bert-multi-gpu | Feel free to fine tune large BERT models | GPU library

 by   HaoyuHu Python Version: v1.1.0 License: Apache-2.0

kandi X-RAY | bert-multi-gpu Summary

kandi X-RAY | bert-multi-gpu Summary

bert-multi-gpu is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch, Tensorflow, Bert applications. bert-multi-gpu has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Feel free to fine tune large BERT models with Multi-GPU and FP16 support.

            kandi-support Support

              bert-multi-gpu has a low active ecosystem.
              It has 176 star(s) with 43 fork(s). There are 7 watchers for this library.
              It had no major release in the last 12 months.
              There are 1 open issues and 25 have been closed. On average issues are closed in 24 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of bert-multi-gpu is v1.1.0

            kandi-Quality Quality

              bert-multi-gpu has 0 bugs and 0 code smells.

            kandi-Security Security

              bert-multi-gpu has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              bert-multi-gpu code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              bert-multi-gpu is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              bert-multi-gpu releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              bert-multi-gpu saves you 1233 person hours of effort in developing the same functionality from scratch.
              It has 2774 lines of code, 174 functions and 11 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed bert-multi-gpu and discovered the below as its top functions. This is intended to give you an instant insight into bert-multi-gpu implemented functionality, and help decide if they suit your requirements.
            • Transformer transformer model
            • Attention layer
            • Apply dropout to input_tensor
            • Get the shape of the tensor
            • A builder for TPUEstimator
            • Create an optimizer
            • Determine if we should use weight decay
            • Apply gradients
            • Loads a vocabulary
            • Return a list of dev examples
            • Apply a sparse gradient op
            • From a json file
            • Get test examples
            • Returns test examples
            • Get dev examples
            • Create dev examples
            • Returns a list of dev examples
            • Creates test examples
            • Saves training data for serving
            • Convert examples to features
            • Creates a attention mask from input_tensor
            • Returns a list of InputExample instances
            • Embed word embedding
            • Embedding postprocessor
            • Validate case - insensitive case
            • Builds a tf input_fn
            • Converts examples into TFRecord
            • Helper function for resource_apply
            • Reads the train examples
            • Tokenize text
            Get all kandi verified functions for this library.

            bert-multi-gpu Key Features

            No Key Features are available at this moment for bert-multi-gpu.

            bert-multi-gpu Examples and Code Snippets

            No Code Snippets are available at this moment for bert-multi-gpu.

            Community Discussions


            Vulkan : How could queues support different features? / VkQueue implementation
            Asked 2022-Apr-03 at 21:56

            In my understanding, VkPhysicalDevice represents an implementation of Vulkan, which could be represented as a GPU and its drivers. We are supposed to record commands with VkCommandBuffers and send them through queues to, potentially, multithread the work we send to the gpu. That is why I understand the fact there can be multiple queues. I understand as well that QueueFamilies groups queues depending on the features they can do (the extensions available for them e.g. presentation, as well as graphics computations, transfer, etc).

            However, if a GPU is able to do Graphics work, why are there queues unable to do so? I heard that using queues with less features could be faster, but why? What is a queue concretely? Is it only tied to vulkan implementation? Or is it related to hardware specific things?

            I just don't understand why queues with different features exist, and even after searching through the Vulkan doc, StackOverflow, vulkan-tutorial and vkguide, the only thing I found was "Queues in Vulkan are an “execution port” for GPUs.", which I don't really understand and on which I can't find anything on google.

            Thank you in advance for your help!



            Answered 2022-Apr-03 at 21:56

            A queue is a thing that consumes and executes commands, such that each queue (theoretically) executes separately from every other queue. You can think of a queue as a mouth, with commands as food.

            Queues within a queue family typically execute commands using the same underlying hardware to process them. This would be like a creature with multiple mouths but all of them connect to the same digestive tract. How much food they can eat is separate from how much food they can digest. Food eaten by one mouth may have to wait for food previously eaten by another to pass through the digestive tract.

