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    James Piggott
    @JamesPiggott
    Hello, I want to run a Tensorflow model I found with a Java app, but I am having difficulty with getting the input just right. Below you can see the result from the layer analysis. I found a few examples for one-dimensional input (mnist) and I got another model working that required integers, but creating Tensor<TFloat32> with dimensions {batch, height, width, channels} is a difficult task. I would like some help.
    input_image=name: "serving_default_input_image:0"
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: -1
      }
      dim {
        size: -1
      }
      dim {
        size: -1
      }
      dim {
        size: 3
      }
    }
    5 replies
    Karl Lessard
    @karllessard

    @JamesPiggott , I don’t know if you are subscribed to the TensorFlow discussion forum but it is the new TF platform for starting new discussion and we’ll slowly migrate the different topics started on this Gitter channel to it.

    That being said, I’ve replied and posted an example of converting BufferedImage instances to a float tensor (TFloat32) in this post, if you can please take a look and maybe continue the discussion from there?

    (BTW sorry but my original snippet was in Kotlin, let me know if you need help to convert it to Java)

    Jakob Sultan Ericsson
    @jakeri
    When is 0.4.0 scheduled to be released?
    Karl Lessard
    @karllessard
    There is still a lot of PR waiting to be merged so there is no clear plan for it yet. Is there anything in particular you are waiting for?
    2 replies
    Karl Lessard
    @karllessard
    Hey everyone, just to let you know that we have just released a hot-fix (0.3.2) for the saved model bundle memory leak that was reported previously in this thread by @jakeri , please try it out!
    3 replies
    Charles Parker
    @charleslparker
    @karllessard - Just a datapoint for you: I'm working with code that loads and closes saved model bundles a lot and the memory behavior is a lot more respectable with 0.3.2. Thanks for releasing this.
    Karl Lessard
    @karllessard
    :+1: :+1:
    Gili Tzabari
    @cowwoc
    @saudet @karllessard Are you guys familiar with any mature implementations of the Temporal Fusion Transformer? Support seems to be very sketchy both for TensorFlow and PyTorch. I found one experimental implementation for TF 1.x, nothing really for 2.x. I also found 3 implementations for PyTorch but all 3 had problems (sample code didn't work for some, others outputted a straight line which other users complained about and no one was able to figure it out)
    Karl Lessard
    @karllessard
    Sorry @cowwoc , I can’t help you out here, maybe @Craigacp knows?
    Adam Pocock
    @Craigacp
    I've not seen any implementations, though I don't work with time series very often. From a quick read through of the paper it looks like an extremely complicated model.
    Try asking on the TF Forum - https://discuss.tensorflow.org/
    Gili Tzabari
    @cowwoc
    Thanks Adam
    torito
    @torito
    Hi, do you know when will be available java tensorflow 2.5.0 ? here it says is in snapshot, and there is any release yet https://github.com/tensorflow/java/#tensorflow-version-support
    raftaa
    @raftaa
    Hi, is there any example/documentation how to use GPU resources in Java? I integrated the tensorflow-core-api-0.3.2-windows-x86_64-gpu.jar etc. But are there any flags needed to be set? I modified the FasterRcnnInception eample to compute images with my own trained modell. Needs approx. 4sec per image while the python gpu-based computation with the same modell and image needs about 0.2 sec... Java log output tells me that "Adding visible gpu devices: 0" and "Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8680 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)".
    raftaa
    @raftaa
    Nevermind. Found the following discussion: tensorflow/java#140
    Adam Pocock
    @Craigacp
    I would expect the cnn based example to run faster on GPUs than CPUs. Did you compare against TF-Java on CPU? Getting the data prep done correctly and transferring images to the GPU is harder to get right in Java than it is in Python, there are more performance pitfalls.
    Also, are you measuring time after the JVM has warmed up? It has a more noticeable warmup time than Python as peak performance only happens a few hundred to thousand calls in after the JIT compiler has compiled most of the hot methods.
    raftaa
    @raftaa
