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submitted by /u/Lazy_Acadia5970 [visit reddit] [comments] |
Hey all!
I’m trying to implement this Transformer article. In the source code they pass a number (n_heads) representing the number of attention heads that should be created, which is used when building the model to create that number of attention heads and save them to a list. Later, when the model is called, the attention heads are iterated over as follows: attn = [self.attn_heads[i](inputs) for i in range(self.n_heads)]. When running this code in graph mode the following error is thrown: OperatorNotAllowedInGraphError: iterating over “tf.Tensor” is not allowed: AutoGraph did convert this function.
How should I go about creating an arbitrary number of the same layer in such a way that it can be iterated over in graph mode?
submitted by /u/EdvardDashD
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Is there an age requirement for the tensorflow developer certificate exam?
submitted by /u/Sad_Combination9971
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TensorFlow was working fine when I only had a 1660ti, but I recently installed a 3070 and the 3070 works fine for other stuff, but not TensorFlow.
It will take a good minute to just do – tf.test.gpu_device_name()
import os os.environ["CUDA_VISIBLE_DEVICES"]="0" import tensorflow as tf tf.test.gpu_device_name()
If CUDA_VISIBLE_DEVICES is changed to “1” (my old card) it will print the device name instantly.
Everything else TensorFlow related will also take super long on the new card.
Is there some box I have to check in the CUDA installation files for the new card to work, or do I need to specially install something to find the new card?
I’m using Jupyter Notebook.
Any help would be appreciated.
submitted by /u/KingGeorge12321
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I was working on an independent project and our school only has AMD GPU with Mac. (we have NVIDIA GPU PC but it is only GTX1030)
submitted by /u/Striking-Warning9533
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Hey everyone š,
I have been taking part solo in an ML challenge by AIcrowd.com which has a cash prize pool of $50,000 š¤
The Machine Learning challenge is for Object Detection enthusiasts, hosted by Amazon Air Prime, called Airborne Object Tracking
The challenge revolves around predicting the future motion of flying airborne objects to avoid collision. Also the dataset they have is pretty lit (11TB in size!! š²), one of the largest collections of flight sequences from aerial vehicles so you might want to look into that.
I am looking for someone to team up in this challenge with. Let me know in the comments if anyone is up!
submitted by /u/desiMLguy
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Categories
Finding All Official Example Projects
I have found the official introduction example guides on the official site https://www.tensorflow.org/tutorials
But I could not find more example codes up until I have bumped in to: https://www.tensorflow.org/text/tutorials/
So is there any other example full projects specialized on topics. (I love their format and the added COLAB just makes it easier to learn it.)
submitted by /u/glassAlloy
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A startup in East Africa is harnessing conversational AI to get the word out about a third wave of COVID-19 passing through the region. It hopes its Mbaza AI Chatbot will lead to partnerships that use the technology to tackle other concerns across the continentās many languages. āCOVID is here to stay, unfortunately, and itās Read article >
The post Tongues Untied: Dataset Starts Global Dialogue in Conversational AI appeared first on The Official NVIDIA Blog.
The recently released Cumulus Linux 4.4. In CL 4.4 provides innovation, advanced features and scale enhancements based on the guiding principles of simplicity.
NVIDIA Cumulus Linux is the industryās most innovative open network operating system that allows you to automate, customize, and scale your data center network like no other.
The recently released Cumulus Linux 4.4. In CL 4.4 provides innovation, advanced features and scale enhancements based on the guiding principles of simplicity.
CL 4.4 includes the following notable new features:
- NVUE (NVIDIA User Experience)
- No license required for users on version 4.4 and forward
- Scale enhancementsĀ
- VxLAN EVPN and Multihoming enhancements
- ISSU ā Warm boot
- QoS and buffer management enhancements
- PTP enhancements
For these features and all other features, please refer to the CL 4.4 User Guide for details.
