Stylegan2 uses network pickle files to store ML models. I transfer trained one model, which I am able to open up on cloud servers. I have been generating images from this model fine with the following setup:
Google Colab: Python 3.6.9, CUDA 10.1, tensorflow-gpu 1.15, CuDNN 7.6.5
However, I cannot open the network pickle file on my local machine, even though I've been trying to replicate that cloud setup the best I can. (I have the right GPU hardware/drivers/etc.)
Local (Windows 10) Python 3.6.9, CUDA 10.1, tensorflow-gpu 1.15, CuDNN 7.6.5
Requires a library 'dnnlib' to be in the PYTHONPATH and for a tf.Session() to be initialized
I get the an assertion error about the pickle.
**Assertion error**: `assert state["version"] in [2,3]`
I find this error very odd because the network pickle works on the cloud--so it was saved properly. Additionally, my local set up can open up other network pickles(ie. ones downloaded from the internet through GET requests), making me think that I have properly set up my PYTHONPATH and initialized a tf.Session. These are prerequisites listed in the Stylegan repo:
"You can import the networks in your own Python code using pickle.load(). For this to work, you need to include the dnnlib source directory in PYTHONPATH and create a default TensorFlow session by calling dnnlib.tflib.init_tf()"
I'm not sure why I cannot open up this pickle in one environment, but can in another. Does anyone have any suggestions as to where I might start looking?
Actually, I figured it out by printing out what version was throwing the error. The version printed was '4'. I realized that this matched the pickle (HIGHEST_PROTOCOL) and that what I needed was the newest pull of the Stylegan2 repository, which included pickle format_version 4 in their allowed versions.
Related
I am using a remote computer in order to run my program on its GPU. My program contains some code with tensorflow functions, and for easier debugging with Pycharm I would like to connect via ssh with remote interpreter to the computer with the GPU. This part can be done easily since Pycharm has this option so I can connect there. However, tensorflow is not loaded automatically so I get import error.
Note that in our institution, we run module load cuda/10.0 and module load tensorflow/1.14.0 each time the computer is loaded. Now this part is the tricky one. Opening a remote terminal creates another session which is not related to the remote interpreter session so it's not affecting remote interpreter modules.
I know that module load in general configures env, however I am not sure how can I export the environment variables to Pycharm's environment variables that are configured before a run.
Any help would be appreciated. Thanks in advance.
The workaround after all was relatively simple: first, I have installed the EnvFile plugin, as it was explained here: https://stackoverflow.com/a/42708476/13236698
Them I created an .env file with a quick script on python, extracting all environment variables and their values from os.environ and wrote them to a file in the following format: <env_variable>=<variable_value>, and saved the file with .env extension. Then I loaded it to PyCharm, and voila - all tensorflow modules were loaded fine.
I am running an existing .ipynb which uses TensorFlow and Python3 of course. I am on Colab Pro and set the runtime to "GPU" with High RAM. As soon as I run the notebook I get the error:
Unrecognized runtime "tensorflow"; defaulting to "python3" Notebook settings
What is this, please?
You're likely using a notebook that was not authored in Colab after uploading it to Python.
That notebook requests an environment named "tensorflow", which was likely sensible to the original author. But, that's not the name of any environment in Colab, which selected a Python 3 runtime by default.
You can safely ignore this message. Or, copying or saving the notebook should update the JSON to name the Colab environment used to save it, thereby suppressing the message in the future.
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 am trying to follow this tutorial:
https://medium.com/#natu.neeraj/training-a-keras-model-on-google-cloud-ml-cb831341c196
to upload and train a Keras model on Google Cloud Platform, but I can't get it to work.
Right now I have downloaded the package from GitHub, and I have created a cloud environment with AI-Platform and a bucket for storage.
