Africa Data Centres Joins NVIDIA DGX-Ready Data Centre Program | Tempemail – Blog – 10 minute

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Africa Data Centres announced it has joined the NVIDIA DGX-Ready Data Centre program, allowing organisations across the African continent to connect with AI-ready facilities for seamless, rapid and cost-effective AI deployments.
Africa Data Centres is the first and only network of interconnected, carrier- and vendor-neutral data centres on the African continent. Being a part of the NVIDIA DGX-Ready Data Centre program will allow companies across a wide range of vertical markets to adopt NVIDIA DGX systems for AI and data analytics hosted locally at Africa Data Centres, along with a range of other managed hosting and colocation services that support business-critical data, applications and back-end systems.
“Africa Data Centres is an ideal partner for NVIDIA’s DGX-Ready Data Centre program. Becoming DGX-Ready Data Centre certified means that Africa Data Centres customers will benefit from lowered costs and greater control as they accelerate their digital transformation. Organisations can entrust their physical infrastructure management and leave the security to Africa Data Centres. Africa Data Centres are ISO 27001 certified and PCI DSS compliant,” says Stephane Duproz, CEO of Africa Data Centres.

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NVIDIA container runtime for Tempemail Linux- Tempemail – Blog – 10 minute

By Pablo Rodriguez Quesada

Introduction
Training and using AI models are tasks that demand significant computational power. Current trends are pointing more to deep neural networks, which include thousands, if not millions of operations per iteration. In the past year, more and more researchers have sounded the alarm on the exploding costs of deep learning. The computing power needed to do AI is now rising seven times faster than ever before [1]. These new needs are making hardware companies create hardware accelerators like Neural processing units, CPUs, and GPUs.
Embedded systems are not an exception to this transformation. We see every day intelligent traffic lights, autonomous vehicles, intelligent IoT devices, and more. The current direction is to have accelerators inside these embedded devices, Systems On-Chip mainly. Hardware developers have embedded small accelerators like GPUs, FPGAs, and more into SOCs, SOMs, and other systems. We call these modern systems: heterogeneous computing architectures.
The use of GPUs on Linux is not something new; we have been able to do so for many years. However, it would be great to accelerate the development and deployment of HPC applications. Containers enable portability, stability, and many other characteristics when deploying an application. For this reason, companies are investing so much in these technologies. For instance, NVIDIA recently started a project that enables CUDA on Docker [2].
One concern when dealing with containers is the loss of performance. However, when comparing the performance of the GPU with and without the containers environment, researchers found that no additional overhead is caused [3]. The consistency in the performance is one of the principal benefits of containers over virtual machines; accessing the GPU is done seamlessly as the kernel stays the constant.
NVIDIA-Docker on Yocto
Together with Matt Madison (Maintainer of meta-tegra layer), we created the required recipes to build and deploy NVIDIA-docker on Tempemail Linux LTS 19 (Yocto 3.0 Zeus).[4]
In this tutorial, you will find how to enable NVIDIA-containers on a custom distribution of Linux and run a small test application that leverages the use of GPUs inside a container.
Description
To enable NVIDIA containers, Docker needs to have the nvidia-containers-runtime which is a modified version of runc that adds a custom pre-start hook to all containers. The nvidia-containers-runtime communicates docker using the library libnvidia-container, which automatically configures GNU/Linux containers leveraging NVIDIA hardware. This library relies on kernel primitives and is designed to be agnostic of the container runtime. All the effort to port these libraries and tools to the Yocto Project was submitted to the community and now is part of the meta-tegra layer which is maintained by Matt Madison.
Note: this setup is based on Linux for Tegra and not the original Yocto Linux kernel
Benefits, and Limitations
The main benefit of GPUs inside containers is the portability and stability in the environment at the time of deployment. Of course, the development also sees benefits in having this portable environment as developers can collaborate more efficiently.
However, there are limitations due to the nature of the NVIDIA environment. Containers are heavy-weight because they are based in Linux4Tegra image that contains libraries required on runtime. On the other hand, because of redistribution limitations, some libraries are not included in the container. This requires runc to mount some property code libraries, losing portability in the process.
Prerequisites
You are required to download NVIDIA property code from their website. To do so, you will need to create an NVIDIA Developer Network account.
Go into https://developer.nvidia.com/embedded/downloads , download the NVIDIA SDK Manager, install it and download all the files for the Jetson board you own. All the effort to port these libraries and tools to the Yocto Project was submited to the community and now is part of the meta-tegra layer which is maintained by Matt Madison.
The required Jetpack version is 4.3
/opt/nvidia/sdkmanager/sdkmanager

Image 1. SDK Manager installation
If you need to include TensorRT in your builds, you must create the subdirectory and move all of the TensorRT packages downloaded by the SDK Manager there.
$ mkdir /home/$USER/Downloads/nvidia/sdkm_downloads/NoDLA
$ cp /home/$USER/Downloads/nvidia/sdkm_downloads/libnv* /home/$USER/Downloads/nvidia/sdkm_downloads/NoDLA

