Intel® Distribution of OpenVINO™ Toolkit
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No module named 'openvino.runtime'; 'openvino' is not a package

jvkloc
Novice
7,928 Views

I installed OpenVINO with pip3 and tried the OpenVINO Runtime API Python tutorial. The first lines of the tutorial are

 

from openvino.runtime import Core

ie = Core()
devices = ie.available_devices

for device in devices:
    device_name = ie.get_property(device, "FULL_DEVICE_NAME")
    print(f"{device}: {device_name}")

 

and they produce a  

 

builtins.ModuleNotFoundError: No module named 'openvino.runtime'; 'openvino' is not a package

 

error. I'm using Ubuntu 20.04, Python 3.8.10 and OpenVINO 2023.0.0. Can you tell me what is wrong? How to make the import work? I do not use any kind of virtual environment and my pip was upgraded to the latest version before installing openvino.

 

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20 Replies
wearyhacker
New Contributor I
7,911 Views
I think you may need to install the openvino-dev python package.
pip install openvino-dev
It is usually better to be in an activated python environment before you do the install
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jvkloc
Novice
7,893 Views

Thank you for the suggestion. I uninstalled openvino and installed openvino-dev but I get the same error. I noticed that opencv-python is installed with openvino. I uninstalled it and tried opencd-contrib-python, also that did not work. Now after uninstalling opencv-python, installing opencv-contrib-python, uninstalling opencv-contrib-python and installing opencv-python I get a 

 

builtins.AttributeError: partially initialized module 'openvino' has no attribute '__path__' (most likely due to a circular import)

 

error as well in addition to the previous error.

 

EDIT:This happens also when I install openvino-dev from virtual environment.

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Peh_Intel
Moderator
7,867 Views

Hi jvkloc,


I am able to download OpenVINO™ from PyPI Repository on Ubuntu 20.04 machine.


Can you check whether OpenVINO™ package has been installed correctly to your Python site-package by running the following command?

pip show openvino-dev



Regards,

Peh


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jvkloc
Novice
7,856 Views

I get the following output with the command you gave:

Name: openvino-dev
Version: 2023.0.0
Summary: OpenVINO(TM) Development Tools
Home-page: https://docs.openvino.ai/latest/index.html
Author: Intel® Corporation
Author-email: openvino_pushbot@intel.com
License: OSI Approved :: Apache Software License
Location: /home/jvkloc/.local/lib/python3.9/site-packages
Requires: texttable, jstyleson, requests, numpy, networkx, openvino-telemetry, pyyaml, addict, defusedxml, opencv-python, pillow, tqdm, openvino, opencv-python, networkx, scipy
Required-by:

 

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Peh_Intel
Moderator
7,829 Views

Hi jvkloc,


From the output, I can see that OpenVINO™ package is installed correctly. But you still faced import issue which I never meet before.


As an alternative, I would suggest you to install OpenVINO™ Runtime on Linux from an Archive File.


However, OpenVINO™ Development Tools can be installed via pypi.org only, which OpenVINO™ Development Tools contains Model Optimizer components: model conversion API, Model Downloader and other Open Model Zoo tools


Could you please check whether Model Optimizer can be imported by running the following command:

mo -h



Regards,

Peh


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jvkloc
Novice
7,784 Views

EDIT: removed double post.

 

 

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jvkloc
Novice
7,783 Views

I do not understand how to see if Model Optimizer can be imported or not from this mo -h output:

usage: main.py [options]

optional arguments:
  -h, --help            show this help message and exit
  --framework FRAMEWORK
                        Name of the framework used to train the input model.

