Not always, but occasionally when running my code this error appears.
At first, I doubted it was a connectivity issue but to do with cashing issue, as discussed on an older Git Issue.
Clearing cache didn't help runtime:
$ rm ~/.cache/huggingface/transformers/ *
Traceback references:
NLTK also gets Error loading stopwords: <urlopen error [Errno -2] Name or service not known.
Last 2 lines re cached_path and get_from_cache.
Cache (before cleared):
$ cd ~/.cache/huggingface/transformers/
(sdg) me#PF2DCSXD:~/.cache/huggingface/transformers$ ls
16a2f78023c8dc511294f0c97b5e10fde3ef9889ad6d11ffaa2a00714e73926e.cf2d0ecb83b6df91b3dbb53f1d1e4c311578bfd3aa0e04934215a49bf9898df0
16a2f78023c8dc511294f0c97b5e10fde3ef9889ad6d11ffaa2a00714e73926e.cf2d0ecb83b6df91b3dbb53f1d1e4c311578bfd3aa0e04934215a49bf9898df0.json
16a2f78023c8dc511294f0c97b5e10fde3ef9889ad6d11ffaa2a00714e73926e.cf2d0ecb83b6df91b3dbb53f1d1e4c311578bfd3aa0e04934215a49bf9898df0.lock
4029f7287fbd5fa400024f6bbfcfeae9c5f7906ea97afcaaa6348ab7c6a9f351.723d8eaff3b27ece543e768287eefb59290362b8ca3b1c18a759ad391dca295a.h5
4029f7287fbd5fa400024f6bbfcfeae9c5f7906ea97afcaaa6348ab7c6a9f351.723d8eaff3b27ece543e768287eefb59290362b8ca3b1c18a759ad391dca295a.h5.json
4029f7287fbd5fa400024f6bbfcfeae9c5f7906ea97afcaaa6348ab7c6a9f351.723d8eaff3b27ece543e768287eefb59290362b8ca3b1c18a759ad391dca295a.h5.lock
684fe667923972fb57f6b4dcb61a3c92763ad89882f3da5da9866baf14f2d60f.c7ed1f96aac49e745788faa77ba0a26a392643a50bb388b9c04ff469e555241f
684fe667923972fb57f6b4dcb61a3c92763ad89882f3da5da9866baf14f2d60f.c7ed1f96aac49e745788faa77ba0a26a392643a50bb388b9c04ff469e555241f.json
684fe667923972fb57f6b4dcb61a3c92763ad89882f3da5da9866baf14f2d60f.c7ed1f96aac49e745788faa77ba0a26a392643a50bb388b9c04ff469e555241f.lock
c0c761a63004025aeadd530c4c27b860ec4ecbe8a00531233de21d865a402598.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b
c0c761a63004025aeadd530c4c27b860ec4ecbe8a00531233de21d865a402598.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.json
c0c761a63004025aeadd530c4c27b860ec4ecbe8a00531233de21d865a402598.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock
fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51
fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51.json
fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51.lock
Code:
from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='gpt2') # Error
set_seed(42)
Traceback:
2022-03-03 10:18:06.803989: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-03-03 10:18:06.804057: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
[nltk_data] Error loading stopwords: <urlopen error [Errno -2] Name or
[nltk_data] service not known>
2022-03-03 10:18:09.216627: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-03-03 10:18:09.216700: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-03-03 10:18:09.216751: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (PF2DCSXD): /proc/driver/nvidia/version does not exist
2022-03-03 10:18:09.217158: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-03-03 10:18:09.235409: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
All model checkpoint layers were used when initializing TFGPT2LMHeadModel.
All the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at gpt2.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.
Traceback (most recent call last):
File "/home/me/miniconda3/envs/sdg/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/me/miniconda3/envs/sdg/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/mnt/c/Users/me/Documents/GitHub/project/foo/bar/__main__.py", line 26, in <module>
nlp_setup()
File "/mnt/c/Users/me/Documents/GitHub/project/foo/bar/utils/Modeling.py", line 37, in nlp_setup
generator = pipeline('text-generation', model='gpt2')
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/pipelines/__init__.py", line 590, in pipeline
tokenizer = AutoTokenizer.from_pretrained(
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 463, in from_pretrained
tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 324, in get_tokenizer_config
resolved_config_file = get_file_from_repo(
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/file_utils.py", line 2235, in get_file_from_repo
resolved_file = cached_path(
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/file_utils.py", line 1846, in cached_path
output_path = get_from_cache(
File "/home/me/miniconda3/envs/sdg/lib/python3.8/site-packages/transformers/file_utils.py", line 2102, in get_from_cache
raise ValueError(
ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on.
Failed Attempts
I closed my IDE and bash terminal. Ran wsl.exe --shutdown in PowerShell. Relaunched IDE and bash terminal with same error.
