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sbrunk / storch

GPU accelerated deep learning and numeric computing for Scala 3.
https://storch.dev
Apache License 2.0
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New snapshot - CUDA error: the provided PTX was compiled with an unsupported toolchain. #62

Closed hmf closed 9 months ago

hmf commented 10 months ago

While working on #61, I rebuilt my dev container. The build failed. Upon investigations I see that the old snapshot does not exist. We now have:

12.3-8.9-1.5.10-SNAPSHOT/ Wed Oct 25 13:34:15 UTC 2023
maven-metadata.xml Wed Oct 25 14:13:45 UTC 2023 393
maven-metadata.xml.md5 Wed Oct 25 14:13:45 UTC 2023 32
maven-metadata.xml.sha1 Wed Oct 25 14:13:45 UTC 2023 40
maven-metadata.xml.sha256 Wed Oct 25 14:13:45 UTC 2023 64
maven-metadata.xml.sha512 Wed Oct 25 14:13:45 UTC 2023 128

I changed the library (Ivy dependencies). Now the build is ok, but execution fails. I suspect the problem may be due to the use of the CUDA 12.3 version. I now get the following message when running an app:

vscode ➜ /workspaces/storch (explore_1) $ ./mill examples.runMain LeNetApp2
[109/116] examples.compile 
[info] compiling 1 Scala source to /workspaces/storch/out/examples/compile.dest/classes ...
[info] done compiling
[116/116] examples.runMain 
[W interface.cpp:47] Warning: Loading nvfuser library failed with: Error in dlopen: libtorch.so: cannot open shared object file: No such file or directory (function LoadingNvfuserLibrary)
Using device: Device(CUDA,-1)
Exception in thread "main" java.lang.ExceptionInInitializerError
        at LeNetApp2.main(LeNet2.scala)
Caused by: java.lang.RuntimeException: CUDA error: the provided PTX was compiled with an unsupported toolchain.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at /home/runner/work/javacpp-presets/javacpp-presets/pytorch/cppbuild/linux-x86_64-gpu/pytorch/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x6c (0x7fc0e40b1d8c in /home/vscode/.javacpp/cache/pytorch-2.0.1-1.5.10-20231025.021128-64-linux-x86_64-gpu.jar/org/bytedeco/pytorch/linux-x86_64-gpu/libc10.so)

Can anyone confirm this is the most probable cause? Of course the issue now is installing the CUDA libraries in the dev-container. Only 12.2 is currently supported. Is installing 12.3 the only way forward?

TIA

sbrunk commented 10 months ago

Yes the native bindings have been updated to CUDA 12.3 as of https://github.com/bytedeco/javacpp-presets/commit/7d56a2cf9badfe9798a59358397f45f3f125894d. Could you check if you have a recent Nvidia driver installed? At least 535. I ran into this issue too with a 530 driver but after updating it started working again.

You can check with nvidia-smi. Even though a 535 driver is CUDA 12.2 it should work with libraries compiled against 12.3.

See also this thread and the CUDA Compatibility docs.

There's some discussion about using upstream libtorch in https://github.com/bytedeco/javacpp-presets/pull/1426 which is compiled against CUDA 12.1 but it's unclear if this is feasible.

Welcome to CUDA depency hell 🙈

hmf commented 10 months ago

@sbrunk Thanks for the help.

I have checked the compatibility matrix. Command nvidia-smireports:

 Driver Version: 535.104.05   CUDA Version: 12.2

nvcc --version reports:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Tue_Aug_15_22:02:13_PDT_2023
Cuda compilation tools, release 12.2, V12.2.140
Build cuda_12.2.r12.2/compiler.33191640_0

All seems to be good. I also have:

vscode ➜ /workspaces/storch (explore_1) $ locate -i libtorch.so
/home/vscode/.javacpp/cache/pytorch-2.0.1-1.5.10-20231025.021128-64-linux-x86_64-gpu.jar/org/bytedeco/pytorch/linux-x86_64-gpu/libtorch.so

