Segmentation Fault when Using SentenceTransformer Inside Docker Container – Docker

The Problem:

A segmentation fault occurs when utilizing the SentenceTransformer model within a Docker container, particularly on Apple Silicon M1 or M2 machines. Despite ample memory allocation to the container, updating libraries and ensuring the code functions properly outside the container, the issue persists within the Docker environment.

The Solutions:

Solution 1: Downgrade PyTorch version

The core issue is that the segmentation fault is caused by a compatibility issue between PyTorch and some libraries (e.g., SentenceTransformer) when running in a Docker container on Apple Silicon M1/M2 machines. To resolve this issue, downgrade the PyTorch version to 2.0.1 by adding torch==2.0.* to the requirements.txt file. This ensures compatibility with other libraries and eliminates the segmentation fault.

This solution is effective because it directly addresses the root cause of the problem, allowing the code to run without crashing inside the Docker container on Apple Silicon machines.

Solution 2: Use the Official PyTorch Docker Image

Title: Utilize the Official PyTorch Docker Image to Address Segmentation Faults

Solution:

To resolve the segmentation fault encountered when running code inside a Docker container on Apple Silicon M1/M2 machines, consider switching to the official PyTorch Docker image. This approach has proven effective in addressing the issue, even when using PyTorch versions 2.1.0 and 2.0.1+cpu.

Steps:

1. Pull the latest official PyTorch Docker image:

   • Pull the latest official PyTorch Docker image: docker pull pytorch/pytorch:latest.

2. Modify Your Dockerfile:

   • Update the FROM line in your Dockerfile to use the official PyTorch image: FROM pytorch/pytorch:latest.

3. Rebuild and Run Your Container:

   • Rebuild your Docker image using the updated Dockerfile.
   • Run your container as usual.

Expected Outcome:

By utilizing the official PyTorch Docker image, you should be able to successfully run your code inside the container without encountering segmentation faults. This solution has been tested and verified to work effectively on Apple Silicon M1/M2 machines.

Solution 3: Update Dependencies: PyTorch

In this case, the solution to the segmentation fault when using SentenceTransformer inside a Docker container is to update the dependencies, specifically PyTorch. The user initially had PyTorch version 2.1.0, which appears to have compatibility issues with the Apple M1 M2 architecture. Updating PyTorch to version 2.1.1 resolved the issue.

To update PyTorch, you could run the following command in your Dockerfile or your Python environment:

pip install --upgrade torch==2.1.1

This will install the latest compatible version of PyTorch for your system. Once PyTorch is updated, the segmentation fault should be resolved.

It’s important to note that the exact version of PyTorch required may vary depending on your specific setup and other dependencies. You can refer to the documentation or release notes for SentenceTransformer and PyTorch to determine the compatible versions for your use case.

Video Explanation: