tensorflow / tensorflow/tensorboard

Error when trying the Notebook Guide : Visualizing Data using the Embedding Projector in TensorBoard

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Description

Environment information (required)

Please run diagnose_tensorboard.py (link below) in the same
environment from which you normally run TensorFlow/TensorBoard, and
paste the output here:

C:\Users\XXXXX\Desktop\XXXXX\XXXXX>python diagnose_tensorboard.py
C:\Users\XXXXX\Desktop\XXXXX\XXXXX\diagnose_tensorboard.py:32: DeprecationWarning: 'pipes' is deprecated and slated for removal in Python 3.13
import pipes

Diagnostics
Diagnostics output 
--- check: autoidentify
INFO: diagnose_tensorboard.py version c6ca9f1d004e2a1bc7c160abc43be229b82cad7e

--- check: general
INFO: sys.version_info: sys.version_info(major=3, minor=12, micro=8, releaselevel='final', serial=0)
INFO: os.name: nt
INFO: os.uname(): N/A
INFO: sys.getwindowsversion(): sys.getwindowsversion(major=10, minor=0, build=19045, platform=2, service_pack='')

--- check: package_management
INFO: has conda-meta: False
INFO: $VIRTUAL_ENV: None

--- check: installed_packages
INFO: installed: tensorboard==2.18.0
INFO: installed: tensorflow==2.18.0
WARNING: no installation among: ['tensorflow-estimator', 'tensorflow-estimator-2.0-preview', 'tf-estimator-nightly']
INFO: installed: tensorboard-data-server==0.7.2

--- check: tensorboard_python_version
INFO: tensorboard.version.VERSION: '2.18.0'

--- check: tensorflow_python_version
2025-01-08 23:15:10.594605: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-01-08 23:15:11.862636: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
INFO: tensorflow.__version__: '2.18.0'
INFO: tensorflow.__git_version__: 'v2.18.0-rc2-4-g6550e4bd802'

--- check: tensorboard_data_server_version
INFO: data server binary: None

--- check: tensorboard_binary_path
INFO: which tensorboard: b'C:\\Users\\XXXXX\\AppData\\Roaming\\Python\\Python312\\Scripts\\tensorboard.exe\r\n'

--- check: addrinfos
socket.has_ipv6 = True
socket.AF_UNSPEC = <AddressFamily.AF_UNSPEC: 0>
socket.SOCK_STREAM = <SocketKind.SOCK_STREAM: 1>
socket.AI_ADDRCONFIG = <AddressInfo.AI_ADDRCONFIG: 1024>
socket.AI_PASSIVE = <AddressInfo.AI_PASSIVE: 1>
Loopback flags: <AddressInfo.AI_ADDRCONFIG: 1024>
Loopback infos: [(<AddressFamily.AF_INET6: 23>, <SocketKind.SOCK_STREAM: 1>, 0, '', ('::1', 0, 0, 0)), (<AddressFamily.AF_INET: 2>, <SocketKind.SOCK_STREAM: 1>, 0, '', ('127.0.0.1', 0))]
Wildcard flags: <AddressInfo.AI_PASSIVE: 1>
Wildcard infos: [(<AddressFamily.AF_INET6: 23>, <SocketKind.SOCK_STREAM: 1>, 0, '', ('::', 0, 0, 0)), (<AddressFamily.AF_INET: 2>, <SocketKind.SOCK_STREAM: 1>, 0, '', ('0.0.0.0', 0))]

--- check: readable_fqdn
INFO: socket.getfqdn(): 'XXXXX'

--- check: stat_tensorboardinfo
INFO: directory: C:\Users\XXXXX\AppData\Local\Temp\.tensorboard-info
INFO: os.stat(...): os.stat_result(st_mode=16895, st_ino=40250921669688513, st_dev=778575332525344801, st_nlink=1, st_uid=0, st_gid=0, st_size=0, st_atime=1736372825, st_mtime=1736173666, st_ctime=1736173666)
INFO: mode: 0o40777

--- check: source_trees_without_genfiles
INFO: tensorboard_roots (1): ['C:\\Users\\XXXXX\\AppData\\Roaming\\Python\\Python312\\site-packages']; bad_roots (0): []

