---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: f34a6fa99d1149c71c6ebf58d43ffbe56d80354c6faf2ac73c6d8156cbb2d5db
  pageDirectory: concepts
  sources:
    - distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - nccl-network-configuration-for-distributed-xgboost
    - NNCFDX
  citations:
    - file: distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md
title: NCCL Network Configuration for Distributed XGBoost
description: Troubleshooting NCCL communication failures in distributed XGBoost training by setting the NCCL_SOCKET_IFNAME environment variable on Databricks clusters.
tags:
  - troubleshooting
  - xgboost
  - nccl
  - distributed-training
timestamp: "2026-06-18T15:32:10.654Z"
---

# NCCL Network Configuration for Distributed XGBoost

**NCCL Network Configuration for Distributed XGBoost** refers to the network interface configuration required when using NCCL (NVIDIA Collective Communications Library) for GPU communication during distributed XGBoost training on Databricks clusters.

## Overview

When performing distributed XGBoost training with GPU clusters, NCCL handles communication between GPUs across different nodes. If NCCL cannot use appropriate network interfaces for this communication, training can fail with connection errors. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

## The NCCL Failure Error

During multi-node GPU training, users may encounter the following error:

```
NCCL failure: remote process exited or there was a network error
```

This error indicates a network communication problem between GPUs in the distributed cluster. The root cause is typically that NCCL cannot identify or use the correct network interfaces for inter-GPU communication. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

## Resolution: Setting NCCL Socket Interface

To resolve this issue, set the Spark configuration property `spark.executorEnv.NCCL_SOCKET_IFNAME` to `eth`. This sets the environment variable `NCCL_SOCKET_IFNAME` to `eth` for all workers in a node. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

### Configuration Step

1. Open your cluster's Spark configuration settings.
2. Add the following configuration:
   ```
   spark.executorEnv.NCCL_SOCKET_IFNAME eth
   ```
3. Apply the configuration and restart the cluster if necessary.

## How It Works

Setting `NCCL_SOCKET_IFNAME` to `eth` tells NCCL to use Ethernet network interfaces for GPU communication. This forces NCCL to communicate over the Ethernet interface rather than potentially using other network interfaces that may not be properly configured for GPU-to-GPU communication. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

## Best Practices

- **Apply the configuration before training starts**: Set the NCCL socket interface in the cluster configuration before launching any distributed XGBoost training jobs.
- **Verify network connectivity**: Check that all GPU nodes in the cluster have functional network interfaces and can communicate with each other.

## Related Concepts

- NCCL (NVIDIA Collective Communications Library)
- [Distributed XGBoost Training](/concepts/distributed-xgboost-training-on-databricks.md)
- GPU Clusters for XGBoost
- [Spark Configuration for Distributed Training](/concepts/workload-yaml-configuration-for-distributed-training.md)

## Sources

- distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md

# Citations

1. [distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md](/references/distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws-08446b58.md)
