Data Transfer and Management

Chiltepin provides specialized tasks for transferring and deleting data between Globus Transfer endpoints. These tasks integrate seamlessly with Chiltepin workflows, allowing you to stage data, process it, and clean up afterward.

Important

Transfer Endpoints vs Compute Endpoints: Globus has two types of endpoints:

• Transfer Endpoints: Used for moving and managing files (documented here)

• Compute Endpoints: Used for executing tasks (see Endpoint Management)

These are configured and managed separately through the Globus service.

Overview

The data module provides two Chiltepin tasks for workflow data management:

• transfer_task: Transfer files/directories between Globus Transfer endpoints

• delete_task: Delete files/directories from Globus Transfer endpoints

These are standard Chiltepin tasks that return futures. You can create dependencies by:

• Using the inputs parameter to pass a list of futures (non-blocking)

• Passing futures as function arguments (when the task signature supports it)

• Calling .result() to wait synchronously (blocking)

Data Transfer Task

The transfer_task function transfers data between two Globus Transfer endpoints.

Basic Usage

from chiltepin.data import transfer_task

# Transfer a single file
transfer_future = transfer_task(
    src_ep="my-laptop",
    dst_ep="hpc-scratch",
    src_path="/Users/me/data/input.dat",
    dst_path="/scratch/project/input.dat",
    executor=["local"]
)

# Wait for transfer to complete
success = transfer_future.result()
if success:
    print("Transfer completed successfully")

Parameters

Parameter	Type	Default	Description
src_ep	string	Required	Source Transfer endpoint name or UUID
dst_ep	string	Required	Destination Transfer endpoint name or UUID
src_path	string	Required	Path to file/directory on source Transfer endpoint
dst_path	string	Required	Path to file/directory on destination Transfer endpoint
timeout	integer	3600	Seconds to wait for transfer completion
polling_interval	integer	30	Seconds between status checks
client	TransferClient	None	Globus TransferClient (auto-created if None)
recursive	boolean	False	Transfer directories recursively
executor	string	Required	Resource name for running the transfer task (must have internet access)

Recursive Transfer

Transfer entire directories recursively:

# Transfer a directory and all its contents
transfer_future = transfer_task(
    src_ep="my-laptop",
    dst_ep="hpc-scratch",
    src_path="/Users/me/project/data/",
    dst_path="/scratch/project/data/",
    recursive=True,
    executor=["local"]
)

Endpoint Names vs UUIDs

You can specify endpoints by their display name or UUID:

# Using display names
transfer_task(
    src_ep="My Laptop",
    dst_ep="HPC Scratch Space",
    ...
)

# Using UUIDs
transfer_task(
    src_ep="12345678-1234-1234-1234-123456789abc",
    dst_ep="87654321-4321-4321-4321-cba987654321",
    ...
)

Tip

UUIDs are more reliable than display names, which can change. Find your endpoint UUIDs at app.globus.org.

Data Deletion Task

The delete_task function removes files or directories from a Globus Transfer endpoint.

Basic Usage

from chiltepin.data import delete_task

# Delete a single file
delete_future = delete_task(
    src_ep="hpc-scratch",
    src_path="/scratch/project/temp.dat",
    executor=["local"]
)

# Wait for deletion to complete
success = delete_future.result()
if success:
    print("File deleted successfully")

Parameters

Parameter	Type	Default	Description
src_ep	string	Required	Transfer endpoint name or UUID where data will be deleted
src_path	string	Required	Path to file/directory to delete
timeout	integer	3600	Seconds to wait for deletion completion
polling_interval	integer	30	Seconds between status checks
client	TransferClient	None	Globus TransferClient (auto-created if None)
recursive	boolean	False	Delete directories recursively
executor	string	Required	Resource name for running the deletion task (must have internet access)

Recursive Deletion

Delete entire directories:

# Delete a directory and all its contents
delete_future = delete_task(
    src_ep="hpc-scratch",
    src_path="/scratch/project/temp_data/",
    recursive=True,
    executor=["local"]
)

Warning

Recursive deletion is permanent and cannot be undone. Use with caution.