Could this be an issue of simply not finding libtorch.so as opposed to getting an incorrect version? Any other diagnostics I could try out?

hmf commented 10 months ago

Strange. I made an update & upgrade. Did a reboot. Now I get (no libtorch warning):

vscode ➜ /workspaces/storch (explore_1) $ ./mill examples.runMain LeNetApp2
[116/116] examples.runMain 
Using device: Device(CUDA,-1)
Exception in thread "main" java.lang.ExceptionInInitializerError
        at LeNetApp2.main(LeNet2.scala)
Caused by: java.lang.RuntimeException: CUDA error: the provided PTX was compiled with an unsupported toolchain.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at /home/runner/work/javacpp-presets/javacpp-presets/pytorch/cppbuild/linux-x86_64-gpu/pytorch/c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x6c (0x7f9fe2a58d8c in /home/vscode/.javacpp/cache/pytorch-2.0.1-1.5.10-20231025.021128-64-linux-x86_64-gpu.jar/org/bytedeco/pytorch/linux-x86_64-gpu/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xfa (0x7f9fe2a1ef6a in /home/vscode/.javacpp/cache/pytorch-2.0.1-1.5.10-20231025.021128-64-linux-x86_64-gpu.jar/org/bytedeco/pytorch/linux-x86_64-gpu/libc10.so)
sbrunk commented 10 months ago

Hmm my assumption about the driver might have been wrong (it might be needed too, but not perhaps it's not enough).

While testing an update to PyTorch 2.1, I just compiled libtorch from source using CUDA 12.3. I tried to run some tests on the GPU then, and ran into the error you're seeing: CUDA error: the provided PTX was compiled with an unsupported toolchain.

I went back to main and the error still occurs. This is a machine with an A 6000 ADA GPU (ampere 86). On my laptop with an older GPU it's working fine though, so I suspect that the issue could be missing support for that 86 arch in our libtorch build in javacpp-presets.

export TORCH_CUDA_ARCH_LIST="5.0;6.0;7.0+PTX"

@hmf what's the GPU you're testing this on?

hmf commented 10 months ago

@sbrunk I have a NVIDIA GeForce RTX 3090 in the VM

EDIT: I assume you are saying that TORCH_CUDA_ARCH_LIST="8.6" should also be included. However, this did run before. I still have the 12.1-8.9 in the VM (not the dev container). So I ran that again. Tests on both 12.1and 12.3 execute correctly.

Now I get he same error in the VM as the dev container for both 12.1and 12.3 versions. I suspect some update might have messed this up.

sbrunk commented 10 months ago

According to https://developer.nvidia.com/cuda-gpus, the RTX 3090 has compute capability 8.6 just like the RTX A6000, supporting my assumption that we might need to update the ARCH_LIST of the libtorch build in JavaCPP.

@HGuillemet do you think this makes sense?

sbrunk commented 10 months ago

EDIT: I assume you are saying that TORCH_CUDA_ARCH_LIST="8.6" should also be included. However, this did run before. I still have the 12.1-8.9 in the VM (not the dev container). So I ran that again. Tests on both 12.1and 12.3 execute correctly.

Now I get he same error in the VM as the dev container for both 12.1and 12.3 versions. I suspect some update might have messed this up.

Hmm yeah that's a bit weird. If I recall correctly I've seen the error too and then it did work again (after upgrading the driver) until I tested again today. Perhaps check your ~/.javacpp/cache folder. In the pytorch folder there, some shared libs are symlinks to the cuda folder and I don't know if it updates them and how that mechanism interacts with snapshots.