Framework-agnostic parameters:
  --model_name MODEL_NAME, -n MODEL_NAME
                        Model_name parameter passed to the final create_ir transform. This parameter is used to name a network in a generated IR and output .xml/.bin files.
  --output_dir OUTPUT_DIR, -o OUTPUT_DIR
                        Directory that stores the generated IR. By default, it is the directory from where the Model Optimizer is launched.
  --freeze_placeholder_with_value FREEZE_PLACEHOLDER_WITH_VALUE
                        Replaces input layer with constant node with provided value, for example: "node_name->True". It will be DEPRECATED in future releases. Use --input option to specify
                        a value for freezing.
  --static_shape        Enables IR generation for fixed input shape (folding `ShapeOf` operations and shape-calculating sub-graphs to `Constant`). Changing model input shape using the
                        OpenVINO Runtime API in runtime may fail for such an IR.
  --use_new_frontend    Force the usage of new Frontend of Model Optimizer for model conversion into IR. The new Frontend is C++ based and is available for ONNX* and PaddlePaddle* models.
                        Model optimizer uses new Frontend for ONNX* and PaddlePaddle* by default that means `--use_new_frontend` and `--use_legacy_frontend` options are not specified.
  --use_legacy_frontend
                        Force the usage of legacy Frontend of Model Optimizer for model conversion into IR. The legacy Frontend is Python based and is available for TensorFlow*, ONNX*,
                        MXNet*, Caffe*, and Kaldi* models.
  --input_model INPUT_MODEL, -m INPUT_MODEL, -w INPUT_MODEL
                        Tensorflow*: a file with a pre-trained model (binary or text .pb file after freezing). Caffe*: a model proto file with model weights.
  --input INPUT         Quoted list of comma-separated input nodes names with shapes, data types, and values for freezing. The order of inputs in converted model is the same as order of
                        specified operation names. The shape and value are specified as comma-separated lists. The data type of input node is specified in braces and can have one of the
                        values: f64 (float64), f32 (float32), f16 (float16), i64 (int64), i32 (int32), u8 (uint8), boolean (bool). Data type is optional. If it's not specified explicitly
                        then there are two options: if input node is a parameter, data type is taken from the original node dtype, if input node is not a parameter, data type is set to f32.
                        Example, to set `input_1` with shape [1,100], and Parameter node `sequence_len` with scalar input with value `150`, and boolean input `is_training` with `False`
                        value use the following format: "input_1[1,100],sequence_len->150,is_training->False". Another example, use the following format to set input port 0 of the node
                        `node_name1` with the shape [3,4] as an input node and freeze output port 1 of the node "node_name2" with the value [20,15] of the int32 type and shape [2]:
                        "0:node_name1[3,4],node_name2:1[2]{i32}->[20,15]".
  --output OUTPUT       The name of the output operation of the model or list of names. For TensorFlow*, do not add :0 to this name.The order of outputs in converted model is the same as
                        order of specified operation names.
  --input_shape INPUT_SHAPE
                        Input shape(s) that should be fed to an input node(s) of the model. Shape is defined as a comma-separated list of integer numbers enclosed in parentheses or square
                        brackets, for example [1,3,227,227] or (1,227,227,3), where the order of dimensions depends on the framework input layout of the model. For example, [N,C,H,W] is
                        used for ONNX* models and [N,H,W,C] for TensorFlow* models. The shape can contain undefined dimensions (? or -1) and should fit the dimensions defined in the input
                        operation of the graph. Boundaries of undefined dimension can be specified with ellipsis, for example [1,1..10,128,128]. One boundary can be undefined, for example
                        [1,..100] or [1,3,1..,1..]. If there are multiple inputs in the model, --input_shape should contain definition of shape for each input separated by a comma, for
                        example: [1,3,227,227],[2,4] for a model with two inputs with 4D and 2D shapes. Alternatively, specify shapes with the --input option.
  --batch BATCH, -b BATCH
                        Set batch size. It applies to 1D or higher dimension inputs. The default dimension index for the batch is zero. Use a label 'n' in --layout or --source_layout option
                        to set the batch dimension. For example, "x(hwnc)" defines the third dimension to be the batch.
  --mean_values MEAN_VALUES
                        Mean values to be used for the input image per channel. Values to be provided in the (R,G,B) or [R,G,B] format. Can be defined for desired input of the model, for
                        example: "--mean_values data[255,255,255],info[255,255,255]". The exact meaning and order of channels depend on how the original model was trained.
  --scale_values SCALE_VALUES
                        Scale values to be used for the input image per channel. Values are provided in the (R,G,B) or [R,G,B] format. Can be defined for desired input of the model, for
                        example: "--scale_values data[255,255,255],info[255,255,255]". The exact meaning and order of channels depend on how the original model was trained. If both
                        --mean_values and --scale_values are specified, the mean is subtracted first and then scale is applied regardless of the order of options in command line.
  --scale SCALE, -s SCALE
                        All input values coming from original network inputs will be divided by this value. When a list of inputs is overridden by the --input parameter, this scale is not
                        applied for any input that does not match with the original input of the model. If both --mean_values and --scale are specified, the mean is subtracted first and
                        then scale is applied regardless of the order of options in command line.
  --reverse_input_channels [REVERSE_INPUT_CHANNELS]
                        Switch the input channels order from RGB to BGR (or vice versa). Applied to original inputs of the model if and only if a number of channels equals 3. When
                        --mean_values/--scale_values are also specified, reversing of channels will be applied to user's input data first, so that numbers in --mean_values and
                        --scale_values go in the order of channels used in the original model. In other words, if both options are specified, then the data flow in the model looks as
                        following: Parameter -> ReverseInputChannels -> Mean apply-> Scale apply -> the original body of the model.
  --source_layout SOURCE_LAYOUT
                        Layout of the input or output of the model in the framework. Layout can be specified in the short form, e.g. nhwc, or in complex form, e.g. "[n,h,w,c]". Example for
                        many names: "in_name1([n,h,w,c]),in_name2(nc),out_name1(n),out_name2(nc)". Layout can be partially defined, "?" can be used to specify undefined layout for one
                        dimension, "..." can be used to specify undefined layout for multiple dimensions, for example "?c??", "nc...", "n...c", etc.
  --target_layout TARGET_LAYOUT
                        Same as --source_layout, but specifies target layout that will be in the model after processing by ModelOptimizer.
  --layout LAYOUT       Combination of --source_layout and --target_layout. Can't be used with either of them. If model has one input it is sufficient to specify layout of this input, for
                        example --layout nhwc. To specify layouts of many tensors, names must be provided, for example: --layout "name1(nchw),name2(nc)". It is possible to instruct
                        ModelOptimizer to change layout, for example: --layout "name1(nhwc->nchw),name2(cn->nc)". Also "*" in long layout form can be used to fuse dimensions, for example
                        "[n,c,...]->[n*c,...]".
  --compress_to_fp16 [COMPRESS_TO_FP16]
                        If the original model has FP32 weights or biases, they are compressed to FP16. All intermediate data is kept in original precision. Option can be specified alone as
                        "--compress_to_fp16", or explicit True/False values can be set, for example: "--compress_to_fp16=False", or "--compress_to_fp16=True"
  --extensions EXTENSIONS
                        Paths or a comma-separated list of paths to libraries (.so or .dll) with extensions. For the legacy MO path (if `--use_legacy_frontend` is used), a directory or a
                        comma-separated list of directories with extensions are supported. To disable all extensions including those that are placed at the default location, pass an empty
                        string.
  --transform TRANSFORM
                        Apply additional transformations. Usage: "--transform transformation_name1[args],transformation_name2..." where [args] is key=value pairs separated by semicolon.
                        Examples: "--transform LowLatency2" or "--transform Pruning" or "--transform LowLatency2[use_const_initializer=False]" or "--transform "MakeStateful[param_res_names=
                        {'input_name_1':'output_name_1','input_name_2':'output_name_2'}]" Available transformations: "LowLatency2", "MakeStateful", "Pruning"
  --transformations_config TRANSFORMATIONS_CONFIG
                        Use the configuration file with transformations description. Transformations file can be specified as relative path from the current directory, as absolute path or
                        as arelative path from the mo root directory.
  --silent [SILENT]     Prevent any output messages except those that correspond to log level equals ERROR, that can be set with the following option: --log_level. By default, log level is
                        already ERROR.
  --log_level {CRITICAL,ERROR,WARN,WARNING,INFO,DEBUG,NOTSET}
                        Logger level of logging massages from MO. Expected one of ['CRITICAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'].
  --version             Version of Model Optimizer
  --progress [PROGRESS]
                        Enable model conversion progress display.
  --stream_output [STREAM_OUTPUT]
                        Switch model conversion progress display to a multiline mode.