I am uploading the files (with the suggested folder structure) to my Cloud Storage bucket (basically to the root of my storage), and then trying the following command in the cloud terminal:
gcloud ai-platform jobs submit training JOB1
--module-name=trainer.cnn_with_keras
--package-path=./trainer
--job-dir=gs://mykerasstorage
--region=europe-north1
--config=gs://mykerasstorage/trainer/cloudml-gpu.yaml
But I get errors, first the cloudml-gpu.yaml file can't be found, it says "no such folder or file", and trying to just remove it, I get errors because it says the --init--.py file is missing, but it isn't, even if it is empty (which it was when I downloaded from the tutorial GitHub). I am Guessing I haven't uploaded it the right way.
Any suggestions of how I should do this? There is really no info on this in the tutorial itself.
I have read in another guide that it is possible to let gcloud package and upload the job directly, but I am not sure how to do this or where to write the commands, in my terminal with gcloud command? Or in the Cloud Shell in the browser? And how do I define the path where my python files are located?
Should mention that I am working with Mac, and pretty new to using Keras and Python.
I was able to follow the tutorial you mentioned successfully, with some modifications along the way.
I will mention all the steps although you made it halfway as you mentioned.
First of all create a Cloud Storage Bucket for the job:
gsutil mb -l europe-north1 gs://keras-cloud-tutorial
To answer your question on where you should write these commands, depends on where you want to store the files that you will download from GitHub. In the tutorial you posted, the writer is using his own computer to run the commands and that's why he initializes the gcloud command with gcloud init. However, you can submit the job from the Cloud Shell too, if you download the needed files there.
The only files we need from the repository are the trainer folder and the setup.py file. So, if we put them in a folder named keras-cloud-tutorial we will have this file structure:
keras-cloud-tutorial/
├── setup.py
└── trainer
├── __init__.py
├── cloudml-gpu.yaml
└── cnn_with_keras.py
Now, a possible reason for the ImportError: No module named eager error is that you might have changed the runtimeVersion inside the cloudml-gpu.yaml file. As we can read here, eager was introduced in Tensorflow 1.5. If you have specified an earlier version, it is expected to experience this error. So the structure of cloudml-gpu.yaml should be like this:
trainingInput:
scaleTier: CUSTOM
# standard_gpu provides 1 GPU. Change to complex_model_m_gpu for 4 GPUs
masterType: standard_gpu
runtimeVersion: "1.5"
Note: "standard_gpu" is a legacy machine type.
Also, the setup.py file should look like this:
from setuptools import setup, find_packages
setup(name='trainer',
version='0.1',
packages=find_packages(),
description='Example on how to run keras on gcloud ml-engine',
author='Username',
author_email='user#gmail.com',
install_requires=[
'keras==2.1.5',
'h5py'
],
zip_safe=False)
Attention: As you can see, I have specified that I want version 2.1.5 of keras. This is because if I don't do that, the latest version is used which has compatibility issues with versions of Tensorflow earlier than 2.0.
If everything is set, you can submit the job by running the following command inside the folder keras-cloud-tutorial:
gcloud ai-platform jobs submit training test_job --module-name=trainer.cnn_with_keras --package-path=./trainer --job-dir=gs://keras-cloud-tutorial --region=europe-west1 --config=trainer/cloudml-gpu.yaml
Note: I used gcloud ai-platform instead of gcloud ml-engine command although both will work. At some point in the future though, gcloud ml-engine will be deprecated.
Attention: Be careful when choosing the region in which the job will be submitted. Some regions do not support GPUs and will throw an error if chosen. For example, if in my command I set the region parameter to europe-north1 instead of europe-west1, I will receive the following error:
ERROR: (gcloud.ai-platform.jobs.submit.training) RESOURCE_EXHAUSTED:
Quota failure for project . The request for 1 K80
accelerators exceeds the allowed maximum of 0 K80, 0 P100, 0 P4, 0 T4,
0 TPU_V2, 0 TPU_V3, 0 V100. To read more about Cloud ML Engine quota,
see https://cloud.google.com/ml-engine/quotas.