I changed the issue title BTW because the other message is just a warning and is not causing issues here.

hmf commented 10 months ago

I figured I would try to install CUDA Toolkit 12.3 in a Dev container just to see what happens. So I get this error when I try it out:

[2023-10-28T15:44:07.599Z] 59.40 Reading state information...
59.40 Package libcudnn8 is not available, but is referred to by another package.
59.40 This may mean that the package is missing, has been obsoleted, or
59.40 is only available from another source
59.40 
59.40 E: Version '8.9.5.29-1+cuda12.3' for 'libcudnn8' was not found
[2023-10-28T15:44:07.599Z] 59.40 ERROR: Feature "NVIDIA CUDA" (ghcr.io/devcontainers/features/nvidia-cuda) failed to install! Look at the documentation at https://github.com/devcontainers/features/tree/main/src/nvidia-cuda for help troubleshooting this error.
------
[2023-10-28T15:44:07.599Z] Dockerfile.extended:80

If I looked at the cuDNN support matrix, I see that the latest version for the CUDA Toolkit Version is 12.2.

I am assuming that libTorch compilation requires and links to CUDA and CuDNN. So how were the JavaCPP binaries created? In the build script I see variables pointing to a CuDNN installation.

Maybe 12.3 is not ready for use? Maybe step back for a 12.2 snapshot first?

sbrunk commented 10 months ago

I am assuming that libTorch compilation requires and links to CUDA and CuDNN. So how were the JavaCPP binaries created? In the build script I see variables pointing to a CuDNN installation.

https://github.com/bytedeco/javacpp-presets/blob/master/.github/actions/deploy-ubuntu/action.yml

I built libtorch from source against CUDA 12.3 and with added support for compute capability 8.0 and 8.6 but I'm still running into the same issue. I didn't check CuDNN before but now I'm seeing that my version is also for 12.2, not sure if this an issue though.

I'll try to compile libtorch against CUDA 12.1 next.

For these tests, I'm using the Ubuntu CUDA/CuDNN packages whose installation is described here: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu

hmf commented 10 months ago

https://github.com/bytedeco/javacpp-presets/blob/master/.github/actions/deploy-ubuntu/action.yml

So CuDNN version is 8.9.5.29-1.cuda12.2, which is the the one I tested with CUDA 12.2.

I built libtorch from source against CUDA 12.3 and with added support for compute capability 8.0 and 8.6

I have a hunch this might not be the issue. I think that if you don't add the capability, you simple don't take advantage of the hardware but it should still execute without problems. As I said, this seemed to work with CUDA 11.8 (and respective CuDNN).

I'll try to compile libtorch against CUDA 12.1 next.

Hope that works. But then 12.2 is also a possible candidate, right?

For these tests, I'm using the Ubuntu CUDA/CuDNN packages whose installation is described

Used the same instructions in the bare VM with the same negative results.

HGuillemet commented 10 months ago

I would also say that the "CUDA error: the provided PTX was compiled with an unsupported toolchain" is related to the CUDA driver version. But with version 535 it should not happen.

You can try running with libtorch binary downloadable from pytorch.org instead of the libraries in JavaCPP (using -Dorg.bytedeco.javacpp.pathsFirst). It will not need PTX.

It's sure that we'd better avoid all problems related to PTX (this error, long startup time when compiling PTX, features lacking, size of cuda cache...) and provide cubins for all major architectures, but we are limited to only 1 or 2 architecture when compiling Pytorch presets because of the compilation time it requires and GitHub actions limits.

That's why I'd like to include the libtorch binaries provided by pytorch.org in JavaCPP presets. But I still need to test what is doable.

sbrunk commented 10 months ago

I've done some tests. I've put the results into the 2.1.0 update PR thread https://github.com/bytedeco/javacpp-presets/pull/1426#issuecomment-1783933140

hmf commented 9 months ago

@sbrunk Can you tell me what the status of this is? Bytedeco snapshot seem to be the same version. I have a CUDA driver 12.2 on the bare metal machine. Tests with Toolkit 11.8, 12.2, 12.1 and 12.3 on the dev container don't seem to work (they use the 12.2 driver). What is the plan for Storch? Move all CUDA to 12.3 (drivers 545.XX.YY)? At this moment, can I assume that the current snapshot won't woprk with the CUDA 12.2 + 535.XXX.YY drivers?

sbrunk commented 9 months ago

Yes but it should start working again once we update to PyTorch 2.1 https://github.com/sbrunk/storch/pull/64 which brings in fixes on the JavaCPP bindings. See https://github.com/bytedeco/javacpp-presets/pull/1426#issuecomment-1786998372.

