Comments (10)
the statement
render(plot_contour(...))
had run successfully in Jupyter Lab, but the image not shown in it.
You can open your .ipynb file with Jupyter Notebook, and you will see the image is just there.
from ax.
@lena-kashtelyan Thanks! I will keep following.
from ax.
@Leonhalt3141 - the main dependency should be Plotly - the init_notebook_plotting()
call in the first cell should be injecting the dependency that you need.
Is there anything that shows up in the browser console log here?
We were seeing issues with plotting in Google Colab (see #83) but they were throwing explicit Python errors (that have now been fixed on master). This seems different.
In general, if you can provide some additional details on how we could reproduce this, it could be helpful.
from ax.
I run the following codes on Jupyter. Did not get any error but not showing graphs.
import torch
import numpy as np
from ax.plot.contour import plot_contour
from ax.plot.trace import optimization_trace_single_method
from ax.service.managed_loop import optimize
from ax.utils.notebook.plotting import render, init_notebook_plotting
from ax.utils.tutorials.cnn_utils import load_mnist, train, evaluate
init_notebook_plotting()
%%
dtype = torch.float
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
%%
def train_evaluate(parameterization):
net = train(train_loader=train_loader, parameters=parameterization, dtype=dtype, device=device)
return evaluate(
net=net,
data_loader=valid_loader,
dtype=dtype,
device=device,
)
%%
best_parameters, values, experiment, model = optimize(
parameters=[
{"name": "lr", "type": "range", "bounds": [1e-6, 0.4], "log_scale": True},
{"name": "momentum", "type": "range", "bounds": [0.0, 1.0]},
],
evaluation_function=train_evaluate,
objective_name='accuracy',
)
%%
render(plot_contour(model=model, param_x='lr', param_y='momentum', metric_name='accuracy'))
The following picture shows a cell after executing render
function.
My Python package environment is shown bellow.
absl-py==0.7.1
affine==2.2.2
alabaster==0.7.10
anaconda-client==1.6.5
anaconda-navigator==1.6.9
anaconda-project==0.8.0
asn1crypto==0.22.0
astor==0.7.1
astroid==1.5.3
astropy==2.0.2
attrs==19.1.0
awscli==1.16.159
ax-platform==0.1.1
Babel==2.5.0
backports.shutil-get-terminal-size==1.0.0
bayesian-optimization==1.0.1
beautifulsoup4==4.6.0
bitarray==0.8.1
bkcharts==0.2
blaze==0.11.3
bleach==2.0.0
bokeh==0.12.10
boto==2.48.0
boto3==1.9.149
botocore==1.12.149
botorch==0.1.0
Bottleneck==1.2.1
certifi==2019.3.9
cffi==1.10.0
chainer==5.4.0
chardet==3.0.4
click==6.7
click-plugins==1.1.1
cligj==0.5.0
cloudpickle==0.4.0
clyent==1.2.2
colorama==0.3.9
conda==4.6.14
conda-build==3.0.27
conda-verify==2.0.0
contextlib2==0.5.5
cryptography==2.6.1
cupy-cuda90==6.0.0
cycler==0.10.0
Cython==0.26.1
cytoolz==0.8.2
dask==0.15.3
datashape==0.5.4
decorator==4.1.2
distributed==1.19.1
Django==2.2.1
dlib==19.17.0
docutils==0.14
entrypoints==0.2.3
ephem==3.7.6.0
et-xmlfile==1.0.1
fastcache==1.0.2
fastrlock==0.4
filelock==2.0.12
Flask==0.12.2
Flask-Cors==3.0.3
gast==0.2.2
GDAL==2.3.3
gevent==1.2.2
glob2==0.5
gmpy2==2.0.8
gpytorch==0.3.2
greenlet==0.4.12
grpcio==1.20.1
h5py==2.9.0
heapdict==1.0.0
html5lib==0.999999999
idna==2.6
imageio==2.2.0
imagesize==0.7.1
ipykernel==4.6.1
ipython==6.1.0
ipython-genutils==0.2.0
ipywidgets==7.0.0
isort==4.2.15
itsdangerous==0.24
jdcal==1.3
jedi==0.10.2
Jinja2==2.9.6
jmespath==0.9.4
jsonschema==2.6.0
jupyter==1.0.0
jupyter-client==5.1.0
jupyter-console==5.2.0
jupyter-core==4.3.0
jupyterlab==0.35.6
jupyterlab-launcher==0.4.0
jupyterlab-server==0.2.0
kaggle==1.5.3
Keras-Applications==1.0.7
Keras-Preprocessing==1.0.9