Disconnecting/ different VPN.
Clear cache $ rm ~/.cache/huggingface/transformers/ *.
make sure you are not loading a tokenizer with an empty path. That solved it for me.
I saw a answer in github which you can have a try:
pass force_download=True to from_pretrained which will override the cache and re-download the files.
Link at :https://github.com/huggingface/transformers/issues/8690 By:patil-suraj
Since I am working in a conda venv and using Poetry for handling dependencies, I needed to re-install torch - a dependency for Hugging Face 🤗 Transformers.
First, install torch:
PyTorch's website lets you chose your exact setup/ specification for install. In my case, the command was
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
Then add to Poetry:
poetry add torch
Both take ages to process. Runtime was back to normal :)
Related
I am recieving an OSerror (withouth any other text) from h5py when loading an h5 model created with keras- tensorflow after updating my enviroment, or working with an up-to-date environment.
I trained some models with keras and tf in the older versions, and also with keras-tf v1.15 and saved them using the model.save('filename.h5') code. Afterwards i am able to load them and work with them further using before the keras.load_model, and now tensorflow.keras.models.load_model without any problems but recieving some warnings that my tf version was not compiled to use the avx2 instructions and so.
The version installed is tensorflow 1.15 using pip install tensorflow-cpu and it seems to work well, my enviroment installed is Anaconda3-2020.02-Windows-x86_64 installed from the anaconda binaries on Windows.
After trying to change the packages to tensorflow-mkl, and needing to update my enviroment because of enviromental conflicts (shows even with the fresh install of anaconda) the OSerror raised by h5py appears.
Using the default enviromental packages from the anaconda binary with tf-cpu seems to work fine, either by cloning the environment. When updating the environment with conda update --all it raises the error either with tfc-cpu or tf-mkl.
The version of h5py in both cases is: '2.10.0' and the error is the following:
Traceback (most recent call last):
File "C:\Users\Oscar\bwSyncAndShare\OPT_PV22WP_intern\pv2wp_control\SIM\Sim_future.py", line 88, in <module>
model = load_model(pathfile_model)
File "C:\Users\Oscar\anaconda3\envs\optimizer2\lib\site-packages\tensorflow_core\python\keras\saving\save.py", line 142, in load_model
isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
File "C:\Users\Oscar\anaconda3\envs\optimizer2\lib\site-packages\h5py\_hl\base.py", line 44, in is_hdf5
return h5f.is_hdf5(filename_encode(fname))
File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py\h5f.pyx", line 156, in h5py.h5f.is_hdf5
OSError
Have anyone had this problem?
I have tried training a model with the updated environment and saving
it, when loading i get the same error.
Updating to tf-cpu v2.3.1
with the base environment and loading works also.
Creating a new env, with conda create -n name python==3.7.x anaconda
and then installing tf, doesn´t work.
i think then some other library is making the problem, but i cannot figure out what is the problem.
I use hd5 instead of h5 as the extension,and solve the problem.
i can load my deep model in colab bu when i want load that model in pc i can't
I want to create executable for my code where i'm using mxnet with pyinstaller.
I got this error
File "mxnet/libinfo.py", line 74, in find_lib_path
RuntimeError: Cannot find the MXNet library.
List of candidates:
/home/rit/test/exe/dist/test/libmxnet.so
/home/rit/test/exe/dist/test/libmxnet.so
/home/rit/test/exe/dist/test/mxnet/libmxnet.so
/home/rit/test/exe/dist/test/mxnet/../../lib/libmxnet.so
/home/rit/test/exe/dist/test/mxnet/../../build/libmxnet.so
Added libmxnet.so though spec file but gave me PyInstallerImportError
Uninstalled mxnet though pip and copied python3.5/dist-packages/mxnet to my project same problem facing.
File "PyInstaller/loader/pyiboot01_bootstrap.py", line 151, in __init__
__main__.PyInstallerImportError: Failed to load dynlib/dll '/home/rit/test/exe/dist/test/libmxnet.so'. Most probably this dynlib/dll was not found when the application was frozen.
Now what is dynlib/dll? How to resolve this error?
Thanks
MXNet needs external binaries to be fed when you freeze it. You can use add-data flag to add libmxnet files to your executable:
pyinstaller -F --add-data="<python_path>/lib/python3.7/site-packages/mxnet/*.so*:./mxnet" script.py
I try to set up the pyFMI on Anaconda (Python 3.6.8)
Installed all the required packages listed on the pyFMI site. The fmu is loaded without the issue but while I try to simulate the fmu I get an error:
Could not find cannot import name 'radau5'
Could not find cannot import name 'dopri5'
Could not find cannot import name 'rodas'
Could not find cannot import name 'odassl'
Could not find ODEPACK functions.
Could not find RADAR5
Could not find GLIMDA.
