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### Start Studio with CloudProvider and Machine

Source: https://lightning.ai/docs/platform/developers/sdk/studio

Basic example of starting a Studio instance with a specific cloud provider and machine type.

```python
from lightning_sdk import Machine, CloudProvider

studio = Studio(name="my-new-studio", create_ok=True, cloud_provider=CloudProvider.AWS)
studio.start(Machine.L4)
```

--------------------------------

### on_start.sh example

Source: https://lightning.ai/docs/platform/build/ai-studio/on-start-actions

Example on_start.sh script that sets an environment variable and starts a server. Environment variables set here are not available in subsequent studio terminals.

```bash
#!/bin/bash

# This script runs every time your Studio starts, from your home directory.

export SOME_IMPORTANT_PATH="/teamspace/studios/this_studio/path/to/some/important/folder"

# start a server
python server.py

```

--------------------------------

### Start VM with CloudProvider

Source: https://lightning.ai/docs/platform/developers/sdk/vms

Basic example of creating a VM instance and starting it with a specific machine type on AWS.

```python
from lightning_sdk import Machine, CloudProvider

vm = VM(name="my-new-vm", create_ok=True, cloud_provider=CloudProvider.AWS)
vm.start(Machine.L4)
```

--------------------------------

### CLIPAPI setup and inference implementation

Source: https://lightning.ai/docs/litserve/api-reference/litapi

This example demonstrates using the setup method to initialize a CLIP model and pre-encode text captions on a specified device. It includes the full LitAPI implementation for image classification.

```python
# pip install torch clip import torch, clip, multiprocessing from PIL import Image from io import BytesIO import litserve as ls  class CLIPAPI(ls.LitAPI): def setup(self, device): # load the model on the device automatically (CPU, GPU etc...)         self.model, self.preprocess = clip.load("ViT-B/32", device=device) # Load candidate captions with open('captions.txt', 'r') as file:             self.candidate_captions = [line.strip() for line in file.readlines()] # Pre-tokenize and pre-encode text captions with torch.no_grad():             self.text_tokens = clip.tokenize(self.candidate_captions).to(self.device)             self.text_embeddings = self.model.encode_text(self.text_tokens) def decode_request(self, request):         image_bytes = bytes.fromhex(request["image_bytes"])         image = Image.open(BytesIO(image_bytes)) return self.preprocess(image).unsqueeze(0).to(self.device) def predict(self, image_tensor): with torch.no_grad():             image_embedding = self.model.encode_image(image_tensor)             similarities = (100.0 * image_embedding @ self.text_embeddings.T).softmax(dim=-1)             max_indices = similarities.argmax(dim=-1) return [self.candidate_captions[idx] for idx in max_indices.cpu().numpy()] def encode_response(self, output): return {"description": output[0]} if __name__ == "__main__":     api = CLIPAPI()     server = ls.LitServer(api, accelerator="auto")     server.run(port=8000)
```

--------------------------------

### Implementing prepare_data and setup for NLP tasks

Source: https://lightning.ai/docs/pytorch/stable/reference/data/datamodule

Example of using prepare_data to tokenize and save data to disk, and setup to load the processed data back.

```python
class LitDataModule(L.LightningDataModule): def prepare_data(self):         dataset = load_Dataset(...)         train_dataset = ...         val_dataset = ... # tokenize # save it to disk def setup(self, stage): # load it back here         dataset = load_dataset_from_disk(...)
```

--------------------------------

### setup(trainer)

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.accelerators.Accelerator

Called by the Trainer to set up the accelerator before the model starts running on the device.

```APIDOC
## setup(trainer)

### Description
Called by the Trainer to set up the accelerator before the model starts running on the device.

### Method
`setup`

### Parameters
- **trainer** (`Trainer`) - Required - the trainer instance

### Return Type
`None`
```

--------------------------------

### Full FSDP Transformer example

Source: https://lightning.ai/docs/pytorch/stable/reference/advanced/model_parallel/fsdp