The main reason why I haven't pushed the update yet is that PyTorch 2.1 adds new data types which we need to add as well.

hmf commented 9 months ago

@sbrunk Thanks for the information.

The main reason why I haven't pushed the update yet is that PyTorch 2.1 adds new data types which we need to add as well.

Hope that does not entail too many changes on my side.

sbrunk commented 9 months ago

I don't think so. The upgrade seems to be quite smooth so far. I've started working on integrating the datatypes and hopefully we'll be able to get it ready in the next few days.

sbrunk commented 9 months ago

Hey @hmf I finally had the bandwidth to get the update to PyTorch 2.1 https://github.com/sbrunk/storch/pull/64 done. Could you give it a try with the latest snapshot to check if the CUDA issues are solved for you?

hmf commented 9 months ago

@sbrunk I was able to compile and run the LeNet example. I did this in both Mill and SBT using a clone of the latest master. Unfortunately the GPU does not seem to be detected. Here is what I get with SBT on a second run: 

sbt:root> examples/runMain LeNetApp
[info] running (fork) LeNetApp 
[info] Using device: Device(CPU,-1)
[info] Epoch: 1 | Batch:    0 | Training loss: 2.2934 | Eval loss: 2.3007 | Eval accuracy: 0.1028
[info] Epoch: 1 | Batch:  200 | Training loss: 0.7505 | Eval loss: 0.7085 | Eval accuracy: 0.7803
[info] Epoch: 1 | Batch:  400 | Training loss: 0.3910 | Eval loss: 0.4682 | Eval accuracy: 0.8660
[info] Epoch: 1 | Batch:  600 | Training loss: 0.2330 | Eval loss: 0.3810 | Eval accuracy: 0.8841
[info] Epoch: 1 | Batch:  800 | Training loss: 0.5794 | Eval loss: 0.3249 | Eval accuracy: 0.9006
[info] Epoch: 1 | Batch: 1000 | Training loss: 0.3650 | Eval loss: 0.2753 | Eval accuracy: 0.9142
[info] Epoch: 1 | Batch: 1200 | Training loss: 0.3777 | Eval loss: 0.2485 | Eval accuracy: 0.9245
[info] Epoch: 1 | Batch: 1400 | Training loss: 0.3084 | Eval loss: 0.2167 | Eval accuracy: 0.9312
[info] Epoch: 1 | Batch: 1600 | Training loss: 0.2847 | Eval loss: 0.2006 | Eval accuracy: 0.9381
[info] Epoch: 1 | Batch: 1800 | Training loss: 0.1045 | Eval loss: 0.1804 | Eval accuracy: 0.9414
[info] Epoch: 2 | Batch:    0 | Training loss: 0.1411 | Eval loss: 0.1729 | Eval accuracy: 0.9434
[info] Epoch: 2 | Batch:  200 | Training loss: 0.2209 | Eval loss: 0.1674 | Eval accuracy: 0.9449
[info] Epoch: 2 | Batch:  400 | Training loss: 0.2056 | Eval loss: 0.1520 | Eval accuracy: 0.9509
[info] Epoch: 2 | Batch:  600 | Training loss: 0.2192 | Eval loss: 0.1472 | Eval accuracy: 0.9538
[info] Epoch: 2 | Batch:  800 | Training loss: 0.0281 | Eval loss: 0.1298 | Eval accuracy: 0.9568
[info] Epoch: 2 | Batch: 1000 | Training loss: 0.1213 | Eval loss: 0.1239 | Eval accuracy: 0.9595
[info] Epoch: 2 | Batch: 1200 | Training loss: 0.1853 | Eval loss: 0.1166 | Eval accuracy: 0.9628
[info] Epoch: 2 | Batch: 1400 | Training loss: 0.0517 | Eval loss: 0.1129 | Eval accuracy: 0.9659
[info] Epoch: 2 | Batch: 1600 | Training loss: 0.0980 | Eval loss: 0.1028 | Eval accuracy: 0.9671
