NVIDIA today reported record revenue for the fourth quarter ended January 31, 2021, of $5.00 billion, up 61 percent from $3.11 billion a year earlier, and up 6 percent from $4.73 billion in the previous quarter. The company’s Gaming and Data Center platforms achieved record revenue for the quarter and year.
Month: February 2021
Categories
How to encrypt a tflite model
Hi,I am trying to run tflite model on browser. It would run client side, I have converted the model to wasm format and am able to run it successfully on browser.
Since, It would be client side, The tflite model would be accessible to everyone. Is it possible to encrypt the model in anyway, So not everyone has access to the model?
The application is built using mediapipe framework, Not sure if it would change the solution.
Thanks!
submitted by /u/cvmldlengineer
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M40 vs 2080S which is better?
So I found out that a tesla M40 has the same amount of cuda cores as a 2080S but 2080S has tensor cores and is a lot more expensive but M40 is a lot cheaper so which would be the best bang for buck?
Price 2080S: 700$ specs: cuda cores 3072 core clock 1815MHz ram 8GB mem clock 2000 (15.5 GB effective) tensor cores: 384
Price M40: 140$(used) specs: cuda cores 3072 core clock 1110MHz ram: 12GB mem clock 1502 (6GBs effective)
Comparing price I would think for coding rendering and AI M40 would be better if you got 2 but tell me what you guys think
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For the first time ever, the NVIDIA Deep Learning Institute (DLI) is making its popular instructor-led workshops available to the general public.
For the first time ever, the NVIDIA Deep Learning Institute (DLI) is making its popular instructor-led workshops available to the general public.
With the launch of public workshops this week, enrollment will be open to individual developers, data scientists, researchers, and students. NVIDIA is increasing accessibility and the number of courses available to participants around the world. Now anyone can learn from world-class NVIDIA instructors in courses on AI, accelerated computing, and data science.
Previously, DLI workshops were only available to large organizations that wanted dedicated and specialized training for their in-house developers, or to individuals attending NVIDIA GTC.
Boost Your Skills with Industry Leading Training
Job growth in the tech industry continues and advanced software development skills in deep learning, data science, and accelerated computing are highly sought after. DLI workshops offer a comprehensive learning experience which includes hands-on exercises and guidance from expert instructors certified by DLI. Courses are delivered virtually and in many time zones to reach developers worldwide. In addition to English, many courses are offered in other languages including Chinese and Japanese.
With the introduction of DLI workshops for individuals, NVIDIA is making it easier for anyone to access world-class training. Registration fees cover learning materials, instructors, and access to fully configured GPU accelerated development servers for hands-on exercises.
The current lineup of DLI workshops for individuals includes:
March 2021
- Fundamentals of Accelerated Computing with CUDA Python
- Applications of AI for Predictive Maintenance
April 2021
- Fundamentals of Deep Learning
- Applications of AI for Anomaly Detection
- Fundamentals of Accelerated Computing with CUDA C/C++
- Building Transformer-Based Natural Language Processing Applications
- Deep Learning for Autonomous Vehicles – Perception
- Fundamentals of Accelerated Data Science with RAPIDS
- Accelerating CUDA C++ Applications with Multiple GPUs
- Fundamentals of Deep Learning for Multi-GPUs
May 2021
- Building Intelligent Recommender Systems
- Fundamentals of Accelerated Data Science with RAPIDS
- Deep Learning for Industrial Inspection
- Building Transformer-Based Natural Language Processing Applications
- Applications of AI for Anomaly Detection
Visit the DLI website for details on each course and the full schedule of upcoming workshops, which is regularly updated with new training opportunities.
A complete list of DLI courses are available in the DLI course catalog.