- '#type': type.googleapis.com/google.rpc.QuotaFailure violations:
- description: The request for 1 K80 accelerators exceeds the allowed maximum of
0 K80, 0 P100, 0 P4, 0 T4, 0 TPU_V2, 0 TPU_V3, 0 V100.
subject:
You can read more about the features of each region here and here.
EDIT:
After the completion of the training job, there should be 3 folders in the bucket that you specified: logs/, model/ and packages/. The model is saved on the model/ folder a an .h5 file. Have in mind that if you set a specific folder for the destination you should include the '/' at the end. For example, you should set gs://my-bucket/output/ instead of gs://mybucket/output. If you do the latter you will end up with folders output, outputlogs and outputmodel. Inside output there should be packages. The job page link should direct to output folder so make sure to check the rest of the bucket too!
In addition, in the AI-Platform job page you should be able to see information regarding CPU, GPU and Network utilization:
Also, I would like to clarify something as I saw that you posted some related questions as an answer:
Your local environment, either it is your personal Mac or the Cloud Shell has nothing to do with the actual training job. You don't need to install any specific package or framework locally. You just need to have the Google Cloud SDK installed (in Cloud Shell is of course already installed) to run the appropriate gcloud and gsutil commands. You can read more on how exactly training jobs on the AI-Platform work here.
I hope that you will find my answer helpful.
I got it to work halfway now by not uploading the files but just running the upload commands from cloud at my local terminal... however there was an error during it running ending in "job failed"
Seems it was trying to import something from the TensorFlow backend called "from tensorflow.python.eager import context" but there was an ImportError: No module named eager
I have tried "pip install tf-nightly" which was suggested at another place, but it says I don't have permission or I am loosing the connection to cloud shell(exactly when I try to run the command).
I have also tried making a virtual environment locally to match that on gcloud (with Conda), and have made an environment with Conda with Python=3.5, Tensorflow=1.14.0 and Keras=2.2.5, which should be supported for gcloud.
The python program works fine in this environment locally, but I still get the (ImportError: No module named eager) when trying to run the job on gcloud.
I am putting the flag --python-version 3.5 when submitting the job, but when I write the command "Python -V" in the google cloud shell, it says Python=2.7. Could this be the issue? I have not fins a way to update the python version with the cloud shell prompt, but it says google cloud should support python 3.5. If this is anyway the issue, any suggestions on how to upgrade python version on google cloud?
It is also possible to manually there a new job in the google cloud web interface, doing this, I get a different error message: ERROR: Could not find a version that satisfies the requirement cnn_with_keras.py (from versions: none) and No matching distribution found for cnn_with_keras.py. Where cnn_with_keras.py is my python code from the tutorial, which runs fine locally.
Really don't know what to do next. Any suggestions or tips would be very helpful!
The issue with the GPU is solved now, it was something so simple as, my google cloud account had GPU settings disabled and needed to be upgraded.
I have installed Tensorflow and Keras by Anaconda (Conda Forge's packages) on Windows. This kind of installation has set Theano as primary backend, so I have checked the keras.json file, surprisingly it has set Tensorflow as the main backend. Furthermore, if I try to remove Theano's installation, Keras stop working. I am supposing that each time I run Keras, it override the json file.
How could I permanently set Tensorflow as primary backend?
In Windows,
Try launching the Anaconda prompt from start -> Anaconda* -> Anaconda prompt
*Anaconda followed by your version, for me it is Anaconda3 (64-bit)
Check whether you can see the below as the first line
set "KERAS_BACKEND=theano"
In this case, by default you can go to the below directory (If you have set a custom install directory you might have to navigate there)
C:\Users\yourusername\AppData\Local\Continuum\Anaconda3\etc\conda\activate.d
and open the keras_activate batch file using notepad
Inside the file edit the line saying
set "KERAS_BACKEND=theano" to
set "KERAS_BACKEND=tensorflow"
You are set to use Keras with tensorflow backend.
As the question ages with time, for anybody coming across this now, the suggestion will be to use tensorflow.keras which is available since TensorFlow 1.15
https://www.tensorflow.org/api_docs/python/tf/keras