world_fl/cloud_deployment.html", + }, + { + title: "Deployment to Kubernetes", + tags: ["advanced", "deployment"], + description: + "Run FLARE inside Amazon Elastic Kubernetes Service (EKS).", + link: "https://nvflare.readthedocs.io/en/main/real_world_fl/kubernetes.html", + }, + { + title: "Helm Chart Deployment", + tags: ["advanced", "deployment"], + description: + "Use the provisioning tool's HelmChartBuilder to generate a reference Helm Chart to deploy FLARE to a local microk8s Kubernetes instance.", + link: "https://nvflare.readthedocs.io/en/main/user_guide/helm_chart.html", + }, + { + title: "Secure Provisioning", + tags: ["advanced", "deployment"], + description: + "Utilize the secure provisioning tool for streamlining the process to provision, start, and operate federated learning with a trusted setup.", + link: "https://nvflare.readthedocs.io/en/main/real_world_fl/overview.html", + }, + { + title: "Preflight Check", + tags: ["advanced", "deployment"], + description: + "Perform preliminary checks before starting up an NVFlare subsystem to catch errors early.", + link: "https://nvflare.readthedocs.io/en/main/user_guide/nvflare_cli/preflight_check.html", + }, + { + title: "FLARE Dashboard", + tags: ["advanced", "deployment"], + description: + "Provides a CLI or UI to collect information of clients and users from different organizations, and generates startup kits with keys and certificates for users to download.", + link: "https://nvflare.readthedocs.io/en/main/user_guide/dashboard_api.html", + }, + { + title: "Federated Policies", + tags: ["advanced", "deployment"], + description: "Example to demonstrate the federated site policies for authorization, resource and data privacy management.", + link: "https://github.com/NVIDIA/NVFlare/blob/main/examples/advanced/federated-policies/README.rst" + }, + { + title: "Custom Authentication", + tags: ["advanced", "deployment"], + description: "Example to demonstrate the custom authentication policy and secure mode.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/custom_authentication/README.rst" + }, + { + title: "Job-Level Authorization", + tags: ["advanced", "deployment"], + description: "Example to demonstrate the job-level authorization policy and secure mode.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/job-level-authorization/README.md" + }, + { + title: "Federated Learning Hub", + tags: ["advanced", "deployment"], + description: "Allow hierarchical interaction between several levels of FLARE FL systems, e.g. Tier-1 (hub) and Tier-2 (sub-systems).", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/fl_hub/README.md" + }, + { + title: "Step-by-Step CIFAR10 Examples", + tags: ["beginner", "algorithm", "pytorch", "deep-learning"], + description: "Step-by-step examples series with CIFAR-10 (image data) to showcase different FLARE features, workflows, and APIs.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/step-by-step/cifar10" + }, + { + title: "Step-by-Step HIGGS Examples", + tags: ["beginner", "algorithm", "pandas", "sklearn", "xgboost"], + description: "Step-by-step examples series with HIGGS (tabular data) to showcase different FLARE features, workflows, and APIs.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/step-by-step/higgs" + }, + { + title: "ML/DL to FL", + tags: ["beginner", "algorithm", "client-api", "numpy", "pytorch", "lightning", "tensorflow"], + description: "Example for converting Deep Learning (DL) code to Federated Learning (FL) using the Client API. Configurations for numpy, pytorch, lighting, and tensorflow.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/ml-to-fl" + }, + { + title: "Hello Scatter and Gather", + tags: ["beginner", "algorithm", "numpy", "controller", "executor"], + description: "Example using the ScatterAndGather workflow to implement Federated Averaging.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-numpy-sag/README.md" + }, + { + title: "Hello Cross-Site Validation", + tags: ["beginner", "algorithm", "numpy", "controller", "executor", "deep-learning"], + description: "Example using the CrossSiteModelEval workflow for cross site evaluation. Demonstrate option to use previous results without training workflow.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-numpy-cross-val/README.md" + }, + { + title: "Hello Cyclic Weight Transfer", + tags: ["beginner", "algorithm", "tensorflow", "controller", "executor", "deep-learning"], + description: "Example using the CyclicController workflow to implement Cyclic Weight Transfer.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-cyclic/README.md" + }, + { + title: "Hello PyTorch", + tags: ["beginner", "algorithm", "pytorch", "controller", "executor", "deep-learning"], + description: "Example of an image classifier with FedAvg using PyTorch as the deep learning training framework.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-pt/README.md" + }, + { + title: "Hello TensorFlow", + tags: ["beginner", "algorithm", "tensorflow", "controller", "executor", "deep-learning"], + description: "Example of an image classifier with FedAvg using TensorFlow as the deep learning training framework.