> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cognite.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Upload point clouds with Python SDK

> Upload large point clouds to Cognite Data Fusion (CDF) using Python SDK to avoid file transfer failures and long upload times.

<Info>
  For large point clouds, we don't recommend uploading through the CDF UI. Those uploads can take a long time and may fail if your computer loses its internet connection or goes into sleep mode.
</Info>

You don't need to compress files into a single zip file - specify the folder containing your point cloud files.

## Prerequisites

Install [Python](https://www.python.org/downloads/) and the [Cognite Python SDK](https://github.com/cognitedata/cognite-sdk-python) in your environment. For more information, see [Get started with Python SDK](/dev/sdks/python/index).

## Run the upload script

Use the Python script to upload point clouds from a folder. It uploads the files, creates a **3D model** and **revision**, and starts processing. When you run the script, sign in through the browser window that opens. To rename the model, change the name in the **Create the 3D model** section of the script.

<Info>
  It'll take a significant amount of time to complete the uploading process. Don't interrupt or shut down the computer until the process is completed.
</Info>

```python theme={"languages":{"custom":["/_languages/kuiper.json","../_languages/kuiper.json"]}}
from cognite.client import global_config, ClientConfig, CogniteClient
from cognite.client.data_classes import ThreeDModelRevisionWrite
from cognite.client.credentials import OAuthInteractive
import json

# Note: Input the details for your environment
# ******************************************************

TENANT_ID = '<TENANT_ID>'
CLIENT_ID = '<CLIENT_ID>'
CDF_CLUSTER = '<CDF_CLUSTER>'
COGNITE_PROJECT = '<PROJECT NAME>'
DIRECTORY_NAME = "<Specify the directory containing all point cloud files>"
AUTHORITY_URL = f"https://login.microsoftonline.com/{TENANT_ID}"

# ******************************************************

BASE_URL = f"https://{CDF_CLUSTER}.cognitedata.com"

# Using OAuth in Azure Entra Id
tokenProvider = OAuthInteractive(
  authority_url=AUTHORITY_URL,
  client_id=CLIENT_ID,
  scopes=[f'{BASE_URL}/.default']
)

clientConfig = ClientConfig(
    client_name="upload point cloud script",
    project=COGNITE_PROJECT,
    credentials=tokenProvider,
    base_url=BASE_URL,
    timeout=300,
)

global_config.default_client_config = clientConfig
client = CogniteClient()

# A directory with point cloud files will be uploaded as individual files
print(f"Uploading files from {DIRECTORY_NAME} .... (Uploading is in progress; it can take hours if the files are large or there are many files)")
directory_upload_result = client.files.upload(DIRECTORY_NAME, recursive=True)
print(f"Number of files uploaded: {len(directory_upload_result)}")

# Create and upload file_id list json file
file_ids = [file.id for file in directory_upload_result]
file_content = {"dataType": "PointCloud", "fileIds": file_ids}
file_name = "FileIdsList.json"
print(f"Uploading {file_name} ....")
with open(file_name, 'w') as outfile:
    json.dump(file_content, outfile)

file_upload_result = client.files.upload(file_name)

# Create the 3D model
model_name = "PointCloud Model"
print(f"Creating 3D model {model_name} ....")
model = client.three_d.models.create(name=model_name)

# Create the 3D revision for the model.
# This will start the processing job.
# Note: The processing time depends on the total size of the files.
print(f"Creating revision for model {model_name} ....")
new_revision_config = ThreeDModelRevisionWrite(file_id=file_upload_result.id)
created_revision = client.three_d.revisions.create(model_id=model.id, revision=new_revision_config)

print("Summary:")
print(f"File id     : {file_upload_result.id}")
print(f"File name   : {file_name}")
print(f"Model name  : {model_name}")
print(f"Model id    : {model.id}")
print(f"Revision id : {created_revision.id}")
print("Check the processing status for the 3D model revision in Cognite Data Fusion UI.")
print("Once the processing is completed, you can select the model revision to visualize it.")
```

<Check>
  When the script finishes, go to [<span class="ui-element">CDF</span>](https://fusion.cognite.com) > <span class="ui-element">Data fusion</span> > <span class="ui-element">Configure</span> > <span class="ui-element">3D</span> and verify that 3D revision processing has started.
  When processing is complete, select the revision to visualize the model.
</Check>