Complete example of training a 1B-parameter Transformer language model on WikiText2 with FSDP. Includes a LightningModule, DataLoader, Trainer setup with FSDPStrategy, and memory summary printing. Reused throughout the guide for optimizing speed and memory usage and comparing to DDP.

```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

import lightning as L
from lightning.pytorch.strategies import FSDPStrategy
from lightning.pytorch.demos import Transformer, WikiText2


class LanguageModel(L.LightningModule):
    def __init__(self, vocab_size):
        super().__init__()
        self.model = Transformer(  # 1B parameters
            vocab_size=vocab_size,
            nlayers=32,
            nhid=4096,
            ninp=1024,
            nhead=64,
        )

    def training_step(self, batch):
        input, target = batch
        output = self.model(input, target)
        loss = F.nll_loss(output, target.view(-1))
        self.log("train_loss", loss, prog_bar=True)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=0.1)


L.seed_everything(42)
# Data
dataset = WikiText2()
train_dataloader = DataLoader(dataset)
# Model
model = LanguageModel(vocab_size=dataset.vocab_size)
# Trainer
trainer = L.Trainer(accelerator="cuda", devices=2, strategy=FSDPStrategy())
trainer.fit(model, train_dataloader)
trainer.print(torch.cuda.memory_summary())
```

--------------------------------

### Submit a Multi-Machine Job with Lightning SDK

Source: https://lightning.ai/docs/platform/train/multi-node-training/sdk-commands

Full example of submitting a multi-machine job. Includes installation and authentication setup, initializing a Studio, running a job with MMT.run, monitoring status, and stopping or deleting the job.

```python
# Install the Lightning SDK
# pip install lightning-sdk
# Export authentication variables
# export LIGHTNING_USER_ID=your-user-id
# export LIGHTNING_API_KEY=your-api-key
from lightning_sdk import Studio, Machine, MMT

# Step 1: Initialize a Studio
studio = Studio(name="batch-processing", teamspace="my-teamspace", user="my-user")
studio.start()
# Step 3: Run a Multi-machine job
job = MMT.run(command="python process_data.py", name="data-job", machine=Machine.CPU, studio=studio, num_machines=2)
# Step 4: Monitor Job Status
print(job.status) # Running, Completed, Failed
# Step 5: Stop or Delete a Job
# job.stop()  # Gracefully stop a running job
# job.delete()  # Cancel and remove the job
# Step 6: Shut Down the Studio
studio.stop()
```

--------------------------------

### Submit a job - full example

Source: https://lightning.ai/docs/platform/developers/sdk/batch

Complete example showing how to initialize a Studio, run a batch job, monitor its status, stop or delete it, and shut down the Studio.

```python
# Install the Lightning SDK
# pip install lightning-sdk

# Export authentication variables
# export LIGHTNING_USER_ID=your-user-id
# export LIGHTNING_API_KEY=your-api-key

from lightning_sdk import Studio, Machine, Job

# Step 1: Initialize a Studio
studio = Studio(name="batch-processing", teamspace="my-teamspace", user="my-user")
studio.start()

# Step 3: Run a Batch Job
job = Job.run(command="python process_data.py", name="data-job", machine=Machine.CPU, studio=studio)

# Step 4: Monitor Job Status
print(job.status)  # Running, Completed, Failed

# Step 5: Stop or Delete a Job
# job.stop()  # Gracefully stop a running job
# job.delete()  # Cancel and remove the job

# Step 6: Shut Down the Studio
studio.stop()
```

--------------------------------

### LambdaCallback usage example

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.callbacks.LambdaCallback

Creates a Trainer with a LambdaCallback that prints 'setup' when the setup hook is called. The example shows the required imports and the callback instantiation.

```python
>>> from lightning.pytorch import Trainer
>>> from lightning.pytorch.callbacks import LambdaCallback
>>> trainer = Trainer(callbacks=[LambdaCallback(setup=lambda *args: print('setup'))])
```

--------------------------------

### setup

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.strategies.ModelParallelStrategy

Sets up the accelerator, plugins and initializes the optimizers (if needed).

```APIDOC
## setup

### Description
Sets up the accelerator, plugins and initializes the optimizers (if needed).

### Method
`setup(trainer)`

### Parameters
- **trainer** (`Trainer`) - Required - The trainer instance.

### Return Type
`None`

