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Error building worker-vllm docker image for mixtral 8x7b - Runpod
R
Runpod
•
3y ago
•
68 replies
wizardjoe
Error building worker-vllm docker image for mixtral 8x7b
I
'm running the following command to build and tag a docker worker image based off of worker
-vllm
:
docker build
-t lesterhnh
/mixtral
-8x7b
-instruct
-v0
.1
-runpod
-
s
e
r
v
e
r
l
e
s
s
:1
.0
-
-build
-arg MODEL
_NAME
=
"mistralai
/Mixtral
-8x7B
-Instruct
-v0
.1
"
-
-build
-arg MODEL
_BASE
_PATH
=
"
/models
"
.
I
'm getting the following error
:
-
-
-
-
-
-
D
o
c
k
e
r
f
i
l
e
:23
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
22
|
# Install torch and vllm based on CUDA version
23
|
>
>
> RUN if
[
[
"
$
{WORKER
_CUDA
_VERSION
}
"
=
= 11
.8
*
]
]
; then
\
24
|
>
>
> python3
.11
-m pip install
-U
-
-force
-reinstall torch
=
=2
.1
.2 xformers
=
=0
.0
.23
.post1
-
-index
-url
https://download.pytorch.org/whl/cu118
;
\
25
|
>
>
> python3
.11
-m pip install
-e git
+
https://github.com/runpod/vllm-fork-for-sls-worker.git@cuda-11.8#egg=vllm
;
\
26
|
>
>
> else
\
27
|
>
>
> python3
.11
-m pip install
-e git
+
https://github.com/runpod/vllm-fork-for-sls-worker.git#egg=vllm
;
\
28
|
>
>
> fi
&
&
\
29
|
>
>
> rm
-rf
/root
/
.cache
/pip
30
|
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
ERROR
: failed to solve
: process
"
/bin
/bash
-o pipefail
-c if
[
[
"
$
{WORKER
_CUDA
_VERSION
}
"
=
= 11
.8
*
]
]
; then python3
.11
-m pip install
-U
-
-force
-reinstall torch
=
=2
.1
.2 xformers
=
=0
.0
.23
.post1
-
-index
-url
https://download.pytorch.org/whl/cu118
; python3
.11
-m pip install
-e git
+
https://github.com/runpod/vllm-fork-for-sls-worker.git@cuda-11.8#egg=vllm
; else python3
.11
-m pip install
-e git
+
https://github.com/runpod/vllm-fork-for-sls-worker.git#egg=vllm
; fi
&
& rm
-rf
/root
/
.cache
/pip
" did not complete successfully
: exit code
: 1
GitHub
GitHub - runpod/vllm-fork-for-sls-worker: A high-throughput and mem...
A high
-throughput and memory
-efficient inference and serving engine for LLMs
- GitHub
- runpod
/vllm
-fork
-for
-sls
-worker
: A high
-throughput and memory
-efficient inference and serving engine for LLMs
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