Register today for a DLI instructor-led workshop for individuals. Space is limited so sign up early. For more information, email nvdli@nvidia.com.
submitted by /u/anotsohypocritesoul
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Categories
Tensorflow pratice vs Mathematics
Hello there,
i often see the following code for regression problems(we have here a linear regression)
import tensorflow.compat.v1 as tf
import numpy as np
import matplotlib.pyplot as plt
learning_rate = 0.01
training_epochs = 100
x_train = np.linspace(-1, 1, 101)
y_train = 2 * x_train + np.random.randn(*x_train.shape) * 0.33
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
def model(X, w):
return tf.multiply(X, w)
w = tf.Variable(0.0, name=”weights”)
y_model = model(X, w)
cost = tf.square(Y-y_model)
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
for epoch in range(training_epochs):
for (x, y) in zip(x_train, y_train):
sess.run(train_op, feed_dict={X: x, Y: y})
w_val = sess.run(w)
sess.close()
plt.scatter(x_train, y_train)
y_learned = x_train*w_val
plt.plot(x_train, y_learned, ‘r’)
plt.show()
But isnt that wrong? My problems are these lines:
for epoch in range(training_epochs):for (x, y) in zip(x_train, y_train):sess.run(train_op, feed_dict={X: x, Y: y})
Why is it a problem? Because if you look how we do it in pure mathematics it doesnt fit. We have the MSE function in math and we do gradient descent over the hole function. But here it seems that they are doing gradient descent just over the parts of MES function in line
“for (x, y) in zip(x_train, y_train):sess.run(train_op, feed_dict={X: x, Y: y})”
What do i mean with that? MSE=g1(x)+g2(x)+…+gn(x) and it seems like they do graph descent on g1(x) then on g2(x) and so on.How does TensorFlow exactly calculus in the back?
My problem is that through feed_dict={X: x, Y: y} only just one function will be called. Lets say x=1 and y=2 Tensorflow will go to X and Y then it will go to def model and only will call one part of the function of MSE lets say g1(x) but you need to go over all MSE with graph descent?
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Categories
Creating a tensor using linspace function
Hello Guys,
I’m challenging myself to create simple 1 dimension tensor that consist of integers, range from 1-10 using the linespace function and with a shape of 6. However I haven’t been successful doing that. How do I fix this ?
My code:
[1,2,3,4,5,6,7,8,9,10]
torch.linspace(1, 1, 10)
submitted by /u/destin95
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I am using Keras for boundary/contour detection using a Unet. When I use binary cross-entropy as the loss, the losses decrease over time as expected the predicted boundaries look reasonable
However, I have tried custom loss for Dice with varying LRs, none of them are working well.
smooth = 1e-6 def dice_coef(y_true, y_pred): y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth) def dice(y_true, y_pred): return 1-dice_coef(y_true, y_pred)
the loss values don’t improve. That is, it will show something like
loss: nan - dice: .9607 - val_loss: nan - val_dice: .9631
I get NaNs for the losses and values for dice and val_dice that barely change as the epochs iterate. This is regardless of what I use for the LR, whether it be .01 to 1e-6
The dimensions of the train images/labels looks like N x H x W x 1, where N is the number of images, H/W are the height/width of each image
can anyone help?
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It’s hard not to feel your best when your car makes every commute a VIP experience. This week, Mercedes-Benz launched the redesigned C-Class sedan and C-Class wagon, packed with new features for the next generation of driving. Both models prominently feature the latest MBUX AI cockpit, powered by NVIDIA, delivering an intelligent user interface for Read article >
The post Feelin’ Like a Million MBUX: AI Cockpit Featured in Popular Mercedes-Benz C-Class appeared first on The Official NVIDIA Blog.
TurboSquid and NVIDIA are collaborating to curate thousands of USD models that are available today and ready to use with NVIDIA Omniverse.
TurboSquid and NVIDIA are collaborating to curate thousands of USD models that are available today and ready to use with NVIDIA Omniverse.
Many developers using Omniverse are experiencing enhanced workflows with virtual collaboration and photorealistic simulation. The open platform, which is available now in open beta, enables teams around the world to simultaneously collaborate in real time, using their favorite 3D applications.
TurboSquid has an extensive library of 3D models that users can easily drag and drop into Omniverse, allowing them to immediately start collaborating with others. This helps developers save time as they can immediately start exploring Omniverse without worrying about importing or exporting content, model preparation, or polycounts. Users can load TurboSquid’s USD models in Omniverse connectors, and Omniverse ensures consistent quality between teams, contractors, and ecosystems.
To get started, download the NVIDIA Omniverse Launcher from nvidia.com/omniverse. Run the Omniverse Launcher and install Omniverse Create or Omniverse View apps, then import TurboSquid 3D content and start creating.
Learn more by visiting TurboSquid’s Omniverse page, and check out the 3D tool sets now available.