TensorFlow*-specific parameters:
  --input_model_is_text [INPUT_MODEL_IS_TEXT]
                        TensorFlow*: treat the input model file as a text protobuf format. If not specified, the Model Optimizer treats it as a binary file by default.
  --input_checkpoint INPUT_CHECKPOINT
                        TensorFlow*: variables file to load.
  --input_meta_graph INPUT_META_GRAPH
                        Tensorflow*: a file with a meta-graph of the model before freezing
  --saved_model_dir SAVED_MODEL_DIR
                        TensorFlow*: directory with a model in SavedModel format of TensorFlow 1.x or 2.x version.
  --saved_model_tags SAVED_MODEL_TAGS
                        Group of tag(s) of the MetaGraphDef to load, in string format, separated by ','. For tag-set contains multiple tags, all tags must be passed in.
  --tensorflow_custom_operations_config_update TENSORFLOW_CUSTOM_OPERATIONS_CONFIG_UPDATE
                        TensorFlow*: update the configuration file with node name patterns with input/output nodes information.
  --tensorflow_object_detection_api_pipeline_config TENSORFLOW_OBJECT_DETECTION_API_PIPELINE_CONFIG
                        TensorFlow*: path to the pipeline configuration file used to generate model created with help of Object Detection API.
  --tensorboard_logdir TENSORBOARD_LOGDIR
                        TensorFlow*: dump the input graph to a given directory that should be used with TensorBoard.
  --tensorflow_custom_layer_libraries TENSORFLOW_CUSTOM_LAYER_LIBRARIES
                        TensorFlow*: comma separated list of shared libraries with TensorFlow* custom operations implementation.

