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Hello world Hello GPU world PyTorch & ⚡⚡⚡ Trainer (1+ cloud GPUs) Train PyTorch (cloud GPU) Train PyTorch (32 cloud GPUs) Deploy a model on cloud GPUs Run a model script XGBoost Streamlit demo # app.py from lightning.app import LightningWork, LightningApp class YourComponent(LightningWork): def run(self): print('RUN ANY PYTHON CODE HERE') component = YourComponent() app = LightningApp(component) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"default","preemptible":false,"shmSize":0},"networkConfig":[{"name":"dzodf","port":61304}]}}] # app.py from lightning.app import LightningWork, LightningApp, CloudCompute class YourComponent(LightningWork): def run(self): print('RUN ANY PYTHON CODE HERE') # run on a cloud machine ("cpu", "gpu", ...) compute = CloudCompute("gpu") component = YourComponent(cloud_compute=compute) app = LightningApp(component) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"qnlgd","port":61516}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu","preemptible":false,"shmSize":0}}}] # app.py from lightning import Trainer from lightning.app import LightningWork, LightningApp, CloudCompute from lightning.app.components import LightningTrainerMultiNode from lightning.pytorch.demos.boring_classes import BoringModel class LightningTrainerDistributed(LightningWork): def run(self): model = BoringModel() trainer = Trainer(max_epochs=10, strategy="ddp") trainer.fit(model) # 8 GPUs: (2 nodes of 4 x v100) component = LightningTrainerMultiNode( LightningTrainerDistributed, num_nodes=4, cloud_compute=CloudCompute("gpu-fast-multi"), # 4 x v100 ) app = LightningApp(component) None [{"name":"root.ws.0","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"ajfrc","port":61553}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.1","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"ttyqc","port":61554}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.2","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"svyej","port":61555}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.3","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"parme","port":61556}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}}] # app.py # ! pip install torch from lightning.app import LightningWork, LightningApp, CloudCompute import torch class PyTorchComponent(LightningWork): def run(self): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = torch.nn.Sequential(torch.nn.Linear(1, 1), torch.nn.ReLU(), torch.nn.Linear(1, 1)) model.to(device) criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.1) for step in range(10000): model.zero_grad() x = torch.tensor([0.8]).to(device) target = torch.tensor([1.0]).to(device) output = model(x) loss = criterion(output, target) print(f'step: {step}. loss {loss}') loss.backward() optimizer.step() compute = CloudCompute('gpu') componet = PyTorchComponent(cloud_compute=compute) app = LightningApp(componet) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"cutdu","port":61584}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu","preemptible":false,"shmSize":0}}}] # app.py # ! pip install torch from lightning.app import LightningWork, LightningApp, CloudCompute from lightning.app.components import MultiNode import torch from torch.nn.parallel.distributed import DistributedDataParallel def distributed_train(local_rank: int, main_address: str, main_port: int, num_nodes: int, node_rank: int, nprocs: int): # 1. SET UP DISTRIBUTED ENVIRONMENT global_rank = local_rank + node_rank * nprocs world_size = num_nodes * nprocs if torch.distributed.is_available() and not torch.distributed.is_initialized(): torch.distributed.init_process_group( "nccl" if torch.cuda.is_available() else "gloo", rank=global_rank, world_size=world_size, init_method=f"tcp://{main_address}:{main_port}", ) # 2. PREPARE DISTRIBUTED MODEL model = torch.nn.Linear(32, 2) device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu") model = DistributedDataParallel(model, device_ids=[local_rank] if torch.cuda.is_available() else None).to(device) # 3. SETUP LOSS AND OPTIMIZER criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=0.01) # 4.TRAIN THE MODEL FOR 50 STEPS for step in range(50): model.zero_grad() x = torch.randn(64, 32).to(device) output = model(x) loss = criterion(output, torch.ones_like(output)) print(f"global_rank: {global_rank} step: {step} loss: {loss}") loss.backward() optimizer.step() # 5. VERIFY ALL COPIES OF THE MODEL HAVE THE SAME WEIGTHS AT END OF TRAINING weight = model.module.weight.clone() torch.distributed.all_reduce(weight) assert torch.equal(model.module.weight, weight / world_size) print("Multi Node Distributed Training Done!") class PyTorchDistributed(LightningWork): def run(self, main_address: str, main_port: int, num_nodes: int, node_rank: int): nprocs = torch.cuda.device_count() if torch.cuda.is_available() else 1 