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Building the required plugin code env PYTHON36 fails.
I'm trying to install your plugin to complete the hands-on portion of the Advanced Visual Designer certification and can't complete the code env build process for PYTHON36 correctly on a Dataiku provided instance for Charles Schwab.
Looks like it's probably a VM host server issue but the inability to troubleshoot these errors by experienced data scientists through the DSS web app is extremely troubling. This is an issue it would take me 10 seconds to fix if I had shell access to the machine hosting the code environments. I understand that the goal is to keep things simple for users, but in it's current form I would never recommend Dataiku to any Data Scientist with the most basic of software development experience.
Try to install plugin with PYTHON36 DSS managed environment
Get Error
Installing build dependencies: started
Installing build dependencies: finished with status 'done'
Getting requirements to build wheel: started
Getting requirements to build wheel: finished with status 'done'
Preparing wheel metadata: started
Preparing wheel metadata: finished with status 'done'
Collecting thinc<7.5.0,>=7.4.1
Using cached thinc-7.4.5-cp36-cp36m-manylinux2014_x86_64.whl (1.1 MB)
Collecting tinydb>=3.0
Using cached tinydb-4.7.0-py3-none-any.whl (24 kB)
Collecting typing-extensions>=3.6.4
Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB)
Collecting wasabi<1.1.0,>=0.4.0
Using cached wasabi-0.10.1-py3-none-any.whl (26 kB)
Collecting zipp>=0.5
Using cached zipp-3.6.0-py3-none-any.whl (5.3 kB)
Collecting packaging
Using cached packaging-21.3-py3-none-any.whl (40 kB)
Collecting pyparsing!=3.0.5,>=2.0.2
Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB)
Collecting scikit-learn
Using cached scikit_learn-0.24.2-cp36-cp36m-manylinux2010_x86_64.whl (22.2 MB)
Collecting joblib>=0.11
Using cached joblib-1.1.0-py2.py3-none-any.whl (306 kB)
Collecting scipy>=0.19.1
Using cached scipy-1.5.4-cp36-cp36m-manylinux1_x86_64.whl (25.9 MB)
Collecting threadpoolctl>=2.0.0
Using cached threadpoolctl-3.1.0-py3-none-any.whl (14 kB)
Collecting sklearn-crfsuite
Using cached sklearn_crfsuite-0.3.6-py2.py3-none-any.whl (12 kB)
Collecting tabulate
Using cached tabulate-0.8.10-py3-none-any.whl (29 kB)
Building wheels for collected packages: sudachipy
Building wheel for sudachipy (PEP 517): started
ERROR: Command errored out with exit status 1:
command: /data/dss1/design/code-envs/python/plugin_nlp-preparation_managed_1/bin/python /data/dss1/design/code-envs/python/plugin_nlp-preparation_managed_1/lib/python3.6/site-packages/pip/_vendor/pep517/_in_process.py build_wheel /tmp/tmpk03ktilv
cwd: /tmp/pip-install-655_cbey/sudachipy_d4e7c533715b4df390539395fb8704a8
Complete output (35 lines):
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.6
creating build/lib.linux-x86_64-3.6/sudachipy
copying py_src/sudachipy/__init__.py -> build/lib.linux-x86_64-3.6/sudachipy
copying py_src/sudachipy/command_line.py -> build/lib.linux-x86_64-3.6/sudachipy
creating build/lib.linux-x86_64-3.6/sudachipy/dictionary
copying py_src/sudachipy/dictionary/__init__.py -> build/lib.linux-x86_64-3.6/sudachipy/dictionary
creating build/lib.linux-x86_64-3.6/sudachipy/tokenizer
copying py_src/sudachipy/tokenizer/__init__.py -> build/lib.linux-x86_64-3.6/sudachipy/tokenizer
creating build/lib.linux-x86_64-3.6/sudachipy/morphemelist
copying py_src/sudachipy/morphemelist/__init__.py -> build/lib.linux-x86_64-3.6/sudachipy/morphemelist
creating build/lib.linux-x86_64-3.6/sudachipy/morpheme
copying py_src/sudachipy/morpheme/__init__.py -> build/lib.linux-x86_64-3.6/sudachipy/morpheme
creating build/lib.linux-x86_64-3.6/sudachipy/resources
copying py_src/sudachipy/resources/sudachi.json -> build/lib.linux-x86_64-3.6/sudachipy/resources
copying py_src/sudachipy/resources/char.def -> build/lib.linux-x86_64-3.6/sudachipy/resources
copying py_src/sudachipy/resources/rewrite.def -> build/lib.linux-x86_64-3.6/sudachipy/resources
copying py_src/sudachipy/resources/unk.def -> build/lib.linux-x86_64-3.6/sudachipy/resources
copying py_src/sudachipy/sudachipy.pyi -> build/lib.linux-x86_64-3.6/sudachipy
running build_ext
running build_rust
error: can't find Rust compiler
Building wheel for sudachipy (PEP 517): finished with status 'error'
Failed to build sudachipy
If you are using an outdated pip version, it is possible a prebuilt wheel is available for this package but pip is not able to install from it. Installing from the wheel would avoid the need for a Rust compiler.
To update pip, run:
pip install --upgrade pip
and then retry package installation.
If you did intend to build this package from source, try installing a Rust compiler from your system package manager and ensure it is on the PATH during installation. Alternatively, rustup (available at https://rustup.rs) is the recommended way to download and update the Rust compiler toolchain.
----------------------------------------
ERROR: Failed building wheel for sudachipy
ERROR: Could not build wheels for sudachipy which use PEP 517 and cannot be installed directly
WARNING: You are using pip version 20.3.3; however, version 21.3.1 is available.
You should consider upgrading via the '/data/dss1/design/code-envs/python/plugin_nlp-preparation_managed_1/bin/python -m pip install --upgrade pip' command.
Expected behavior
I expect the
Screenshots
Additional context
Add any other context about the problem here.
safari
DSS version 9
The text was updated successfully, but these errors were encountered:
Building the required plugin code env PYTHON36 fails.
I'm trying to install your plugin to complete the hands-on portion of the Advanced Visual Designer certification and can't complete the code env build process for PYTHON36 correctly on a Dataiku provided instance for Charles Schwab.
Looks like it's probably a VM host server issue but the inability to troubleshoot these errors by experienced data scientists through the DSS web app is extremely troubling. This is an issue it would take me 10 seconds to fix if I had shell access to the machine hosting the code environments. I understand that the goal is to keep things simple for users, but in it's current form I would never recommend Dataiku to any Data Scientist with the most basic of software development experience.
To Reproduce
Steps to reproduce the behavior:
Expected behavior
I expect the
Screenshots
Additional context
Add any other context about the problem here.
The text was updated successfully, but these errors were encountered: