eed1957f03
Added instructions for resolving 'invalid tar header' error on Kylin OS with an ARM64 architecture on Atlas300I hardware during docker
pull, including steps for offline loading of docker images.
---
### What this PR does / why we need it?
The primary motivation for this PR is to address a critical `docker
pull` failure that occurs on specific, yet important, enterprise
environments. Specifically, when operating on **Kylin OS (麒麟操作系统) with
an ARM64 architecture on Atlas300I hardware**, users frequently
encounter an `archive/tar: invalid tar header` error, which completely
blocks the setup process. This issue has been consistently reproduced,
with multiple retries failing with the same error, confirming that it is
a persistent environmental problem rather than a transient network
issue.
<img width="2060" height="525" alt="image"
src="https://github.com/user-attachments/assets/6c1c5728-de27-476f-8df4-723564fc290b"
/>
This guide provides a robust, step-by-step workaround using an
offline-loading method (`docker save` on a host machine and `docker
load` on the target machine). This solution is crucial for enabling
users on this platform to use vLLM.
This contribution does not directly fix an existing issue number, but it
proactively solves a significant environmental and usability problem for
a growing user base.
### Does this PR introduce _any_ user-facing change?
No.It does not alter any code, APIs, interfaces, or existing behavior of
the vLLM project.
### How was this patch tested?
The instructions and troubleshooting steps in this guide were validated
through a real-world, end-to-end test case on the my hardware and OS.
The testing process was as follows:
1. **Problem Reproduction**: An attempt was made to directly `docker
pull` the `vllm-ascend:v0.10.0rc1-310p` image on a target machine
running Kylin OS (ARM64). The `invalid tar header` failure was
successfully and consistently reproduced, confirming the existence of
the problem.
2. **Solution Implementation**: The workaround detailed in the guide was
executed:
* On a separate host machine (Ubuntu x86_64), the image was successfully
pulled using the `--platform linux/arm64` flag.
* The image was then saved to a `.tar` archive using `docker save`.
* The `.tar` archive was transferred to the target Kylin OS machine.
* The image was successfully loaded from the archive using `docker load
-i ...`.
3. **End-to-End Validation**: After loading the image, the vLLM
container was launched on the target machine following the instructions
in the guide. Both online inference (via `curl` to the API server) and
offline inference (via the Python script) were executed successfully,
confirming that the entire workflow described in the document is
accurate and effective.
Since this is a documentation-only change based on a validated workflow,
no new unit or integration tests were added to the codebase.
- vLLM version: v0.11.0rc3
- vLLM main:
83f478bb19
---------
Signed-off-by: Liwx <liweixuan1014@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
vLLM Ascend Plugin documents
Live doc: https://vllm-ascend.readthedocs.io
Build the docs
# Install dependencies.
pip install -r requirements-docs.txt
# Build the docs.
make clean
make html
# Build the docs with translation
make intl
# Open the docs with your browser
python -m http.server -d _build/html/
Launch your browser and open:
- English version: http://localhost:8000
- Chinese version: http://localhost:8000/zh_CN