I’ve set up my own server, and am doing chapter 1 of the fastbook. For the pets example, the training works fine, however the following:
learner.show_results()
does nothing. There is no error, and I can see a progress bar for a bit, but then there are no images displayed afterwards.
Any idea on how to find out what’s wrong? I’ve looked at the source code but I get lost here. The preds does get generated (length 64 vector), but then somehow something goes wrong.
The same thing happens when I call show_results on the unet_learner for the image segmentation example in chapter 1.
Actually, when I do dls.show_batch() nothing happens either. Getting closer to the cause? What is the .show being called in this line, and how can I test it independently from the rest:
Can you explain how you’ve setup your server? OS? How did you install fastai?
(Generally when following the book that’s not recommended too)
Thanks Zachary. I know it is recommended to use cloud services, it’s not just for following along with the book though.
I have a Ubuntu 20.04 box with a GTX 1080 Ti (driver version 470.57.02, which seems to work fine for training). I installed Miniconda as suggested in “Lesson 0”, here is the output of conda list if that helps:
# Name Version Build Channel
_libgcc_mutex 0.1 main
_openmp_mutex 4.5 1_gnu
_pytorch_select 0.2 gpu_0
adal 1.2.7 pypi_0 pypi
aiohttp 3.7.4.post0 py37h5e8e339_0 fastchan
argon2-cffi 20.1.0 py37h27cfd23_1
asn1crypto 1.4.0 py_0
async-timeout 3.0.1 py37h06a4308_0
async_generator 1.10 py37h28b3542_0
attrs 21.2.0 pyhd3eb1b0_0
azure-cognitiveservices-search-imagesearch 2.0.0 pypi_0 pypi
azure-common 1.1.27 pypi_0 pypi
backcall 0.2.0 pyhd3eb1b0_0
beautifulsoup4 4.9.3 pyha847dfd_0
blas 1.0 mkl
bleach 4.0.0 pyhd3eb1b0_0
boto 2.49.0 py37_0
boto3 1.17.7 pyhd3eb1b0_0
botocore 1.20.7 pyhd8ed1ab_0 fastchan
bottleneck 1.3.2 py37heb32a55_1
brotlipy 0.7.0 py37h27cfd23_1003
ca-certificates 2021.7.5 h06a4308_1
cachetools 4.2.2 pyhd3eb1b0_0
cairo 1.14.12 h8948797_3
catalogue 2.0.4 py37h89c1867_0 fastchan
certifi 2021.5.30 py37h06a4308_0
cffi 1.14.0 py37h2e261b9_0
chardet 4.0.0 py37h06a4308_1003
click 7.1.2 pypi_0 pypi
colorama 0.4.4 pyhd3eb1b0_0
cryptography 2.3.1 py37hc365091_0
cudatoolkit 10.0.130 0
cudnn 7.6.5 cuda10.0_0
cycler 0.10.0 py37_0
cymem 2.0.5 py37h2531618_0
cython-blis 0.7.4 py37h27cfd23_1
dataclasses 0.8 pyh6d0b6a4_7
dbus 1.13.18 hb2f20db_0
decorator 5.0.9 pyhd3eb1b0_0
defusedxml 0.7.1 pyhd3eb1b0_0
entrypoints 0.3 py37_0
expat 2.4.1 h2531618_2
fastai 2.5.0 pypi_0 pypi
fastbook 0.0.18 pypi_0 pypi
fastcore 1.3.25 py_0 fastai
fastdownload 0.0.3 pypi_0 pypi
fastprogress 1.0.0 pyh39e3cac_0 fastchan
fastrelease 0.1.12 pypi_0 pypi
fastscript 1.0.0 0 fastai
fontconfig 2.13.1 h6c09931_0
freetype 2.10.4 h5ab3b9f_0
fribidi 1.0.10 h7b6447c_0
ghapi 0.1.19 pypi_0 pypi
glib 2.63.1 h5a9c865_0