Traceback (most recent call last):
File "assimulo\solvers\../lib/sundials_callbacks_ida_cvode.pxi", line 240, in assimulo.solvers.sundials.cv_jac
File "C:\Users\d60378\AppData\Local\Continuum\anaconda3\lib\site-packages\pyfmi\simulation\assimulo_interface.py", line 733, in j
A = self._model._get_A(add_diag=True, output_matrix=self._A)
File "src\pyfmi\fmi.pyx", line 6041, in pyfmi.fmi.FMUModelBase2._get_A
File "src\pyfmi\fmi.pyx", line 7592, in pyfmi.fmi.FMUModelME2._get_directional_proxy
File "src\pyfmi\fmi.pyx", line 5989, in pyfmi.fmi.FMUModelBase2._get_directional_proxy
TypeError: Expected tuple, got dict_keys
Traceback (most recent call last):
File "<ipython-input-1-6c340902ef15>", line 28, in <module>
res = model.simulate(options=opts,start_time=tstart, final_time=tstart+172200)
File "src\pyfmi\fmi.pyx", line 7522, in pyfmi.fmi.FMUModelME2.simulate
File "src\pyfmi\fmi.pyx", line 304, in pyfmi.fmi.ModelBase._exec_simulate_algorithm
File "src\pyfmi\fmi.pyx", line 300, in pyfmi.fmi.ModelBase._exec_simulate_algorithm
File "C:\Users\d60378\AppData\Local\Continuum\anaconda3\lib\site-packages\pyfmi\fmi_algorithm_drivers.py", line 520, in solve
self.simulator.simulate(self.final_time, self.ncp)
File "assimulo\ode.pyx", line 168, in assimulo.ode.ODE.simulate
File "assimulo\ode.pyx", line 288, in assimulo.ode.ODE.simulate
File "assimulo\explicit_ode.pyx", line 101, in assimulo.explicit_ode.Explicit_ODE._simulate
File "assimulo\explicit_ode.pyx", line 187, in assimulo.explicit_ode.Explicit_ODE._simulate
File "assimulo\solvers\sundials.pyx", line 1894, in assimulo.solvers.sundials.CVode.integrate
File "assimulo\solvers\sundials.pyx", line 1926, in assimulo.solvers.sundials.CVode.integrate
CVodeError: {-1: 'The solver took max internal steps but could not reach tout.', -2: 'The solver could not satisfy the accuracy demanded by the user for some internal step.', -3: 'Error test failures occurred too many times during one internal time step or minimum step size was reached.', -4: 'Convergence test failures occurred too many times during one internal time step or minimum step size was reached.', -5: 'The linear solvers initialization function failed.', -6: 'The linear solvers setup function failed in an unrecoverable manner.', -7: 'The linear solvers solve function failed in an unrecoverable manner.', -8: 'The user-provided rhs function failed in an unrecoverable manner.', -9: 'The right-hand side function failed at the first call.', -10: 'The right-hand side function had repeated recoverable errors.', -11: 'The right-hand side function had a recoverable error, but no recovery is possible.', -12: 'The rootfinding function failed in an unrecoverable manner.', -20: 'A memory allocation failed.', -21: 'The cvode_mem argument was NULL.', -22: 'One of the function inputs is illegal.', -23: 'The CVode memory block was not allocated by a call to CVodeMalloc.', -24: 'The derivative order k is larger than the order used.', -25: 'The time t is outside the last step taken.', -26: 'The output derivative vector is NULL.', -27: 'The output and initial times are too close to each other.', -41: 'The sensitivity right-hand side function failed unrecoverable.'}
Would appreciate any hints where to look for the possible issue.
Kelamahim, how have you installed the PyFMI package? I have used
conda install -c chria pyfmi
and it works.
Only
Could not find RADAR5
Could not find GLIMDA are shown in the execution, but my models works. Hope this helps.
The solution is downgrading to Anaconda 3 Python 3.6.2 and installing with conda the pyfmi version 2.4.0
I have been using Anaconda2 (Conda 4.6.8/python 2.7.15). Here's the installation process:
FMIL is built from source code using CMake
pyfmi is installed via conda install -c chria pyfmi
assimulo is installed via conda install -c conda-forge assimulo
wxPython 2.8.12.1 (classic) is installed via the Windows installer available on sourceforge
Other dependencies can be installed from pip
I also saw the following warning messages after loading pyfmi in python, but my simulation doesn't seem to be affected:
Could not find cannot import name radau5
Could not find cannot import name dopri5
Could not find cannot import name rodas
Could not find cannot import name odassl
Could not find ODEPACK functions.
Could not find RADAR5
Could not find GLIMDA.
HTH
pyFMi is also available from the conda-forge channel:
https://anaconda.org/conda-forge/pyfmi
I permanentely added that channel, because it has reproducible builds and a huge number of packages, so usually dependencies can be resolved.