Workflow Integration

Transfer and deletion tasks integrate seamlessly with Chiltepin workflows using the inputs parameter for non-blocking dependencies.

Stage, Process, Cleanup Pattern

A common pattern is to stage data, process it, then clean up:

from chiltepin import run_workflow
from chiltepin.tasks import python_task
from chiltepin.data import transfer_task, delete_task

@python_task
def analyze_data(input_path):
    # Process the data file
    import pandas as pd
    df = pd.read_csv(input_path)
    result = df.mean().to_dict()
    return result

# Load configuration and start workflow
with run_workflow("config.yaml"):
    # Stage data to compute resource
    stage_in = transfer_task(
        src_ep="my-laptop",
        dst_ep="hpc-scratch",
        src_path="/Users/me/data/dataset.csv",
        dst_path="/scratch/project/dataset.csv",
        executor=["local"]
    )

    # Process the staged data (waits for transfer via inputs)
    analysis = analyze_data(
        "/scratch/project/dataset.csv",
        executor=["compute"],
        inputs=[stage_in]  # Non-blocking dependency
    )

    # Clean up staged data (waits for processing via inputs)
    cleanup = delete_task(
        src_ep="hpc-scratch",
        src_path="/scratch/project/dataset.csv",
        executor=["local"],
        inputs=[analysis]  # Non-blocking dependency
    )

    # Get results (blocks until analysis completes)
    results = analysis.result()
    print(f"Analysis results: {results}")

    # Ensure cleanup completes before exiting
    cleanup.result()

Multiple File Transfers

Transfer multiple files in parallel:

from chiltepin.data import transfer_task

# Transfer multiple input files in parallel
files = ["sim1.dat", "sim2.dat", "sim3.dat"]

transfers = []
for filename in files:
    future = transfer_task(
        src_ep="my-laptop",
        dst_ep="hpc-scratch",
        src_path=f"/Users/me/data/{filename}",
        dst_path=f"/scratch/project/{filename}",
        executor=["local"]
    )
    transfers.append(future)

# Wait for all transfers to complete
for t in transfers:
    assert t.result(), "Transfer failed"

Waiting for Multiple Tasks

To run a transfer or deletion after multiple tasks complete, pass them via the inputs parameter:

from chiltepin.data import transfer_task, delete_task
from chiltepin.tasks import python_task

@python_task
def generate_config():
    # Generate config file
    with open("/scratch/config.json", "w") as f:
        f.write('{"param": 1}')
    return True

@python_task
def generate_input():
    # Generate input file
    with open("/scratch/input.dat", "w") as f:
        f.write("data")
    return True

# Generate files in parallel
config_ready = generate_config(executor=["compute"])
input_ready = generate_input(executor=["compute"])

# Wait for both files before transferring (non-blocking dependency)
transfer = transfer_task(
    src_ep="hpc-scratch",
    dst_ep="my-laptop",
    src_path="/scratch/",
    dst_path="/results/",
    recursive=True,
    executor=["local"],
    inputs=[config_ready, input_ready]  # Multiple dependencies
)

transfer.result()  # Wait for transfer

Data Pipeline with Transfers

Build complete data pipelines using the inputs parameter for non-blocking dependencies:

@python_task
def preprocess(input_path, output_path):
    # Preprocessing step
    import pandas as pd
    df = pd.read_csv(input_path)
    df_clean = df.dropna()
    df_clean.to_csv(output_path, index=False)
    return output_path

@python_task
def analyze(clean_data_path):
    # Analysis step
    import pandas as pd
    df = pd.read_csv(clean_data_path)
    return df.describe().to_dict()