google-api-core 1.25.1 pyhd3eb1b0_0
google-auth 1.28.0 pyhd3eb1b0_0
google-cloud-core 1.6.0 pyhd3eb1b0_0
google-cloud-storage 1.40.0 pyhd3eb1b0_0
google-crc32c 1.1.2 py37h27cfd23_0
google-resumable-media 1.3.1 pyhd3eb1b0_1
googleapis-common-protos 1.53.0 py37h06a4308_0
graphite2 1.3.14 h23475e2_0
graphviz 2.40.1 h21bd128_2
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
harfbuzz 1.8.8 hffaf4a1_0
icu 58.2 he6710b0_3
idna 2.10 pyhd3eb1b0_0
importlib-metadata 3.10.0 py37h06a4308_0
importlib_metadata 3.10.0 hd3eb1b0_0
intel-openmp 2021.3.0 h06a4308_3350
ipykernel 5.3.4 py37h5ca1d4c_0
ipython 7.26.0 py37hb070fc8_0
ipython_genutils 0.2.0 pyhd3eb1b0_1
ipywidgets 7.6.3 pyhd3eb1b0_1
isodate 0.6.0 pypi_0 pypi
jbig 2.1 hdba287a_0
jedi 0.18.0 py37h06a4308_1
jinja2 3.0.1 pyhd3eb1b0_0
jmespath 0.10.0 py_0
joblib 1.0.1 pypi_0 pypi
jpeg 9d h36c2ea0_0 fastchan
jsonschema 3.2.0 py_2
jupyter 1.0.0 py37_7
jupyter_client 6.1.12 pyhd3eb1b0_0
jupyter_console 6.4.0 pyhd3eb1b0_0
jupyter_core 4.7.1 py37h06a4308_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
kiwisolver 1.3.1 py37h2531618_0
lcms2 2.12 h3be6417_0
lerc 2.2.1 h2531618_0
libcrc32c 1.1.1 he6710b0_2
libdeflate 1.7 h27cfd23_5
libedit 3.1.20210216 h27cfd23_1
libffi 3.2.1 hf484d3e_1007
libgcc-ng 9.3.0 h5101ec6_17
libgfortran-ng 7.5.0 h14aa051_19 fastchan
libgfortran4 7.5.0 h14aa051_19 fastchan
libgomp 9.3.0 h5101ec6_17
libpng 1.6.37 hbc83047_0
libprotobuf 3.16.0 h780b84a_0 fastchan
libsodium 1.0.18 h7b6447c_0
libstdcxx-ng 9.3.0 hd4cf53a_17
libtiff 4.3.0 hf544144_1 fastchan
libuuid 1.0.3 h1bed415_2
libwebp-base 1.2.0 h27cfd23_0
libxcb 1.14 h7b6447c_0
libxml2 2.9.12 h03d6c58_0
lz4-c 1.9.3 h295c915_1
markupsafe 2.0.1 py37h27cfd23_0
matplotlib 3.3.2 0 fastchan
matplotlib-base 3.3.2 py37h817c723_0
matplotlib-inline 0.1.2 pyhd3eb1b0_2
mistune 0.8.4 py37h14c3975_1001
mkl 2020.2 256
mkl-service 2.3.0 py37he8ac12f_0
mkl_fft 1.3.0 py37h54f3939_0
mkl_random 1.1.1 py37h0573a6f_0
msrest 0.6.21 pypi_0 pypi
msrestazure 0.6.4 pypi_0 pypi
multidict 5.1.0 py37h27cfd23_2
murmurhash 1.0.5 py37h2531618_0
nbclient 0.5.3 pyhd3eb1b0_0
nbconvert 5.6.1 pypi_0 pypi
nbdev 1.1.19 pypi_0 pypi
nbformat 5.1.3 pyhd3eb1b0_0
ncurses 6.2 he6710b0_1
nest-asyncio 1.5.1 pyhd3eb1b0_0
ninja 1.10.2 hff7bd54_1
notebook 6.4.0 py37h06a4308_0
numexpr 2.7.3 py37hb2eb853_0
numpy 1.19.2 py37h54aff64_0
numpy-base 1.19.2 py37hfa32c7d_0
nvidia-ml-py3 7.352.0 py_0 fastchan
oauthlib 3.1.1 pypi_0 pypi
olefile 0.46 py37_0
openjpeg 2.3.0 h05c96fa_1
openssl 1.0.2u h7b6447c_0
packaging 21.0 pyhd3eb1b0_0
pandas 1.3.1 py37h8c16a72_0
pandocfilters 1.4.3 py37h06a4308_1
pango 1.42.4 h049681c_0
parso 0.8.2 pyhd3eb1b0_0
pathy 0.4.0 pyhd8ed1ab_0 fastchan
pcre 8.45 h295c915_0
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 8.3.1 py37h2c7a002_0
pip 21.2.2 py37h06a4308_0
pixman 0.40.0 h7b6447c_0
preshed 3.0.5 py37h2531618_4
