I tried python -m tf2onnx.convert --saved-model [file_name] --output [onnx_file_name]. but It is run by tensorflow = 2.4.4 automatically.
I want to run tensorflow version 1 code. Is this code have a option of it?
You can install TensorFlow version 1, I also trying to use
tf.compat.v1.layers that also work with the result . You may need to
use model.save to have .pb format and convert by the program.
I used python -m tf2onnx.convert --saved-model [model file] --output [onnx file name].onnx --opset 13 and I solved it.
Related
I try to convert caffe model. I am using coremltools v5.
this is my code
import coremltools
caffe_model = ('oxford102.caffemodel', 'deploy.prototxt')
labels = 'flower-labels.txt'
coreml_model = coremltools.converters.caffe.convert(
caffe_model,
class_labels=labels,
image_input_names='data'
)
coreml_model.save('FlowerClassifier.mlmodel')
I convert using below command
python3 convert-script.py
And i get an error message like below.
error message
Does anybody face this problem and have solution on it?
I just came across this as I was having the same problem. The caffe support is not available in the newer versions of coremltools API. To make this code run an older version of coremltools (such as 3.4) must be used, which requires using Python 2.7 - which is best done in a virtual environment.
I assume you've solved your issue already, but I added this in case anyone else stumbles onto this question.
There are several solutions according to your case:
I had the same issue on my M1 Mac. You can resolve the same by duplicating your Terminal, and running it with Rosetta.(This worked for me)
cd ~/.virtualenvs/<your venv name here>/bin
mkdir bk; cp python bk; mv -f bk/python .;rmdir bk
codesign -s - --preserve-metadata=identifier,entitlements,flags,runtime -f python
Fore more solutions and issue you can watch this issue on github
I had the same error running python 3.7
In the virtualenv, solution is to run:
pip install coremltools==3.0
Don't have to change python versions and just rerun the script
Is that possible to generate texts from OpenAI GPT-2 using TensorFlowJS?
If not what is the limitation, like model format or ...?
I don't see any reason as to why not, other than maybe some operation that is in gpt-2 that is not supported by tensorflowjs.
I don't know how to do it, but here's a nice starting point:
install.sh
python3 -m pip install -q git+https://github.com/huggingface/transformers.git
python3 -m pip install tensorflow
save.py
from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# add the EOS token as PAD token to avoid warnings
model = TFGPT2LMHeadModel.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id)
model.save("./test_gpt2")
that will give you a SavedModel file. Now you can try figure out the input and output nodes, and use tensorflowjs_converter to try and convert it. Pointer: https://www.tensorflow.org/js/tutorials/conversion/import_saved_model.
I was wondering if I could install RAPIDS library (executing machine learning tasks entirely on GPU) in Google Colaboratory notebook?
I've done some research but I've not been able to find the way to do that...
This is now possible with the new T4 instances https://medium.com/rapids-ai/run-rapids-on-google-colab-for-free-1617ac6323a8
To enable cuGraph too, you can replace the wget command with:
!conda install -c nvidia/label/cuda10.0 -c rapidsai/label/cuda10.0 -c pytorch \
-c numba -c conda-forge -c numba -c defaults \
boost cudf=0.6 cuml=0.6 python=3.6 cugraph=0.6 -y
Dec 2019 update
New process for RAPIDS v0.11+
Because
RAPIDS v0.11 has dependencies (pyarrow) which were
not covered by the prior install script,
the notebooks-contrib repo, which contains RAPIDS demo notebooks (e.g.
colab_notebooks) and the Colab install script, now follows RAPIDS standard version-specific branch structure*
and some Colab users still enjoy v0.10,
our honorable notebooks-contrib overlord taureandyernv has updated the script which now:
If running v0.11 or higher, updates pyarrow library to 0.15.x.
Here's the code cell to run in Colab for v0.11:
# Install RAPIDS
!wget -nc https://raw.githubusercontent.com/rapidsai/notebooks-contrib/890b04ed8687da6e3a100c81f449ff6f7b559956/utils/rapids-colab.sh
!bash rapids-colab.sh
import sys, os
dist_package_index = sys.path.index("/usr/local/lib/python3.6/dist-packages")
sys.path = sys.path[:dist_package_index] + ["/usr/local/lib/python3.6/site-packages"] + sys.path[dist_package_index:]
sys.path
if os.path.exists('update_pyarrow.py'): ## This file only exists if you're using RAPIDS version 0.11 or higher
exec(open("update_pyarrow.py").read(), globals())
For a walk thru setting up Colab & implementing this script, see How to Install RAPIDS in Google Colab
-* e.g. branch-0.11 for v0.11 and branch-0.12 for v0.12 with default set to the current version
Looks like various subparts are not yet pip-installable so the only way to get them on colab would be to build them on colab, which might be more effort than you're interested in investing in this :)
https://github.com/rapidsai/cudf/issues/285 is the issue to watch for rapidsai/cudf (presumably the other rapidsai/ libs will follow suit).
Latest solution;
!wget -nc https://github.com/rapidsai/notebooks-extended/raw/master/utils/rapids-colab.sh
!bash rapids-colab.sh
import sys, os
sys.path.append('/usr/local/lib/python3.6/site-packages/')
os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'
os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'
was pushed a few days ago, see issues #104 or #110, or the full rapids-colab.sh script for more info.
Note: instillation currently requires a Tesla T4 instance, checking for this can be done with;
# check gpu type
!nvidia-smi
import pynvml
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
device_name = pynvml.nvmlDeviceGetName(handle)
# your dolphin is broken, please reset & try again
if device_name != b'Tesla T4':
raise Exception("""Unfortunately this instance does not have a T4 GPU.
Please make sure you've configured Colab to request a GPU instance type.
Sometimes Colab allocates a Tesla K80 instead of a T4. Resetting the instance.
If you get a K80 GPU, try Runtime -> Reset all runtimes...""")
# got a T4, good to go
else:
print('Woo! You got the right kind of GPU!')
when I use the command pip install tensorflow the download is only 99% complete and terminated at that point. How can I install tensorflow using google cloud shell.
Instead of installing it by yourself you can use the machine learning api and use TensorFlow for training or inference. Just follow this guidelines: https://cloud.google.com/ml/docs/quickstarts/training
You can submit a TensorFlow job like this:
gcloud beta ml jobs submit training ${JOB_NAME} \
--package-path=trainer \
--module-name=trainer.task \
--staging-bucket="${TRAIN_BUCKET}" \
--region=us-central1 \
-- \
--train_dir="${TRAIN_PATH}/train"
I'm trying use a retrained inception-v3 model in tensorflow-serving. But it seems that I have to provide a 'checkpoint'. I was wondering how do I get those 'checkpoints'? The retrain.py returns me a retrained_graph.pb. I followed this tutorial (https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0)
Thank you!
You may want to look into the API changes for Tensorflow 1.0
https://www.tensorflow.org/install/migration
to make it work to create a checkpoint file/s instead of .pb file.
Also refer to:
https://www.tensorflow.org/tutorials/image_retraining
Here is an example of how to download the checkpoints for the latest edition of pre-trained Inception V3 model:
$ CHECKPOINT_DIR=/tmp/checkpoints
$ mkdir ${CHECKPOINT_DIR}
$ wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
$ tar -xvf inception_v3_2016_08_28.tar.gz
$ mv inception_v3.ckpt ${CHECKPOINT_DIR}
$ rm inception_v3_2016_08_28.tar.gz