Using Word2vec and Doc2vec methods provided by Gensim, they have a distributed version which uses BLAS, ATLAS, etc to speedup (details here). However, is it supporting GPU mode? Is it possible to get GPU working if using Gensim?
Thank you for your question. Using GPU is on the Gensim roadmap. Will appreciate any input that you have about it.
There is a version of word2vec running on keras by #niitsuma called word2veckeras.
The code that runs on latest Keras version is in this fork and branch https://github.com/SimonPavlik/word2vec-keras-in-gensim/tree/keras106
#SimonPavlik has run performance test on this code. He found that a single gpu is slower than multiple CPUs for word2vec.
Regards
Lev
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Okay, so I've worked on a bunch of Deep Learning projects and internships now and I've never had to do heavy training. But lately I've been thinking of doing some Transfer Learning for which I'll need to run my code on a GPU. Now I have a system with Windows 10 and a dedicated NVIDIA GeForce 940M GPU. I've been doing a lot of research online, but I'm still a bit confused. I haven't installed the NVIDIA Cuda Toolkit or cuDNN or tensorflow-gpu on my system yet. I currently use tensorflow and pytorch to train my DL models. Here are my queries -
When I define a tensor in tf or pytorch, it is a cpu tensor by default. So, all the training I've been doing so far has been on the CPU. So, if I make sure to install the correct versions of Cuda and cuDNN and tensorflow-gpu (specifically for tensorflow), I can run my models on my GPU using tf-gpu and pytorch and that's it? (I'm aware of the torch.cuda.is_available() in pytorch to ensure pytorch can access my GPU and the device_lib module in tf to check if my gpu is visible to tensorflow)(I'm also aware of the fact that tf doesnt support all Nvidia GPUs)
Why does tf have a separate module for GPU support? PyTorch doesnt seem to have that and all you need to do is cast your tensor from cpu() to cuda() to switch between them.
Why install cuDNN? I know it is a high-level API CUDA built for support to train Deep Neural Nets on the GPU. But do tf-gpu and torch use these in the backend while training on the gpu?
After tf == 1.15, did they combine CPU and GPU support all into one package?
First of all unfortunately 940M is a kinda weak GPU for training. I suggest you use Google colab for faster training but of course, it would be faster than the CPU. So here my answers to your four questions.
1-) Yes if you install the requirements correctly, then you can run on GPU. You can manually place your data to your GPU as well. You can check implementations on TensorFlow. In PyTorch, you should specify the device that you want to use. As you said you should do device = torch.device("cuda" if args.cuda else "cpu") then for models and data you should always call .to(device) Then it will automatically use GPU if available.
2-) PyTorch also needs extra installation (module) for GPU support. However, with recent updates both TF and PyTorch are easy to use for GPU compatible code.
3-) Both Tensorflow and PyTorch is based on cuDNN. You can use them without cuDNN but as far as I know, it hurts the performance but I'm not sure about this topic.
4-) No they are still different packages. tensorflow-gpu==1.15 and tensorflow==1.15 what they did with tf2, was making the tensorflow more like Keras. So it is more simplified then 1.15 or before.
Rest was already answered by regarding 3) cudNN optimizes layer and such operations on hardware level and those implementations are pure black magic. It is incredibly hard to write CUDA code that properly utilizes your GPU (how load data into the GPU, how to actually perform them using matrices etc. )
These are the instruction to solve the assignments?
Convert your TensorFlow model to UFF
Use TensorRT’s C++ API to parse your model to convert it to a CUDA engine.
TensorRT engine would automatically optimize your model and perform steps
like fusing layers, converting the weights to FP16 (or INT8 if you prefer) and
optimize to run on Tensor Cores, and so on.
Can anyone tell me how to proceed with this assignment because I don't have GPU in my laptop and is it possible to do this in google colab or AWS free account.
And what are the things or packages I have to install for running TensorRT in my laptop or google colab?
so I haven't used .uff but I used .onnx but from what I've seen the process is similar.
