ml-indie-tools 0.12.0

A collection of tools for low-resource indie machine learning development

A collection of machine learning tools for low-resource research and experiments

Description

pip install ml-indie-tools

This module contains of a collection of tools useable for researchers with limited access to compute-resources and who change between laptop, Colab-instances and local workstations with a graphics card.

env_tools checks the current environment, and populates a number of flags that allow identification of run-time environment and available accelerator hardware. For Colab instances, it provides tools to mount Google Drive for persistent data- and model-storage.

The usage scenarios are:

Env	MLX	Pytorch TPU	Pytorch GPU	Jax TPU	Jax GPU
Colab	/	/	+	+	+
Workstation with Nvidia	/	/	+	/	+
Apple Silicon	+	/	+	/	+e

(+: supported, /: not supported, +e: experimental)

Gutenberg_Dataset and Text_Dataset are NLP libraries that provide text data and can be used in conjuction with Huggingface Datasets or directly with ML libraries.

ALU_Dataset is a toy-dataset that allows training of integer arithmetic and logical (ALU) operations.

env_tools

A collection of tools that allow moving machine learning projects between local hardware and colab instances.

Examples

Local laptop:

from ml_indie_tools.env_tools import MLEnv
ml_env = MLEnv(platform='pt', accelator='fastest')
ml_env.describe()  # -> 'OS: Darwin, Python: 3.12 (Conda) Pytorch: 2.1, GPU: METAL'
ml_env.is_gpu   # -> True
ml_env.is_mlx  # -> True
ml_env.gpu_type  # -> 'METAL'

Colab instance:

# !pip install -U ml_indie_tools
from ml_indie_tools.env_tools import MLEnv
ml_env = MLEnv(platform='pt', accelerator='fastest')
print(ml_env.describe())
print(ml_env.gpu_type)

Output:

DEBUG:MLEnv:Pytorch version: 2.1
DEBUG:MLEnv:GPU available
DEBUG:MLEnv:You are on a Jupyter instance.
DEBUG:MLEnv:You are on a Colab instance.
INFO:MLEnv:OS: Linux, Python: 3.12, Colab Jupyter Notebook Pytorch: 2.1, GPU: Tesla K80
OS: Linux, Python: 3.12, Colab Jupyter Notebook Pytorch: 2.1, GPU: Tesla K80
Tesla K80

Project paths

ml_env.init_paths('my_project', 'my_model') will give a list of paths that are adapted for local and colab usage

Local project:

ml_env.init_paths("my_project", "my_model")  
# -> ('.', '.', './model/my_model', './data', './logs')

The list contains <root-path>, <project-path> (both are ., the current directory for local projects), <model-path> to save model and weights, <data-path> for training data and <log-path> for logs.

Those paths (with exception of ./logs) are moved to Google Drive for Colab instances:

On Google Colab:

# INFO:MLEnv:You will now be asked to authenticate Google Drive access in order to store training data (cache) and model state.
# INFO:MLEnv:Changes will only happen within Google Drive directory `My Drive/Colab Notebooks/<project-name>`.
# DEBUG:MLEnv:Root path: /content/drive/My Drive
# Mounted at /content/drive
('/content/drive/My Drive',
 '/content/drive/My Drive/Colab Notebooks/my_project',
 '/content/drive/My Drive/Colab Notebooks/my_project/model/my_model',
 '/content/drive/My Drive/Colab Notebooks/my_project/data',
 './logs')

See the env_tools API documentation for details.

Gutenberg_Dataset

Gutenberg_Dataset makes books from Project Gutenberg available as dataset.

This module can either work with a local mirror of Project Gutenberg, or download files on demand. Files that are downloaded are cached to prevent unnecessary load on Gutenberg's servers.

Working with a local mirror of Project Gutenberg

If you plan to use a lot of files (hundreds or more) from Gutenberg, a local mirror might be the best solution. Have a look at Project Gutenberg's notes on mirrors.

A mirror image suitable for this project can be made with:

rsync -zarv --dry-run --prune-empty-dirs --del --include="*/" --include='*.'{txt,pdf,ALL} --exclude="*" aleph.gutenberg.org::gutenberg ./gutenberg_mirror

It's not mandatory to include pdf-files, since they are currently not used. Please review the --dry-run flag.