Creating the project
$ git clone –branch WRLINUX_10_19_BASE https://github.com/WindRiver-Labs/wrlinux-x.git
$ ./wrlinux-x/setup.sh –all-layers –dl-layers –templates feature/docker

Note: –distro wrlinux-graphics can be used for some applications that require x11.
Add meta-tegra layer
DISCAIMER: meta-tegra is a community maintained layer not supported by Tempemail at the time of writing
$ git clone https://github.com/madisongh/meta-tegra.git layers/meta-tegra
$ cd layers/meta-tegra
$ git checkout 11a02d02a7098350638d7bf3a6c1a3946d3432fd
$ cd –

Tested with: https://github.com/madisongh/meta-tegra/commit/11a02d02a7098350638d7bf3a6c1a3946d3432fd
. $ ./environment-setup-x86_64-wrlinuxsdk-linux
. $ ./oe-init-build-env

$ bitbake-layers add-layer ../layers/meta-tegra/
$ bitbake-layers add-layer ../layers/meta-tegra/contrib

Configure the project
$ echo “BB_NO_NETWORK = ‘0’” >> conf/local.conf
$ echo ‘INHERIT_DISTRO_remove = “whitelist”‘ >> conf/local.conf

Set the machine to your Jetson Board
$ echo “MACHINE=’jetson-nano-qspi-sd'” >> conf/local.conf
$ echo “PREFERRED_PROVIDER_virtual/kernel = ‘linux-tegra'” >> conf/local.conf

CUDA cannot be compiled with GCC versions higher than 7. Set GCC version to 7.%:
$ echo ‘GCCVERSION = “7.%”‘ >> conf/local.conf
$ echo “require contrib/conf/include/gcc-compat.conf” >> conf/local.conf

Set the IMAGE export type to tegraflash for ease of deployment.
$ echo ‘IMAGE_CLASSES += “image_types_tegra”‘ >> conf/local.conf
$ echo ‘IMAGE_FSTYPES = “tegraflash”‘ >> conf/local.conf

Change the docker version, add nvidia-container-runtime.
$ echo ‘IMAGE_INSTALL_remove = “docker”‘ >> conf/local.conf
$ echo ‘IMAGE_INSTALL_append = ” docker-ce”‘ >> conf/local.conf

Fix tini build error
$ echo ‘SECURITY_CFLAGS_pn-tini_append = ” ${SECURITY_NOPIE_CFLAGS}”‘ >> conf/local.conf

Set NVIDIA download location
$ echo “NVIDIA_DEVNET_MIRROR=’file:///home/$USER/Downloads/nvidia/sdkm_downloads'” >> conf/local.conf
$ echo ‘CUDA_BINARIES_NATIVE = “cuda-binaries-ubuntu1604-native”‘ >> conf/local.conf

Add the Nvidia containers runtime, AI libraries and the AI libraries CSV files
$ echo ‘IMAGE_INSTALL_append = ” nvidia-docker nvidia-container-runtime cudnn tensorrt libvisionworks libvisionworks-sfm libvisionworks-tracking cuda-container-csv cudnn-container-csv tensorrt-container-csv libvisionworks-container-csv libvisionworks-sfm-container-csv libvisionworks-tracking-container-csv”‘ >> conf/local.conf

Enable ldconfig required by the nvidia-container-runtime
$ echo ‘DISTRO_FEATURES_append = ” ldconfig”‘ >> conf/local.conf

Build the project
$ bitbake wrlinux-image-glibc-std

Burn the image into the SD card
$ unzip wrlinux-image-glibc-std-sato-jetson-nano-qspi-sd-20200226004915.tegraflash.zip -d wrlinux-jetson-nano
$ cd wrlinux-jetson-nano

Connect the Jetson Board to your computer using the micro USB cable as shown in the image:
Image 2. Recovery mode setup for Jetson Nano
Image 3. Pins Diagram for Jetson Nano
After connecting the board, run:
$ sudo ./dosdcard.sh

This command will create the file wrlinux-image-glibc-std.sdcard that contains the SD card image required to boot.
Burn the Image to the SD Card:
$ sudo dd if=wrlinux-image-glibc-std.sdcard of=/dev/***** bs=8k

Warning: substitute the of= device to the one that points to your sdcardFailure to do so can lead to unexpected erase of hard disks
Deploy the target
Boot up the board and find the ip address with the command ifconfig.
Then, ssh into the machine and run docker:
$ ssh [email protected]

Create tensorflow_demo.py using the example from the “Train and evaluate with Keras” section in the Tensorflow documentation:
#!/usr/bin/python3
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(784,), name=’digits’)
x = layers.Dense(64, activation=’relu’, name=’dense_1′)(inputs)
x = layers.Dense(64, activation=’relu’, name=’dense_2′)(x)
outputs = layers.Dense(10, name=’predictions’)(x)

model = keras.Model(inputs=inputs, outputs=outputs)

