Direct upload and download

Get Endpoint via Float16

This tutorial guides you through upload and download using Float16's storage.

• Float16 CLI installed

• Logged into Float16 account

• VSCode or preferred text editor recommended

Step 1 : Create and start the project

float16 project create --instance h100
float16 project start

If you didn't start the project, You can't use storage command before start the project.

Step 2 : Prepare the script

https://github.com/float16-cloud/examples/tree/main/official/spot/torch-train-and-infernce-mnist

(download-mnist-datasets.py)

import os
from torchvision import datasets, transforms

def download_mnist(data_path):
    if not os.path.exists(data_path):
        os.makedirs(data_path)
    
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])

    # Download training data
    train_dataset = datasets.MNIST(root=data_path, train=True, download=True, transform=transform)
    
    # Download test data
    test_dataset = datasets.MNIST(root=data_path, train=False, download=True, transform=transform)

    print(f"MNIST dataset downloaded and saved to {data_path}")

if __name__ == "__main__":
    data_path = "../mnist-datasets"  # You can change this to your preferred location
    download_mnist(data_path)

Step 3.1 : Upload via CLI

After downloaded. Use this command to upload datasets directory to remote path.

float16 storage upload -f ./mnist-datasets -d datasets

• The storage upload command use direct connect between your local machine direct to

Step 3.2 : Upload via Website

Step 4.1 : Download the file(s) via CLI

float16 storage download -f datasets -d ./local_datasets

Step 4.2 : Download the file(s) via Website

Congratulations! You've successfully use your first server mode on Float16's serverless GPU platform.

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