Comments (2)
Maven is a build tool. It does not fully solve deployment. You do not need libtensorflow_jni_gpu
to build. What is needed to make GPU work at runtime is a deployment that includes the correct native artifacts. The reason to declare things in a Maven POM at runtime
scope is to document that they are necessary and/or desirable for the project to function optimally at runtime. Maven has some limited support for synthesizing a simple runtime environment based on the project's dependencies, so that e.g. you can run from an IDE or via (in the case of SciJava projects) mvn -Pexec
on the command line. But these are not the intended primary deployment vehicles. In the case of GPU support for TensorFlow within ImageJ, we just need to ship the correct natives in the correct jars/<platform>
and/or lib/<platform>
directories. The only real "problem" to solve, IMHO, is how to make the imagej-maven-plugin's copy-jars
goal smart enough to populate an ImageJ installation in a cross-platform way. But this problem is not specific to imagej-tensorflow.
from imagej-tensorflow.
Oh, I see so it seems to be simpler to solve than what I was thinking.
Since it's not specific to this repo, I close here.
from imagej-tensorflow.
Related Issues (14)
- Cached models only compared by name and tag HOT 4
- Add options dialog to control CPU/GPU mode, and graphics card selection
- Feature Request: Object Detection or Instance Segmentation HOT 1
- Add cache clearing API
- Cache system variable improvements
- java.lang.IllegalStateException: close() has been called on the Graph HOT 6
- Try to implement the TensorFlow version control in my code HOT 1
- Upgrade to TensorFlow 2
- jdk.internal.loader.ClassLoaders$AppClassLoader cannot be cast to class java.net.URLClassLoader HOT 2
- install tensorflow through script interface HOT 1
- Use ByteBuffers for all image types HOT 2
- Cache directory location during development HOT 5
- Policy for NN models distribution HOT 3
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