torch.multiprocessing.spawn( distributed_train, args=(main_address, main_port, num_nodes, node_rank, nprocs), nprocs=nprocs ) # 32 GPUs: (8 nodes x 4 v 100) compute = CloudCompute("gpu-fast-multi") # 4xV100 component = MultiNode(PyTorchDistributed, num_nodes=8, cloud_compute=compute) app = LightningApp(component) None [{"name":"root.ws.0","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"whhby","port":61613}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.1","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"yhjtf","port":61614}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.2","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"rqwkt","port":61615}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.3","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"pjdsj","port":61616}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.4","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"efdor","port":61617}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.5","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"pxmso","port":61618}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.6","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"feevy","port":61619}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}},{"name":"root.ws.7","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"tbmse","port":61620}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu-fast-multi","preemptible":false,"shmSize":0}}}] # !pip install torchvision from lightning.app import LightningApp, CloudCompute from lightning.app.components.serve import PythonServer, Image, Number import base64, io, torchvision, torch from PIL import Image as PILImage class PyTorchServer(PythonServer): def setup(self): self._model = torchvision.models.resnet18(pretrained=True) self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self._model.to(self._device) def predict(self, request): image = base64.b64decode(request.image.encode("utf-8")) image = PILImage.open(io.BytesIO(image)) transforms = torchvision.transforms.Compose([ torchvision.transforms.Resize(224), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) image = transforms(image) image = image.to(self._device) prediction = self._model(image.unsqueeze(0)) return {"prediction": prediction.argmax().item()} component = PyTorchServer( input_type=Image, output_type=Number, cloud_compute=CloudCompute('gpu') ) app = LightningApp(component) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"umqqg","port":7777}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"gpu","preemptible":false,"shmSize":0}}}] # app.py # !curl https://raw.githubusercontent.com/Lightning-AI/lightning/master/examples/app/multi_node/pl_boring_script.py -o pl_boring_script.py from lightning.app import LightningApp, CloudCompute from lightning.app.components.training import LightningTrainerScript # run script that trains PyTorch with the Lightning Trainer model_script = 'pl_boring_script.py' component = LightningTrainerScript( model_script, num_nodes=1, cloud_compute=CloudCompute("gpu") ) app = LightningApp(component) None [] # app.py # !pip install scikit-learn xgboost from lightning.app import LightningWork, LightningApp from sklearn import datasets from sklearn.model_selection import train_test_split from xgboost import XGBClassifier class XGBoostComponent(LightningWork): def run(self): iris = datasets.load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) bst = XGBClassifier(verbosity=3) bst.fit(X_train, y_train) preds = bst.predict(X_test) print(f'preds: {preds}') app = LightningApp(XGBoostComponent()) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"tggba","port":61729}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"default","preemptible":false,"shmSize":0}}}] # app.py # !pip install streamlit omegaconf scipy # !pip install torch from lightning.app import LightningApp import torch from io import BytesIO from functools import partial from scipy.io.wavfile import write import streamlit as st class StreamlitApp(app.components.ServeStreamlit): def build_model(self): sample_rate = 48000 model, _ = torch.hub.load('snakers4/silero-models', model='silero_tts',speaker="v3_en") return partial(model.apply_tts, sample_rate=sample_rate, speaker="en_0"), sample_rate def render(self): st.title("Text To Speech") text = st.text_input("Text:", "Lightning Apps are the best!") if text: model, sample_rate = self.model audio_numpy = model(text).numpy() audio = BytesIO() write(audio, sample_rate, audio_numpy) audio.seek(0) st.audio(audio) app = LightningApp(StreamlitApp()) None [{"name":"root.work","spec":{"buildSpec":{"commands":[],"pythonDependencies":{"packageManager":"PACKAGE_MANAGER_PIP","packages":""}},"drives":[],"networkConfig":[{"name":"hpyaz","port":61763}],"userRequestedComputeConfig":{"count":1,"diskSize":0,"name":"default","preemptible":false,"shmSize":0}}}]

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