    Thanks Adam, somehow it's running faster now. Honestly I have no clue which changes yielded to this result. Guess it has something to do with what you called "warming up the JVM": the first image still takes about 4sec. The following images however take 200ms. That's fast enough by now.
    Adam Pocock
    @Craigacp
    There are a few things that warm up, it could be the JVM (the first one won't be compiled it'll interpret the Java code which is slower, the first one may have to do a bunch more memory allocation etc), or it could be that we don't ship the right compute level bits for your GPU so when it loads TF the CUDA driver has to compile a bunch of things. I think our builds are the same as the python builds in that respect, but the point at which the CUDA compilation happens might be different.
    Karl Lessard
    @karllessard
    Under the TF hood, XLA does a bunch of JIT compilation as well
    raftaa
    @raftaa
    Hi. Sorry for another stupid beginner question but I didn't found any information how to read a *.pbtxt file in Java. Is there any parser that can be used? By now I just want to read my simple "label_map.pbtxt" file with some annotations. I'd write a simple parser by my own for this but as there are more complex pb files I'd like to use the generic approach. I found some discussions about this topic. Seems to be quite an issue in the past. Is it still the case in the newest Java TF version?
    Adam Pocock
    @Craigacp
    What protobufs are stored in the pbtxt file?
    raftaa
    @raftaa
    It's just an annotation for training a custom object detection - similar to https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#create-label-map
    Adam Pocock
    @Craigacp
    So that's this proto - https://github.com/tensorflow/models/blob/238922e98dd0e8254b5c0921b241a1f5a151782f/research/object_detection/protos/string_int_label_map.proto. You should be able to download that and compile it yourself with protoc to read that file.
    abelehx
    @abelehx
    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java
    Is there an example that supports tensorFlow version 2.4.1, i.e., tensorFlow Java API version 0.3.1, where can I find it?
    Ben Roberson
    @ninjabem
    Hello, is there any public documentation on how memory is allocated/deallocated in the new java lib tensorflow-core vs the old libtensorflow?
    Karl Lessard
    @karllessard
    There is not a lot of official documentation describing this no, unfortunately. On the other hand, both versions support pretty much the same paradigm, where resources that are backed natively must be either explicitly released or using a try-with-resources block. Such resources are pretty much the same: tensors, saved model bundles, graphs, sessions and (only in the new version) concrete functions.
    Also in both versions, you can partially rely on the garbage collector in eager mode to get rid of small objects but closing resources or the whole eager session when done with them is recommended.
    Ben Roberson
    @ninjabem
    Oh, thanks for the response @karllessard! That's not the answer I was expecting. I have a high throughput application that's been in prod for about 3 years using libtensorflow and I'm testing a migration to the new tensorflow-core. With a minimal change to the new lib, I'm seeing GC times jump 20x. After diving into the gc logs, I'm seeing hundreds of thousands of PhantomReference being discovered and most of the increased GC time being used for the Object Copy phase. The magnitude of the Object Copy phase time seems to be positively correlated with the magnitude of the PhantomReference discovered. I don't have detailed knowledge of tensorflow-core internals, so I'm trying to understand what is going on and how to mitigate the symptoms I'm seeing. Any help would be greatly appreciated. Are there any JVM config tweaks or GC tweaks that might help clearing the PhantomReference more quickly?
    Adam Pocock
    @Craigacp
    What versions of TF-Java and Java are you using, and what GC settings do you currently have?
    Ben Roberson
    @ninjabem
    I'm using Java 11, TF-Java 0.3.2, my gc settings are -Xms18g -Xmx18g -XX:+UseG1GC -XX:+MaxGCPauseMillis=200
    I've tried using a few different Xms/Xmx heap settings to see if it just needed more memory to achieve a steady state but those experiments didn't affect the GC times.
    Adam Pocock
    @Craigacp
    If you're closing the tensors that are returned from your model then the references should be dead and be removed along with the rest of the memory. Do you have a code snippet somewhere we could look at?