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(60000, 784).astype(‘float32’) / 255
x_test = x_test.reshape(10000, 784).astype(‘float32’) / 255

y_train = y_train.astype(‘float32’)
y_test = y_test.astype(‘float32’)

# Reserve 10,000 samples for validation
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]

model.compile(optimizer=keras.optimizers.RMSprop(), # Optimizer
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# List of metrics to monitor
metrics=[‘sparse_categorical_accuracy’])

print(‘# Fit model on training data’)
history = model.fit(x_train, y_train,
batch_size=64,
epochs=3,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(x_val, y_val))

print(‘nhistory dict:’, history.history)

# Evaluate the model on the test data using `evaluate`
print(‘n# Evaluate on test data’)
results = model.evaluate(x_test, y_test, batch_size=128)
print(‘test loss, test acc:’, results)

# Generate predictions (probabilities — the output of the last layer)
# on new data using `predict`
print(‘n# Generate predictions for 3 samples’)
predictions = model.predict(x_test[:3])
print(‘predictions shape:’, predictions.shape)

Create a Dockerfile:
FROM tianxiang84/l4t-base:all

WORKDIR /root
COPY tensorflow_demo.py .

ENTRYPOINT [“/usr/bin/python3”]
CMD [“/root/tensorflow_demo.py”]

Build the container:
# docker build -t l4t-tensorflow .

Run the container:
# docker run –runtime nvidia -it l4t-tensorflow

Results
Note the use of the GPU0:
2020-04-22 21:13:56.969319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-04-22 21:13:58.210600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 268 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)

Conclusions
The use of NVIDIA-containers allows a smooth deployment of AI applications. Once you have your Linux distribution running containers with the custom NVIDIA runtime, getting a Neural Network to work is as simple as running one command. Getting an NVIDIA Tegra board to run computing-intensive workloads is now easier than ever.
With the provided custom runc engine that allows the use of CUDA and other related libraries, you will be running applications as if they were on bare-metal.
One of the possibilities the containers offer is combining this setup with Kubernetes or the NVIDIA EGX Platform so that you can do the orchestration. The Kubernetes Device Plugins distribute and manage workloads across multiple acceleration devices, giving you high availability as well as other benefits. Combined with other technologies such as Tensorflow and OpenCV, and you will have an army of edge devices ready to run your Intelligent applications for you.
References

[1] K. Hao, “The computing power needed to train AI is now rising seven times faster than ever before”, MIT Technology Review, Nov. 2019. [Online]. Available: https://www.technologyreview.com/s/614700/the- computing-power-needed-to-train-ai-is-now-rising-seven-times-faster-than-ever-before.
[2] Nvidia, nvidia-docker, [Online; accessed 15. Mar. 2020], Feb. 2020. [Online]. Available:https://github.com/NVIDIA/nvidia-docker.
[3] L. Benedicic and M. Gila, “Accessing gpus from containers in hpc”, 2016. [Online]. Available: http://sc16.supercomputing.org/sc-archive/tech_poster/poster_files/post187s2-file3.pdf.
[4] M. Madison, Container runtime for master, [Online; accessed 30. Mar. 2020], Mar. 2020. [Online]. Available:https://github.com/madisongh/meta-tegra/pull/266

All product names, logos, and brands are property of their respective owners.All company, product and service names used in this software are for identification purposes only. Tempemail are registered trademarks of Tempemail Systems.
Disclaimer of Warranty / No Support: Tempemail does not provide support and maintenance services for this software, under Tempemail’s standard Software Support and Maintenance Agreement or otherwise. Unless required by applicable law, Tempemail provides the software (and each contributor provides its contribution) on an “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, either express or implied, including, without limitation, any warranties of TITLE, NONINFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the software and assume ay risks associated with your exercise of permissions under the license.
TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.
Docker is a trademark of Docker, Inc.
NVIDIA, NVIDIA EGX, CUDA, Jetson, and Tegra are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

Tempemail , Tempmail Temp email addressess (10 minutes emails)– When you want to create account on some forum or social media, like Facebook, Reddit, Twitter, TikTok you have to enter information about your e-mail box to get an activation link. Unfortunately, after registration, this social media sends you dozens of messages with useless information, which you are not interested in. To avoid that, visit this Temp mail generator: tempemail.co and you will have a Temp mail disposable address and end up on a bunch of spam lists. This email will expire after 10 minute so you can call this Temp mail 10 minute email. Our service is free! Let’s enjoy!