# Stage raw data
stage_raw = transfer_task(
    src_ep="my-laptop",
    dst_ep="hpc-scratch",
    src_path="/data/raw.csv",
    dst_path="/scratch/raw.csv",
    executor=["local"]
)

# Preprocess (waits for transfer via inputs)
preprocess_future = preprocess(
    "/scratch/raw.csv",
    "/scratch/clean.csv",
    executor=["compute"],
    inputs=[stage_raw]  # Wait for transfer non-blocking
)

# Analyze (waits for preprocess completing and passing output path)
analysis_future = analyze(
    preprocess_future,  # Parsl waits for this future and passes the output path
    executor=["compute"]
)

# Stage results back (waits for analysis via inputs)
stage_out = transfer_task(
    src_ep="hpc-scratch",
    dst_ep="my-laptop",
    src_path="/scratch/clean.csv",
    dst_path="/data/cleaned_output.csv",
    executor=["local"],
    inputs=[analysis_future]  # Wait for analysis non-blocking
)

# Cleanup remote files (waits for stage_out via inputs)
cleanup = delete_task(
    src_ep="hpc-scratch",
    src_path="/scratch/",
    recursive=True,
    executor=["local"],
    inputs=[stage_out]  # Wait for stage out non-blocking
)

# Get results when needed
results = analysis_future.result()
print(f"Analysis results: {results}")

# Ensure cleanup completes before exiting
cleanup.result()

Authentication

Data transfer tasks require Globus authentication. Use the Chiltepin login command:

$ chiltepin login

This authenticates you with Globus and grants the necessary permissions for data transfers. The authentication persists across workflow runs until you log out.

Note

You only need to authenticate once. The credentials are cached and reused for subsequent transfers.

Setting Up Data Endpoints

To use data transfer tasks, you need access to Globus Transfer endpoints:

1. Personal Endpoints: Install Globus Connect Personal on your laptop/workstation (globus.org/globus-connect-personal)

2. Institutional Endpoints: Many HPC centers provide pre-configured Globus endpoints. Check with your institution’s documentation.

3. Guest Collections: Create shareable collections for specific directories

Visit the Globus File Manager to view and manage your endpoints.

Best Practices

1. Use Descriptive Endpoint Names: Clear names make workflows easier to understand and maintain.

2. Check Transfer Success: Always check the result of transfer/delete tasks:

   success = transfer_future.result()
   assert success, "Transfer failed"
   

3. Handle Permissions: Ensure you have read permissions on source endpoints and write permissions on destination endpoints.

4. Set Appropriate Timeouts: Large transfers may need longer timeouts. The default is 1 hour (3600 seconds).

5. Create Dependencies Properly: Use the inputs parameter to create non-blocking dependencies between tasks. Reserve .result() for when you actually need the data or must wait synchronously.

6. Cleanup Staged Data: Always delete temporary staged data to avoid filling up scratch space.

7. Test Endpoints First: Verify endpoints are set up correctly by doing a manual transfer through the Globus web interface before automating.

8. Use Absolute Paths: Always use absolute paths for both source and destination to avoid ambiguity.

Troubleshooting

Transfer Not Starting

• Verify you’ve authenticated: chiltepin login

• Check endpoint names/UUIDs are correct

• Ensure both endpoints are activated (visit Globus File Manager)

Permission Denied

• Verify you have read permissions on the source endpoint

• Verify you have write permissions on the destination endpoint

• Some endpoints require explicit activation in the Globus web interface

Transfer Timing Out

• Increase the timeout parameter for large transfers

• Check network connectivity between endpoints

• Verify endpoints are online and not in maintenance mode

Endpoint Not Found

• Check endpoint name spelling (case-sensitive)

• Try using the endpoint UUID instead of display name

• Verify the endpoint is visible in your Globus File Manager

See Also

• Tasks - General task documentation

• Endpoint Management - Globus Compute endpoints for task execution

• API Reference - Full API reference for the data module

• Globus Documentation - Official Globus guides