Once a mirror of at least all of Gutenberg's *.txt files and of index-file GUTINDEX.ALL has been generated, it can be used via:

from ml_indie_tools.Gutenberg_Dataset import Gutenberg_Dataset
gd = Gutenberg_Dataset(root_url='./gutenberg_mirror')  # Assuming this is the file-path to the mirror image

Working without a remote mirror

from ml_indie_tools.Gutenberg_Dataset import Gutenberg_Dataset
gd = Gutenberg_Dataset()  # the default Gutenberg site is used. Alternative specify a specific mirror with `root_url=http://...`.

Getting Gutenberg books

After using one of the two methods to instantiate the gd object:

gd.load_index()  # load the index of books

Then get a list of books (array). Each entry is a dict with meta-data: search_result is a list of dictionaries containing meta-data without the actual book-text.

search_result = gd.search({'author': ['kant', 'goethe'], 'language': ['german', 'english']})

Insert the actual book text into the dictionaries. Note that download count is limited if using a remote server.

search_result = gd.insert_book_texts(search_result)
# search_result entries now contain an additional field `text` with the filtered text of the book.
import pandas as pd
df = pd.DataFrame(search_result)  # Display results as Pandas DataFrame
df

See the Gutenberg_Dataset API documentation for details.

Text_Dataset

A library for character, word, or dynamical ngram tokenization.

import logging
logging.basicConfig(encoding='utf-8', level=logging.INFO)
from ml_indie_tools.Gutenberg_Dataset import Gutenberg_Dataset
from ml_indie_tools.Text_Dataset import Text_Dataset

gd=Gutenberg_Dataset()
gd.load_index()
bl=gd.search({'title': ['proleg', 'hermen'], 'language': ['english']})
bl=gd.insert_book_texts(bl)
for i in range(len(bl)):
    print(bl[i]['title'])

Prolegomena to the Study of Hegel's Philosophy
Kant's Prolegomena
The Cornish Fishermen's Watch Night and Other Stories
Prolegomena to the History of Israel
Legge Prolegomena

tl = Text_Dataset(bl)  # bl contains a list of texts (books from Gutenberg)
tl.source_highlight("If we write anything that contains parts of the sources, like: that is their motto, then a highlight will be applied.")

INFO:Datasets:Loaded 5 texts
If we write anything t[4]hat contains[1] parts of the s[4]ources, like: that is t[1]heir motto[4], then a highlight will be a[1]pplied.
Sources: Julius Wellhausen: Prolegomena to the History of Israel[4], William Wallace and G. W. F. Hegel: Prolegomena to the Study of Hegel's Philosophy[1]

test_text="That would be a valid argument if we hadn't defeated it's assumptions way before."
print(f"Text length {len(test_text)}, {test_text}")
tokenizer='ngram'
tl.init_tokenizer(tokenizer=tokenizer)
st = tl.tokenize(test_text)
print(f"Token-count: {len(st)}, {st}")

Text length 81, That would be a valid argument if we hadn't defeated it's assumptions way before. Token-count: 27, [1447, 3688, 1722, 4711, 4880, 1210, 1393, 4393, 2382, 1352, 3655, 1972, 1939, 44, 23, 3333, 1871, 4975, 2967, 2884, 2216, 2382, 3048, 1546, 4589, 2272, 30]

test2="ðƒ "+test_text
print(f"Text length {len(test2)}, {test2}")
el=tl.encode(test2)
print(f"Token-count: {len(el)}, {el}")

Text length 84, ðƒ That would be a valid argument if we hadn't defeated it's assumptions way before. Token-count: 29, ['<unk>', '<unk>', 1397, 3688, 1722, 4711, 4880, 1210, 1393, 4393, 2382, 1352, 3655, 1972, 1939, 44, 23, 3333, 1871, 4975, 2967, 2884, 2216, 2382, 3048, 1546, 4589, 2272, 30]

See the Text_Dataset API documentation for details.

ALU_Dataset

See the ALU_Dataset API documentation for details. A sample project is at ALU_Net

keras_custom_layers

A collection of Keras residual- and self-attention layers

See the keras_custom_layers API documentation for details.

