Comments (5)
First thing to check is that you've installed tensorflow with gpu support. The default tensorflow package is cpu only.
pip3 install --user tensorflow-gpu
from lisa.
The package is tensorflow-gpu 1.9.0. @patverga
from lisa.
Does tensorflow output a line like:
2019-01-22 12:22:22.434234: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1098] Created TensorFlow
device (/job:localhost/replica:0/task:0/device:GPU:0 with 11428 MB memory)
-> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id:
0000:82:00.0, compute capability: 5.2)
If so, then it's using GPU. If not, then you likely have some kind of
configuration issue.
I would expect GPU usage to fluctuate a lot during evaluation, and in fact
for most of the time to be spent on CPU since the code calls the official
CoNLL evaluation scripts (perl). Currently I believe evaluation uses the
same batch size as training, but you could increase it depending on your
GPU's memory to make better use of the GPU.
The code currently doesn't have a "predict" mode, which simply outputs
predictions for sentences without evaluating. This may be more the
functionality you desire, and I'm happy to accept pull requests :)
from lisa.
Does tensorflow output a line like:
2019-01-22 12:22:22.434234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1098] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11428 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:82:00.0, compute capability: 5.2)
If so, then it's using GPU. If not, then you likely have some kind of
configuration issue.I would expect GPU usage to fluctuate a lot during evaluation, and in fact
for most of the time to be spent on CPU since the code calls the official
CoNLL evaluation scripts (perl). Currently I believe evaluation uses the
same batch size as training, but you could increase it depending on your
GPU's memory to make better use of the GPU.The code currently doesn't have a "predict" mode, which simply outputs
predictions for sentences without evaluating. This may be more the
functionality you desire, and I'm happy to accept pull requests :)
Thanks for your patient answer. I suddenly found that I set the parameter of debug
as 1
, which caused the high frequency of evaluation for validation and the low GPU usage.
from lisa.
from lisa.
Related Issues (9)
- eval the model with golden parsing HOT 1
- Invalid argument error during training HOT 14
- Question on your paper. HOT 6
- Question about use of nn_utils.MLP in srl_bilinear HOT 2
- training SA model
- Use elmo and other word embedding in new framework
- I want to konw the way to preprocess CoNLL 2012 dataset,could you give some instructor?
- Question about the evaluation of CoNLL-2005 in the end-to-end setting. HOT 3
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from lisa.