```

--------------------------------

### Install torchvision nightly

Source: https://lightning.ai/docs/thunder/stable/basic/mlp_mnist

Command to install the required nightly version of torchvision for the MNIST example.

```bash
pip install --pre torchvision --index-url https://download.pytorch.org/whl/nightly/cu121
```

--------------------------------

### setup(_trainer_)

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.strategies.FSDPStrategy

Sets up the accelerator, plugins and initializes the optimizers (if needed).

```APIDOC
## setup(_trainer_)

### Description
Sets up the accelerator, plugins and initializes the optimizers (if needed).

### Method
setup(_trainer_)

### Parameters
- **trainer** (`Trainer`) - The trainer instance.

### Return Type
`None`

```

--------------------------------

### Clone and install the example

Source: https://lightning.ai/docs/platform/build/sandbox/examples/code-agent-langchain

Clone the SDK repo and install the local 'brain' dependencies. Note that torch and transformers are installed inside the sandbox at create time, not here.

```bash
git clone https://github.com/Lightning-AI/sdk.git
cd sdk/python/examples
# Install the local "brain" dependencies (torch + transformers are installed
# *in the sandbox* at create time, not here).
pip install -r langchain/requirements.txt
```

--------------------------------

### Set up model and optimizers with setup_module

Source: https://lightning.ai/docs/fabric/stable/full-api-reference/api/generated/lightning.fabric.fabric.Fabric

Demonstrates setting up a model independently of optimizers, which is useful for strategies like FSDP.

```python
# Set up model first (useful for FSDP) model = fabric.setup_module(model) # Then create and set up optimizer optimizer = torch.optim.Adam(model.parameters()) optimizer = fabric.setup_optimizers(optimizer)
```

--------------------------------

### setup(module, *optimizers, scheduler=None, move_to_device=True, _reapply_compile=True)

Source: https://lightning.ai/docs/fabric/stable/full-api-reference/api/generated/lightning.fabric.fabric.Fabric

Set up a model and its optimizers for accelerated training. Returns the wrapped module and optimizers, and optionally the scheduler.

```APIDOC
## setup(module, *optimizers, scheduler=None, move_to_device=True, _reapply_compile=True)

### Description
Set up a model and its optimizers for accelerated training.

### Method
setup

### Signature
setup(module, *optimizers, scheduler=None, move_to_device=True, _reapply_compile=True)

### Parameters
- **module** (Module) - Required - A torch.nn.Module to set up.
- ***optimizers** (Optimizer) - Optional - The optimizer(s) to set up. Can be zero or more optimizers.
- **scheduler** (Optional[_LRScheduler]) - Optional - An optional learning rate scheduler to set up. Must be provided after optimizers if used.
- **move_to_device** (bool) - Optional - If set True (default), moves the model to the correct device. Set this to False and alternatively use to_device() manually.
- **_reapply_compile** (bool) - Optional - If True (default), and the model was torch.compiled before, the corresponding torch._dynamo.OptimizedModule wrapper will be removed and reapplied with the same settings after the model was set up by the strategy (e.g., after the model was wrapped by DDP, FSDP etc.). Set it to False if compiling DDP/FSDP is causing issues.

### Return Type
Any

### Returns
If no optimizers are passed, returns the wrapped module. If optimizers are passed, returns a tuple containing the wrapped module and optimizers, and optionally the scheduler if provided, in the same order they were passed in.

### Note
For certain strategies like FSDP, you may need to set up the model first using setup_module(), then create the optimizer, and finally set up the optimizer using setup_optimizers().

### Example
```python
# Basic usage
model, optimizer = fabric.setup(model, optimizer)

# With multiple optimizers and scheduler
model, opt1, opt2, scheduler = fabric.setup(model, opt1, opt2, scheduler=scheduler)

# Model only
model = fabric.setup(model)
```
```

--------------------------------

### Example: Compute STOI with short_time_objective_intelligibility

Source: https://lightning.ai/docs/torchmetrics/stable/audio/short_time_objective_intelligibility