External projects

Checkout the following jupyter notebook based projects for example-usage:

Text generation

• tensor-poet
• torch-poet
• transformer-poet
• torch-transformer-poet, using pytorch transformers from Andrej Karpathy's nanoGPT as implemented in ng-video-lecture

Arithmetic and logic operations

• ALU_Net

History

• (2024-04-28, 0.12.0) JAX support for Apple Silicon via jax-metal added (experimental).
• (2024-03-25, 0.11.0) Tensorflow support completely removed, maintenance is simply too much effort due to continous API changes.
• (2024-02-21, 0.10.4) More tests with bytegrams.
• (2023-11-14, 0.9.3) Fix/hack for reloading of checkpoints for compiled models (torch puts weights in some sub-object: _orig_mod)
• (2023-11-13, 0.9.0) Breaking API change to Folder_Dataset, load_index() can be called multiple times additively.
• (2023-04-2, 0.8.168) Two transformer variants: a transformer with 'yoke', MultiHeadSelfAttentionWithCompression (a layer that compresses information, forcing abstraction), and version with state: MultiHeadSelfAttentionWithCompressionState, a state is combined with the yoke-layer, allowing the most 'abstract' information of the transformer to be maintained in a recurrent manner. See torch-transformer-poet for examples.
• (2023-04-01, 0.8.90) API changes: WIP!
• (2023-03-31, 0.8.0) Put compression/state experiments in separate model.
• (2023-03-30, 0.7.0) Cleanup of bottleneck mechanism to force abstraction. Dropout behave again normal (hacks removed).
• (2023-03-28, 0.6.0) Add dropout>1.0 paramater to MultiHeadSelfAttention (torch): replaces 'normal' dropout with a linear compression by 4.0/dropout. The linear layers no longer map n -> 4n -> n, but n -> 4n/dropout -> n. This reduces the amount of information, the net can propagate, forcing compression. Sigma_compression uses different compressions rates: max in the middle layers, and non at start end end layers, linearly interpolating between them.
• (2023-02-01, 0.5.6) load_checkpoint(), optionally only load params. Incompatible API-change for load- and save_checkpoint() methods!
• (2023-01-31, 0.5.4) Add top_k parameter to generator. Apple MPS users beware, MPS currently limits top_k to max 16.
• (2023-01-30, 0.5.3) Add use_aliases parameter to Folder- and Calibre datasets.
• (2023-01-27, 0.5.2) Add alias field to local datasets to protect privacy of local document names.
• (2023-01-27, 0.5.0) Acquire training data from Calibre library (Calibre_Dataset), the documents must be in text format in Calibre, or get training data from a folder containing text files (Folder_Dataset). Text_Dataset can now contain texts from Gutenberg, Calibre or a folder of text files.
• (2023-01-26, 0.4.4) Add save/load tokenizer to Text_Dataset to enable reusing tokenizer data.
• (2023-01-22, 0.4.3) Add temperature parameter to generator.
• (2023-01-21, 0.4.2) Start of port of pytorch transformers from Andrej Karpathy's nanoGPT as implemented in ng-video-lecture. Additional tests with Apple Silicon MPS and pytorch 2.0 nightly.
• (2022-12-13, 0.4.0) The great cleanup: neither recurrence nor gated memory improved the transformer architecture, so they are removed again.
• (2022-12-11, 0.3.17) Testversion for slightly handwavy recurrent attention
• (2022-06-19, 0.3.1) get_random_item(index) that works with all tokenization strategies, get_unique_token_count() added.
• (2022-06-19, 0.3.0) Breaking change in Text_Dataset get_item() behavior, old API didn't fit with tokenization.
• (2022-06-19, 0.2.0) Language agnostic dynamic ngram tokenizer.
• (2022-06-07, 0.1.5) Support for pytorch nightly 1.13dev MPS, Apple Metal acceleration on Apple Silicon.
• (2022-03-27, 0.1.4) Bugfixes to Gutenberg search and load_book and get_book.
• (2022-03-15, 0.1.2) env_tools.init() no longer uses tf.compat.v1.disable_eager_executition() since there are rumors about old code-paths being used. Use tf.function() instead, or call with env_tools.init(..., old_disable_eager=True) which continues to use the old v1 API.
• (2022-03-12, 0.1.0) First version for external use.
• (2021-12-26, 0.0.x) First pre-alpha versions published for testing purposes, not ready for use.