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On the Road Again: GeForce NOW Alliance Expanding to Turkey, Saudi Arabia and Australia

Bringing more games to more gamers, our GeForce NOW game-streaming service is coming soon to Turkey, Saudi Arabia and Australia. Turkcell, Zain KSA and Pentanet are the latest telcos to join the GeForce NOW Alliance. By placing NVIDIA RTX Servers on the edge, GeForce NOW Alliance partners deliver even lower latency gaming experiences. And this Read article >

The post On the Road Again: GeForce NOW Alliance Expanding to Turkey, Saudi Arabia and Australia appeared first on The Official NVIDIA Blog.

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Misc

Take Note: Otter.ai CEO Sam Liang on Bringing Live Captions to a Meeting Near You

Sam Liang is making things easier for the creators of the NVIDIA AI Podcast — and just about every remote worker. He’s the CEO and co-founder of Otter.ai, which uses AI to produce speech-to-text transcriptions in real time or from recording uploads. The platform has a range of capabilities, from differentiating between multiple people, to Read article >

The post Take Note: Otter.ai CEO Sam Liang on Bringing Live Captions to a Meeting Near You appeared first on The Official NVIDIA Blog.

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Misc

Batch training in tf 2.0

When performing custom batch training in the training loop,
which one should be used?

tf.gradient_tape or train_on_batch?

What is the difference?

submitted by /u/SuccMyStrangerThings

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I got this error while trying to run the webcam_demo.py example in Posenet library from tensorflow. how to resolve this? #46575

I got this error/warning while trying to run the webcam_demo.py
example in Posenet library from Tensorflow. how to resolve
this?

This is the Git Repo from where I forked this
code : posenet-python

and This is my Output Screen :

>>>

RESTART: A:PythonScriptsPosenet-Forked —
OGCodeposenet-python-masterwebcam_demo.py

Cannot find model file ./_modelsmodel-mobilenet_v1_101.pb,
converting from tfjs…

WARNING:tensorflow:From
A:Pythonlibsite-packagestensorflowpythontoolsfreeze_graph.py:127:
checkpoint_exists (from
tensorflow.python.training.checkpoint_management) is deprecated and
will be removed in a future version.

Instructions for updating:

Use standard file APIs to check for files with this prefix.

Traceback (most recent call last):

File “A:PythonScriptsPosenet-Forked —
OGCodeposenet-python-masterwebcam_demo.py”, line 66, in
<module>

main()

File “A:PythonScriptsPosenet-Forked —
OGCodeposenet-python-masterwebcam_demo.py”, line 20, in main

model_cfg, model_outputs = posenet.load_model(args.model,
sess)

File “A:PythonScriptsPosenet-Forked —
OGCodeposenet-python-masterposenetmodel.py“, line 42, in load_model

convert(model_ord, model_dir, check=False)

File “A:PythonScriptsPosenet-Forked —
OGCodeposenet-python-masterposenetconvertertfjs2python.py“, line 198, in
convert

initializer_nodes=””)

File
“A:Pythonlibsite-packagestensorflowpythontoolsfreeze_graph.py”,
line 361, in freeze_graph

checkpoint_version=checkpoint_version)

File
“A:Pythonlibsite-packagestensorflowpythontoolsfreeze_graph.py”,
line 190, in freeze_graph_with_def_protos

var_list=var_list, write_version=checkpoint_version)

File
“A:Pythonlibsite-packagestensorflowpythontrainingsaver.py“, line 835, in __init__

self.build()

File
“A:Pythonlibsite-packagestensorflowpythontrainingsaver.py“, line 847, in build

self._build(self._filename, build_save=True,
build_restore=True)

File
“A:Pythonlibsite-packagestensorflowpythontrainingsaver.py“, line 885, in _build

build_restore=build_restore)

File
“A:Pythonlibsite-packagestensorflowpythontrainingsaver.py“, line 489, in _build_internal

names_to_saveables)

File
“A:Pythonlibsite-packagestensorflowpythontrainingsavingsaveable_object_util.py”,
line 362, in validate_and_slice_inputs

for converted_saveable_object in saveable_objects_for_op(op,
name):

File
“A:Pythonlibsite-packagestensorflowpythontrainingsavingsaveable_object_util.py”,
line 223, in saveable_objects_for_op

yield ResourceVariableSaveable(variable, “”, name)

File
“A:Pythonlibsite-packagestensorflowpythontrainingsavingsaveable_object_util.py”,
line 95, in __init__

self.handle_op = var.op.inputs[0]