NVIDIA container runtime for Tempemail Linux- Tempemail – Blog – 10 minute

By Pablo Rodriguez Quesada

Introduction
Training and using AI models are tasks that demand significant computational power. Current trends are pointing more to deep neural networks, which include thousands, if not millions of operations per iteration. In the past year, more and more researchers have sounded the alarm on the exploding costs of deep learning. The computing power needed to do AI is now rising seven times faster than ever before [1]. These new needs are making hardware companies create hardware accelerators like Neural processing units, CPUs, and GPUs.
Embedded systems are not an exception to this transformation. We see every day intelligent traffic lights, autonomous vehicles, intelligent IoT devices, and more. The current direction is to have accelerators inside these embedded devices, Systems On-Chip mainly. Hardware developers have embedded small accelerators like GPUs, FPGAs, and more into SOCs, SOMs, and other systems. We call these modern systems: heterogeneous computing architectures.
The use of GPUs on Linux is not something new; we have been able to do so for many years. However, it would be great to accelerate the development and deployment of HPC applications. Containers enable portability, stability, and many other characteristics when deploying an application. For this reason, companies are investing so much in these technologies. For instance, NVIDIA recently started a project that enables CUDA on Docker [2].
One concern when dealing with containers is the loss of performance. However, when comparing the performance of the GPU with and without the containers environment, researchers found that no additional overhead is caused [3]. The consistency in the performance is one of the principal benefits of containers over virtual machines; accessing the GPU is done seamlessly as the kernel stays the constant.
NVIDIA-Docker on Yocto
Together with Matt Madison (Maintainer of meta-tegra layer), we created the required recipes to build and deploy NVIDIA-docker on Tempemail Linux LTS 19 (Yocto 3.0 Zeus).[4]
In this tutorial, you will find how to enable NVIDIA-containers on a custom distribution of Linux and run a small test application that leverages the use of GPUs inside a container.
Description
To enable NVIDIA containers, Docker needs to have the nvidia-containers-runtime which is a modified version of runc that adds a custom pre-start hook to all containers. The nvidia-containers-runtime communicates docker using the library libnvidia-container, which automatically configures GNU/Linux containers leveraging NVIDIA hardware. This library relies on kernel primitives and is designed to be agnostic of the container runtime. All the effort to port these libraries and tools to the Yocto Project was submitted to the community and now is part of the meta-tegra layer which is maintained by Matt Madison.
Note: this setup is based on Linux for Tegra and not the original Yocto Linux kernel
Benefits, and Limitations
The main benefit of GPUs inside containers is the portability and stability in the environment at the time of deployment. Of course, the development also sees benefits in having this portable environment as developers can collaborate more efficiently.
However, there are limitations due to the nature of the NVIDIA environment. Containers are heavy-weight because they are based in Linux4Tegra image that contains libraries required on runtime. On the other hand, because of redistribution limitations, some libraries are not included in the container. This requires runc to mount some property code libraries, losing portability in the process.
Prerequisites
You are required to download NVIDIA property code from their website. To do so, you will need to create an NVIDIA Developer Network account.
Go into https://developer.nvidia.com/embedded/downloads , download the NVIDIA SDK Manager, install it and download all the files for the Jetson board you own. All the effort to port these libraries and tools to the Yocto Project was submited to the community and now is part of the meta-tegra layer which is maintained by Matt Madison.
The required Jetpack version is 4.3
/opt/nvidia/sdkmanager/sdkmanager

Image 1. SDK Manager installation
If you need to include TensorRT in your builds, you must create the subdirectory and move all of the TensorRT packages downloaded by the SDK Manager there.
$ mkdir /home/$USER/Downloads/nvidia/sdkm_downloads/NoDLA
$ cp /home/$USER/Downloads/nvidia/sdkm_downloads/libnv* /home/$USER/Downloads/nvidia/sdkm_downloads/NoDLA

Creating the project
$ git clone –branch WRLINUX_10_19_BASE https://github.com/WindRiver-Labs/wrlinux-x.git
$ ./wrlinux-x/setup.sh –all-layers –dl-layers –templates feature/docker

Note: –distro wrlinux-graphics can be used for some applications that require x11.
Add meta-tegra layer
DISCAIMER: meta-tegra is a community maintained layer not supported by Tempemail at the time of writing
$ git clone https://github.com/madisongh/meta-tegra.git layers/meta-tegra
$ cd layers/meta-tegra
$ git checkout 11a02d02a7098350638d7bf3a6c1a3946d3432fd
$ cd –

Tested with: https://github.com/madisongh/meta-tegra/commit/11a02d02a7098350638d7bf3a6c1a3946d3432fd
. $ ./environment-setup-x86_64-wrlinuxsdk-linux
. $ ./oe-init-build-env

$ bitbake-layers add-layer ../layers/meta-tegra/
$ bitbake-layers add-layer ../layers/meta-tegra/contrib

Configure the project
$ echo “BB_NO_NETWORK = ‘0’” >> conf/local.conf
$ echo ‘INHERIT_DISTRO_remove = “whitelist”‘ >> conf/local.conf

Set the machine to your Jetson Board
$ echo “MACHINE=’jetson-nano-qspi-sd'” >> conf/local.conf
$ echo “PREFERRED_PROVIDER_virtual/kernel = ‘linux-tegra'” >> conf/local.conf

CUDA cannot be compiled with GCC versions higher than 7. Set GCC version to 7.%:
$ echo ‘GCCVERSION = “7.%”‘ >> conf/local.conf
$ echo “require contrib/conf/include/gcc-compat.conf” >> conf/local.conf