            Queues from different families may (or may not) have distinct underlying execution hardware. This would be like a creature with multiple mouths and multiple digestive tracts. If a mouth eats, that food need not wait for food from a different mouth to digest.

            Of course, distinct underlying execution hardware is typically distinct for a reason. Several GPUs have specialized DMA hardware for doing copies to/from device-local memory. Such hardware will typically expose a queue family that only allows transfer operations, and those transfer operations may be restricted in their byte alignment compared to transfers done on graphics-capable queues.

            Note that these are general rules. Sometimes queues within a family do execute on different hardware, and sometimes queues between families use much of the same hardware. The API and implementations don't always make this clear, so you may have to benchmark different circumstances.



            OpenCL local memory exists on Mali/Adreno GPU
            Asked 2022-Mar-30 at 09:39
            1. Does OpenCL local memory really exist on Mali/Adreno GPU or they only exist in some special mobile phones?
            2. If they exist, in which case should we use local memory, such as GEMM/Conv or other cl kernel?


            Answered 2022-Mar-24 at 15:27

            Interesting question. OpenCL defines a number of conceptual memories including local memory, constant memory, global memory, and private memory. And physically as you know, the hardware implementation of these memories is hardware dependent. For instance, some may emulate local memory using cache or system memory instead of having physical memory.

            AFAIK, ARM Mali GPU does not have local memory, whereas Qualcomm Adreno GPU does have local memory.

            For instance below table shows the definition of each memory in OpenCL and their relative latency and physical locations in Adreno GPU cited from OpenCL Optimization and Best Practices for Qualcomm Adreno GPUs∗

            Answer updated:

            as commented by SK-logic below, Mali6xx have a local memory (shared with cache).



            How to force gpu usage with JavaFX?
            Asked 2022-Mar-09 at 05:23

            I use JavaFX with Java 8 and i set this properties before launching my app
            The verbose mode for prism gives me this :



            Answered 2022-Mar-09 at 05:23

            For those who are trying to solve a similar issue, it might be coming from the java.exe executable not using the gpu you want as a default device, you can change that in Windows' settings.



            GPU's not showing up on GKE Node even though they show up in GKE NodePool
            Asked 2022-Mar-03 at 08:30

            I'm trying to setup a Google Kubernetes Engine cluster with GPU's in the nodes loosely following these instructions, because I'm programmatically deploying using the Python client.

            For some reason I can create a cluster with a NodePool that contains GPU's

            ...But, the nodes in the NodePool don't have access to those GPUs.

            I've already installed the NVIDIA DaemonSet with this yaml file:

            You can see that it's there in this image:

            For some reason those 2 lines always seem to be in status "ContainerCreating" and "PodInitializing". They never flip green to status = "Running". How can I get the GPU's in the NodePool to become available in the node(s)?


            Based on comments I ran the following commands on the 2 NVIDIA pods; kubectl describe pod POD_NAME --namespace kube-system.

            To do this I opened the UI KUBECTL command terminal on the node. Then I ran the following commands:

            gcloud container clusters get-credentials CLUSTER-NAME --zone ZONE --project PROJECT-NAME

            Then, I called kubectl describe pod nvidia-gpu-device-plugin-UID --namespace kube-system and got this output:



            Answered 2022-Mar-03 at 08:30

            According the docker image that the container is trying to pull (gke-nvidia-installer:fixed), it looks like you're trying use Ubuntu daemonset instead of cos.

            You should run kubectl apply -f

            This will apply the right daemonset for your cos node pool, as stated here.

            In addition, please verify your node pool has the scope which is needed to pull the image. You can should see it in your node pool page in GCP Console, under Security -> Access scopes (The relevant service is Storage).



            "Attempting to perform BLAS operation using StreamExecutor without BLAS support" error occurs
            Asked 2022-Feb-21 at 16:09

            my computer has only 1 GPU.