[info] Epoch: 2 | Batch: 1800 | Training loss: 0.1265 | Eval loss: 0.0968 | Eval accuracy: 0.9693
[info] Epoch: 3 | Batch:    0 | Training loss: 0.1374 | Eval loss: 0.0927 | Eval accuracy: 0.9706
[info] Epoch: 3 | Batch:  200 | Training loss: 0.2114 | Eval loss: 0.0883 | Eval accuracy: 0.9722
[info] Epoch: 3 | Batch:  400 | Training loss: 0.0732 | Eval loss: 0.0893 | Eval accuracy: 0.9711
[info] Epoch: 3 | Batch:  600 | Training loss: 0.1169 | Eval loss: 0.0814 | Eval accuracy: 0.9743
[info] Epoch: 3 | Batch:  800 | Training loss: 0.1020 | Eval loss: 0.0783 | Eval accuracy: 0.9752
[info] Epoch: 3 | Batch: 1000 | Training loss: 0.0209 | Eval loss: 0.0789 | Eval accuracy: 0.9751
[info] Epoch: 3 | Batch: 1200 | Training loss: 0.0231 | Eval loss: 0.0762 | Eval accuracy: 0.9756
[info] Epoch: 3 | Batch: 1400 | Training loss: 0.0405 | Eval loss: 0.0747 | Eval accuracy: 0.9762
[info] Epoch: 3 | Batch: 1600 | Training loss: 0.0203 | Eval loss: 0.0704 | Eval accuracy: 0.9773
[info] Epoch: 3 | Batch: 1800 | Training loss: 0.0165 | Eval loss: 0.0651 | Eval accuracy: 0.9800
[info] Epoch: 4 | Batch:    0 | Training loss: 0.0210 | Eval loss: 0.0682 | Eval accuracy: 0.9789
[info] Epoch: 4 | Batch:  200 | Training loss: 0.1051 | Eval loss: 0.0620 | Eval accuracy: 0.9812
[info] Epoch: 4 | Batch:  400 | Training loss: 0.1084 | Eval loss: 0.0628 | Eval accuracy: 0.9801
[info] Epoch: 4 | Batch:  600 | Training loss: 0.0055 | Eval loss: 0.0624 | Eval accuracy: 0.9802
[info] Epoch: 4 | Batch:  800 | Training loss: 0.0484 | Eval loss: 0.0593 | Eval accuracy: 0.9820
[info] Epoch: 4 | Batch: 1000 | Training loss: 0.0068 | Eval loss: 0.0591 | Eval accuracy: 0.9820
[info] Epoch: 4 | Batch: 1200 | Training loss: 0.1645 | Eval loss: 0.0563 | Eval accuracy: 0.9829
[info] Epoch: 4 | Batch: 1400 | Training loss: 0.0205 | Eval loss: 0.0565 | Eval accuracy: 0.9827
[info] Epoch: 4 | Batch: 1600 | Training loss: 0.0576 | Eval loss: 0.0536 | Eval accuracy: 0.9836
[info] Epoch: 4 | Batch: 1800 | Training loss: 0.0070 | Eval loss: 0.0540 | Eval accuracy: 0.9835
[info] Epoch: 5 | Batch:    0 | Training loss: 0.0614 | Eval loss: 0.0535 | Eval accuracy: 0.9833
[info] Epoch: 5 | Batch:  200 | Training loss: 0.0102 | Eval loss: 0.0514 | Eval accuracy: 0.9846
[info] Epoch: 5 | Batch:  400 | Training loss: 0.1576 | Eval loss: 0.0535 | Eval accuracy: 0.9819
[info] Epoch: 5 | Batch:  600 | Training loss: 0.1332 | Eval loss: 0.0520 | Eval accuracy: 0.9837
[info] Epoch: 5 | Batch:  800 | Training loss: 0.0251 | Eval loss: 0.0537 | Eval accuracy: 0.9820
[info] Epoch: 5 | Batch: 1000 | Training loss: 0.2369 | Eval loss: 0.0542 | Eval accuracy: 0.9835
[info] Epoch: 5 | Batch: 1200 | Training loss: 0.3197 | Eval loss: 0.0494 | Eval accuracy: 0.9856
[info] Epoch: 5 | Batch: 1400 | Training loss: 0.0147 | Eval loss: 0.0529 | Eval accuracy: 0.9831
[info] Epoch: 5 | Batch: 1600 | Training loss: 0.0183 | Eval loss: 0.0539 | Eval accuracy: 0.9832
[info] Epoch: 5 | Batch: 1800 | Training loss: 0.1724 | Eval loss: 0.0500 | Eval accuracy: 0.9844
[success] Total time: 41 s, completed Nov 27, 2023, 8:59:31 AM

