Shouldn’t these be protected by a
PointerScope or something? https://github.com/tensorflow/java/blob/4c1c2718ef1c4c386891d92cf0d774d7e77a7dc1/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/internal/c_api/AbstractTF_Tensor.java#L101
String tensors are being handled differently since TF2.4 so it’s possible we are observing some side-effects of it
also @ninjabem did you tried with the latest snapshot (0.4.0-SNAPSHOT)? It has tensorflow/java@709f570 that is related to string tensor deallocation, I’ll be curious to see if that could improve your situation as well.
The log will tell us to what kind of Pointer they are attached to though, so if @ninjabem can let us know what those are, it could help.
We're calling
SavedModelBundle.exporter("/data/").withSession(sess).withSignature(mySig).export to save a model. It seems to save out fine (according to the cli tool). But when we're loading it from the Python side using tf.saved_model.load("/data/"), we're getting the following error in ValueError: Feeds must be tensors. Any ideas ?
Assume it's probably something silly we're doing
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.