Set the IMAGE export type to tegraflash for ease of deployment.
$ echo ‘IMAGE_CLASSES += “image_types_tegra”‘ >> conf/local.conf
$ echo ‘IMAGE_FSTYPES = “tegraflash”‘ >> conf/local.conf

Change the docker version, add nvidia-container-runtime.
$ echo ‘IMAGE_INSTALL_remove = “docker”‘ >> conf/local.conf
$ echo ‘IMAGE_INSTALL_append = ” docker-ce”‘ >> conf/local.conf

Fix tini build error
$ echo ‘SECURITY_CFLAGS_pn-tini_append = ” ${SECURITY_NOPIE_CFLAGS}”‘ >> conf/local.conf

Set NVIDIA download location
$ echo “NVIDIA_DEVNET_MIRROR=’file:///home/$USER/Downloads/nvidia/sdkm_downloads'” >> conf/local.conf
$ echo ‘CUDA_BINARIES_NATIVE = “cuda-binaries-ubuntu1604-native”‘ >> conf/local.conf

Add the Nvidia containers runtime, AI libraries and the AI libraries CSV files
$ echo ‘IMAGE_INSTALL_append = ” nvidia-docker nvidia-container-runtime cudnn tensorrt libvisionworks libvisionworks-sfm libvisionworks-tracking cuda-container-csv cudnn-container-csv tensorrt-container-csv libvisionworks-container-csv libvisionworks-sfm-container-csv libvisionworks-tracking-container-csv”‘ >> conf/local.conf

Enable ldconfig required by the nvidia-container-runtime
$ echo ‘DISTRO_FEATURES_append = ” ldconfig”‘ >> conf/local.conf

Build the project
$ bitbake wrlinux-image-glibc-std

Burn the image into the SD card
$ unzip wrlinux-image-glibc-std-sato-jetson-nano-qspi-sd-20200226004915.tegraflash.zip -d wrlinux-jetson-nano
$ cd wrlinux-jetson-nano

Connect the Jetson Board to your computer using the micro USB cable as shown in the image:
Image 2. Recovery mode setup for Jetson Nano
Image 3. Pins Diagram for Jetson Nano
After connecting the board, run:
$ sudo ./dosdcard.sh

This command will create the file wrlinux-image-glibc-std.sdcard that contains the SD card image required to boot.
Burn the Image to the SD Card:
$ sudo dd if=wrlinux-image-glibc-std.sdcard of=/dev/***** bs=8k

Warning: substitute the of= device to the one that points to your sdcardFailure to do so can lead to unexpected erase of hard disks
Deploy the target
Boot up the board and find the ip address with the command ifconfig.
Then, ssh into the machine and run docker:
$ ssh [email protected]

Create tensorflow_demo.py using the example from the “Train and evaluate with Keras” section in the Tensorflow documentation:
#!/usr/bin/python3
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

inputs = keras.Input(shape=(784,), name=’digits’)
x = layers.Dense(64, activation=’relu’, name=’dense_1′)(inputs)
x = layers.Dense(64, activation=’relu’, name=’dense_2′)(x)
outputs = layers.Dense(10, name=’predictions’)(x)

model = keras.Model(inputs=inputs, outputs=outputs)

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(60000, 784).astype(‘float32’) / 255
x_test = x_test.reshape(10000, 784).astype(‘float32’) / 255

y_train = y_train.astype(‘float32’)
y_test = y_test.astype(‘float32’)

# Reserve 10,000 samples for validation
x_val = x_train[-10000:]
y_val = y_train[-10000:]
x_train = x_train[:-10000]
y_train = y_train[:-10000]

model.compile(optimizer=keras.optimizers.RMSprop(), # Optimizer
# Loss function to minimize
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
# List of metrics to monitor
metrics=[‘sparse_categorical_accuracy’])

print(‘# Fit model on training data’)
history = model.fit(x_train, y_train,
batch_size=64,
epochs=3,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(x_val, y_val))

print(‘nhistory dict:’, history.history)

# Evaluate the model on the test data using `evaluate`
print(‘n# Evaluate on test data’)
results = model.evaluate(x_test, y_test, batch_size=128)
print(‘test loss, test acc:’, results)

# Generate predictions (probabilities — the output of the last layer)
# on new data using `predict`
print(‘n# Generate predictions for 3 samples’)
predictions = model.predict(x_test[:3])
print(‘predictions shape:’, predictions.shape)

Create a Dockerfile:
FROM tianxiang84/l4t-base:all

WORKDIR /root
COPY tensorflow_demo.py .

ENTRYPOINT [“/usr/bin/python3”]
CMD [“/root/tensorflow_demo.py”]

Build the container:
# docker build -t l4t-tensorflow .

