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Error upon installing #42
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Perhaps the same as #32? |
I am having the same issue both locally and on a cluster. Both have Julia 1.5.3 and CUDA 11.0.
Trying on CUDA 10.1 yields a similar error:
|
What is the |
Locally
On cluster
|
My first errors were produced with the latest version release. On master locally, I get
On master on the cluster, the errors are the same. |
That looks like an issue with the local CUDA setup. We should really just setup lazy artifacts to make these errors go away entirely. |
Hit the same issue (I think) on a GPU machine with Julia 1.6.1 and a fresh environment: julia> versioninfo()
Julia Version 1.6.1
Commit 6aaedecc44 (2021-04-23 05:59 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, broadwell)
(@v1.6) pkg> st
Status `~/.julia/environments/v1.6/Project.toml`
[052768ef] CUDA v3.3.3
[587475ba] Flux v0.12.4
[7073ff75] IJulia v1.23.2
[6a2ea274] Torch v0.1.2
julia> using CUDA; CUDA.versioninfo()
CUDA toolkit 11.3.1, artifact installation
CUDA driver 11.2.0
NVIDIA driver 460.73.1
Libraries:
- CUBLAS: 11.5.1
- CURAND: 10.2.4
- CUFFT: 10.4.2
- CUSOLVER: 11.1.2
- CUSPARSE: 11.6.0
- CUPTI: 14.0.0
- NVML: 11.0.0+460.73.1
- CUDNN: 8.20.0 (for CUDA 11.3.0)
- CUTENSOR: 1.3.0 (for CUDA 11.2.0)
Toolchain:
- Julia: 1.6.1
- LLVM: 11.0.1
- PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0
- Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80
1 device:
0: Tesla T4 (sm_75, 14.414 GiB / 14.756 GiB available)
julia> using Torch
[ Info: Precompiling Torch [6a2ea274-3061-11ea-0d63-ff850051a295]
ERROR: LoadError: InitError: could not load library "/home/jupyter/.julia/artifacts/d6ce2ca09ab00964151aaeae71179deb8f9800d1/lib/libdoeye_caml.so"
libcublas.so.10: cannot open shared object file: No such file or directory
Stacktrace:
[1] dlopen(s::String, flags::UInt32; throw_error::Bool)
@ Base.Libc.Libdl ./libdl.jl:114
[2] dlopen (repeats 2 times)
@ ./libdl.jl:114 [inlined]
[3] __init__()
@ Torch_jll ~/.julia/packages/Torch_jll/sFQc0/src/wrappers/x86_64-linux-gnu-cxx11.jl:57
[4] _include_from_serialized(path::String, depmods::Vector{Any})
@ Base ./loading.jl:674
[5] _require_search_from_serialized(pkg::Base.PkgId, sourcepath::String)
@ Base ./loading.jl:760
[6] _require(pkg::Base.PkgId)
@ Base ./loading.jl:998
[7] require(uuidkey::Base.PkgId)
@ Base ./loading.jl:914
[8] require(into::Module, mod::Symbol)
@ Base ./loading.jl:901
[9] include
@ ./Base.jl:386 [inlined]
[10] include_package_for_output(pkg::Base.PkgId, input::String, depot_path::Vector{String}, dl_load_path::Vector{String}, load_path::Vector{String}, concrete_deps::Vector{Pair{Base.PkgId, UInt64}}, source::Nothing)
@ Base ./loading.jl:1213
[11] top-level scope
@ none:1
[12] eval
@ ./boot.jl:360 [inlined]
[13] eval(x::Expr)
@ Base.MainInclude ./client.jl:446
[14] top-level scope
@ none:1
during initialization of module Torch_jll
in expression starting at /home/jupyter/.julia/packages/Torch/fIKJf/src/Torch.jl:1
ERROR: Failed to precompile Torch [6a2ea274-3061-11ea-0d63-ff850051a295] to /home/jupyter/.julia/compiled/v1.6/Torch/jl_Yw2dNx.
Stacktrace:
[1] error(s::String)
@ Base ./error.jl:33
[2] compilecache(pkg::Base.PkgId, path::String, internal_stderr::Base.TTY, internal_stdout::Base.TTY)
@ Base ./loading.jl:1360
[3] compilecache(pkg::Base.PkgId, path::String)
@ Base ./loading.jl:1306
[4] _require(pkg::Base.PkgId)
@ Base ./loading.jl:1021
[5] require(uuidkey::Base.PkgId)
@ Base ./loading.jl:914
[6] require(into::Module, mod::Symbol)
@ Base ./loading.jl:901
[7] top-level scope
@ ~/.julia/packages/CUDA/02Kjq/src/initialization.jl:52 is there any recommended workaround? |
For this issue, one workaround you could try is to link the cuda library by |
Using Julia 1.5.3 on a computer with GPU:
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