We are using tfagents in tensorflow for reinforcement learning, because of limitations with static computation graphs we are planning to migrate our code to pytorch.
tfagents is great and have very good documentation and reduce a lot of time doing the same task again
We are wondering if the pytorch community have a similar kind of stuff?
rllib is an alternative which supports PyTorch.
Related
I am new to the object detection field, currently want to build a faster-rcnn model to recognize multiple objects within an image.
I have went through several tutorials including the official tutorial from TensorFlow GitHub [https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md#installation], roboflow and online tutorial such as [https://pub.towardsai.net/training-faster-r-cnn-using-tensorflow-object-detection-api-with-a-custom-dataset-88dd525666fd]. But none of them works for me, mostly due to package version conflict and lack of instructions.
Therefore, I would like to ask where can I find a proper guide or tutorial for building a faster-rcnn model?
You can follow this very informative tutorial https://gilberttanner.com/blog/tensorflow-object-detection-with-tensorflow-2-creating-a-custom-model. You can use FasterRCNN from Tf2 detection Zoo https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md instead of EfficientDet0.
I am currently working with neural networks in keras and I know that it works with tensorflow in the back-end, I have it installed on the GPU, but I don't know if keras uses the GPU or if it is something completely different from tensorflow.
TensorFlow is a mid-level framework that performs operations on tensors. Keras is a high-level API that simplifies the creation and training of neural networks. Keras doesn't do any of the tensor ops itself; it delegates those to its backend, which is a mid-level framework of your choosing: TensorFlow, CNTK, or Theano. Each of those frameworks can be configured to do the tensor ops in whatever ways they can (as far as I am aware, each of them can use either CPUs or GPUs). Keras, however, doesn't really care how the ops get done. It just tells the backend to do them, and they get done.
I come from a sort of HPC background and I am just starting to learn about machine learning in general and TensorFlow in particular. I was initially surprised to find out that distributed TensorFlow is designed to communicate with TCP/IP by default though it makes sense in hindsight given what Google is and the kind of hardware it uses most commonly.
I am interested in experimenting with TensorFlow in a parallel way with MPI on a cluster. From my perspective, this should be advantageous because latency should be much lower due to MPI's use of Remote Direct Memory Access (RDMA) across machines without shared memory.
So my question is, why doesn't this approach seem to be more common given the increasing popularity of TensorFlow and machine learning ? Isn't latency a bottleneck ? Is there some typical problem that is solved, that makes this sort of solution impractical? Are there likely to be any meaningful differences between calling TensorFlow functions in a parallel way vs implementing MPI calls inside of the TensorFlow library ?
Thanks
It seems tensorflow already supports MPI, as stated at https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/mpi
MPI support for tensorflow was also discussed at https://arxiv.org/abs/1603.02339
Generally speaking, keep in mind MPI is best at sending/receiving messages, but not so great at sending notifications and acting upon events.
Last but not least, MPI support of multi-threaded applications (e.g. MPI_THREAD_MULTIPLE) has not always been production-ready among MPI implementation s.
These were two general statements and i honestly do not know if they are relevant for tensorflow.
According to the doc in Tensorflow git repo，actually tf utilizes gRPC library by detault, which is based on HTTP2 protocol, rather than TCP/IP protocol, and this paper should give you some insight, hope this information is useful.
I am testing the new Tensorflow Object Detection API in Python, and I succeeded in installing it on Windows using docker. However, my trained model (Faster RCNN resnet101 COCO) takes up to 15 seconds to make a prediction (with very good accuracy though), probably because I only use Tensorflow CPU.
My three questions are:
Considering the latency, where is the problem? I heard Faster RCNN was a good model for low latency visual detection, is it because of the CPU-only execution?
With such latency, is it possible to make efficient realtime video processing by using tensorflow GPU, or should I use a more popular model like YOLO?
The popular mean to use tensorflow GPU in docker is nvidia-docker but is not supported on windows. Should I continue to look for a docker (or conda) solution for local prediction, or should I deploy my model directly to a virtual instance with GPU (I am comfortable with Google Cloud Platform)?
Any advice and/or good practice concerning real-time video processing with Tensorflow is very welcome!
Considering the latency, where is the problem ? I heard Faster RCNN
was a good model for low latency visual detection, is it because of
the CPU-only execution ?
Of course, it's because you are using CPU.
With such latency, is it possible to make efficient realtime video
processing by using tensorflow GPU, or should I use a more popular
model like YOLO ?
Yolo is fast, but I once used it for face and accuracy was not that great. But a good alternative.
The popular mean to use tensorflow GPU in docker is nvidia-docker but
is not supported on windows. Should I continue to look for a docker
(or conda) solution for local prediction, or should I deploy my model
directly to a virtual instance with GPU (I am comfortable with Google
Cloud Platform) ?
I think you can still use your local GPU in windows, as Tensorflow supports GPU on python.
And here is an example, simply to do that. It has a client which can read webcam or IP cam stream. The server is using Tensorflow python GPU version and ready to use pre-trained model for predictions.
Unfortunately, Tensoflow does not support tensorflow-serving on windows. Also as you said Nvidia-Docker is not supported on windows. Bash on windows has no support for GPU either. So I think this is the only easy way to go for now.
I have been studying neural networks for some weeks. Furthermore even if I always used R the Keras library in Python was really helpful with someone with a small programming background like me.
Keras it's a very nice interface which allows the customization I need without even invoking the backend, unless for some custom loss metrics I used.
Being that straightforward is also the Hardware specification, which for example allows you to switch from the CPU of the machine where you have your Python+Keras installed to the machine (compatible) GPU, allowing to exploit the strong parallelization of neural networks when training them.
I was wondering if there is anything which allows you to switch to hadoop cluster training of neural networks with the same kind of ease.
Moreover is there some hadoop open source cluster available to do so?
Thank you for your help