Caffe*-specific parameters:
  --input_proto INPUT_PROTO, -d INPUT_PROTO
                        Deploy-ready prototxt file that contains a topology structure and layer attributes
  --caffe_parser_path CAFFE_PARSER_PATH
                        Path to Python Caffe* parser generated from caffe.proto
  --k K                 Path to CustomLayersMapping.xml to register custom layers
  --disable_omitting_optional [DISABLE_OMITTING_OPTIONAL]
                        Disable omitting optional attributes to be used for custom layers. Use this option if you want to transfer all attributes of a custom layer to IR. Default behavior
                        is to transfer the attributes with default values and the attributes defined by the user to IR.
  --enable_flattening_nested_params [ENABLE_FLATTENING_NESTED_PARAMS]
                        Enable flattening optional params to be used for custom layers. Use this option if you want to transfer attributes of a custom layer to IR with flattened nested
                        parameters. Default behavior is to transfer the attributes without flattening nested parameters.

MXNet-specific parameters:
  --input_symbol INPUT_SYMBOL
                        Symbol file (for example, model-symbol.json) that contains a topology structure and layer attributes
  --nd_prefix_name ND_PREFIX_NAME
                        Prefix name for args.nd and argx.nd files.
  --pretrained_model_name PRETRAINED_MODEL_NAME
                        Name of a pretrained MXNet model without extension and epoch number. This model will be merged with args.nd and argx.nd files
  --save_params_from_nd [SAVE_PARAMS_FROM_ND]
                        Enable saving built parameters file from .nd files
  --legacy_mxnet_model [LEGACY_MXNET_MODEL]
                        Enable MXNet loader to make a model compatible with the latest MXNet version. Use only if your model was trained with MXNet version lower than 1.0.0
  --enable_ssd_gluoncv [ENABLE_SSD_GLUONCV]
                        Enable pattern matchers replacers for converting gluoncv ssd topologies.

Kaldi-specific parameters:
  --counts COUNTS       Path to the counts file
  --remove_output_softmax [REMOVE_OUTPUT_SOFTMAX]
                        Removes the SoftMax layer that is the output layer
  --remove_memory [REMOVE_MEMORY]
                        Removes the Memory layer and use additional inputs outputs instead

 

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jvkloc
Novice
7,794 Views

My reply including mo -h output never seems to appear on this page. Maybe there's a length limit to posts? Anyway, it is quite long output and I don't know how to check if Model Optimizer can be imported or not. What am I looking for int the mo -h output?

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wearyhacker
New Contributor I
7,778 Views
I suggest you try and locate where pip has installed your openvino packages. Try this in your home directory.

find . -name "openvino_dev-2022.3.1.dist-info"

If that does not find anything try this
sudo find / -name "openvino_dev-2022.3.1.dist-info"
One of these shoud find something. Go the the directory where the file is and list its contents. The directory should be under the standard python paths or under a python virtual environment directory.

If it is not then python will never find the openvino module.

Once again I strongly recommend doing all the pip installations in an activated python virtual environment. It much easier to find stuff then. All the installations will then be under the directory that you created when the virtual environment was created.
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jvkloc
Novice
7,761 Views

The openvino_dev-2023.0.0.dist-info is found:

/home/jvkloc/.local/lib/python3.9/site-packages/openvino_dev-2023.0.0.dist-info/

Looks ok path to me, that's where other packages are, too. When I run python -c "from openvino.runtime import Core" I get

Traceback (most recent call last):
  File "/usr/lib/command-not-found", line 28, in <module>
    from CommandNotFound import CommandNotFound
  File "/usr/lib/python3/dist-packages/CommandNotFound/CommandNotFound.py", line 19, in <module>
    from CommandNotFound.db.db import SqliteDatabase
  File "/usr/lib/python3/dist-packages/CommandNotFound/db/db.py", line 5, in <module>
    import apt_pkg
ModuleNotFoundError: No module named 'apt_pkg'

I got the same error also after

sudo apt-get install --reinstall python3-apt

The files in openvino_dev-2023.0.0.dist-info are

dev-third-party-programs.txt
entry_points.txt
INSTALLER
LICENSE
METADATA
omz-third-party-programs.txt
readme.txt
RECORD
top_level.txt
WHEEL

