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jupyter lab fixes #1139

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merged 5 commits into from
Jan 22, 2024
Merged

jupyter lab fixes #1139

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c225f88
add a basic notebook for lab users in the root
winglian Jan 17, 2024
e5aec3a
update notebook and fix cors for jupyter
winglian Jan 17, 2024
b4f8039
cell is code
winglian Jan 17, 2024
cfa197f
fix eval batch size check
winglian Jan 17, 2024
784faac
remove intro notebook
winglian Jan 17, 2024
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2 changes: 1 addition & 1 deletion docker/Dockerfile-cloud
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ ENV HF_HUB_ENABLE_HF_TRANSFER="1"

COPY scripts/cloud-entrypoint.sh /root/cloud-entrypoint.sh

RUN pip install jupyterlab notebook && \
RUN pip install jupyterlab notebook ipywidgets && \
jupyter lab clean
RUN apt install --yes --no-install-recommends openssh-server tmux && \
mkdir -p ~/.ssh && \
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2 changes: 1 addition & 1 deletion scripts/cloud-entrypoint.sh
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Expand Up @@ -25,7 +25,7 @@ fi

if [ "$JUPYTER_DISABLE" != "1" ]; then
# Run Jupyter Lab in the background
jupyter lab --allow-root --ip 0.0.0.0 &
jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* --ServerApp.preferred_dir=/workspace &
fi

# Execute the passed arguments (CMD)
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20 changes: 13 additions & 7 deletions src/axolotl/cli/train.py
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Expand Up @@ -3,9 +3,11 @@
"""
import logging
from pathlib import Path
from typing import Tuple

import fire
import transformers
from transformers import PreTrainedModel, PreTrainedTokenizer

from axolotl.cli import (
check_accelerate_default_config,
Expand All @@ -24,19 +26,23 @@
def do_cli(config: Path = Path("examples/"), **kwargs):
# pylint: disable=duplicate-code
parsed_cfg = load_cfg(config, **kwargs)
print_axolotl_text_art()
check_accelerate_default_config()
check_user_token()
parser = transformers.HfArgumentParser((TrainerCliArgs))
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
return_remaining_strings=True
)
return do_train(parsed_cfg, parsed_cli_args)


if parsed_cfg.rl:
dataset_meta = load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
print_axolotl_text_art()
check_accelerate_default_config()
check_user_token()
if cfg.rl:
dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
else:
dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)


if __name__ == "__main__":
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7 changes: 4 additions & 3 deletions src/axolotl/core/trainer_builder.py
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Expand Up @@ -746,9 +746,10 @@ def build(self, total_num_steps):
training_arguments_kwargs[
"per_device_train_batch_size"
] = self.cfg.micro_batch_size
training_arguments_kwargs[
"per_device_eval_batch_size"
] = self.cfg.eval_batch_size
if self.cfg.eval_batch_size:
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if not set, it'll default to 8 https://huggingface.co/docs/transformers/v4.36.1/en/main_classes/trainer#transformers.TrainingArguments.per_device_eval_batch_size

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Right, but I'm trying to prevent it from getting set to None here which results in a downstream error I found.

training_arguments_kwargs[
"per_device_eval_batch_size"
] = self.cfg.eval_batch_size
training_arguments_kwargs[
"gradient_accumulation_steps"
] = self.cfg.gradient_accumulation_steps
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3 changes: 2 additions & 1 deletion src/axolotl/utils/bench.py
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Expand Up @@ -20,7 +20,8 @@ def wrapper(*args, **kwargs):
device = kwargs.get("device", args[0] if args else None)

if (
not torch.cuda.is_available()
device is None
or not torch.cuda.is_available()
or device == "auto"
or torch.device(device).type == "cpu"
):
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8 changes: 6 additions & 2 deletions src/axolotl/utils/models.py
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Expand Up @@ -2,7 +2,7 @@
import logging
import math
import os
from typing import Any, Optional, Tuple, Union # noqa: F401
from typing import Any, Dict, Optional, Tuple, Union # noqa: F401

import addict
import bitsandbytes as bnb
Expand Down Expand Up @@ -339,7 +339,11 @@ def load_model(
LOG.info("patching _expand_mask")
hijack_expand_mask()

model_kwargs = {}
model_kwargs: Dict[str, Any] = {}

if cfg.model_kwargs:
for key, val in model_kwargs.items():
model_kwargs[key] = val

max_memory = cfg.max_memory
device_map = cfg.device_map
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