tensorboard | TensorFlow 's Visualization Toolkit | Machine Learning library

 by   tensorflow TypeScript Version: 2.16.2 License: Apache-2.0

kandi X-RAY | tensorboard Summary

kandi X-RAY | tensorboard Summary

tensorboard is a TypeScript library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Docker, Prometheus applications. tensorboard has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

The first step in using TensorBoard is acquiring data from your TensorFlow run. For this, you need summary ops. Summary ops are ops, just like tf.matmul and tf.nn.relu, which means they take in tensors, produce tensors, and are evaluated from within a TensorFlow graph. However, summary ops have a twist: the Tensors they produce contain serialized protobufs, which are written to disk and sent to TensorBoard. To visualize the summary data in TensorBoard, you should evaluate the summary op, retrieve the result, and then write that result to disk using a summary.FileWriter. A full explanation, with examples, is in the tutorial.
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              tensorboard has a medium active ecosystem.
              It has 6270 star(s) with 1595 fork(s). There are 191 watchers for this library.
              There were 7 major release(s) in the last 6 months.
              There are 581 open issues and 1196 have been closed. On average issues are closed in 56 days. There are 54 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of tensorboard is 2.16.2

            kandi-Quality Quality

              tensorboard has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              tensorboard 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

              tensorboard releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.
              It has 57467 lines of code, 3607 functions and 1482 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

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            tensorboard Key Features

            No Key Features are available at this moment for tensorboard.

            tensorboard Examples and Code Snippets

            Compute a summary of a tensorboard .
            pythondot img1Lines of Code : 118dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def audio(name, tensor, sample_rate, max_outputs=3, collections=None,
                      family=None):
              # pylint: disable=line-too-long
              """Outputs a `Summary` protocol buffer with audio.
            
              The summary has up to `max_outputs` summary values containing audi  
            Initialize the TensorBoard .
            pythondot img2Lines of Code : 51dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def ensure_initialized(self):
                """Initialize handle and devices if not already done so."""
                if self._initialized:
                  return
                with self._initialize_lock:
                  if self._initialized:
                    return
                  assert self._context_devices is None  
            Convert TensorArrayReadV3 to Tensorboard .
            pythondot img3Lines of Code : 44dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _convert_tensor_array_read_v3(pfor_input):
              handle = pfor_input.unstacked_input(0)
              index, index_stacked, _ = pfor_input.input(1)
              dtype = pfor_input.get_attr("dtype")
              flow, flow_stacked, _ = pfor_input.input(2)
              if flow_stacked:
                flow =  
            neural network gives TypeError: ('Keyword argument not understood:', 'training')
            Pythondot img4Lines of Code : 65dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            x = Input(shape=(1,))
            x = Dense(200,activation="relu")(x)
            x = Dropout(0.1)(x, training=True)
            x = Dense(2)(x)
            out = tfp.layers.DistributionLambda(normal_exp, name='normal_exp')(x)
            
            import numpy as np
            import matplotli
            Weird `glibc==2.17` conflict when trying to conda install tensorflow 1.4.1
            Pythondot img5Lines of Code : 113dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Looking for: ['python=3', 'tensorflow-gpu=1.4.1']
            
            conda-forge/linux-64                                        Using cache
            conda-forge/noarch                                          Using cache
            pkgs/main/linux-64                          
            No such file or directory: '/opt/anaconda3/lib/python3.8/site-packages/rtree/lib'
            Pythondot img6Lines of Code : 4dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            python is /opt/anaconda3/bin/python
            python is /usr/local/bin/python
            python is /usr/bin/python
            
            detach().cpu() kills kernel
            Pythondot img7Lines of Code : 8dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
                #tensor: 128(pictures at the time ) * 784 (28*28)
                Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28 
                Objgrid= make_grid(Objdata[:num],nrow=4).permute
            Error installing tensorflow with pip, no module named 'termcolor'
            Pythondot img8Lines of Code : 2dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            pip install termcolor‑1.1.0‑py2.py3‑none‑any.whl
            
