Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [nvidia]

23 questions
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1 answer

How to make my Neural Netwok run on GPU instead of CPU

I have installed Anaconda3 and have installed latest versions of Keras and Tensorflow. Running this command : from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) I find the Notebook is running in CPU: [name:…
Deni Avinash
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10
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2 answers

interpret results of nvidia-smi

Every 1.0s: nvidia-smi Tue Feb 20 12:49:34 2018 Tue Feb 20 12:49:34…
kRazzy R
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3
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2 answers

What does images per second mean when benchmarking Deep Learning GPU?

I've been reviewing performance of several NVIDIA GPU's and I see that typically results are presented in terms of "images per second" that can be processed. Experiments are typically being performed on classical network architectures such as Alex…
Javierfdr
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2
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1 answer

Is Nvidia Jetson product family also suitable for machine learing model training?

I recently came accross these products (Nvidia Jetson) and they are all tagged as "edge", so i think they are designed only for machine learning inference and not model training. They are quite interesting for their low power consumpion and price…
Skary
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2
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1 answer

What are "belief maps" and "affinity maps"?

When evaluating Nvidia-Deep Object Pose Estimation, I came across these terms, belief maps and affinity maps. I haven't been able to find a satisfying answer online on what these terms mean.
rajput
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2
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1 answer

How can I monitor the usage of NVLink connections?

If I'm running a Keras model on some Nvidia GPUs which are connected via NVLink, how can I monitor the usage of the NVLink connections? I want to be sure the NVLink connections are being used and see at what rate.
user3731622
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Is attention cache useful during transformer pretraining?

I am looking at the MegatronLM implementation, and the only thing that is cached are the results of xK and xV computation: https://github.com/NVIDIA/Megatron-LM/blob/b44dca25727c294a7f825e74a3c4a53744cc8404/megatron/model/transformer.py#L339 Which…
LOST
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1
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1 answer

DIGITS Docker container not picking up GPU

I am running DIGITS Docker container but for some reason it fails to recognize host's GPU: it does not report any GPUs (where I expect 1 to be reported) so in the upper right corner of the DIGITS home page there is no indication of any GPUs and also…
Bojan Komazec
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1
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1 answer

Not able to connect to GPU on Google Colab

I'm trying to use tensorflow with a GPU on Google Colab. I followed the steps listed at https://www.tensorflow.org/install/gpu I confirmed that gpu is visible and CUDA is installed with the commands - !nvcc --version !nvidia-smi This works as…
anirudh
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Why doesn't this CNN model need fetures for reducing overfitting?

I found this CNN model by Nvidia end-to-end-deeplearning and with training this model, I'm wondering why this model doesn't need to have dropout layers to reduce overfitting. Neither, this doesn't have activation function. I know we can tune the…
Yuki.U
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1 answer

Two different GPUs for Keras (Python)?

One question guys, someone knows if it should be ok to get one more GPU of type Nvidia Geforce GTX 1070 (gaming version), given that now I have GTX 1070 Titanium? They don't have another Titanium card available here, so I have to get a different…
user68541
1
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1 answer

How to setup my Ubuntu PC for these Deep Learning frameworks?

I need to use both NVIDIA Digits and tensorflow Object Detection API for different deep learning purposes. I am a bit lost with the compatibility issues, especially for what concerns CUDA. So which version should I choose for DIGITS, Tensorflow-gpu,…
firion
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Does it make sense to parallelize machine learning algorithms as part of PhD research?

I'm developing machine learning algorithms to aid in the diagnosis and prognosis of various cancers for my PhD. My lab is an Nvidia teaching center (CUDA). My supervisor thinks that I need to also optimize ML by parallelizing it in CUDA. However,…
Sterls
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Huge variance for RandomForestRegressor models

The experiment is the following: train a RFR with a 15k train rows get predictions on 8k test rows, save predictions as y_hat0 remove 1 random row from the training set and retrain the RFR save prediction for the newly trained model as…
Oleg
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Is there a reason not to wirk with AMP (automatic mixed precision)?

According to: Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs It's better to use AMP (with 16 Floating Point) due to: Shorter training time. Lower memory requirements, enabling larger batch sizes, larger…
user3668129
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