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Entrypoint for all backend cape webservices

License: Apache License 2.0

Python 95.89% HTML 0.22% Dockerfile 3.89%
cape deep-learning machine-learning machine-reading python question-answering search

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cape-webservices's Issues

Default user & passwords

Hi,
I'm trying to test the solution but there's or I can't find the admin user or password.
I can install the docker image, but once it is running I can't get any valid response.
Thanks

cape-webservices not launching on mac, but fine on an AWS box

Hi guys,

Thank you so much for having open sourced this :)

On my Mac I cannot run any of the examples in your readme with Docker, but it's fine on an AWS t2.medium instance. That sounds extremely weird to me, so even if I don't mind experimenting on a Linux box, I'm reporting this here to save somebody else a bit of time.

Mac config:

~ system_profiler SPHardwareDataType
Hardware:

    Hardware Overview:

      Model Name: MacBook Pro
      Model Identifier: MacBookPro14,3
      Processor Name: Intel Core i7
      Processor Speed: 2.8 GHz
      Number of Processors: 1
      Total Number of Cores: 4
      L2 Cache (per Core): 256 KB
      L3 Cache: 6 MB
      Memory: 16 GB
      Boot ROM Version: MBP143.0167.B00
      SMC Version (system): 2.45f0
      Serial Number (system):*********
      Hardware UUID: *********

Example output:

~ docker run -ti bloomsburyai/cape ipython3                                
Python 3.6.5 (default, Apr  1 2018, 05:46:30) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.5.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from cape_responder.responder_core import Responder
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp: In function ‘PyObject* __pyx_pw_12grouper_core_1get_n_words_before_position(PyObject*, PyObject*, PyObject*)’:
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp:1584:15: warning: ‘__pyx_v_remaining_words’ may be used uninitialized in this function [-Wmaybe-uninitialized]
     __pyx_t_4 = (-__pyx_v_remaining_words);
     ~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp:1381:7: note: ‘__pyx_v_remaining_words’ was declared here
   int __pyx_v_remaining_words;
       ^~~~~~~~~~~~~~~~~~~~~~~
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp: In function ‘PyObject* __pyx_pw_12grouper_core_3get_n_words_after_position(PyObject*, PyObject*, PyObject*)’:
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp:1930:42: warning: ‘__pyx_v_remaining_words’ may be used uninitialized in this function [-Wmaybe-uninitialized]
     __pyx_t_6 = (__pyx_v_remaining_words - 1);
                 ~~~~~~~~~~~~~~~~~~~~~~~~~^~~~
/root/.pyxbld/temp.linux-x86_64-3.6/pyrex/grouper_core.cpp:1742:7: note: ‘__pyx_v_remaining_words’ was declared here
   int __pyx_v_remaining_words;
       ^~~~~~~~~~~~~~~~~~~~~~~
/usr/local/lib/python3.6/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use the retry module or similar alternatives.

In [2]: Responder.get_answers_from_documents('my-token','How easy is Cape to use', text ="Cape is an open source large-scale question answering system and is super easy to use!")
2018-08-18 12:39:58.475848: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Had pre-trained word embeddings for 84902 of 181076 words
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/docqa/nn/attention.py:85: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/docqa/nn/attention.py:85: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
INFO:tensorflow:Restoring parameters from /usr/local/lib/python3.6/dist-packages/cape_document_qa/storage/models/production_ready_model/save/checkpoint-123456789
INFO:tensorflow:Restoring parameters from /usr/local/lib/python3.6/dist-packages/cape_document_qa/storage/models/production_ready_model/save/checkpoint-123456789

And then the container just dies. If I do docker inspect, I get the following state:

"State": {
            "Status": "exited",
            "Running": false,
            "Paused": false,
            "Restarting": false,
            "OOMKilled": false,
            "Dead": false,
            "Pid": 0,
            "ExitCode": 137,
            "Error": "",
            "StartedAt": "2018-08-18T12:39:24.1657046Z",
            "FinishedAt": "2018-08-18T12:40:21.0737114Z"
}

So it looks like there is an OOM error somewhere, but this is definitely weird given my Mac's configuration.

ngrok gets expired after a while

After running the docker images, the ngrok url gets expired after a while. Any way to log into the ngrok, so that it doesn't get expired?

Also, can we can have an option to use, without ngrok? Just normal localhost would be good.

Running as a distributed cluster

In the README.md file it mentions several ways to run the webapp, including As a distributed cluster, however there are no details on how to do this and what benefits could be expected from this approach. Is there any more information available? If not can there be?

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