            Tensorflow : convolutional autoencoder via subclassing
            Pythondot img9Lines of Code : 90dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from tensorflow.keras import layers, Model
            from tensorflow.keras.datasets import mnist
            from tensorflow.keras.callbacks import TensorBoard
            import numpy as np
            
            class Encoder(Model):
                def __init__(self, name: str = "encoder"):
                    supe
            module 'tensorboard.summary._tf.summary' has no attribute 'FileWriter'
            Pythondot img10Lines of Code : 4dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            Writer = tf.summary.create_file_writer(LOG_DIR)
            
            Writer = tf.compat.v1.summary.FileWriter(LOG_DIR, tfs.graph)
            

            Community Discussions

            QUESTION

            Tensorboard Profiler: Failed to load libcupti (is it installed and accessible?)
            Asked 2022-Mar-21 at 18:36

            I'm trying to analyze my tensorflow application. The training runs well, but I get Failed to load libcupti (is it installed and accessible?) if I open the Profile-Tab in Tensorboard.

            My configuration is:

            • Windows 10
            • Python 3.9.7
            • Tensorflow 2.6.0
            • CUDA Toolkit 11.2
            • cuDNN 8.1.1 (installed as here by copying files as described)
            • Visual Studio Professional 2019

            CUDA_PATH is C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2

            My Path-Variable contains:

            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64
            • C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include
            • C:\Program Files\NVIDIA Corporation\Nsight Systems 2020.4.3\target-windows-x64

            conda list (only relevant packages):

            ...

            ANSWER

            Answered 2022-Mar-21 at 18:36

            Hidden in the log output of jupyter I found an this error message: Could not load dynamic library 'cupti64_113.dll': dlerror: cupti64_113.dll not found

            With this error message and that hint I was able to solve the problem: I copied cupti64_2020.3.0.dll in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\CUPTI\lib64 and renamed it to cupti64_113.dll and now the profiler works.

            Source https://stackoverflow.com/questions/71023977

            QUESTION

            Tensorboard profiler is trying to use the wrong version of libcupti
            Asked 2022-Mar-16 at 10:39

            So I'm trying to set up a GPU profiler on tensorboard but I am getting this error:

            ...

            ANSWER

            Answered 2022-Mar-16 at 10:39

            TensorFlow 2.8 doesn't support CUDA 11.6. but requires 11.2 see docs

            Seems you need to get in touch with the VM's owner to update the dependencies

            Source https://stackoverflow.com/questions/71409516

            QUESTION

            Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
            Asked 2022-Mar-08 at 14:22

            I am training a Unet segmentation model for binary class. The dataset is loaded in tensorflow data pipeline. The images are in (512, 512, 3) shape, masks are in (512, 512, 1) shape. The model expects the input in (512, 512, 3) shape. But I am getting the following error. Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

            Here are the images in metadata dataframe.

            Randomly sampling the indices to select the training and validation set

            ...

            ANSWER

            Answered 2022-Mar-08 at 13:38

            Use train_batches in model.fit and not train_images. Also, you do not need to use repeat(), which causes an infinite dataset if you do not specify how many times you want to repeat your dataset. Regarding your labels error, try rewriting your model like this:

            Source https://stackoverflow.com/questions/71395504

            QUESTION

            Export tensorboard (with pytorch) data into csv with python
            Asked 2022-Feb-23 at 16:44

            I have Tensorboard data and want it to download all of the csv files behind the data, but I could not find anything from the official documentation. From StackOverflow, I found only this question which is 7 years old and also it's about TensorFlow while I am using PyTorch.

            We can do this manually, as we can see in the screenshot, manually there is an option. I wonder if we can do that via code or it is not possible? As I have a lot of data to process.

            ...

            ANSWER

            Answered 2022-Feb-23 at 16:44

            With the help of this script Below is the shortest working code it gets all of the data in dataframe then you can play further.

