It's also a little easier to operate on code snippets, as the gitter mobile client (at least on Android) is pretty poor at formatting (and threading for that matter).
But it's up to you.
There's a little bit a of a bump at first to prevent easy spam attacks, but after the first couple of posts all that stuff should go away.
^ discussion moved here
So to the question “is there an easy way”, I would say “no” but is there a way, I would say “maybe”. Here’s a starting point for investigating in that direction in case you are interested.
hi, one question concerning restricting GPU performance of TF: In Python we managed to restrict the GPU memory of our TF process to a certain amount of the GPU menory with: " tf.config.set_logical_device_configuration(gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=8192)])". In Java we tried to use setPerProcessGpuMemoryFraction to restrict it to 50%. The configuration in java is implemented as follows:
GPUOptions gpu = ConfigProto.getDefaultInstance().getGpuOptions().toBuilder().setPerProcessGpuMemoryFraction(0.5).setAllowGrowth(true).build();
ConfigProto config = ConfigProto.newBuilder(ConfigProto.getDefaultInstance()).setLogDevicePlacement(true).mergeGpuOptions(gpu).build();
However, it doesn't work and we get an exception: org.tensorflow.exceptions.TensorFlowException: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, [...]
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, [...]
I just noticed that the GPU memory is allocated when loading a model with "SavedModelBundle.load(...)". The GPUOptions in the ConfigProto are used later when opening a session e.g. to classify an image (at least in our code). How is this meant to be done? How can I set the GPU options before loading a model?
Hi folks, just a question, is there a way to attach the tensorflow logging output to my log4j file logger?
In python, i was able to pass my "log handler" with "tf.get_logger().addHandler(logHandler)" to receive all log events into my file logger.
Now I'm searching for a simular solution in Java to log all the Tensorflow events.
In python, i was able to pass my "log handler" with "tf.get_logger().addHandler(logHandler)" to receive all log events into my file logger.
Now I'm searching for a simular solution in Java to log all the Tensorflow events.
I think it's the only one under active development. This project (https://github.com/eaplatanios/tensorflow_scala) has Scala bindings to the C API directly, but hasn't been updated to track newer versions of TensorFlow and I'm not sure of it's status.
On that @eaplatanios , do you think it could be interesting to « merge » tf-scala in tf-java so you won’t have to maintain a separate project by yourself? Are you aware of any important feature currently missing in tf-java, but present in tf-scala, that would enhance user experience for Scala developers?
HI there. Are there any self-contained example projects to look at? I'm trying this, using sbt 1.5.5:
lazy val deps = new {
val tensorflow = "0.3.3"
}
lazy val root = project.in(file("."))
.settings(
scalaVersion := "3.0.2",
libraryDependencies ++= Seq(
"org.tensorflow" % "tensorflow-core-api" % deps.tensorflow,
"org.tensorflow" % "tensorflow-core-api" % deps.tensorflow classifier "linux-x86_64-mkl",
"org.tensorflow" % "tensorflow-framework" % deps.tensorflow,
)
)and
package de.sciss.tf
import org.tensorflow.EagerSession
object Test:
def main(args: Array[String]): Unit =
val input = Array(-10.0, -5.0, 0.0, 5.0, 10.0)
val options = EagerSession.options()
val session = EagerSession.initDefault(options)
println("Here")
session.close()But running it just tells me something for JNI isn't working yet:
Warning: Could not load Loader: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path: [/usr/java/packages/lib, /usr/lib/x86_64-linux-gnu/jni, /lib/x86_64-linux-gnu, /usr/lib/x86_64-linux-gnu, /usr/lib/jni, /lib, /usr/lib]
Warning: Could not load Pointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path: [/usr/java/packages/lib, /usr/lib/x86_64-linux-gnu/jni, /lib/x86_64-linux-gnu, /usr/lib/x86_64-linux-gnu, /usr/lib/jni, /lib, /usr/lib]
Warning: Could not load BytePointer: java.lang.UnsatisfiedLinkError: no jnijavacpp in java.library.path: [/usr/java/packages/lib, /usr/lib/x86_64-linux-gnu/jni, /lib/x86_64-linux-gnu, /usr/lib/x86_64-linux-gnu, /usr/lib/jni, /lib, /usr/lib]
2021-10-03 16:07:13.749139: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-10-03 16:07:13.749982: I external/org_tensorflow/tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Here
Exception in thread "main" java.lang.IllegalStateException: Default eager session cannot be closed
at org.tensorflow.EagerSession.close(EagerSession.java:261)
at de.sciss.tf.Test$.main(Test.scala:11)
at de.sciss.tf.Test.main(Test.scala)
Like a Java example showing simple cycle of training an ANN, then creating some predictions.
Trying to work through this tutorial https://www.baeldung.com/tensorflow-java - but
Tensor.create seems to no longer exist. How do you create a constant scalar in the current version?
and there is a more type safe API than stringly typed
opBuilder?
Ok, getting the hang of it. Is this the idiomatic way?
val g = new Graph()
val ops = Ops.create(g)
import ops.*
val a = constant(3.0)
val b = constant(2.0)
val x = placeholder(classOf[TFloat64])
val y = placeholder(classOf[TFloat64])
val ax = math.mul(a, x)
val by = math.mul(b, y)
val z = math.add(ax, by)
val session = new Session(g)
val r = session.runner()
val tensor = r.fetch(z)
.feed(x, TFloat64.scalarOf(3.0))
.feed(y, TFloat64.scalarOf(6.0))
.run
.get(0)
tensor match
case tf: TFloat64 => println(tf.getDouble()) // 21.0
case other => println(s"OTHER: $other")