I have tried for a fews days to use Tensorflow profiler to measure resource usage. But I can't understand the interface in order to get the data I need. I think it has something to do with "Metrics" option in the trace viewer, but it is not clear to me how to get information about CPU, Memory, Disk usage and etc from the interface in a certain interval. Everything appears to be in "ns". If you happen to know a tutorial that goes deep in the trace viewer, please. Let me know.
If this kind of profiling is not possible, also, let me know.
This image shows the "Metrics" option that I refer to
Please help.
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first of all, I am new in Anylogic and no idea to know about this case. If you don't mind please tell me resource for complete optimization material resource.
I have operation process above, and want to know how many minimize person for each shift I and II for process I and Process II. should I have variabel for resource pool? and how to link it to optimization objective?
Please help me and tell step by step. Thank You!
Go to File->New->Experiment and select Optimization from the list. There on the right side, you need to define your objective function and constraints. This topic is explained here in AnyLogic documentation. You should have a parameter for each of the resources. In the optimization objective you need to say minimize noResourcePoolProcess1 for example. And a good constraint would be utilization being below 85% for example. Otherwise minimization will not care if your system is throttled.
I have found this post but am still unclear on what the redzone_checker kernel is doing and why. Specifically, should it be taking > 90% of my application's runtime? TensorBoard reports that it is taking the vast majority of the runtime of my JAX code, and I'd like to know
Is it actually the case that this kernel is taking way too much time, or is this a side effect of profiling JAX with TensorBoard (i.e., the output is misleading in some way)?
Is there a way to reduce the amount of time taken by the redzone_checker kernel? Is that even a good idea?
Thanks in advance for any insights.
make sure warmup before profiling.
it maybe jit compiling time.
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
My TensorBoard log files grow huge because – it seems – every image summary ever generated is stored. This even though in TensorBoard, it seems like I can only look at the most recent image. And I only need the most recent image anyway.
Is there a way to let TensorBoard know that I only need the latest iamge? I looked at the SummaryWriter API docs but there is no obvious flag.
Hi I work on TensorBoard. To the best of my knowledge, the logs are append only. However when TensorBoard loads them into memory, it uses reservoir sampling so they don't consume all your memory. In the future, we may be implementing a system that will reservoir sampling during the writing phase, or possibly, a tool for compressing logs so they only contain what TensorBoard needs.
While TensorBoard image dashboard only shows the most recent image at the moment, we'd be hesitant to write tools to remove the previous ones, since we may be extending the dashboard to show more than the most recent sample.
I am training convnets with Tensorflow and skflow, on an EC2 instance I share with other people. For all of us to be able to work at the same time, I'd like to limit the fraction of available GPU memory which is allocated.
This question does it with Tensorflow, but since I'm using sklfow I'm never using a tf.Session().
Is it possible to do the same thing through skflow ?
At this moment, you can only control the number of cores (num_cores) to be used in estimators by passing this parameter to estimator.
One can add gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) to tf.ConfigProto as suggested by this question you linked to achieve what you need.
Feel free to submit a PR to make changes here as well as adding this additional parameters to all estimators. Otherwise, I'll make the changes some time this week.
Edit:
I have made the changes to allow those options. Please check "Building A Model Using Different GPU Configurations" example in examples folder. Let me know if there's any particular need or other options you want to add. Pull requests are always welcomed!