Run the container:
# docker run –runtime nvidia -it l4t-tensorflow

Results
Note the use of the GPU0:
2020-04-22 21:13:56.969319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2020-04-22 21:13:58.210600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 268 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3)

Conclusions
The use of NVIDIA-containers allows a smooth deployment of AI applications. Once you have your Linux distribution running containers with the custom NVIDIA runtime, getting a Neural Network to work is as simple as running one command. Getting an NVIDIA Tegra board to run computing-intensive workloads is now easier than ever.
With the provided custom runc engine that allows the use of CUDA and other related libraries, you will be running applications as if they were on bare-metal.
One of the possibilities the containers offer is combining this setup with Kubernetes or the NVIDIA EGX Platform so that you can do the orchestration. The Kubernetes Device Plugins distribute and manage workloads across multiple acceleration devices, giving you high availability as well as other benefits. Combined with other technologies such as Tensorflow and OpenCV, and you will have an army of edge devices ready to run your Intelligent applications for you.
References

[1] K. Hao, “The computing power needed to train AI is now rising seven times faster than ever before”, MIT Technology Review, Nov. 2019. [Online]. Available: https://www.technologyreview.com/s/614700/the- computing-power-needed-to-train-ai-is-now-rising-seven-times-faster-than-ever-before.
[2] Nvidia, nvidia-docker, [Online; accessed 15. Mar. 2020], Feb. 2020. [Online]. Available:https://github.com/NVIDIA/nvidia-docker.
[3] L. Benedicic and M. Gila, “Accessing gpus from containers in hpc”, 2016. [Online]. Available: http://sc16.supercomputing.org/sc-archive/tech_poster/poster_files/post187s2-file3.pdf.
[4] M. Madison, Container runtime for master, [Online; accessed 30. Mar. 2020], Mar. 2020. [Online]. Available:https://github.com/madisongh/meta-tegra/pull/266

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Nvidia launches chip aimed at data centre economics – Hardware- Tempemail – Blog – 10 minute

Semiconductor firm Nvidia on Thursday announced a new chip that can be digitally split up to run several different programs on one physical chip, a first for the company that matches a key capability on many of Intel’s chips.
The notion behind what the Santa Clara, California-based company calls its A100 chip is simple: Help the owners of data centres get every bit of computing power possible out of the physical chips they purchase by ensuring the chip never sits idle.
The same principle helped power the rise of cloud computing over the past two decades and helped Intel build a massive data centre business.
When software developers turn to a cloud computing provider such as Amazon.com or Microsoft for computing power, they do not rent a full physical server inside a data centre.
Instead they rent a software-based slice of a physical server called a “virtual machine.”
Such virtualisation technology came about because software developers realised that powerful and pricey servers often ran far below full computing capacity. By slicing physical machines into smaller virtual ones, developers could cram more software on to them, similar to the puzzle game Tetris. Amazon, Microsoft and others built profitable cloud businesses out of wringing every bit of computing power from their hardware and selling that power to millions of customers.
But the technology has been mostly limited to processor chips from Intel and similar chips such as those from MAD.
Nvidia said Thursday that its new A100 chip can be split into seven “instances.”
For Nvida, that solves a practical problem.
Nvidia sells chips for artificial intelligence tasks. The market for those chips breaks into two parts.
“Training” requires a powerful chip to, for example, analyse millions of images to train an algorithm to recognise faces.
But once the algorithm is trained, “inference” tasks need only a fraction of the computing power to scan a single image and spot a face.
Nvidia is hoping the A100 can replace both, being used as a big single chip for training and split into smaller inference chips.
Customers who want to test the theory will pay a steep price of US$200,000 for Nvidia’s DGX server built around the A100 chips.
In a call with reporters, chief executive Jensen Huang argued the math will work in Nvidia’s favour, saying the computing power in the DGX A100 was equal to that of 75 traditional servers that would cost US$5,000 each.
“Because it’s fungible, you don’t have to buy all these different types of servers. Utilisation will be higher,” he said.
“You’ve got 75 times the performance of a $5,000 server, and you don’t have to buy all the cables.”

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Nvidia turns to driver-assistance market as robo-taxis stall – Benchmarking Change- Tempemail – Blog – 10 minute

 Nvidia Corp, whose semiconductors power data centers, autonomous cars and robots, said on Thursday it plans to enter the market for technology that helps cars with automated lane-keeping, cruise control and other driver-assistance features.
The move, announced as part of the chip company’s annual conference, which was held online this year, represents a change in direction for Nvidia. Until now, the Santa Clara, California-based company has supplied key technology aimed at making autonomous vehicles that require much more sophisticated computers.
But such vehicles, some of which are known as “robo-taxis,” remain years away from mass adoption. Even before the coronavirus pandemic hammered the world economy, automakers such as General Motors Co and Ford Motor Co were dialing down their expectations for self-driving cars.
Many of the driver-assistance features that the new Nvidia system will enable, by contrast, are already available on high-end vehicles with technology from providers such as Mobileye, the Israeli firm owned by Nvidia data center rival Intel Corp.
Danny Shapiro, senior director of automotive at Nvidia, said the shift in strategy is aimed at meeting the existing needs of automakers that struggle with maintaining two systems – one for the driver assistance available today, and one for more advanced self-driving technology for the future.
The new Nvidia system means automakers will be able to use one system for both, saving engineering efforts and using some of the self-driving technology to improve the driver assistance functions, Shapiro said.
“We have a single architecture that will enable the automaker to span every potential level of automation they want to deliver and put that software-updatable system in every single vehicle,” Shapiro said.
Nvidia’s new self-driving technology uses the “Orin” processing chip the company launched in December. Shapiro said he expected vehicles using the system could start production in early 2023.
Shapiro declined to comment on pricing or potential automaker customers. However, he said the Nvidia chips will be part of a larger system that includes cameras and will likely be built by traditional automotive suppliers such as Continental AG, ZF Friedrichshafen AG or Robert Bosch.
“We’re the (artificial intelligence) brain that would go into this,” Shapiro said.