According to the documentation, with TensorFlow you can do something like:
from tensorflow.python.compiler.tensorrt import trt_convert as trt
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=['logits', 'classes'])
frozen_graph = converter.convert()
In TensorFlow1.0, so they have it pretty straight forward, TrtGraphConverter has the option to serialized for FP16 like:
converter = trt.TrtGraphConverter(
input_saved_model_dir=input_saved_model_dir,
max_workspace_size_bytes=(11<32),
precision_mode=”FP16”,
maximum_cached_engines=100)
See the preciosion_mode part, once you have serialized you can load the networks easily on TensorRT, some good examples using cpp are here.
Unfortunately, you'll need a nvidia gpu with FP16 support, check this support matrix.
If I'm correct, Google Colab offered a Tesla K80 GPU which does not have FP16 support. I'm not sure about AWS but I'm certain the free tier does not have gpus.
Your cheapest option could be buying a Jetson Nano which is around ~90$, it's a very powerful board and I'm sure you'll use it in the future. Or you could rent some AWS gpu server, but that is a bit expensive and the setup progress is a pain.
Best of luck!
Export and convert your TensorFlow model into .onnx file.
Then, use this onnx-tensorrt tool to do the CUDA engine file conversion.
I recently started learning Keras and TensorFlow. I am testing out a few models currently on the MNIST dataset (pretty basic stuff). I wanted to know, exactly how much my model is consuming memory-wise, during training and inference. I tried googling but did not find much info.
I came across Nvidia-smi. I tried using config.gpu_options.allow_growth = True option but still am not able to use the exact memory python.exe is consuming due to some issues with Nvidia-smi. I know that I could run a separate pass of train and inference, but this is too cumbersome. It is very easy if I could just find the right API to do the job.
Tensorflow being such a well known and well-used library, I am hoping to find a better and faster way to get to these numbers.
Finally, once again my question is:
How to get the exact memory usage for a Keras model during training and inference.
Relevant specs:
OS: Windows 10
GPU: GTX 1050
TensorFlow version: 1.14
Please let me know if any other details are required.
Thanks!
I am new to Tensorflow, I am using retrain.py to train some images. In case I have a larger data base of 10000 images and I have a GPU capable system. How can i use retrain.py to run on my Nvidia GPU. So that training will be done faster.
I am following the steps from the link below
https://www.tensorflow.org/hub/tutorials/image_retraining
To get GPU support, be sure to install the PIP package tensorflow-gpu instead of plain tensorflow. You should see some performance benefits from that for retrain.py. That said, retrain.py shows its age (far predating TF Hub) and does not utilize GPUs so well, because it does not properly batch images when extracting bottleneck values.
If you are ready to live on the cutting edge of TF 2.0.0alpha0 (announced last week), take a look at Hub's
examples/colab/tf2_image_retraining.ipynb which is considerably smaller, faster (if you use a GPU), and even supports fine-tuning the image module.
I am currently using Lenovo Ideapad PC with AMD Radeon graphics in it. I am trying to run an image classifier model using convolutional neural networks. The dataset contains 50000 images and it takes too long to train the model. Can someone tell me how can I use my AMD GPU to fasten the process. I think AMD Graphics does not support CUDA. So is there any way around?
PS: I am using Ubuntu 17.10
What you're asking for is OpenCL support, or in more grandiose terms: the democratization of accelerated devices. There seems to be tentative support for OpenCL, I see some people testing it as of early 2018, but it doesn't appear fully baked yet. The issue has been tracked for quite some time here:
https://github.com/tensorflow/tensorflow/issues/22
You should also be aware of development on XLA, an attempt to virtualize tensorflow over an LLVM (or LLVM-like) virtualization layer making it more portable. It's currently cited as being in alpha as of early 2018.
https://www.tensorflow.org/performance/xla/
There isn't yet a simple solution, but these are the two efforts to follow along these lines.