    Ben Roberson
    @ninjabem
    Unfortunately I can't copy/paste internal code, but I might be able to build a mock that is similar. Improperly closed tensors (unclosed tensors) was one of my first thoughts as well but I don't think the symptoms point in that direction. If I wasn't closing the tensors it would present as a memory leak with the symptoms being a monotonically increasing number of PhantomReferences right? I'm not seeing that pattern. I'm seeing the number of PhantomReferences jump from 200k to 800k over 8 seconds and two young gen collections. Then I see it drop from 800k to 200k over ~4 minutes and ~60 young gen collections. So the tensors do appear to be closing and their resources being collected...eventually
    (BTW, I would be very happy to discover I'm doing something wrong, so I'm definitely open that possibility)
    Adam Pocock
    @Craigacp
    We do have a check in place to close resources using the GC, but you should definitely close them manually.
    The memory leak wouldn't be visible on the Java side as they are allocated on the native heap.
    Are you using eager mode or a session?
    Samuel Audet
    @saudet
    @ninjabem It sounds like something's not getting deallocated manually, and JavaCPP is forced to call System.gc() to clear enough memory. You can see if that's happening or not from the log, for example, by setting the "org.bytedeco.javacpp.logger.debug" system property to "true". In any case, to get best performance, you'll want to disable all that and deallocate everything manually anyway, see tensorflow/java#313 for some info about that.
    Ben Roberson
    @ninjabem
    @Craigacp I'm using a session. I am manually closing all input and output tensors using try-with-resources. I mocked up an example of how things are structured. Input tensors are wrapped in a batch and closed via try-with-resources. The output tensor is also closed via try-with-resources.
    Adam Pocock
    @Craigacp
    Thanks, does this mocked up version exhibit the same PhantomReference behaviour? Also, when you observed the PhantomReferences are they subclasses of org.bytedeco.javacpp.Pointer.DeallocatorReference?
    Ben Roberson
    @ninjabem
    I haven't done any stress testing of this mock yet. I'd have to fill out the feature packing part, mock a full training dataset, and then train a model. That's a bit of work. I was hoping this mock would show that I'm properly closing input and output tensors?
    Ben Roberson
    @ninjabem
    I took a few heap dumps and the #1 leak suspect (according to Eclipse MAT) is org.bytedeco.javacpp.Pointer$NativeDeallocator with several hundred thousand instances.
    @saudet Thanks for the debug log setting; I'll set it and see what I see. I read through the tensorflow/java#313 and I think I'm doing the right things. I'm using a long living session, and closing all my input/output tensors using try-with-resources. Is there anything else I should be doing that I might have missed in that thread?
    Samuel Audet
    @saudet
    Well, I'm not sure what they are referring to by dbx.close(), but maybe you have something similar in your code?
    abelehx
    @abelehx
    https://github.com/tensorflow/java-models/blob/master/tensorflow-examples-legacy/label_image/src/main/java/LabelImage.java
    is tesorflow java for 1.4. I'm looking for an example of LabelImage.java for Tensorfow Java version 0.3.1 or 0.3.2.Where can I find it? Thank you.
    Adam Pocock
    @Craigacp
    We haven't converted that example to 0.3.2, however there are many similar examples for 0.3.2 here https://github.com/tensorflow/java-models/tree/master/tensorflow-examples/src/main/java/org/tensorflow/model/examples.
    Karl Lessard
    @karllessard
    @ninjabem , I’m looking right now at your example, stupid question but just to clear out possible issues, I assume that the model targeted operation only returns a single output (which is "fancy/model:0” in your case)? Because all outputs must be explicitly closed even if they are not being used.
    Karl Lessard
    @karllessard

    Also (sorry if it is off topic) but in your predicator, you can easily navigate through the list of floats directly from the predTensor by casting it to a TFloat32, instead of passing by a tmp array.

    try (Tensor predTensor = (TFloat32)runner.fetch(this.graphOperation).run().get(0)) {
          predTensor.scalars().forEach(s -> predictions.add(s.getFloat()));
     }

    or something like that