            Source https://stackoverflow.com/questions/71239557

            QUESTION

            Colab: (0) UNIMPLEMENTED: DNN library is not found
            Asked 2022-Feb-08 at 19:27

            I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:

            ...

            ANSWER

            Answered 2022-Feb-07 at 09:19

            It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason

            Source https://stackoverflow.com/questions/71000120

            QUESTION

            AWS Elastic Beanstalk - Failing to install requirements.txt on deployment
            Asked 2022-Feb-05 at 22:37

            I have tried the similar problems' solutions on here but none seem to work. It seems that I get a memory error when installing tensorflow from requirements.txt. Does anyone know of a workaround? I believe that installing with --no-cache-dir would fix it but I can't figure out how to get EB to do that. Thank you.

            Logs:

            ...

            ANSWER

            Answered 2022-Feb-05 at 22:37

            The error says MemoryError. You must upgrade your ec2 instance to something with more memory. tensorflow is very memory hungry application.

            Source https://stackoverflow.com/questions/71002698

            QUESTION

            What is the proper way to make an object with unpickable fields pickable?
            Asked 2022-Jan-26 at 00:11

            For me what I do is detect what is unpickable and make it into a string (I guess I could have deleted it too but then it will falsely tell me that field didn't exist but I'd rather have it exist but be a string). But I wanted to know if there was a less hacky more official way to do this.

            Current code I use:

            ...

            ANSWER

            Answered 2022-Jan-19 at 22:30

            Yes, a try/except is the best way to go about this.

            Per the docs, pickle is capable of recursively pickling objects, that is to say, if you have a list of objects that are pickleable, it will pickle all objects inside of that list if you attempt to pickle that list. This means that you cannot feasibly test to see if an object is pickleable without pickling it. Because of that, your structure of:

            Source https://stackoverflow.com/questions/70128335

            QUESTION

            Stablebaselines3 logging reward with custom gym
            Asked 2021-Dec-25 at 01:10

            I have this custom callback to log the reward in my custom vectorized environment, but the reward appears in console as always [0] and is not logged in tensorboard at all

            ...

            ANSWER

            Answered 2021-Dec-25 at 01:10

            You need to add [0] as indexing,

            so where you wrote self.logger.record('reward', self.training_env.get_attr('total_reward')) you just need to index with self.logger.record('reward', self.training_env.get_attr ('total_reward')[0])

            Source https://stackoverflow.com/questions/70468394

            QUESTION

            How to make conda use its own gcc version?
            Asked 2021-Dec-12 at 16:12

            I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:

            ...

            ANSWER

            Answered 2021-Dec-12 at 16:12

            Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.

            Source https://stackoverflow.com/questions/70316504

            QUESTION

            How to extract loss and accuracy from logger by each epoch in pytorch lightning?
            Asked 2021-Dec-04 at 00:07

            I want to extract all data to make the plot, not with tensorboard. My understanding is all log with loss and accuracy is stored in a defined directory since tensorboard draw the line graph.

            ...

            ANSWER

            Answered 2021-Sep-22 at 23:47

            Lightning do not store all logs by itself. All it does is streams them into the logger instance and the logger decides what to do.

            The best way to retrieve all logged metrics is by having a custom callback:

            Source https://stackoverflow.com/questions/69276961

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

            Vulnerabilities

            No vulnerabilities reported

            Install tensorboard

            You can download it from GitHub.

            Support

            Update: the experimental --reload_multifile=true option can now be used to poll all "active" files in a directory for new data, defined as any file that received new data within --reload_multifile_inactive_secs seconds ago, defaulting to 4000. No. TensorBoard expects that only one events file will be written to at a time, and multiple summary writers means multiple events files. If you are running a distributed TensorFlow instance, we encourage you to designate a single worker as the "chief" that is responsible for all summary processing. See supervisor.py for an example.
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