Shows how to compute the STOI metric using the functional interface. Requires pystoi to be installed (e.g., via pip install torchmetrics[audio] or pip install pystoi). The example uses random tensors of shape (8000,) and a sampling frequency of 8000 Hz, returning a tensor value.

```python
>>> from torch import randn
>>> from torchmetrics.functional.audio.stoi import short_time_objective_intelligibility
>>> preds = randn(8000)
>>> target = randn(8000)
>>> short_time_objective_intelligibility(preds, target, 8000).float()
tensor(-0.084...)
```

--------------------------------

### Set up optimizers with setup_optimizers

Source: https://lightning.ai/docs/fabric/stable/full-api-reference/api/generated/lightning.fabric.fabric.Fabric

Demonstrates setting up single or multiple optimizers for accelerated training.

```python
# Single optimizer optimizer = fabric.setup_optimizers(optimizer) # Multiple optimizers opt1, opt2 = fabric.setup_optimizers(opt1, opt2)
```

--------------------------------

### setup

Source: https://lightning.ai/docs/litserve/api-reference/litapi

The setup method is called once at the start of the server's lifecycle. It is used to initialize models, databases, caches, and other necessary systems. The method accepts a device string to specify the hardware (CPU, GPU, MPS) or a list of specific GPUs.

```APIDOC
## setup

### Description
Called once at the start of the server's lifecycle to initialize models, databases, caches, and other necessary systems. Centralizes all setup tasks, simplifying complex configurations like multiple models or external resources.

### Method Signature
`def setup(self, device: str)`

### Parameters
- **device** (str) - Required - String representing the device (CPU, GPU, MPS). Can also specify specific GPUs by passing a list [1, 2, 3].

### Example
```python
# pip install torch clip
import torch, clip, multiprocessing
from PIL import Image
from io import BytesIO
import litserve as ls

class CLIPAPI(ls.LitAPI):
    def setup(self, device):
        # load the model on the device automatically (CPU, GPU etc...)
        self.model, self.preprocess = clip.load("ViT-B/32", device=device)
        # Load candidate captions
        with open('captions.txt', 'r') as file:
            self.candidate_captions = [line.strip() for line in file.readlines()]
        # Pre-tokenize and pre-encode text captions
        with torch.no_grad():
            self.text_tokens = clip.tokenize(self.candidate_captions).to(self.device)
            self.text_embeddings = self.model.encode_text(self.text_tokens)

    def decode_request(self, request):
        image_bytes = bytes.fromhex(request["image_bytes"])
        image = Image.open(BytesIO(image_bytes))
        return self.preprocess(image).unsqueeze(0).to(self.device)

    def predict(self, image_tensor):
        with torch.no_grad():
            image_embedding = self.model.encode_image(image_tensor)
            similarities = (100.0 * image_embedding @ self.text_embeddings.T).softmax(dim=-1)
            max_indices = similarities.argmax(dim=-1)
        return [self.candidate_captions[idx] for idx in max_indices.cpu().numpy()]

    def encode_response(self, output):
        return {"description": output[0]}

if __name__ == "__main__":
    api = CLIPAPI()
    server = ls.LitServer(api, accelerator="auto")
    server.run(port=8000)
```
```

--------------------------------

### Install the lightning-sdk

Source: https://lightning.ai/docs/platform/build/ai-studio

Install the Lightning SDK with pip. This is required to automate Studio operations as shown in the following pipeline example.

```bash
pip install lightning-sdk

```

--------------------------------

### Initialize and manage a Studio

Source: https://lightning.ai/docs/platform/developers/sdk/studio

Demonstrates basic Studio initialization, starting, stopping, and duplication.

```python
from lightning_sdk import Studio

studio = Studio(name='my-studio-name', teamspace='my-teamspace', org='my-org')
studio.start()
studio.stop()
duplicated_studio = studio.duplicate()
```