NVUE ā NVIDIA User Experience
The new release introduces NVUE. NVUE is a brand-new management infrastructure in Cumulus Linux. It was created with the intention to create a better, more efficient, and standard way to manage and automate your network.
This API-driven infrastructure defines an object model for every element in the NOS, and exposes a brand new CLI and a REST API. This new infrastructure enables adding more clients, and with all the API clients having the interface to the same models, it is our first step towards the support of standard YANG models and other programmable APIs in the future.
Cumulus 4.4 provides you the option to explore and use NVUE. Please note that in 4.4 the legacy NCLU command is still available as the default CLI, and you can decide which CLI you would like to enable manually.
*NCLU and NVUE do not interoperateā.Ā For further clarification, please refer to the Cumulus Linux 4.4 User Guide.
No License required
From CL 4.4 there is no need anymore to install a license key, you turn up the system and itās up and running, simple as that.
Scale enhancementsĀ
Multiple VLAN-aware bridges mean that you can have overlapping and unique VLANs across bridges. Additionally, multiple VNIs (VxLAN tunnels) can now be mapped to a single Linux ādeviceā.
These new capabilities allow for thousands of tunnels: An order of magnitude more than the previous release! We also increased the number of allowed VLANs and the number of IPv4 LPMs, and our plan is to continue and increase the scale in different vectors, so follow us up on the next release.
VxLAN EVPN and Multihoming enhancements
Multihoming is the modern way to design your high availability in your VxLAN EVPN environment. It simplifies the network, removes the need for MLAG, and the need for dedicated peer links.
In this version we are adding HERP (Head End Replication) support for Multihoming. HREP removes the need for multicast. We also added support for SVI-IP reuse across racks for scalability.
In addition to the multihoming enhancements, we have added support for Downstream VNIs, enabling additional VxLAN EVPN use cases.
ISSU ā Warm boot
Cumulus 4.4 offers a variety of design and technology options for a safe upgrade progress with minimal impact, spending less ākeeping the lights onā and more time innovating.
In particular, ISSU (In service Software update) allows software updates throughout a process that assures no or limited hit on the traffic.
In this release we are also introducing the warm-boot option. This provides the ability to restart and upgrade the operating system in a single switch with no traffic impact. This is in addition to the fast-boot option which we already support that allows software updates with a very short downtime.
QoS and buffer management enhancements
Our Spectrum systems based on the Spectrum series ASICs offer a full shared buffer for complete fairness in your datacenter network. Complementing this, we now support improved default behavior with the default dynamic buffer configuration. We also added support for ETS (Enhanced Transmission Selection).
PTP enhancements (as Early Access)Ā
In Cumulus 4.4 we present some impressive new features weāve added to our PTP feature set.Ā Here is a partial list of the PTP new features:
- PTP Boundary Clock: Spectrum2 and Spectrum3 based platforms
- PTP Support for VLAN, LAG (per port), L2 (per VLAN), L3, VRF & IPv6
- User configurable TTL and DSCP values
- PTPv1 message forwarding
As this is only a partial list of our new features, please refer to the Cumulus Linux datasheet for more details.
Finally, NVIDIA AIRāÆis also updated with Cumulus 4.4. Create your first networking digital twin simulation, and check out and upgrade your environment to take advantage of all the new capabilities!To learn more, visit the NVIDIA ethernet switching solutions webpage.
Introducing NVIDIAās āfirst big betā on the digital biology revolution, NVIDIA CEO Jensen Huang Wednesday unveiled Cambridge-1, a $100 million investment that promises to harness partnerships across the U.K. for breakthroughs with a āglobal impact.ā The U.K.ās most powerful supercomputer, Cambridge-1 will advance research at AstraZeneca, GSK, Kingās College London, Oxford Nanopore, and Guyās and Read article >
The post NVIDIA CEO Unveils āFirst Big Betā on Digital Biology Revolution with UK-Based Cambridge-1 appeared first on The Official NVIDIA Blog.