Knowing that you can set the max. memory amount for GPUs for Tensorflow,
I wonder how can I prevent tensorflow**.js** from using all my gpu ram. I didn't found anything in the API documentation.
Not yet. Currently you can only select to register the tensorflow backend by using tf.setBackend which will use all the available gpu.
use tensorflow backend by using
tf.setBackend('tensorflow')
use webgl or cpu in the brower by using
tf.setBackend('cpu')
tf.setBackend('webgl') // if using tfjs with the browser
Related
I'm trying to do object detection with yolo. I want to use gpu instead of cpu, but as you can see, I get the following error;
DNN MODULE WAS NOT BUILT WITH CUDA BACKEND; SWITCHING TO CPU
I am using windows 10. can you help?
I want to run inference on the CPU; although my machine has a GPU.
I wonder if it's possible to force TensorFlow to use the CPU rather than the GPU?
By default, TensorFlow will automatically use GPU for inference, but since my GPU is not good (OOM'ed), I wonder if there's a setting to force Tensorflow to use the CPU for inference?
For inference, I used:
tf.contrib.predictor.from_saved_model("PATH")
Assuming you're using TensorFlow 2.0, please check out this issue on GitHub:
[TF 2.0] How to globally force CPU?
The solution seems to be to hide the GPU devices from TensorFlow. You can do that using one of the methodologies described below:
TensorFlow 2.0:
my_devices = tf.config.experimental.list_physical_devices(device_type='CPU')
tf.config.experimental.set_visible_devices(devices= my_devices, device_type='CPU')
TensorFlow 2.1:
tf.config.set_visible_devices([], 'GPU')
(Credit to #ymodak and #henrysky, who answered the question on the GitHub issue.)
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.
Reading :
https://www.tensorflow.org/versions/r0.10/resources/faq.html it states :
Does TensorFlow make use of all the devices (GPUs and CPUs) available
on my machine?
TensorFlow supports multiple GPUs and CPUs. See the how-to
documentation on using GPUs with TensorFlow for details of how
TensorFlow assigns operations to devices, and the CIFAR-10 tutorial
for an example model that uses multiple GPUs.
Note that TensorFlow only uses GPU devices with a compute capability
greater than 3.5.
Does this mean Tensorflow can automatically make use of all CPU's on given machine or does it ned to be explicitly configured ?
CPUs are used via a "device" which is just a threadpool. You can control the number of threads if you feel like you need more:
sess = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=NUM_THREADS))
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