--- check: full_pip_freeze
INFO: pip freeze --all:
absl-py==2.1.0
anyio==4.8.0
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==3.0.0
astunparse==1.6.3
async-lru==2.0.4
attrs==24.3.0
babel==2.16.0
beautifulsoup4==4.12.3
bleach==6.2.0
certifi==2024.12.14
cffi==1.17.1
charset-normalizer==3.4.1
click==8.1.8
colorama==0.4.6
comm==0.2.2
contourpy==1.3.1
cycler==0.12.1
debugpy==1.8.11
decorator==5.1.1
defusedxml==0.7.1
dm-tree==0.1.8
docstring_parser==0.16
etils==1.11.0
executing==2.1.0
fastjsonschema==2.21.1
filelock==3.16.1
flatbuffers==24.12.23
fonttools==4.55.3
fqdn==1.5.1
fsspec==2024.10.0
gast==0.6.0
google-pasta==0.2.0
googleapis-common-protos==1.66.0
grpcio==1.69.0
h11==0.14.0
h5py==3.12.1
httpcore==1.0.7
httpx==0.28.1
idna==3.10
immutabledict==4.2.1
importlib_resources==6.5.2
ipykernel==6.29.5
ipython==8.31.0
ipywidgets==8.1.5
isoduration==20.11.0
jedi==0.19.2
Jinja2==3.1.4
joblib==1.4.2
json5==0.10.0
jsonpointer==3.0.0
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
jupyter==1.1.1
jupyter-console==6.6.3
jupyter-events==0.11.0
jupyter-lsp==2.2.5
jupyter_client==8.6.3
jupyter_core==5.7.2
jupyter_server==2.15.0
jupyter_server_terminals==0.5.3
jupyterlab==4.3.4
jupyterlab_pygments==0.3.0
jupyterlab_server==2.27.3
jupyterlab_widgets==3.0.13
keras==3.8.0
kiwisolver==1.4.8
libclang==18.1.1
Markdown==3.7
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.10.0
matplotlib-inline==0.1.7
mdurl==0.1.2
mistune==3.1.0
ml-dtypes==0.4.1
mpmath==1.3.0
namex==0.0.8
nbclient==0.10.2
nbconvert==7.16.5
nbformat==5.10.4
nest-asyncio==1.6.0
networkx==3.4.2
notebook==7.3.2
notebook_shim==0.2.4
numpy==2.0.2
opt_einsum==3.4.0
optree==0.13.1
overrides==7.7.0
packaging==24.2
pandas==2.2.3
pandocfilters==1.5.1
parso==0.8.4
patsy==1.0.1
pillow==11.0.0
pip==24.3.1
platformdirs==4.3.6
prometheus_client==0.21.1
promise==2.3
prompt_toolkit==3.0.48
protobuf==5.29.3
psutil==6.1.1
pure_eval==0.2.3
pyarrow==18.1.0
pycparser==2.22
Pygments==2.18.0
pyparsing==3.2.1
python-dateutil==2.9.0.post0
python-json-logger==3.2.1
pytz==2024.2
pywin32==308
pywinpty==2.0.14
PyYAML==6.0.2
pyzmq==26.2.0
referencing==0.35.1
requests==2.32.3
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==13.9.4
rpds-py==0.22.3
scikit-learn==1.6.0
scipy==1.15.0
seaborn==0.13.2
Send2Trash==1.8.3
setuptools==70.2.0
simple-parsing==0.1.6
six==1.17.0
sniffio==1.3.1
soupsieve==2.6
stack-data==0.6.3
statsmodels==0.14.4
sympy==1.13.1
tensorboard==2.18.0
tensorboard-data-server==0.7.2
tensorflow==2.18.0
tensorflow-datasets==4.9.7
tensorflow-metadata==1.16.1
tensorflow_intel==2.18.0
termcolor==2.5.0
terminado==0.18.1
threadpoolctl==3.5.0
tinycss2==1.4.0
toml==0.10.2
torch==2.7.0.dev20250106+cu126
torchaudio==2.6.0.dev20250107+cu126
torchvision==0.22.0.dev20250107+cu126
tornado==6.4.2
tqdm==4.67.1
traitlets==5.14.3
types-python-dateutil==2.9.0.20241206
typing_extensions==4.12.2
tzdata==2024.2
uri-template==1.3.0
urllib3==2.3.0
wcwidth==0.2.13
webcolors==24.11.1
webencodings==0.5.1
websocket-client==1.8.0
Werkzeug==3.1.3
wheel==0.45.1
widgetsnbextension==4.0.13
wrapt==1.17.0
zipp==3.21.0
Next steps

No action items identified. Please copy ALL of the above output,
including the lines containing only backticks, into your GitHub issue
or comment. Be sure to redact any sensitive information.

C:\Users\XXXXX\XXXXX\XXXXX\XXXXX>

Issue description

When trying the tutorial notebook here : https://github.com/tensorflow/tensorboard/blob/master/docs/tensorboard_projector_plugin.ipynb

I encounter an issue when trying to run the second cell :

(train_data, test_data), info = tfds.load(
    "imdb_reviews/subwords8k",
    split=(tfds.Split.TRAIN, tfds.Split.TEST),
    with_info=True,
    as_supervised=True,
)

Here is the error :

ValueError: Failed to construct dataset "imdb_reviews", builder_kwargs "{'config': 'subwords8k', 'data_dir': None}": BuilderConfig subwords8k not found with version None. Available: ['plain_text']

I think the argument name="imdb_reviews/subwords8k" is no longer an option, this causes the error. I tried to replace with plain_text and the loading worked.

But plain text and embeddings do not match so the rest of the example notebook will not execute

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with docs/tensorboard_projector_plugin.ipynb and inspect the second cell containing the tfds.load call for imdb_reviews/subwords8k. Check which dataset configuration is available in the reported environment, then update the notebook so its data and embeddings remain compatible. Run the notebook through the projector example to confirm the later cells execute.

Written by the indexing model from the issue text.

Assessment

Tech stack
jupyter-notebook, python
Domain
documentation, machine-learning
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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