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COVID 19 impact: NVIDIA Expands Free Access to GPU Virtualization Software to Support Remote Workers- Tempemail – Blog – 10 minute

Read Article
In the aftermath of COVID19, with many companies needing to quickly support employees now working remotely, NVIDIA is expanding free, 90-day virtual GPU software evaluation from 128 to 500 licenses.
With vGPU software licenses, companies can use their on-premises NVIDIA GPUs to provide accelerated virtual infrastructure so people can work and collaborate from anywhere. Companies can also temporarily repurpose NVIDIA GPUs being used on other projects to support their remote workers.
Every organization is working hard to address these needs: Healthcare providers are supporting care from new locations. Schools are expanding their virtual classrooms. Agencies are coordinating critical services.
Whether supporting financial professionals working with data on multiple screens, scientists conducting research, or designers working in graphics-intensive applications, enterprises are faced with different workloads that have different requirements.
NVIDIA offers a variety of customized vGPU software to meet these diverse needs. All three tiers of the company’s specialized vGPU software are available through the expanded free licensing:

NVIDIA GRID software delivers responsive VDI by virtualizing systems and applications for knowledge workers.
NVIDIA Quadro Virtual Data Center Workstation software provides workstation-class performance for creators using high-end graphics applications.
NVIDIA Virtual Compute Server software accelerates server virtualization with GPUs to power the most compute-intensive workflows, such as AI, deep learning and data science on a virtual machine.

Virtualized Performance, Enterprise Security and Broad Ecosystem Support
In addition to providing high performance and reducing latency for remote workers, NVIDIA vGPU software ensures protection for sensitive data and digital assets, which remain in the data center and aren’t saved to local client devices. This is an important security requirement for remote work across many industries, including visual effects and design, as well as for research and development.
NVIDIA vGPU software is certified on a broad ecosystem of hypervisors, platforms, user applications and management software to help IT teams quickly scale out support for remote workers.
Companies can deploy virtual workstations, compute and VDI from their on-prem data centers by installing the vGPU software licenses on all NVIDIA GPUs based on the Pascal, Volta and Turing architectures, including NVIDIA Quadro RTX 6000 and RTX 8000 GPUs, and NVIDIA M10 and M60 GPUs.

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Nvidia tweets, then deletes a mysterious teaser showing an animated blinking eye – Blog – 10 minute

What just happened? After effectively canceling its annual GTC conference and shifting it to an online-only format (and then later scrapping that in favor of a series of news posts), Nvidia is using alternative marketing strategies to hype up its upcoming announcements. Today, for example, Nvidia’s Australia and New Zealand-focused Twitter account, @NvidiaANZ, tweeted a mysterious teaser: an eyeball emoji, a date (March 19, 2020), and a strange video with what appeared to be a blinking eye.
Frankly, nobody seems to know what this tease means. The tweet was removed shortly after going live, which could mean it was mistakenly posted. Alternatively, its removal might be part of Nvidia’s marketing strategy here — companies have been known to “accidentally” leak content in the past when it serves their interests.
Either way, tech news site eTeknix managed to grab a screenshot of the tweet before it was deleted, which you can see below (though, of course, it is not animated).

As we said, nobody seems to understand what the tweet is referring to. It could be pointing toward a new advancement in foveated rendering — an eye-tracking feature for VR that aims to boost performance — or it could somehow be related to Nvidia’s Ampere GPU architecture.
Regardless, we won’t have to wait long to find out what Nvidia has up its sleeve. Assuming the March 19 date shown in the tweet is accurate, we can probably expect the hardware giant to start announcing its newest technology and products in just over a week.
If you have any theories regarding the potential context for this tweet, please feel free to drop them in the comments below. We will try to reach out to Nvidia for comment, but we don’t expect to receive a response.

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Nvidia CEO Jensen Huang won’t deliver his annual GTC keynote this year – Blog – 10 minute

What just happened? A few days ago, Nvidia revealed that it would be shifting its annual GPU Technology Conference to an online-only event due to coronavirus (COVID-19) fears. Details were still scarce, but the company did at least promise that CEO Jensen Huang’s yearly keynote would take place via a live stream. Now, it seems Nvidia has changed tack again. According to a new announcement, Huang will not be giving a keynote this year after all.
This news will certainly disappoint those who were looking forward to seeing Huang speak on stage, but for most others, there’s no need to worry. While Huang will not be giving a keynote, all the announcements that would have been shared within his speech will instead be published as news posts on Nvidia’s official website.
These announcements will go live on Tuesday, March 24, and they will be followed up by an investor call with Huang, which will be accessible to “other listeners.” Presumably, “other listeners” refers to the press, but it’s also possible that Nvidia is discussing some way to open the call up to the public as well.

