I'm using Tensorflow on Windows but when I try to launch Tensorboard opening http://localhost:6006 the browser shows a blank page
I have added the codeline
writer = tf.train.SummaryWriter('mypath/my_graph', sess.graph)
to my Tensorflow model and launched tensorboard with
tensorboard --logdir="mypath/my_graph"
Here the console output:
Following mrry suggestion I have updated to 0.12.0rc1 and now the Tensorboard page is shown but unfortunately I cannot see any graph, and is missing also the left panel to upload a graph file manually that I can see in some screenshots of the official guide.
Tried also to use
writer = tf.summary.FileWriter('mypath/my_graph', sess.graph)
following the deprecation hint
EDIT
I have found the problem. If I launch tensorboard --logdir="mypath/my_graph" TensorBoard is unable to load the path and looks always for the graph files in the default user path C:\Users\andrew\mygraph\ if I run console as user or C:\Windows\System32 if I run console as administrator. This is a bug and should be fixed.
The 0.12.0rc0 (Release Candidate 0) release of TensorFlow on Windows contains a broken version of TensorBoard. We recently made a new release (0.12.0rc1, Release Candidate 1) that contains a fix for TensorBoard on Windows. You can upgrade by following the instructions for installing the latest release on Windows, or simply typing pip install --upgrade tensorflow at the command prompt.
In ubuntu we can use:
tensorboard --logdir=/home/user/graph/
In Windows we have to change the command prompt to the directory in which the graph file is placed and then use:
tensorboard --logdir=\home\user\graph\
Related
I installed tensorboard 2.9.0 with annaconda. And I am using Ubuntu.
Now suppose I have two event files:
1/ppo/summary/eventfile1
1/sac/summary/eventfile2
If I launch tensorboard with --logdir=1, it cannot find any event files. But if I launch it with --logdir=1/ppo, it can find evenfile1. In theory, if I launch it with --logdir=1, it should find both event files.
I only have this problem after updating my virtual env recently, but I cannot locate the exact reason, nor can I revert to a previous working evn.
Could you please help me
I installed Uiniversal Sentence Encoder (Tensorflow 2) in 2 virtual environment with Ananconda. One is on Mac, anther is on Ubuntu.
All worked with following:
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
Installed with:
conda create -n my-tf2-env python=3.6 tensorflow
conda init bash
conda activate my-tf2-env
conda install -c conda-forge tensorflow-hub
But, for unknown reason after 3 weeks, Mac does not work with following error which fails at:
model = hub.load(module_url)
Error: SavedModel file does not exist at: /var/folders/99/8rwn_9hx3jj9x3qz6yf0j2f00000gp/T/tfhub_modules/063d866c06683311b44b4992fd46003be952409c/{saved_model.pbtxt|saved_model.pb}
On Mac, I recreated new env with same procedure but has same error.
On Ubuntu, all works well.
I want to know how to fix Mac. Thank you for help.
What I attempted on Mac is that I tried to download "https://tfhub.dev/google/universal-sentence-encoder/4" to local drive and load it from local drive in future, not from web url. This process was not finished and not successful yet. I don't remember if there is anything downloaded to Mac with this attempt, that might corrupted Tensorflow-hub on login user account of my Mac.
This error usually occurs when the saved_model.pb is not present in the path specified in the module_url.
For example, if we consider the Folder structure as shown in the screenshot below,
The code,
import tensorflow_hub as hub
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
and
import tensorflow_hub as hub
module_url = "/home/mothukuru/Downloads/Hub"
model = hub.load(module_url)
work successfully.
But if saved_model.pb is not present in that Folder as shown below,
Executing the code,
import tensorflow_hub as hub
module_url = "/home/mothukuru/Downloads/Hub"
model = hub.load(module_url)
results in the below error,
OSError: SavedModel file does not exist at: /home/mothukuru/Downloads/Hub/{saved_model.pbtxt|saved_model.pb}
In your specific case, executing the code while the Download of the Model was in progress might have resulted in the error.
As stated in the comment, deleting the Downloaded File can fix the problem.
Please let me know if this answer has not resolved your issue and I will be happy to modify it accordingly.
TF Published some additional guidelines on caching models apparently in response to questions about this issue.
In my case, I was running this locally on Mac via a jupyter notebook.
I was not sure how to "Delete the download file" as suggest in the other answer, but I found this resolved my issue:
https://www.tensorflow.org/hub/caching#reading_from_remote_storage
Reading from remote storage
Users can instruct the tensorflow_hub
library to directly read models from remote storage (GCS) instead of
downloading the models locally with
os.environ["TFHUB_MODEL_LOAD_FORMAT"] = "UNCOMPRESSED"
or by setting the command-line flag --tfhub_model_load_format to UNCOMPRESSED. This way, no caching directory is needed, which is especially helpful in environments that provide little disk space but a fast internet connection.
I ran that command in my notebook, and then the error was immediately resolved.