            Below is what I get the result by entering someone's code



            Answered 2021-Oct-12 at 08:52

            For the benefit of community providing solution here

            This problem is because when keras run with gpu, it uses almost all vram. So we needed to give memory_limit for each notebook as shown below



            SSBO CPU mapping returning correct data, but data is 'different' to the SSBO on GPU
            Asked 2022-Feb-10 at 13:25

            I've run into an issue while attempting to use SSBOs as follows:



            Answered 2022-Feb-10 at 13:25

            GLSL structs and C++ structs have different rules on alignment. For structs, the spec states:

            If the member is a structure, the base alignment of the structure is N, where N is the largest base alignment value of any of its members, and rounded up to the base alignment of a vec4. The individual members of this substructure are then assigned offsets by applying this set of rules recursively, where the base offset of the first member of the sub-structure is equal to the aligned offset of the structure. The structure may have padding at the end; the base offset of the member following the sub-structure is rounded up to the next multiple of the base alignment of the structure.

            Let's analyze the struct:



            Julia CUDA - Reduce matrix columns
            Asked 2022-Jan-21 at 18:57

            Consider the following kernel, which reduces along the rows of a 2-D matrix



            Answered 2022-Jan-21 at 18:57


            Use of tf.GradientTape() exhausts all the gpu memory, without it it doesn't matter
            Asked 2022-Jan-07 at 11:47

            I'm working on Convolution Tasnet, model size I made is about 5.05 million variables.

            I want to train this using custom training loops, and the problem is,



            Answered 2022-Jan-07 at 11:08

            Gradient tape triggers automatic differentiation which requires tracking gradients on all your weights and activations. Autodiff requires multiple more memory. This is normal. You'll have to manually tune your batch size until you find one that works, then tune your LR. Usually, the tune just means guess & check or grid search. (I am working on a product to do all of that for you but I'm not here to plug it).



            Why does nvidia-smi return "GPU access blocked by the operating system" in WSL2 under Windows 10 21H2
            Asked 2021-Nov-18 at 19:20
            Installing CUDA on WSL2

            I've installed Windows 10 21H2 on both my desktop (AMD 5950X system with RTX3080) and my laptop (Dell XPS 9560 with i7-7700HQ and GTX1050) following the instructions on

            1. Install CUDA-capable driver in Windows
            2. Update WSL2 kernel in PowerShell: wsl --update
            3. Install CUDA toolkit in Ubuntu 20.04 in WSL2 (Note that you don't install a CUDA driver in WSL2, the instructions explicitly tell that the CUDA driver should not be installed.):


            Answered 2021-Nov-18 at 19:20

            Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop. Checking Windows version by running winver reports that my OS is still 21H1. Of course CUDA in WSL2 will not work in Windows 10 without 21H2.

            After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.



            How to run Pytorch on Macbook pro (M1) GPU?
            Asked 2021-Nov-18 at 03:08

            I tried to train a model using PyTorch on my Macbook pro. It uses the new generation apple M1 CPU. However, PyTorch couldn't recognize my GPUs.



            Answered 2021-Nov-18 at 03:08

            It looks like PyTorch support for the M1 GPU is in the works, but is not yet complete.

            From @soumith on GitHub:

            So, here's an update. We plan to get the M1 GPU supported. @albanD, @ezyang and a few core-devs have been looking into it. I can't confirm/deny the involvement of any other folks right now.

            So, what we have so far is that we had a prototype that was just about okay. We took the wrong approach (more graph-matching-ish), and the user-experience wasn't great -- some operations were really fast, some were really slow, there wasn't a smooth experience overall. One had to guess-work which of their workflows would be fast.

            So, we're completely re-writing it using a new approach, which I think is a lot closer to your good ole PyTorch, but it is going to take some time. I don't think we're going to hit a public alpha in the next ~4 months.

            We will open up development of this backend as soon as we can.

            That post:

            TL;DR: a public beta is at least 4 months out.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install bert-multi-gpu

            You can download it from GitHub.
            You can use bert-multi-gpu like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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