tensorflow / tensorflow/tensorboard

Batch accuracy and batch loss are not being plotted in browser or vscode plugin

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Dominant language
TypeScript
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Forks
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Avg merge
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Merged PRs (30d)
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Description

This link in the bug report text did not work for me:

https://raw.githubusercontent.com/tensorflow/tensorboard/master/tensorboard/tools/diagnose_tensorboard.py

/home/user/work/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 df7af2c6fc0e4c4a5b47aeae078bc7ad95777ffa

--- check: general
INFO: sys.version_info: sys.version_info(major=3, minor=12, micro=2, releaselevel='final', serial=0)
INFO: os.name: posix
INFO: os.uname(): posix.uname_result(sysname='Linux', nodename='beast', release='6.6.21-1-lts', version='#1 SMP PREEMPT_DYNAMIC Wed, 06 Mar 2024 16:59:55 +0000', machine='x86_64')
INFO: sys.getwindowsversion(): N/A

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

--- check: installed_packages
INFO: installed: tensorboard==2.16.2
INFO: installed: tensorflow==2.16.1
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: '2.16.2'

--- check: tensorflow_python_version
2024-03-14 13:58:36.829200: I tensorflow/core/util/port.cc:113] 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`.
2024-03-14 13:58:36.851097: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-03-14 13:58:37.239354: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO: tensorflow.__version__: '2.16.1'
INFO: tensorflow.__git_version__: 'v2.16.1-0-g5bc9d26649c'

--- check: tensorboard_data_server_version
INFO: data server binary: '/home/john/work/Sleep/.venv/lib/python3.12/site-packages/tensorboard_data_server/bin/server'
INFO: data server binary version: b'rustboard 0.7.2'

--- check: tensorboard_binary_path
INFO: which tensorboard: b'/home/john/work/Sleep/.venv/bin/tensorboard\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: 32>
socket.AI_PASSIVE = <AddressInfo.AI_PASSIVE: 1>
Loopback flags: <AddressInfo.AI_ADDRCONFIG: 32>
Loopback infos: [(<AddressFamily.AF_INET6: 10>, <SocketKind.SOCK_STREAM: 1>, 6, '', ('::1', 0, 0, 0)), (<AddressFamily.AF_INET: 2>, <SocketKind.SOCK_STREAM: 1>, 6, '', ('127.0.0.1', 0))]
Wildcard flags: <AddressInfo.AI_PASSIVE: 1>
Wildcard infos: [(<AddressFamily.AF_INET: 2>, <SocketKind.SOCK_STREAM: 1>, 6, '', ('0.0.0.0', 0)), (<AddressFamily.AF_INET6: 10>, <SocketKind.SOCK_STREAM: 1>, 6, '', ('::', 0, 0, 0))]

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

--- check: stat_tensorboardinfo
INFO: directory: /tmp/.tensorboard-info
INFO: os.stat(...): os.stat_result(st_mode=16895, st_ino=1853, st_dev=44, st_nlink=2, st_uid=1000, st_gid=1000, st_size=40, st_atime=1710438727, st_mtime=1710438986, st_ctime=1710438986)
INFO: mode: 0o40777

--- check: source_trees_without_genfiles
INFO: tensorboard_roots (1): ['/home/john/work/Sleep/.venv/lib/python3.12/site-packages']; bad_roots (0): []

--- check: full_pip_freeze
INFO: pip freeze --all:
absl-py==2.1.0
asttokens==2.4.1
astunparse==1.6.3
bidict==0.23.1
biosppy==2.1.2
certifi==2024.2.2
charset-normalizer==3.3.2
colorama==0.4.6
colorlog==6.8.2
comm==0.2.2
contourpy==1.2.0
cycler==0.12.1
debugpy==1.8.1
decorator==5.1.1
dm-tree==0.1.8
easydev==0.13.1
edfio==0.4.0
executing==2.0.1
flatbuffers==24.3.7
fonttools==4.49.0
future==1.0.0
gast==0.5.4
google-pasta==0.2.0
grpcio==1.62.1
h5py==3.10.0
idna==3.6
ipykernel==6.29.3
ipython==8.22.2
jedi==0.19.1
Jinja2==3.1.3
joblib==1.3.2
jupyter_client==8.6.1
jupyter_core==5.7.2
keras==3.0.5
kiwisolver==1.4.5
lazy_loader==0.3
libclang==16.0.6
lightgbm==4.3.0
line-profiler==4.1.2
lxml==5.1.0
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.8.3
matplotlib-inline==0.1.6
mdurl==0.1.2
ml-dtypes==0.3.2
mne==1.6.1
namex==0.0.7
nest-asyncio==1.6.0
nolds==0.5.2
numpy==1.26.4
nvidia-cublas-cu12==12.3.4.1
nvidia-cuda-cupti-cu12==12.3.101
nvidia-cuda-nvcc-cu12==12.3.107
nvidia-cuda-nvrtc-cu12==12.3.107
nvidia-cuda-runtime-cu12==12.3.101
nvidia-cudnn-cu12==8.9.7.29
nvidia-cufft-cu12==11.0.12.1
nvidia-curand-cu12==10.3.4.107
nvidia-cusolver-cu12==11.5.4.101
nvidia-cusparse-cu12==12.2.0.103
nvidia-nccl-cu12==2.19.3
nvidia-nvjitlink-cu12==12.3.101
opencv-python==4.9.0.80
opt-einsum==3.3.0
packaging==24.0
pandas==2.2.1
parso==0.8.3
pexpect==4.9.0
pillow==10.2.0
pip==24.0
platformdirs==4.2.0
pooch==1.8.1
prompt-toolkit==3.0.43
protobuf==4.25.3
psutil==5.9.8
ptyprocess==0.7.0
pure-eval==0.2.2
Pygments==2.17.2
pyhrv==0.4.1
pyparsing==3.1.2
python-dateutil==2.9.0.post0
pytz==2024.1
PyWavelets==1.5.0
pyzmq==25.1.2
requests==2.31.0
rich==13.7.1
scikit-learn==1.4.1.post1
scipy==1.12.0
seaborn==0.13.2
setuptools==69.2.0
shortuuid==1.0.13
six==1.16.0
spectrum==0.8.1
stack-data==0.6.3
tensorboard==2.16.2
tensorboard-data-server==0.7.2
tensorflow==2.16.1
termcolor==2.4.0
threadpoolctl==3.3.0
tornado==6.4
tqdm==4.66.2
traitlets==5.14.2
typing_extensions==4.10.0
tzdata==2024.1
urllib3==2.2.1
wcwidth==0.2.13
Werkzeug==3.0.1
wheel==0.43.0
wrapt==1.16.0

In vscode plugin and Firefox, the same issue:
image

Issue description

The batch_accuracy and batch_loss are not being plotted. Their is a single dot at the center, but this screenshot was taken after some 3100 batches, so there should have been a line plotted for both.

Callbacks in my model.fit:

        callbacks=[
            tf.keras.callbacks.TensorBoard(log_dir=LOG_PATH, update_freq="batch"),
            chkpt_callback,
        ],

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 by reproducing the missing batch_accuracy and batch_loss plots with the shown tf.keras.callbacks.TensorBoard configuration, comparing browser and VSCode plugin behavior. Trace how these batch metrics are logged and rendered, then verify that both series display all recorded batches rather than a single centered point.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, typescript
Domain
frontend, machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
35/100

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