DataBloomNVIDIA is the second-best place to work in the U.S. according to a ranking released today by Glassdoor. The site’s Best Places to Work in 2021 list rates the 100 best U.S. companies with more than 1,000 employees, based on how their own employees rate career opportunities, company culture and senior management. The survey’s top Read article >
The post Glassdoor Ranks NVIDIA No. 2 in Latest Best Places to Work List appeared first on The Official NVIDIA Blog.
Version control is used to keep track of modifications made in a
software code. Similarly, when building machine learning (ML)
systems, it is essential to track things, such as the datasets used
to train the model, the hyperparameters and pipeline used, the
version of tensorflow used to create the model, and many more.
ML artifacts’ history and lineage are very complicated than a
simple, linear log. Git can be used to track the code to one
extent, but we need something to track your models, datasets, and
more. The complexity of ML code and artifacts like models,
datasets, and much more requires a similar approach.
submitted by /u/ai-lover
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def load_data(dir_list, image_size):
X = []
Y = []
image_width, image_height = image_size
for directory in dir_list:
for filename in listdir(directory):
image = cv2.imread(directory + ‘//’ + filename)
imgres = resize(image, (240,240,3))
img_resized = cv2.resize(imgres, dsize = (image_width,
image_height),interpolation=cv2.INTER_CUBIC)
X.append(image)
if directory[-3:] == ‘yes’:
Y.append([1])
else:
Y.append([0])
X = np.array(X)
Y = np.array(Y)
X, Y = shuffle(X, Y)
print(f’Number of examples is: {len(X)}’)
print(f’X shape is: {X.shape}’)
print(f’y shape is: {Y.shape}’)
return X, Y
———————————————–
yes = ‘yes’
no = ‘no’
IMG_WIDTH, IMG_HEIGHT = (240, 240)
X, Y = load_data([yes, no], (IMG_WIDTH, IMG_HEIGHT))
——————————————————–
OUTPUT:
Number of examples is: 253
X shape is: (253,)
Y shape is: (253, 1)
——————————————————-
The X-Shape should be (253,240,240,3), however, I do not know
why it is missing the other numbers. Thank you for helping.
submitted by /u/-KingKrazy-
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Cooking on video calls with friends. Getting to the end of Netflix’s endless content well. Going 10 months without a haircut. Over the past year, we all found different ways to keep ourselves occupied. Gaming, however, is a longer-term trend that promises to continue remaking global culture for years to come. Over 2.5 billion gamers Read article >
The post Thought Gaming Was Big in 2020? 2021 Is Amped Up for More appeared first on The Official NVIDIA Blog.
Bringing more gaming capabilities to millions more gamers, NVIDIA on Tuesday announced more than 70 new laptops will feature GeForce RTX 30 Series Laptop GPUs and unveiled the NVIDIA GeForce RTX 3060 graphics card for desktops. All are powered by the award-winning NVIDIA Ampere GPU architecture, the second generation of RTX with enhanced Ray Tracing Read article >
The post NVIDIA Introduces GeForce RTX 30 Series Laptops, RTX 3060 Graphics Cards, New RTX Games & Features in Special Event appeared first on The Official NVIDIA Blog.
The latest NVIDIA Studio laptops, powered by new NVIDIA GeForce RTX 30 Series Laptop GPUs, are empowering the next generation of creativity. And they bring a host of updates to change how fast creators work. New models come equipped with up to 16GB of video memory, pixel-accurate displays with 1440p and 4K options, and GPU Read article >
The post The Ultimate Creative Machines: NVIDIA Studio Laptops Now with GeForce RTX 30 Series Laptop GPUs appeared first on The Official NVIDIA Blog.
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Quick question, so, i’ve got some model (an autoencoder to be
exact) written in a sequential manner and let’s say it looks
something like this
input = keras.Input() conv = layers.Conv2D()(input) flatten = layers.Flatten()(conv) dense = layers.Dense()(flatten) out = layers.Dense()(dense) model = keras.Model(inputs=input, outputs=out) model.compile()
and let’s say i’ve trained that model. If i then use some layers
of that model to make another one like this
new_model = keras.Model(inputs=input, outputs=conv)
will that new model be already trained? I guess it poses more
global question, does keras.Model() create separate object which
uses those layers vairables you’ve written just to know the
structure, like a Class description or does it actually acts upon
those variables during actions like .fit()?
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Kick off 2021 with a listen to the newest episode of RAPIDSFire: the Accelerated Data Science Podcast!
Kick off 2021 with a listen to the newest episode of RAPIDSFire: the Accelerated Data Science Podcast!
Host Paul Mahler sits down with AI Infrastructure Manager at NVIDIA, Bartley Richardson, and Senior Cybersecurity Data Scientist at NVIDIA, Rachel Allen.
In this episode, they discuss the intersection of Data Science and Cybersecurity, specifically, how NVIDIA GPUs are helping solve problems that have plagued experts in the cybersecurity space for years.
Log files generated by various systems in a company’s infrastructure can leave traces of attacks a data scientist can turn into actionable insight. The problem, historically, has been that logs do not conform to any standard format and the relevant information can be difficult to extract.
Traditionally, this has been done through regular expressions. Bartley and Rachel discuss how their extension of the BERT model, cyBERT, turns logs into usable data faster and more efficiently than was previously possible.
They also discuss the ready-made cybersecurity pipeline available in the RAPIDS CLX GitHub repo, super-fast GPU tokenization for NLP applications, and what the future holds for the RAPIDS CLX team.
This podcast is a great way for anyone in the data science, NLP, or cybersecurity spaces to begin the work year inspired and engaged. Listen to the latest episode here.
Viet Anh Nguyen was awarded the Jetson Project of the Month for his Advanced Driver Assistance System (ADAS). This prototype, which runs on a NVIDIA Jetson Nano, aids a driver with collision, lane departure and speeding warnings.
Viet Anh Nguyen was awarded the Jetson Project of the Month for his Advanced Driver Assistance System (ADAS). This prototype, which runs on a NVIDIA Jetson Nano, aids a driver with collision, lane departure and speeding warnings.
Viet Anh’s goal is to serve the low-end and older car models with his solution. He chose Jetson Nano for its affordability and its capability to run deep learning models. In addition, his prototype has an LCD screen for the user to interact and a speaker to convey warning messages. The core AI functionality is implemented in two modules – “Object Detection” module for collision and speeding warnings, and “Lane Detection” module for lane departure warnings.
CenterNet, trained on Berkeley DeepDrive Dataset, is used for the main object detection. Viet Anh evaluated the model on Resnet-18 and MobileNet backbones. He chose the CenterNet model with ResNet-18 backbone, optimized with NVIDIA TensorRT, for its balance between speed and accuracy on Jetson Nano with an inference time of 62 ms per frame. For speeding warnings, the output of the object detection model is fed to a ResNet-18 network, which then identifies the traffic signs.
In the ‘Lane Detection’ module, a modified U-Net model was trained using the Mapillary Vista Dataset. After optimizing the model using TensorRT, he was able to achieve 14FPS on Jetson Nano for a 384×384 image with FP16 precision. In the below demonstration, the prototype can be seen working on pre-recorded videos and in the CARLA simulator.
Prototype solutions such as ‘ADAS on Jetson Nano’ enable developers to build comprehensive yet low-cost solutions for the ADAS aftermarket category.
Viet Anh has done a great job documenting his journey to build this prototype in a series of blogs (Part 1 – Background and system design, Part 2 -Software design and Part 3 – Machine Learning (DNNs)) and has shared the code here.