Here is what nvidia-smi reports:

Mon Nov 27 09:05:07 2023       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 3090        On  | 00000000:00:06.0 Off |                  N/A |
| 30%   32C    P8              25W / 350W |      3MiB / 24576MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+

+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |

The above was tested on a clean dev container. Are the versions of CUDA and the NVIDIA driver ok?

EDIT: Same tests and results on my laptop on bare metal (has about the same setup).

sbrunk commented 9 months ago

@hmf just to be sure when trying to run with sbt did you enable GPU in the sbt build? I.e.

set ThisBuild / enableGPU := true

It defaults to false currently. If I set it to true, the build switches to GPU-enabled LibTorch and adds cuda-redist as dependency. With that, the LeNet example works on the machines I've tried so far.

The example checks torch.cuda.isAvailable and falls back to CPU otherwise.

It doesn't hurt to also delete your .javacpp/cache directory and start fresh.

hmf commented 9 months ago

@sbrunk You are correct that I did not set the GPU in sbt. I then set the GPU and that failed. So as per your suggestion, I removed the .javacpp/cache and tried again. Unfortunately I still get a Using device: Device(CUDA,-1). Maybe I have some problem when downloading the JavaCPP libs or OS CUDA installation.

Could you confirm that the javaCPP libraries are correct? I have the following results:

EDIT: think there is something wrong with my OS. Will have to dig into it. 