Note: I assume this is slower, especially if you do not have a fast internet connection, since what you are doing is telling the program to not locally cache (store) a copy and to just download it on demand.
I’m using a python notebook in gGloud datalab to re-train a neural network.
From the notebook I call retrain.py with
!python -m retrain --bottleneck_dir=../tf_files/bottlenecks --how_many_training_steps=500 --model_dir=../tf_files/models/ --summaries_dir=../tf_files/training_summaries/'mobilenet_1.0_224' --output_graph=../tf_files/retrained_graph.pb --output_labels=../tf_files/retrained_labels.txt --architecture='mobilenet_1.0_224' --image_dir=../tf_files/flower_photos
Within retrain.py I import the Tensorboard with
from google.datalab.ml import TensorBoard as tb
followed by the main function that does a bunch of things for the training process followed by:
inti=tf.global_variables_initializer()
sess.run(init)
tb.start('./tmp/retrain_logs’)
Executing retrain.py, a neural network will be trained, and TensorBoard will be activated (as stated in the output of my notebook copied below)
TensorBoard 1.8.0 at http://3439c553be9b:59199 (Press CTRL+C to quit)
{'text/html':TensorBoard was started successfully with pid 7707. Click here to access it.}
I tried to see the TensorBoard by:
clicking on the link provided (http://3439c553be9b:59199). A site on my web-brower opens but is empty.
I used gCloud Shell to connect with 'datalab connect --port=59199 .' This brings me to my files on gCloud, but not to a TensorBoard.
Can someone tell me how to access the TensorBoard please?
Thank you,
Julia
Just clicking on the link should work.
Could you check to see if you have a firewall rule that is preventing this?
I think the first string "TensorBoard 1.8.0 at http://3439c553be9b:59199 (Press CTRL+C to quit) " was output by the new tensorboard version. The direct link won't work.
The second string "TensorBoard was started successfully with pid 7707. Click here to access it.", the word "here" should be backed up by a hyperlink. See code https://github.com/googledatalab/pydatalab/blob/master/google/datalab/ml/_tensorboard.py#L73. That link should work. Do you get that link?
Recently I wrote a my custom operator (and its gradient) in python following this post
Tensorflow: Custom operation used in two networks simultaneously produces nan
Tensorflow runs with no error and the prediction gives expected accuracy. However, when I want to visualize this graph with tensorboard. I find that I cannot open the subgraph to see its structures. But it's gradient subgraph can be opened and seen. Does anyone has some idea about this problem?
Fig.1: subgraph fc1 cannot be opened but gradient/fc1 can be opened.
I can open the fc1 metanode on TensorBoard 0.1.8.
What version of TensorBoard you are using? You can find the version via running
python -c 'from tensorboard import version; print(version.VERSION)'
After that, could you try upgrading tensorboard via
pip install --upgrade tensorflow-tensorboard
and let me know if the issue persists?
I'm able to use TensorFlow just fine. But I can't yet use TensorBoard at all. I'm following the instructions on tensorflow.org's Visualizing Learning page.
When I run
tensorboard --logdir=/tmp/mnist_logs --debug
I get the following
INFO:tensorflow:TensorBoard is in debug mode.
INFO:tensorflow:Starting TensorBoard in directory /private/tmp/mnist_logs
INFO:tensorflow:TensorBoard path_to_run is: {'/tmp/mnist_logs': None}
INFO:tensorflow:Adding events from directory /tmp/mnist_logs
INFO:tensorflow:Constructing EventAccumulator for /tmp/mnist_logs
DEBUG:tensorflow:Opening a record reader pointing at /tmp/mnist_logs/events.out.tfevents.1457792617.Rafaels-MacBook-Air
WARNING:tensorflow:IOError [Errno 2] No such file or directory: '/usr/local/lib/python3.5/site-packages/tensorflow/tensorboard/TAG' on path /usr/local/lib/python3.5/site-packages/tensorflow/tensorboard/TAG
WARNING:tensorflow:Unable to read TensorBoard tag
Starting TensorBoard on port 6006
(You can navigate to http://0.0.0.0:6006)
DEBUG:tensorflow:No more events in /tmp/mnist_logs/events.out.tfevents.1457792617.Rafaels-MacBook-Air
INFO:tensorflow:No more files in /tmp/mnist_logs
DEBUG:tensorflow:No more events in /tmp/mnist_logs/events.out.tfevents.1457792617.Rafaels-MacBook-Air
INFO:tensorflow:No more files in /tmp/mnist_logs
INFO:tensorflow:Multiplexer done loading. Load took 0.9 secs
If I nagivate to http://0.0.0.0:6006, I see a blank white page.
[Stack: OSX, Google Chrome, Python 3, TensorFlow installed via pip]
Try navigating to localhost:6006 instead (equivalent to http://127.0.0.1:6006/).
It works fine for me while http://0.0.0.0:6006 yields no result.
I had a similar issue with TensorBoard 1.4 (no errors on start but black page in browser). In my case it helped to set the host explicitly to 127.0.0.1 with --host