--------------------------------

### Quickstart Sandbox Execution

Source: https://lightning.ai/docs/platform/developers/sdk/sandbox/js-sandbox-sdk

Demonstrates creating a sandbox, running a simple shell command, and cleaning up the environment. Credentials are automatically read from environment variables or can be configured via Sandbox.configure.

```ts
import { Sandbox } from "@lightningai/sdk";

const sandbox = await Sandbox.create({ name: "quickstart", instanceType: "cpu-1" });

const result = await sandbox.runCommand("echo", ["Hello, World!"]);
console.log(result.output.trimEnd()); // Hello, World!
console.log(result.exitCode);         // 0

await sandbox.delete();
```

--------------------------------

### Install toolchains at runtime

Source: https://lightning.ai/docs/platform/developers/sdk/sandbox/python-sandbox-sdk

Example of installing additional software like GCC inside a sandbox using run_command, which requires network egress.

```python
sandbox = Sandbox.create(name="build", instance_type="cpu-1", runtime="python313")
# Install a toolchain (needs egress — see Network policy) then use it:
sandbox.run_command("sh", ["-c", "apt-get update && apt-get install -y gcc && gcc --version"])
```

--------------------------------

### Setup models and optimizers with fabric.setup

Source: https://lightning.ai/docs/fabric/stable/full-api-reference/api/fabric_methods

The setup method prepares models, optimizers, and optional schedulers for accelerated training. It automatically moves objects to the correct device and handles precision casting.

```python
model = nn.Linear(32, 64)
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=1.0, end_factor=0.3, total_iters=10)

# Set up model and optimizer for accelerated training
model, optimizer = fabric.setup(model, optimizer)

# If you don't want Fabric to set the device
model, optimizer = fabric.setup(model, optimizer, move_to_device=False)

# If you want to additionally register a learning rate scheduler with compatible strategies such as DeepSpeed
model, optimizer, scheduler = fabric.setup(model, optimizer, scheduler)
```

--------------------------------

### Setup multi-language sandbox with run_and_wait

Source: https://lightning.ai/docs/platform/build/sandbox/examples/safe-code-execution

Creates a python313 sandbox and installs Node.js, Ruby, and PHP using the run_and_wait helper to handle the installation process.

```python
def setup_multi_language_sandbox() -> Sandbox:
    """Create a sandbox and install Node.js, Ruby and PHP alongside Python."""
    print("🏖️  Creating sandbox (python313 base)...")

    sandbox = Sandbox.create(
        name="example-safe-code-execution",
        instance_type="cpu-1",
        runtime="python313",
        timeout=30 * MINUTES_MS,
    )

    print(f"Sandbox ID: {sandbox.sandbox_id}")

    print("🏖️  Installing nodejs, ruby and php (this can take a minute)...")
    install_script = (
        "set -eux; "
        "export DEBIAN_FRONTEND=noninteractive; "
        "apt-get update; "
        "apt-get install -y nodejs ruby php-cli"
    )
    exit_code, output = run_and_wait(sandbox, install_script, timeout=600)
    if exit_code != 0:
        raise RuntimeError(
            f"toolchain install failed (exit {exit_code}):\n{output}"
        )

    return sandbox
```

--------------------------------

### DDPStrategy.setup

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.strategies.DDPStrategy

Sets up the accelerator, plugins and initializes the optimizers (if needed).

```APIDOC
## DDPStrategy.setup

### Description
Sets up the accelerator, plugins and initializes the optimizers (if needed).

### Method
setup

### Parameters
- **trainer** (`Trainer`) - The trainer instance.

### Return Type
`None`
```

--------------------------------

### LitServer with accelerator='cpu'

Source: https://lightning.ai/docs/litserve/api-reference/litserver

Example of starting a LitServer with accelerator set to 'cpu'.

```python
import litserve as ls

api = ls.examples.SimpleLitAPI()
server = ls.LitServer(api, accelerator='cpu')
server.run(port=8000)
```

--------------------------------

### setup_environment()

Source: https://lightning.ai/docs/fabric/stable/full-api-reference/api/generated/lightning.fabric.strategies.DDPStrategy

Setup any processes or distributed connections.

```APIDOC
## setup_environment()

### Description
Setup any processes or distributed connections. This must be called by the framework at the beginning of every process, before any distributed communication takes place.

### Method
`setup_environment()`

### Return Type
`None`