The following worked for me, in Anaconda3 with Python 3.6:
conda config --append channels conda-forge
conda install pyfmi
conda list
When training tensor-flow object detection api using the typical steps in google cloud VM. After configuring all the dependence, when i try to run the train.py script the error [ImportError: No module named 'tensorflow.python.eager'] poped up. I already trained using the same steps in my local PC without any errors. I couldn't find any solution related to this error.
System Info: gcloud VM; TensorFlow-GPU 1.3.0; Python 3.5; CUDA 8.0 /cuDNN 6.0:
script running command:
$ python3 train.py --logtostderr --train_dir=training/ --pipeline_config_path=ssd_mobilenet_v1_lap.config
Error:
Traceback (most recent call last): File "train.py", line 49, in
from object_detection import trainer File "/usr/local/lib/python3.5/dist-packages/object_detection-0.1-py3.5.egg/object_detection/trainer.py", line 33 , in
from deployment import model_deploy File "/home/ragulh28/project/models/research/slim/deployment/model_deploy.py",
line 106, in
from tensorflow.python.eager import context ImportError: No module named 'tensorflow.python.eager'
This issue is caused by a dependency on the new TF Eager API some of the newer models in slim use. They require the latest version of tensorflow, which is why the library is not being found.
Our apologies for the inconvenience. As a workaround, could you try checking out an older version of the Tensorflow Object Detection API? This commit should be a good candidate.
I have followed the instructions to install Theano an GPUArray from source (git versions), in the system folders (not as a user). The GPUArray tests run just fine without errors.
The problem is Theano only works with GPU if I run as root. Running the example to test gpu:
(python35) rll#ip-30-92:~$ THEANO_FLAGS=device=cuda python temp.py
ERROR (theano.gpuarray): Could not initialize pygpu, support disabled
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/theano/gpuarray/__init__.py", line 179, in <module>
use(config.device)
File "/usr/local/lib/python3.5/dist-packages/theano/gpuarray/__init__.py", line 166, in use
init_dev(device, preallocate=preallocate)
File "/usr/local/lib/python3.5/dist-packages/theano/gpuarray/__init__.py", line 73, in init_dev
context.cudnn_handle = dnn._make_handle(context)
File "/usr/local/lib/python3.5/dist-packages/theano/gpuarray/dnn.py", line 83, in _make_handle
cudnn = _dnn_lib()
File "/usr/local/lib/python3.5/dist-packages/theano/gpuarray/dnn.py", line 70, in _dnn_lib
raise RuntimeError('Could not find cudnn library (looked for v5* or v6*)')
RuntimeError: Could not find cudnn library (looked for v5* or v6*)
[Elemwise{exp,no_inplace}(<TensorType(float64, vector)>)]
Looping 1000 times took 3.201078 seconds
Result is [ 1.23178032 1.61879341 1.52278065 ..., 2.20771815 2.29967753
1.62323285]
Used the cpu
If run as root it works, although there is still an error related to cuDNN not being able to identify the devices maybe:
(python35) rll#ip-30-92:~$ sudo THEANO_FLAGS=device=cuda python3 temp.py
Can not use cuDNN on context None: cannot compile with cuDNN. We got this error:
b'/tmp/try_flags_bg7m03hd.c:4:19: fatal error: cudnn.h: No such file or directory\ncompilation terminated.\n'
Mapped name None to device cuda: TITAN X (Pascal) (0000:01:00.0)
[GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float64, vector)>), HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.390976 seconds
Result is [ 1.23178032 1.61879341 1.52278065 ..., 2.20771815 2.29967753
1.62323285]
Used the gpu
There are 2 Titan X on this machine. Works fine with Tensorflow. I am not using .theanorc file, but I have set both:
(python35) rll#ip-30-92:~$ echo $LD_LIBRARY_PATH
/usr/local/cuda-8.0/lib64
(python35) rll#ip-30-92:~$ echo $CUDA_ROOT
/usr/local/cuda-8.0/
I did everything as per the instructions, and despite some warnings there were no errors.
I don't think it is a permissions error on the compile dir .theano, because if I chown the .theano dir the behaviour is the same.
How can I fix this?
I have finally found the problem. There is an aspect missing in the instructions to install Theano which is that you have to verify if LIBRARY_PATH is set and add the cuda libraries to it (note that it is not the LD_LIBRARY_PATH).
If it is not set just export it and you will be good to go. So for temporary fix:
export LIBRARY_PATH=/usr/local/cuda-8.0/lib64
To persist it may depend on the system, but in general you can add to the /etc/environment, adding a line:
LIBRARY_PATH=/usr/local/cuda-8.0/lib64
This fixed the message when root, and fixed cuda for the regular user.