IndexError: tuple index out of range

>>>

My Git
Issue Link

submitted by /u/Section_Disastrous

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Misc

Tensorflow implementation of Bleu score

I’m looking for a tensorflow implementation of BLEU score
similar to the nltk implementation. The reason I can’t use nltk is
because I need to calculate bleu score per each TPU replica result.
I cannot append predictions across replicas and then use nltk to
calculate BLEU for the entire corpus as I would prefer. The reason
is described in this stackoverflow post
https://stackoverflow.com/questions/60842868/how-can-i-merge-the-results-from-strategy-in-tensorflow-2

submitted by /u/International_Fix_94

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Need help with intent based personal assistant / chatbot

Hello all! I have spent some time working on my chatbot and it’s
working pretty well. I have a json file that stores all my intents,
but I have come across a problem that I don’t know how to solve. I
want to have an “other” tag. This tag should be called whenever the
input doesn’t match any other patterns or tags. The goal of this is
so that if no tags are matched, I have a separate set of
instructions for my program to follow in such cases. Does anyone
have any idea how I can go about this? Is there a certain pattern I
should have or what? Also, another question I have is what if a
certain pattern I have has variables in it, for example, “Play
Clocks by Coldplay”. In the case of “Play {songName} by {artist}” a
constant pattern cannot be used since the user can come up with any
combination of song names and artists. Any help is appreciated.
Thank you in advance!

submitted by /u/Rafhay101

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Tensorflow: Confusion on how javascript bundling with rollup affects exports/namespaces/etc.

submitted by /u/ApproximateIdentity

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A Trusted Companion: AI Software Keeps Drivers Safe and Focused on the Road Ahead

NVIDIA DRIVE IX is an open, scalable cockpit software platform that provides AI functions to enable a full range of in-cabin experiences, including intelligent visualization with augmented reality and virtual reality, conversational AI and interior sensing. 

The post A Trusted Companion: AI Software Keeps Drivers Safe and Focused on the Road Ahead appeared first on The Official NVIDIA Blog.

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Misc

NVIDIA Launches Storefront in AWS Marketplace to Accelerate and Simplify AI Workflows

To help data scientists and developers simplify their AI workflows, we have collaborated with Amazon Web Services (AWS) to bring NVIDIA NGC software resources directly to the AWS Marketplace.

Enterprises across industries are adopting AI to drive business growth and they’re relying on cloud infrastructure to develop and deploy their solutions.

To help data scientists and developers simplify their AI workflows, we have collaborated with Amazon Web Services (AWS) to bring NVIDIA NGC software resources directly to the AWS Marketplace. The AWS Marketplace is where customers find, buy and immediately start using software and services that run on AWS.

The NVIDIA NGC catalog provides GPU-optimized AI software for data engineers, data scientists, developers, and DevOps teams so they can focus on building and deploying their AI solutions faster.

More than 250,000 unique users have now downloaded over 1 million of the AI containers, pretrained models, application frameworks, Helm charts and other machine learning resources available on the NGC catalog.

Available free of charge, the software from the NGC catalog is optimized to run on NVIDIA GPU cloud instances, such as the Amazon EC2 P4d instance featuring the record-breaking performance of NVIDIA A100 Tensor Core GPUs.

Instant Access to Performance-Optimized AI Software

NGC software in AWS Marketplace provides a number of benefits to help data scientists and developers build AI solutions.

  • Faster software discovery: Through the AWS Marketplace, developers and data scientists can access the latest versions of NVIDIA’s AI software with a single click.
  • The latest NVIDIA software: The NGC software in AWS Marketplace is automatically updated to the latest versions as soon as they’re available in the NGC catalog. The software is constantly optimized, and the monthly releases give users access to the latest features and performance improvements.
  • Simplified software deployment: Users of Amazon EC2, Amazon SageMaker, Amazon Elastic Kubernetes Service (EKS) and Amazon Elastic Container Service (ECS) can quickly subscribe, pull and run NGC software on NVIDIA GPU instances, all within the AWS console. Additionally, SageMaker users can simplify their workflows by eliminating the need to first store a container in Amazon Elastic Container Registry (ECR).
  • Continuous integration and development: NGC Helm charts are also available in AWS Marketplace to help DevOps teams quickly and consistently deploy their services.

Here’s a step-by-step guide to quickly discover the NGC software and run an object detection service on Amazon EC2 instances.

Accelerate your AI development on NVIDIA GPU-powered AWS services today with the NGC catalog in AWS Marketplace

Categories
Misc

A WebAssembly Powered Augmented Reality Sudoku Solver


A WebAssembly Powered Augmented Reality Sudoku Solver
submitted by /u/SpatialComputing

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