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/hello-world/hello-tf2/README.md" + }, + { + title: "Simulated Federated Learning with CIFAR-10", + tags: ["intermediate", "algorithm", "simulator", "pytorch", "learner", "model-controller-api", "deep-learning"], + description: "Running FedAvg, FedProx, FedOpt, and SCAFFOLD algorithms using the FL simulator.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10/cifar10-sim/README.md" + }, + { + title: "Real-world Federated Learning with CIFAR-10", + tags: ["intermediate", "algorithm", "poc", "pytorch", "learner", "deep-learning", "homomorphic-encryption"], + description: "Provisioning secure workspace and running FedAvg with streaming of TensorBoard metrics to the server during training and homomorphic encryption.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/cifar10/cifar10-real-world/README.md" + }, + { + title: "Logistic Regression with Newton-Raphton", + tags: ["intermediate", "algorithm", "client-api", "model-controller-api", "machine-learning"], + description: + "Federated binary classification via logistic regression with second-order Newton-Raphson optimization.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/lr-newton-raphson", + }, + { + title: "Survival Analysis with Federated Kaplan-Meier", + tags: ["intermediate", "algorithm", "healthcare", "client-api", "model-controller-api", "homomorphic-encryption", "analytics"], + description: + "Kaplan-Meier survival analysis in federated setting without and with secure features via time-binning and Homomorphic Encryption (HE).", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/kaplan-meier-he", + }, + { + title: "Swarm Learning", + tags: ["intermediate", "algorithm", "pytorch", "learner", "deep-learning"], + description: + "Example using Swarm Learning and Client-Controlled Cross-site Evaluation workflows.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/swarm_learning", + }, + { + title: "Federated Linear Model with Scikit-learn", + tags: ["intermediate", "algorithm", "sklearn", "machine-learning"], + description: "Using scikit-learn for federated linear model learning on tabular data.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/sklearn-linear/README.md" + }, + { + title: "Federated K-Means Clustering with Scikit-learn", + tags: ["intermediate", "algorithm", "sklearn", "machine-learning"], + description: "Using scikit-learn and k-Means for federated clustering on tabular data.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/sklearn-kmeans/README.md" + }, + { + title: "Federated SVM with Scikit-learn", + tags: ["intermediate", "algorithm", "sklearn", "machine-learning"], + description: "Using scikit-learn and SVM for federated model learning on tabular data.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/sklearn-svm/README.md" + }, + { + title: "Histogram-based FL for XGBoost", + tags: ["advanced", "algorithm", "xgboost", "machine-learning"], + description: "Histogram-based algorithm for XGBoost.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/xgboost/histogram-based/README.md" + }, + { + title: "Tree-based Federated Learning for XGBoost", + tags: ["advanced", "algorithm", "xgboost", "machine-learning"], + description: "Tree-based algorithms including bagging and cyclic approaches for XGBoost.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/xgboost/tree-based/README.md" + }, + { + title: "Federated Learning for Random Forest based on XGBoost", + tags: ["advanced", "algorithm", "xgboost", "machine-learning"], + description: "Example of using FLARE with scikit-learn and Random Forest.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/random_forest/README.md" + }, + { + title: "Federated Vertical XGBoost", + tags: ["advanced", "algorithm", "xgboost", "machine-learning"], + description: "Example using Private Set Intersection and XGBoost on vertically split HIGGS data.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/vertical_xgboost/README.md" + }, + { + title: "NVFlare + MONAI integration", + tags: ["advanced", "algorithm", "healthcare", "monai", "deep-learning"], + description: "Using FLARE to train a 3D medical image analysis model using federated averaging (FedAvg) and MONAI Bundle.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/integration/monai/README.md" + }, + { + title: "Federated Learning with Differential Privacy for BraTS18 segmentation", + tags: ["advanced", "algorithm", "healthcare", "differential-privacy", "monai", "deep-learning"], + description: "Illustrates the use of differential privacy for training brain tumor segmentation models using federated learning.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/brats18/README.md" + }, + { + title: "Federated Learning for Prostate Segmentation from Multi-source Data", + tags: ["advanced", "algorithm", "healthcare", "monai", "deep-learning"], + description: "Example of training a multi-institutional prostate segmentation model using FedAvg, FedProx, and Ditto.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/prostate/README.md" + }, + { + title: "Federated Statistics for Images", + tags: ["advanced", "algorithm", "pandas", "analytics"], + description: "Example of gathering local image histogram to compute the global dataset histograms.