Segment 11 warning

[shoraj]

Unable to run datawig code in google colab

[felixbiessmann]

Could you provide any hints on what errors you got?

[shoraj]

Whenever I am trying to run the "import datawig" command the google collab session is crashing and restarting. I tried to check the log it showed me a warning which says Segmentation fault: 11 and [bt] (0) /usr/local/lib/python3.6/dist-packages/mxnet/libmxnet.so(+0x3c27360) [0x7f813614c360] .

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On Fri, Mar 13, 2020 at 4:00 PM felixbiessmann notifications@github.com wrote:

Could you provide any hints on what errors you got?

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[felixbiessmann]

Hm, not sure to what extent this is a datawig issue, also we never have tested this in colab, but we'll look into it asap (probably after the ICML review deadline next week).

[shoraj]

Thank You.

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On Fri, Mar 13, 2020 at 4:44 PM felixbiessmann notifications@github.com wrote:

Hm, not sure to what extent this is a datawig issue, also we never have tested this in colab, but we'll look into it asap (probably after the ICML review deadline next week).

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[felixbiessmann]

so it seems to be a backwards incompatibility issue with mxnet, picking a random older version seems to work. the below example just ran through in a colab notebook:

!pip install mxnet==1.3.0
!pip install datawig

import datawig, numpy

# generate some data with simple nonlinear dependency
df = datawig.utils.generate_df_numeric() 
# mask 10% of the values
df_with_missing = df.mask(numpy.random.rand(*df.shape) > .9)

# impute missing values
df_with_missing_imputed = datawig.SimpleImputer.complete(df_with_missing)

df_with_missing_imputed

We'll fix the mxnet version number in the next release, sorry about that.

[shoraj]

Don't be sorry, actually, I had tried running datawig a few days ago, it was absolutely fine. only from yesterday, I am facing the problem. All the best, thank you for your immediate reply.

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On Fri, 13 Mar 2020, 4:50 pm felixbiessmann, notifications@github.com wrote:

so it seems to be a backwards incompatibility issue with mxnet, picking a random older version seems to work. the below example just ran through in a colab notebook:

!pip install mxnet==1.3.0!pip install datawig import datawig, numpy

generate some data with simple nonlinear dependency

df = datawig.utils.generate_df_numeric() # mask 10% of the values df_with_missing = df.mask(numpy.random.rand(*df.shape) > .9)

impute missing values

df_with_missing_imputed = datawig.SimpleImputer.complete(df_with_missing)

df_with_missing_imputed

We'll fix the mxnet version number in the next release, sorry about that.

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[felixbiessmann]

Should be solved with latest release, please reopen if problem persists