```

--------------------------------

### LitServer with devices=4

Source: https://lightning.ai/docs/litserve/api-reference/litserver

Example of starting a LitServer with 4 GPUs (accelerator='cuda', devices=4).

```python
import litserve as ls

api = ls.examples.SimpleLitAPI()
server = ls.LitServer(api, accelerator='cuda', devices=4)
server.run(port=8000)
```

--------------------------------

### Compute VMAF score and features with video_multi_method_assessment_fusion

Source: https://lightning.ai/docs/torchmetrics/stable/video/vmaf

Example usage of the functional VMAF metric. Requires vmaf-torch to be installed (e.g., via pip install torchmetrics[video]). The example creates random video tensors, computes the VMAF score, and then with features=True retrieves a dictionary of elementary features like 'vmaf' and 'integer_adm2'.

```python
>>> import torch
>>> from torchmetrics.functional.video import video_multi_method_assessment_fusion
>>> # 2 videos, 3 channels, 10 frames, 32x32 resolution
>>> preds = torch.rand(2, 3, 10, 32, 32, generator=torch.manual_seed(42))
>>> target = torch.rand(2, 3, 10, 32, 32, generator=torch.manual_seed(43))
>>> vmaf_score = video_multi_method_assessment_fusion(preds, target)
>>> torch.round(vmaf_score, decimals=2)
tensor([[ 9.9900, 15.9000, 14.2600, 16.6100, 15.9100, 14.3000, 13.5800, 13.4900, 15.4700, 20.2800],
        [ 6.2500, 11.3000, 17.3000, 11.4600, 19.0600, 14.9300, 14.0500, 14.4100, 12.4700, 14.8200]])
>>> vmaf_dict = video_multi_method_assessment_fusion(preds, target, features=True)
>>> # show a couple of features, more features are available
>>> vmaf_dict['vmaf'].round(decimals=2)
tensor([[ 9.9900, 15.9000, 14.2600, 16.6100, 15.9100, 14.3000, 13.5800, 13.4900, 15.4700, 20.2800],
        [ 6.2500, 11.3000, 17.3000, 11.4600, 19.0600, 14.9300, 14.0500, 14.4100, 12.4700, 14.8200]])
>>> vmaf_dict['integer_adm2'].round(decimals=2)
tensor([[0.4500, 0.4500, 0.3600, 0.4700, 0.4300, 0.3600, 0.3900, 0.4100, 0.3700, 0.4700],
        [0.4200, 0.3900, 0.4400, 0.3700, 0.4500, 0.3900, 0.3800, 0.4800, 0.3900, 0.3900]])
```

--------------------------------

### Full example: submit a multi-machine job

Source: https://lightning.ai/docs/platform/developers/sdk/multi-machine

Complete example showing how to initialize a Studio, run a multi-machine job, monitor its status, and stop or delete it.

```python
# Install the Lightning SDK
# pip install lightning-sdk

# Export authentication variables
# export LIGHTNING_USER_ID=your-user-id
# export LIGHTNING_API_KEY=your-api-key

from lightning_sdk import Studio, Machine, MMT

# Step 1: Initialize a Studio
studio = Studio(name="batch-processing", teamspace="my-teamspace", user="my-user")
studio.start()

# Step 3: Run a Multi-machine job
job = MMT.run(command="python process_data.py", name="data-job", machine=Machine.CPU, studio=studio, num_machines=2)

# Step 4: Monitor Job Status
print(job.status)  # Running, Completed, Failed

# Step 5: Stop or Delete a Job
# job.stop()  # Gracefully stop a running job
# job.delete()  # Cancel and remove the job

# Step 6: Shut Down the Studio
studio.stop()
```

--------------------------------

### BinaryFBetaScore example with int preds

Source: https://lightning.ai/docs/torchmetrics/stable/classification/fbeta_score

Shows how to use BinaryFBetaScore with beta=2.0. The example uses integer tensors for preds and target, creates the metric, and calls it to get the F-beta score.

```python
>>> from torch import tensor
>>> from torchmetrics.classification import BinaryFBetaScore
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0, 0, 1, 1, 0, 1])
>>> metric = BinaryFBetaScore(beta=2.0)
>>> metric(preds, target)
tensor(0.6667)
```

--------------------------------

### Implementing setup in MNISTDataModule

Source: https://lightning.ai/docs/pytorch/stable/reference/data/datamodule

Shows how to use the setup method to perform data splits and dataset creation. This method is called from every process.

```python
import lightning as L  class MNISTDataModule(L.LightningDataModule): def setup(self, stage: str): # Assign Train/val split(s) for use in Dataloaders if stage == "fit":             mnist_full = MNIST(self.data_dir, train=True, download=True, transform=self.transform)             self.mnist_train, self.mnist_val = random_split(                 mnist_full, [55000, 5000], generator=torch.Generator().manual_seed(42) ) # Assign Test split(s) for use in Dataloaders if stage == "test":             self.mnist_test = MNIST(self.data_dir, train=False, download=True, transform=self.transform)
```

--------------------------------

### LightningCLI.setup_parser

Source: https://lightning.ai/docs/pytorch/stable/optional-api/api/lightning.pytorch.cli.LightningCLI

Initialize and setup the parser, subcommands, and arguments. Returns None.

```APIDOC
## LightningCLI.setup_parser

### Description
Initialize and setup the parser, subcommands, and arguments.

### Method
setup_parser

### Signature
setup_parser(add_subcommands, main_kwargs, subparser_kwargs)

### Parameters
- **add_subcommands** - Required - Whether to add subcommands.
- **main_kwargs** - Required - Main parser keyword arguments.
- **subparser_kwargs** - Required - Subparser keyword arguments.

### Returns
- **None** - No return value.
```

--------------------------------

### Push from Linux

Source: https://lightning.ai/docs/platform/inference/deploy-containers/lightning-container-registry

Install and start Docker on Linux, then upload a container to Lightning registry.

```bash
# [Install Docker] Setup Docker
# install
curl -fsSL https://get.docker.com | bash

# [Start Docker] Make sure Docker is running
# start
sudo systemctl restart docker

# [Upload] Upload the container (same vLLM container as above) to Lightning registry
# upload
lightning container upload my-vllm-server --teamspace org-or-user-name/teamspace-name --tag latest
```

--------------------------------

### Start a Studio

Source: https://lightning.ai/docs/platform/build/ai-studio/cli

Starts an existing Studio. --name and --teamspace are optional and will be prompted if not provided as arguments or in the config file.

```bash
lightning studio start --name <STUDIO_NAME> --teamspace <OWNER_NAME>/<TEAMSPACE_NAME> --machine T4
```

--------------------------------

### Run LitServe server with basic, production, and development configurations

Source: https://lightning.ai/docs/litserve/api-reference/litserver

Shows how to start a LitServe server with the default configuration, a production setup with multiple API servers and warning-level logging, and a development setup with debug logging, pretty logs, and client file generation.

```python
import litserve as ls

api = ls.examples.SimpleLitAPI()
server = ls.LitServer(api)
# Basic
server.run()
# Production
server.run(
    port=8080,
    num_api_servers=4,
    log_level="warning"
)
# Development
server.run(
    log_level="debug",
    pretty_logs=True,
    generate_client_file=True
)
```

--------------------------------

### Push from Mac

Source: https://lightning.ai/docs/platform/inference/deploy-containers/lightning-container-registry

Install and start Docker on macOS, then upload a container to Lightning registry.

```bash
# [Install] Install Docker if it's not already installed
# install
brew install docker

[Run Docker] Make sure Docker is running
# start
open -a Docker

# [Upload] Upload the vLLM container built earlier to the organization or username and teamspace
# upload
lightning container upload my-vllm-server --teamspace org-or-user-name/teamspace-name --tag latest
```

--------------------------------

### Configuring fixed optimizer and scheduler

Source: https://lightning.ai/docs/pytorch/stable/reference/cli/lightning_cli_advanced_3

Example configuration structure for a fixed optimizer and scheduler setup.

```yaml
optimizer: lr: 0.01 lr_scheduler: gamma: 0.2 model: ... trainer: ...
```
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