Does anyone has success in training small command recognition models on synthetic dataset? The full details is the following: I need a small model to run a command recognition (about 30 commands) on embedded device. It looks like NVIDIA NeMo MatchboxNet is a good solution, but I have no standard dataset covering my set of commands. The model should be adapted to a broad variation of speakers. Obtaining real dataset seems difficult. I consider using NVIDIA models like Waveglow/Flowtron to …
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 models, or larger inputs. So is there a reason not to work with FP16 ? Which models / datasets / solutions we will need to use FP32 and not FP16 ? Can I find an example in kaggle which we must use FP32 and …
I am following steps mentioned in below link for installing Rapids in Google Colab. However as soon as I run cell no 4, the folders I see in file section disappear and I see "Connecting to a runtime to enable file browsing." in Files section. https://colab.research.google.com/drive/1rY7Ln6rEE1pOlfSHCYOVaqt8OvDO35J0#forceEdit=true&offline=true&sandboxMode=true How can I fix this?
I installed spacy 3 in a venv and tried to execute: spacy.require_gpu() Then I got this as output: >>> spacy.require_gpu() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/.virtualenvs/spacy3/lib/python3.8/site-packages/thinc/util.py", line 187, in require_gpu raise ValueError("GPU is not accessible. Was the library installed correctly?") ValueError: GPU is not accessible. Was the library installed correctly? How can I get rid of this? Im using: nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.119.04 Driver Version: 450.119.04 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | …
I am trying to run some PyTorch scripts on a remote GPU server. While calling the script in the ubuntu terminal i start as:CUDA_VISIBLE_DEVICES=0(or whichever is available) python3 <script.py>. Also, used the following snippet in the code and used .to(device) on the model, input and target tensors. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(device) I have confirmed that my model and data and target tensors are mounted on the cuda device. But the GPU shows 0 percentage utilization all …
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 during the training phase, DIGITS uses only CPU. I have GeForce GT 640 graphics card: $ nvidia-smi -L GPU 0: GeForce GT 640 (UUID: GPU-f2583df9-404d-2564-d332-e7878a94d087) $ lspci ... VGA compatible controller: …
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 expected giving - nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Sun_Jul_28_19:07:16_PDT_2019 Cuda compilation tools, release 10.1, V10.1.243 Wed Nov 20 10:58:14 2019 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 430.50 Driver Version: 418.67 CUDA Version: 10.1 | |-------------------------------+----------------------+----------------------+ | GPU …
I installed cuDNN and cuda toolkit 11 in my machine. Now I installed tensorflow-gpu in pip3 of version 2.2.0. But while running neural network, cpu is being used 90% with four cores and gpu is being used just 7%-15%. But I want my cpu will not be used.
I am learning about "Image Denoising using Autoencoders". So, now I want to build and train a model. Hence, when I read into how Nvidia generated the dataset, I came across: We used about 1000 different scenes and created a series of 16 progressive images for each scene. To train the denoiser, images were rendered from the scene data at 1 sample per pixel, then 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, and …
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: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 2888773992010593937 ] This is my Nvidia version: nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Sat_Aug_25_21:08:04_Central_Daylight_Time_2018 Cuda compilation tools, release 10.0, V10.0.130 running nvidia-smi, I'm getting this result: I want to make the neural network …
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 number of epochs and it reduces overfitting. I'm curious why this model works better without those layers?
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 (eg: Jetson Nano) so i hope they are suitable also for model trainig. So i would ask if someone may clarify this aspect about the focus of the product.
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.
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.
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 one, but closely similar, and I wonder if for using Keras (with TensorFlow backend), will it work fine? They are not exactly the same cards, but similar enough maybe. I want 2 GPUs for …
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 Net or GoogLeNet. I'm wondering if a given number of images per second, say 15000, means that 15000 images can be processed by iteration or for fully learning the network with that amount of images?. I suppose that if I have 15000 …
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, CUDA and Object Detection API in order to make everything work?
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, as I see it, a model is trained once and there is no need to train again. Testing a model is also not time consuming. My interests are in ML, not Parallel Processing. 1) Should I spend a …
I'm using NVIDIA DIGITS 3.0 to do training for detecting direction of a ball throw. My Dataset contains 400+ binary images each for left and right throw with the following specs in DIGITS: Image Type: Grayscale, JPG Image Size: 256 x 256 Resize Transformation: Fill My Classification Model specs: Solver Type: NAG Networks: GoogLeNet The rest are default values Problem: I separated out 8 images (4 for left, 4 for right) from my 400+ training dataset to do testing on …