prometheus_client 0.11.0 pyhd3eb1b0_0
prompt-toolkit 3.0.17 pyh06a4308_0
prompt_toolkit 3.0.17 hd3eb1b0_0
protobuf 3.16.0 py37hcd2ae1e_0 fastchan
ptyprocess 0.7.0 pyhd3eb1b0_2
pyasn1 0.4.8 py_0
pyasn1-modules 0.2.8 py_0
pycparser 2.20 py_2
pydantic 1.7.3 py37h27cfd23_0
pygments 2.9.0 pyhd3eb1b0_0
pyjwt 2.1.0 pypi_0 pypi
pyopenssl 19.0.0 py37_0
pyparsing 2.4.7 pyhd3eb1b0_0
pyqt 5.9.2 py37h05f1152_2
pyrsistent 0.17.3 py37h7b6447c_0
pysocks 1.7.1 py37_1
python 3.7.0 h6e4f718_3
python-dateutil 2.8.2 pyhd3eb1b0_0
python-graphviz 0.16 pyhd3eb1b0_1
python_abi 3.7 1_cp37m fastchan
pytorch-gpu 1.3.1 0
pytz 2021.1 pyhd3eb1b0_0
pyyaml 5.4.1 py37h27cfd23_1
pyzmq 20.0.0 py37h2531618_1
qt 5.9.6 h8703b6f_2
qtconsole 5.1.0 pyhd3eb1b0_0
qtpy 1.9.0 py_0
readline 7.0 h7b6447c_5
requests 2.25.1 pyhd3eb1b0_0
requests-oauthlib 1.3.0 pypi_0 pypi
rsa 4.7.2 pyhd3eb1b0_1
s3transfer 0.3.6 pyhd3eb1b0_0
scikit-learn 0.24.2 pypi_0 pypi
scipy 1.6.2 py37h91f5cce_0
send2trash 1.5.0 pyhd3eb1b0_1
sentencepiece 0.1.96 pypi_0 pypi
setuptools 52.0.0 py37h06a4308_0
shellingham 1.3.1 py_0
sip 4.19.8 py37hf484d3e_0
six 1.16.0 pyhd3eb1b0_0
smart_open 2.2.1 py_0
soupsieve 2.2.1 pyhd3eb1b0_0
spacy 3.0.3 py37h6dcda5c_0 fastchan
spacy-legacy 3.0.8 pyhd8ed1ab_0 fastchan
sqlite 3.33.0 h62c20be_0
srsly 2.4.0 py37hcd2ae1e_1 fastchan
terminado 0.9.4 py37h06a4308_0
testpath 0.5.0 pyhd3eb1b0_0
thinc 8.0.3 py37hda21425_1 fastchan
threadpoolctl 2.2.0 pypi_0 pypi
tk 8.6.10 hbc83047_0
torch 1.9.0 pypi_0 pypi
torchvision 0.10.0 pypi_0 pypi
tornado 6.1 py37h27cfd23_0
tqdm 4.62.0 pyhd3eb1b0_1
traitlets 5.0.5 pyhd3eb1b0_0
typer 0.3.1 py_0 fastchan
typing-extensions 3.10.0.0 hd3eb1b0_0
typing_extensions 3.10.0.0 pyh06a4308_0
urllib3 1.26.6 pyhd3eb1b0_1
wasabi 0.8.2 pyhd3eb1b0_0
wcwidth 0.2.5 py_0
webencodings 0.5.1 py37_1
wheel 0.36.2 pyhd3eb1b0_0
widgetsnbextension 3.5.1 py37_0
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h7b6447c_0
yarl 1.5.1 py37h7b6447c_0
zeromq 4.3.4 h2531618_0
zipp 3.5.0 pyhd3eb1b0_0
zlib 1.2.11 h7b6447c_3
zstd 1.5.0 ha95c52a_0 fastchan
Additional note: I am also unable to plot the confusion matrix. Same thing as in the other cases, there is no error message when calling this:
interp = ClassificationInterpretation.from_learner(learner)
or this:
interp.plot_confusion_matrix()
However the matrix is just not displayed.
Could it be a matplotlib issue? Or do show_batch and show_results use something else?
Ah! If I run plt.show(), it now shows all the missing stuff at once!
And if I run
%matplotlib inline
then all the commands show what they’re supposed to (without needing plot.show()).
IiRC you need to set a notebook magic ( %% ) for plotting inline. Are you setting that?
Right!
%matplotlib inline
does the trick. Phew! Thanks.
Shouldn’t this be part of all notebooks then?
Usually they are. But not all environments actually need it.
Eg Colab doesn’t