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Fix five alt image text references erroneously interpreted as substitution directives in sphinx build #180

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Jun 17, 2022
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Fix five alt image text references erroneously interpreted as substitution directives in sphinx build #180

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10 changes: 5 additions & 5 deletions lightning_examples/finetuning-scheduler/finetuning-scheduler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@
#
# 2. Alter the schedule as desired.
#
# ![side_by_side_yaml](side_by_side_yaml.png){height="327px" width="800px"}
# ![side-by-side-yaml](side_by_side_yaml.png){height="327px" width="800px"}
#
# 3. Once the finetuning schedule has been altered as desired, pass it to
# [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) to commence scheduled training:
Expand All @@ -105,7 +105,7 @@
#
# **Tip:** Use of regex expressions can be convenient for specifying more complex schedules. Also, a per-phase base maximum lr can be specified:
#
# ![emphasized_yaml](emphasized_yaml.png){height="380px" width="800px"}
# ![emphasized-yaml](emphasized_yaml.png){height="380px" width="800px"}
#
# </div>
#
Expand Down Expand Up @@ -645,8 +645,8 @@ def train() -> None:
# produced in the scenarios [here](https://drive.google.com/file/d/1t7myBgcqcZ9ax_IT9QVk-vFH_l_o5UXB/view?usp=sharing)
# (caution, ~3.5GB).
#
# [![fts_explicit_accuracy](fts_explicit_accuracy.png){height="315px" width="492px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D)
# [![nofts_baseline](nofts_baseline_accuracy.png){height="316px" width="505px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D)
# [![fts-explicit-accuracy](fts_explicit_accuracy.png){height="315px" width="492px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOnRydWUsIm5vZnRzX2Jhc2VsaW5lIjpmYWxzZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D)
# [![nofts-baseline](nofts_baseline_accuracy.png){height="316px" width="505px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6dHJ1ZSwiZnRzX2ltcGxpY2l0IjpmYWxzZX0%3D)
#
# Note there could be around ~1% variation in performance from the tensorboard summaries generated by this notebook
# which uses DP and 1 GPU.
Expand All @@ -656,7 +656,7 @@ def train() -> None:
# greater finetuning flexibility for model exploration in research. For example, glancing at DeBERTa-v3's implicit training
# run, a critical tuning transition point is immediately apparent:
#
# [![implicit_training_transition](implicit_training_transition.png){height="272px" width="494px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D)
# [![implicit-training-transition](implicit_training_transition.png){height="272px" width="494px"}](https://tensorboard.dev/experiment/n7U8XhrzRbmvVzC4SQSpWw/#scalars&_smoothingWeight=0&runSelectionState=eyJmdHNfZXhwbGljaXQiOmZhbHNlLCJub2Z0c19iYXNlbGluZSI6ZmFsc2UsImZ0c19pbXBsaWNpdCI6dHJ1ZX0%3D)
#
# Our `val_loss` begins a precipitous decline at step 3119 which corresponds to phase 17 in the schedule. Referring to our
# schedule, in phase 17 we're beginning tuning the attention parameters of our 10th encoder layer (of 11). Interesting!
Expand Down