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/federated-statistics/image_stats/README.md" + }, + { + title: "Federated Statistics for DataFrame", + tags: ["advanced", "algorithm", "pandas", "analytics"], + description: "Example of gathering local statistics summary from Pandas DataFrame to compute the global dataset statistics.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/federated-statistics/df_stats/README.md" + }, + { + title: "MONAI FLARE Integration Experiment Tracking", + tags: ["advanced", "algorithm", "experiment-tracking", "monai"], + description: "Example using FLARE and MONAI integration with experiment tracking streaming from clients to server.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/integration/monai/examples/spleen_ct_segmentation_local/README.md#51-experiment-tracking-with-mlflow" + }, + { + title: "KeyCloak Site Authentication Integration", + tags: ["advanced", "algorithm", "security"], + description: "Demonstrate KeyCloak integration for supporting site-specific authentication.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/keycloak-site-authentication/README.md" + }, + { + title: "NLP-NER", + tags: ["advanced", "algorithm", "huggingface", "deep-learning"], + description: "Illustrates both BERT and GPT-2 models from Hugging Face on a Named Entity Recognition (NER) task using the NCBI disease dataset.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/nlp-ner/README.md" + }, + { + title: "Parameter Efficient Fine Turning", + tags: ["advanced", "algorithm", "nemo", "llm"], + description: "Example utilizing NeMo's PEFT methods to adapt a LLM to a downstream task.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/integration/nemo/examples/peft" + }, + { + title: "Prompt-Tuning Example", + tags: ["advanced", "algorithm", "nemo", "llm"], + description: "Example for using FLARE with NeMo for LLM prompt learning.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/integration/nemo/examples/prompt_learning" + }, + { + title: "Supervised Fine Tuning (SFT)", + tags: ["advanced", "algorithm", "nemo", "llm"], + description: "Example to fine-tune all parameters of a LLM on supervised data.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/integration/nemo/examples/supervised_fine_tuning" + }, + { + title: "LLM Tuning via HuggingFace SFT Trainer", + tags: ["advanced", "algorithm", "huggingface", "llm"], + description: "Example for using FLARE with a HuggingFace trainer for LLM tuning tasks.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/llm_hf" + }, + { + title: "Federated GNN: Protein Classification", + tags: ["advanced", "algorithm", "healthcare", "pytorch", "deep-learning"], + description: "Example using GNNs for Protein Classification using PPI dataset using GraphSAGE.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/gnn#federated-gnn-on-graph-dataset-using-inductive-learning" + }, + { + title: "Federated GNN: Financial Transaction Classification", + tags: ["advanced", "algorithm", "finance", "pytorch", "deep-learning"], + description: "Example using GNNs for Financial Transaction Classification with Elliptic++ dataset using GraphSAGE.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/gnn#federated-gnn-on-graph-dataset-using-inductive-learning" + }, + { + title: "Financial Application with Federated XGBoost Methods", + tags: ["advanced", "algorithm", "xgboost", "finance"], + description: "Example using XGBoost in vertical and horizontal approaches to train a federated model to perform fraud detection with a finance dataset.", + link: "https://github.com/NVIDIA/NVFlare/tree/main/examples/advanced/finance" + }, +] + +const level = ["beginner", "intermediate", "advanced"]; +const tutorial_category = ["algorithm", "tools", "deployment"] +const flare_api = ["client-api", "model-controller-api", "learner", "executor", "controller"]; +const framework = ["pytorch", "tensorflow", "lightning", "xgboost", "sklearn", "numpy", "nemo", "monai", "huggingface"]; +const application_type = ["machine-learning", "deep-learning", "llm", "analytics"]; +const industry_domain = ["healthcare", "finance"]; +const privacy_algorithm = ["homomorphic-encryption", "differential-privacy"]; + +const tag_list = [ + { + name: "Experience Level", + id: "level", + list: level, + }, + { + name: "Tutorial Category", + id: "tutorial-category", + list: tutorial_category, + }, + { + name: "Application Type", + id: "application-type", + list: application_type, + }, + { + name: "Framework", + id: "framework", + list: framework, + }, + { + name: "Industry Domain", + id: "industry-domain", + list: industry_domain, + }, + { + name: "FLARE API", + id: "flare-api", + list: flare_api, + }, + { + name: "Privacy Algorithm", + id: "privacy-algorithm", + list: privacy_algorithm, + }, +] + +--- + +
+
+
+ + + Back to home + +
+ +
+

+ Tutorial Catalog +

+

+ Explore our extensive catalog of tutorials of FLARE features and examples for various applications. + Use the tags to filter by categories such as level, applications, frameworks, etc. + or search for keywords. + + To read more, visit our + documentation and + GitHub. +

+
+ +
+ +
+ +
+ +
+ + +
+
+ +
+ { + tag_list.map((category) => ( +
+

+ +

+ +
+ )) + } +
+
+ + +
+ + +
+ + +
+

+ +

+
+ + +
+
+
    + { + highlights.map((tutorial) => ( + + ))} +
+
+
+ + + +
+ { + highlights.map((item, index) => ( + + )) + } +
+ + +
+
+
+ +
+
+
+
+
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