---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 33ba2c54f850244fd03d98823d72203e92e4e6163a8539665d5cd6a087032956
  pageDirectory: concepts
  sources:
    - distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - nccl-network-communication-failure-in-multi-node-gpu-training
    - NNCFIMGT
  citations:
    - file: distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md
title: NCCL Network Communication Failure in Multi-Node GPU Training
description: Troubleshooting NCCL failures during multi-node GPU training by setting spark.executorEnv.NCCL_SOCKET_IFNAME to eth for correct network interface selection.
tags:
  - troubleshooting
  - nccl
  - gpu
  - networking
  - xgboost
timestamp: "2026-06-18T12:05:52.130Z"
---

# NCCL Network Communication Failure in Multi-Node GPU Training

**NCCL Network Communication Failure** is an error that occurs during multi-node GPU training when NCCL (NVIDIA Collective Communications Library) cannot establish proper network communication between GPUs across different nodes. This error manifests as a failure message indicating that a remote process exited or that there was a network error.

## Error Message

When this failure occurs during multi-node training, users may encounter a message similar to:

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

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

## Cause

The error typically arises when NCCL cannot use certain network interfaces for GPU communication between nodes. NCCL relies on specific network interfaces to facilitate collective communication operations across distributed GPUs. When these interfaces are misconfigured, unavailable, or incorrectly identified, NCCL fails to establish the required communication channels. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

This issue is particularly relevant in cloud environments or multi-node clusters where network interface naming conventions may differ from NCCL's default expectations. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

## Solution

### Setting the NCCL Socket Interface Environment Variable

To resolve the NCCL network communication failure, set the `NCCL_SOCKET_IFNAME` environment variable to specify which network interface NCCL should use for GPU communication. The recommended value is `eth`, which instructs NCCL to use Ethernet interfaces. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

#### For Spark Clusters Using sparkdl.xgboost

In Spark clusters where distributed training is performed using [sparkdl.xgboost](/concepts/sparkdlxgboost-module.md), set the cluster's Spark configuration to propagate the environment variable to all workers: ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

```python
spark.conf.set("spark.executorEnv.NCCL_SOCKET_IFNAME", "eth")
```

This sets the `NCCL_SOCKET_IFNAME` environment variable to `eth` for all workers in a node, ensuring NCCL uses the correct network interface for GPU communication. ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

#### For Direct NCCL Usage

If running distributed training outside of Spark, set the environment variable directly: ^[distributed-training-of-xgboost-models-using-sparkdlxgboost-databricks-on-aws.md]

```bash
export NCCL_SOCKET_IFNAME=eth
```

## Related Concepts

- [Distributed XGBoost Training](/concepts/distributed-xgboost-training-on-databricks.md) — Multi-node training that uses NCCL for GPU communication
- GPU Training Configuration — Settings for enabling GPU-based training
- Apache Spark Configuration — Spark configuration parameters affecting distributed workloads
- NCCL Collective Communications — The NVIDIA library for multi-GPU communication
- Multi-Node GPU Training — Distributed training across multiple nodes with GPUs

## 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)