 

 

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jvkloc
Novice
7,758 Views

After some adventures I don't get any output with

python3 -c "from openvino.runtime import Core"

But when I run the code

from openvino.runtime import Core
ie = Core()
devices = ie.available_devices
for device in devices:
    device_name = ie.get_property(device, "FULL_DEVICE_NAME")
    print(f"{device}: {device_name}")

I still get 

builtins.AttributeError: partially initialized module 'openvino' has no attribute '__path__' (most likely due to a circular import)

During handling of the above exception, another exception occurred:

builtins.ModuleNotFoundError: No module named 'openvino.runtime'; 'openvino' is not a package

It is the import line that causes the errors. So I didn't get anywhere from the initial situation.

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wearyhacker
New Contributor I
7,742 Views
I think you need to install python-apt via pip not apt-get .

pip install python-apt

Once again I would suggest that start again with a python virtual environment.

python -m venv myopenvino-dev
source myopenvino-dev/bin/activate
pip install openvino-dev
python
>>>from openvino.runtime import Core
...



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jvkloc
Novice
7,717 Views

Thank you for your patience. Now the code works in terminal:

(openvino) jvkloc@lenovo:~$ python
Python 3.9.5 (default, Nov 23 2021, 15:27:38)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from openvino.runtime import Core
>>> ie = Core()
>>> devices = ie.available_devices
>>> for device in devices:
...     device_name = ie.get_property(device, "FULL_DEVICE_NAME")
...     print(f"{device}: {device_name}")
...
CPU: Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
>>> quit()
(openvino) jvkloc@lenovo:~$

However, I still get the same errors in my IDE (Wing Personal 9, version 9.0.2.1), also after setting the project to use the openvino venv created fro the install:

shot-2023-06-29_10-17-16.jpg

Is there some additional setup needed for using venv in IDEs?

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wearyhacker
New Contributor I
7,706 Views

The answer is no but your ide needs to run the activate script. It looks like your ide is telling you what to do to acheive this.. The activate script needs to be run as a sequence of commands in the same shell instance that will be running python. Thats why when you do this from a shell command line you use the 'source' command. I am guessing that what is being described in your screenshot is how to do that. I think it may also be an idea to stop it using any inherited environment.

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Berdy
New Contributor I
7,688 Views

In my experience (mostly with Eclipse), you need to add the Python interpreter PATH in which venv is created. The IDE is attempting to import from the site-packages of the main Python install.

 

The program execution from within the IDE needs to however search for imports at the venv site-packages. Typically, ~/.venv/<project venv dir>/lib/<python-version>/site-packages/ (sometimes lib64 instead of lib). In Ubuntu the path may be different, but still needs to point at the venv site-packages location. Change the path for the Python interpreter and it should work.

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jvkloc
Novice
7,674 Views

Thank you for the advice. I tried with lib and lib64 with no success. Also, this Wing tutorial says that only the Python Executable needs to be set for virtualenv (I tried also with virtualenv, instead of venv, but no success).

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Berdy
New Contributor I
7,672 Views

Hi jvkloc,

Sorry for the confusion, I was trying to explain what is going on under the hood. The tutorial is correct, you only need to specify where the interpreter is located. The site-packages will be found accordingly whether under lib, lib64, or anywhere else.

I am not familiar with your IDE, but it should have a setting for the project's Python interpreter. You need to set that to the python executable file under the virtual environment.

If you are running Linux, with the virtual environment enabled, run the command "which python". It will show you the path you need to set in the IDE.

 

 

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Peh_Intel
Moderator
7,560 Views

Hi jvkloc,


Yes, you are able to import Model Optimizer.


Besides, you are doing the right way in using the existing virtualenv with Wing.


However, you still keep getting the same error:

builtins.ModuleNotFoundError: No module named 'openvino.runtime'; 'openvino' is not a package


This is because you are running a Python script which its name conflicts with the package name.

 

Please rename your Python script (home/jvkloc/Documents/Sonify/openvino.py) to any name except ‘openvino’.



Regards,

Peh


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jvkloc
Novice
7,518 Views

Finally the correct answer to my problem. Thank you. Renaming the file did it.

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Peh_Intel
Moderator
7,489 Views

Hi jvkloc,


Thank you for confirming your problem is resolved. I am glad to hear everything is going well.


This thread will no longer be monitored since this issue has been resolved. If you need any additional information from Intel, please submit a new question.



Regards,

Peh


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