Commit 9f5eabcf authored by Mohammad Shoeybi's avatar Mohammad Shoeybi
Browse files

Merge branch 'remove-pkg' into 'main' 

Remove outdated packaging files.

See merge request ADLR/megatron-lm!331
parents 5ab64637 6c00f448

.gitignore

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__pycache__

# Distribution / packaging

build/

dist/

*.egg-info/
 
 No newline at end of file

MANIFEST.in

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include megatron/data/Makefile

include megatron/data/helpers.cpp

README.md

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@@ -48,13 +48,6 @@ We have tested Megatron with [NGC's PyTorch container](https://ngc.nvidia.com/ca 


To use this repository, please install the latest supported versions of PyTorch with GPU support (python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3 and above) and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.12-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation, or downstream tasks.



<!--

To use megatron you can either clone the repo or install it via pip (make sure python3-dev is installed):

<pre>

pip install megatron-lm

</pre>

-->



## Downloading Checkpoints

We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) and [GPT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1).

megatron/__init__.py

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# limitations under the License.

import torch



from .package_info import (

    __description__,

    __contact_names__,

    __url__,

    __download_url__,

    __keywords__,

    __license__,

    __package_name__,

    __version__,

)



from .global_vars import get_args

from .global_vars import get_current_global_batch_size

from .global_vars import get_num_microbatches

megatron/package_info.py

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# coding=utf-8

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



MAJOR = 1

MINOR = 1.5



# Use the following formatting: (major, minor)

VERSION = (MAJOR, MINOR)



__version__ = '.'.join(map(str, VERSION))

__package_name__ = 'megatron-lm'

__contact_names__ = 'NVIDIA INC'

__url__ = 'https://github.com/NVIDIA/Megatron-LM'

__download_url__ = 'https://github.com/NVIDIA/Megatron-LM/releases'

__description__ = 'Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.'

__license__ = 'See https://github.com/NVIDIA/Megatron-LM/blob/master/LICENSE'

__keywords__ = 'deep learning, Megatron, gpu, NLP, nvidia, pytorch, torch, language'