kiwisolver==1.1.0
lazy-object-proxy==1.3.1
llvmlite==0.20.0
locket==0.2.0
lxml==4.1.0
Markdown==3.1
MarkupSafe==1.0
matplotlib==2.2.2
mccabe==0.6.1
mistune==0.7.4
mock==3.0.5
mpl-finance==0.10.0
mpmath==0.19
msgpack-python==0.4.8
multipledispatch==0.4.9
navigator-updater==0.1.0
nbconvert==5.3.1
nbformat==4.4.0
networkx==2.0
nltk==3.2.4
nose==1.3.7
notebook==5.0.0
numba==0.35.0+10.g143f70e90
numexpr==2.6.2
numpy==1.16.3
numpydoc==0.7.0
odo==0.5.1
olefile==0.44
openpyxl==2.4.8
packaging==16.8
pandas==0.24.2
pandocfilters==1.4.2
partd==0.3.8
path.py==10.3.1
pathlib2==2.3.0
patsy==0.4.1
pep8==1.7.0
pexpect==4.2.1
pickleshare==0.7.4
Pillow==6.0.0
pkginfo==1.4.1
plotly==2.7.0
ply==3.10
prompt-toolkit==1.0.15
protobuf==3.7.1
psutil==5.4.0
ptyprocess==0.5.2
py==1.4.34
pyasn1==0.4.5
pycodestyle==2.3.1
pycosat==0.6.3
pycparser==2.18
pycrypto==2.6.1
pycurl==7.43.0.2
pyflakes==1.6.0
Pygments==2.2.0
PyKrige==1.4.1
pylint==1.7.4
pyodbc==4.0.17
pyOpenSSL==17.2.0
pyparsing==2.2.0
PySocks==1.6.7
pystan==2.19.0.0
pytest==3.2.1
python-dateutil==2.6.1
python-slugify==3.0.2
pytz==2017.2
PyWavelets==0.5.2
PyYAML==3.12
pyzmq==16.0.2
QtAwesome==0.4.4
qtconsole==4.3.1
QtPy==1.3.1
rasterio==1.0.1
requests==2.18.4
rope==0.10.5
rsa==3.4.2
ruamel-yaml==0.11.14
s3transfer==0.2.0
scikit-image==0.13.0
scikit-learn==0.19.1
scipy==1.2.1
seaborn==0.8
simplegeneric==0.8.1
simplejson==3.16.0
singledispatch==3.4.0.3
six==1.11.0
sklearn==0.0
snowballstemmer==1.2.1
snuggs==1.4.6
sortedcollections==0.5.3
sortedcontainers==1.5.7
Sphinx==1.6.3
sphinxcontrib-websupport==1.0.1
spyder==3.2.4
SQLAlchemy==1.1.13
sqlparse==0.3.0
statsmodels==0.8.0
sympy==1.1.1
tables==3.5.1
tblib==1.3.2
tensorboard==1.13.1
tensorflow==1.13.1
tensorflow-estimator==1.13.0
tensorflow-gpu==1.13.1
tensorflow-probability==0.6.0
termcolor==1.1.0
terminado==0.6
testpath==0.3.1
text-unidecode==1.2
toolz==0.8.2
torch==1.1.0
torchvision==0.2.2
tornado==4.5.2
tqdm==4.32.1
traitlets==4.3.2
typing==3.6.2
unicodecsv==0.14.1
urllib3==1.22
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.12.2
widgetsnbextension==3.0.2
wrapt==1.10.11
xlrd==1.1.0
XlsxWriter==1.0.2
xlwt==1.3.0
zict==0.1.3
from ax.
@Leonhalt3141 - I've identified the issue - the Plotly resource is not being loaded in Jupyter Labs since require.js is not exposed by default on the page. We will have a fix for this next week, but if you need the plots to work sooner than that, I'd recommend using vanilla Jupyter (not Jupyter Labs). The plots should all work without any issues there.
from ax.
@kkashin Thank you for your help. I will keep following the commits and reflect the fix!
from ax.
Thanks @summererror - that's a useful workaround for now! We're close to making this work for Jupyter Labs directly - should be in master by early next week.
from ax.
Migration to jupyterlab-friendly plotting has been tested successfully.
from ax.
@Leonhalt3141, @summererror, just to be specific, the plots should now render in JupyterLabs if you have the master version of Ax installed; the fix for JupyterLabs will also be included in the next stable release.
from ax.
This is now fixed on the latest stable version, 0.1.6.
from ax.
Related Issues (20)
- attach trials HOT 8
- Multi-task BO with Service API HOT 2
- constrains HOT 17
- Question : Generation 12 trials HOT 8
- Question: Multi-Task Multi Objective HOT 1
- MOO not respecting nonlinear constraints HOT 1
- Problem with Fixed parameters if nonlinear_inequality_constraint is imposed
- Safe optimization in the Service API HOT 4
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 1
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from ax.