Nvidia says “continuing public health uncertainties” related to COVID-19 are the primary reason for the shut-down of Huang’s keynote. However, other parts of “GTC Digital,” such as live webinars, research posters, and recorded talks, will still be available on March 25.
GTC is the latest in a long series of tech conferences that have seen substantial format changes or even outright cancellations due to COVID-19. We hope, for the sake of the public, that these steps will prove effective when it comes to slowing down the spread of this virus.

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Nvidia is shifting GTC 2020 conference to an online-only event – Blog – 10 minute

In context: 2020 is set to be a big year for PC hardware and gaming, but unfortunately some of the most exciting events for both of these industries have seen some unavoidable disruptions. Due to the ongoing coronavirus epidemic, many companies have pulled out of large conferences like the annual Game Developers Conference and PAX East — the former has been canceled entirely. While Nvidia isn’t going as far as outright cancellation, it is adopting an alternative approach for its GPU Technology Conference (GTC) this year.
In an announcement published today, Nvidia revealed that GTC 2020 will be an online-only event due to COVID-2019 (coronavirus) concerns. Nvidia is currently in talks with speakers who were originally scheduled to talk at GTC, and the company hopes to have those talks published online in the “weeks ahead.”
Nvidia CEO and founder Jensen Huang will still be delivering his annual keynote via livestream. For viewers at home (and most of us here at TechSpot), the change won’t be too significant. Watching the event online is typically a more efficient and affordable way to get any relevant information. The exact time and other details of the online livestream will be announced by Nvidia soon.

So, what should you expect to see during GTC? We hope Nvidia reveals its next-gen Ampere-based consumer GPUs, which could have the RTX 3000-series branding. Recent leaks suggest they’ll be quite a bit stronger than current RTX 2000-series cards.
We’ll update you if Nvidia releases any further details about its online GTC plans. If you purchased a conference pass already, Nvidia says it will contact you about a “full refund.”

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Nvidia GPU with 7,552 CUDA cores spotted in benchmark database – Blog – 10 minute

Through the looking glass: A pair of next-gen Nvidia graphics cards have been discovered in the Geekbench database. One has 118 compute units, and the other 108. Given compute units generally contain 64 cores, the two cards are implied to have 7,552 and 6,912 CUDA cores, respectively… with a catch. Geekbench counts compute units, but the structure of compute units can vary from generation to generation. Nvidia is also known to modify core configurations between generations.
When transitioning from Pascal to Turing, Nvidia halved the number of CUDA cores from 128 to 64 per Streaming Multiprocessor (colloquially, the compute unit). However, while Pascal has FP32 ALUs as the backbone of a CUDA core, Turing pairs an FP32 ALU with an INT32 ALU in every CUDA core, increasing the performance of each core by about one-third.
You can read more about this in our Navi vs. Turing architecture comparison.
Nvidia could boost the per-core performance again with the next generation, or, as rumors suggest, go the other way and increase the ratio of FP32 ALUs to INT32 ALUs in an attempt to increase efficiency. The bottom line is, until Nvidia tells us how they’re configuring their next-gen architecture, nothing is guaranteed. What Geekbench registers as a compute unit may be a device we’re unfamiliar with, and contain CUDA cores that perform better or worse than what we’re used to.

Model
Mystery GPU 1
Mystery GPU 2
Quadro RTX 8000

CUs/SMs
118
108
72

CUDA Cores
7552
6912
4608

Clock Speed
1110 MHz
1010 MHz
1770 MHz

Memory
24 GB
48 GB
48 GB

But let’s not spoil all the fun. These GPUs are, without a doubt, next-gen hardware that offer unprecedented levels of performance.
Contained within the Geekbench entries are the GPUs’ OpenCL benchmark scores. The big one reaches 184,096 points and the little one (isn’t that an oxymoron) gets 141,654. For comparison, the RTX 2080 Ti gets roughly 130,000.
It’s also a pretty safe bet that this pair are underperforming members of their species. The big one had a maximum clock, as recorded by Geekbench, of 1.11 GHz. The little one ran at 1.01 GHz. By the time the silicon graduates from engineering sample status they’ll probably reach full-blooded clocks of well over 1.5 GHz, and their performance will improve accordingly.
At a guess, I’d say that this pair are prototypes of next generation Quadro flagships. Their respective memory capacities of 48 GB and 24 GB exclude them from being gaming cards. But Nvidia uses almost identical silicon for its flagship Quadro and GeForce cards, so you could estimate the sequel to the RTX 2080 Ti to have about 7,000 cores – whatever those cores are made of.

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