sbt:root> set ThisBuild / enableGPU := true
[info] Defining ThisBuild / enableGPU
[info] The new value will be used by core / javaCppPresetLibs
[info] Reapplying settings...
[info] set scmInfo to https://github.com/hmf/storch
[info] set current project to root (in build file:/workspaces/storch/)
sbt:root> examples/runMain LeNetApp
[info] Updating 
https://repo1.maven.org/maven2/org/typelevel/shapeless3-typeable_3/3.3.0/shapeless3-typeable_3-3.3.0.pom
    0.0% [          ] 0B (0B / s)
https://repo1.maven.org/maven2/com/lihaoyi/os-lib_3/0.9.1/os-lib_3-0.9.1.pom
  100.0% [##########] 1.6 KiB (52.5 KiB / s)
https://repo1.maven.org/maven2/org/scala-lang/scala3-library_3/3.3.1/scala3-library_3-3.3.1.pom
  100.0% [##########] 3.7 KiB (121.8 KiB / s)
https://repo1.maven.org/maven2/org/typelevel/shapeless3-typeable_3/3.3.0/shapeless3-typeable_3-3.3.0.pom
    0.0% [          ] 0B (0B / s)
https://repo1.maven.org/maven2/com/lihaoyi/sourcecode_3/0.3.0/sourcecode_3-0.3.0.pom
  100.0% [##########] 1.4 KiB (27.7 KiB / s)
https://repo1.maven.org/maven2/org/typelevel/shapeless3-typeable_3/3.3.0/shapeless3-typeable_3-3.3.0.pom
  100.0% [##########] 2.6 KiB (50.3 KiB / s)
https://repo1.maven.org/maven2/org/typelevel/spire_3/0.18.0/spire_3-0.18.0.pom
    0.0% [          ] 0B (0B / s)
https://repo1.maven.org/maven2/org/typelevel/spire_3/0.18.0/spire_3-0.18.0.pom
  100.0% [##########] 2.5 KiB (19.1 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/cuda/12.3-8.9-1.5.10-SNAPSHOT/maven-metadata.xml
  100.0% [##########] 3.0 KiB (7.4 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/javacpp/1.5.10-SNAPSHOT/maven-metadata.xml
  100.0% [##########] 3.4 KiB (7.9 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/mkl/2023.1-1.5.10-SNAPSHOT/maven-metadata.xml
  100.0% [##########] 2.6 KiB (6.0 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/openblas/0.3.23-1.5.10-SNAPSHOT/maven-metadata.xml
  100.0% [##########] 3.4 KiB (8.2 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/pytorch/2.1.0-1.5.10-SNAPSHOT/maven-metadata.xml
  100.0% [##########] 2.3 KiB (5.6 KiB / s)
https://oss.sonatype.org/content/repositories/snapshots/org/bytedeco/cuda/12.3-8.9-1.5.10-SNAPSHOT/cuda-12.3-8.9-1.5.10-20231115.112…
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[info] Epoch: 4 | Batch: 1200 | Training loss: 0.1867 | Eval loss: 0.0592 | Eval accuracy: 0.9806
[info] Epoch: 4 | Batch: 1400 | Training loss: 0.0501 | Eval loss: 0.0580 | Eval accuracy: 0.9810
[info] Epoch: 4 | Batch: 1600 | Training loss: 0.0252 | Eval loss: 0.0564 | Eval accuracy: 0.9828
[info] Epoch: 4 | Batch: 1800 | Training loss: 0.0509 | Eval loss: 0.0581 | Eval accuracy: 0.9805
[info] Epoch: 5 | Batch:    0 | Training loss: 0.0419 | Eval loss: 0.0580 | Eval accuracy: 0.9811
[info] Epoch: 5 | Batch:  200 | Training loss: 0.0306 | Eval loss: 0.0533 | Eval accuracy: 0.9826
[info] Epoch: 5 | Batch:  400 | Training loss: 0.0983 | Eval loss: 0.0555 | Eval accuracy: 0.9816
[info] Epoch: 5 | Batch:  600 | Training loss: 0.0867 | Eval loss: 0.0544 | Eval accuracy: 0.9816
[info] Epoch: 5 | Batch:  800 | Training loss: 0.1454 | Eval loss: 0.0527 | Eval accuracy: 0.9817
[info] Epoch: 5 | Batch: 1000 | Training loss: 0.2604 | Eval loss: 0.0519 | Eval accuracy: 0.9821
[info] Epoch: 5 | Batch: 1200 | Training loss: 0.2010 | Eval loss: 0.0502 | Eval accuracy: 0.9833
[info] Epoch: 5 | Batch: 1400 | Training loss: 0.0247 | Eval loss: 0.0495 | Eval accuracy: 0.9827
[info] Epoch: 5 | Batch: 1600 | Training loss: 0.0443 | Eval loss: 0.0525 | Eval accuracy: 0.9830
[info] Epoch: 5 | Batch: 1800 | Training loss: 0.0868 | Eval loss: 0.0533 | Eval accuracy: 0.9822
[success] Total time: 154 s (02:34), completed Nov 30, 2023, 1:06:24 PM
sbt:root>

TIA

sbrunk commented 9 months ago

Using device: Device(CUDA,-1) seems fine I get that too. In contrast to PyTorch LibTorch returns -1 when you don't explicitly enumerate a GPU I think.

Could you check the output of nvidia-smi or nvtop to see if it is actually using the GPU?

hmf commented 9 months ago

@sbrunk You are correct. nvidia-smi shows the GPU is being used. I will merge this into my latest and see how that goes. Thank you.