diff --git a/.gitignore b/.gitignore index fae498b..3db280a 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,5 @@ requirements.txt /rust-tfrecord /.venv /target_maturin +__pycache__/ +.DS_Store diff --git a/README.md b/README.md index 143f495..ee5f66c 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,9 @@ Examples are loaded into native PyTorch `Tensor`s. The wheel can be installed on any Linux system with Python 3.8 or higher: - pip3 install rustfrecord +```bash +pip3 install rustfrecord +```` ## Getting Started @@ -43,14 +45,11 @@ Repo: https://github.com/gavrie/rustfrecord To develop this package (not just use it), you need to install the Rust compiler and the Python development headers. - pip install uv - uv venv - source .venv/bin/activate - - uv pip compile pyproject.toml -o requirements.txt - uv pip install -r requirements.txt +```python +pip install uv # if needed - export LIBTORCH_USE_PYTORCH=1 - CARGO_TARGET_DIR=target_maturin maturin develop +export LIBTORCH_USE_PYTORCH=1 +CARGO_TARGET_DIR=target_maturin maturin develop - python main.py \ No newline at end of file +uv run pytest -sv test_rustfrecord.py +``` \ No newline at end of file diff --git a/images/ferris.png b/images/ferris.png new file mode 100644 index 0000000..9660859 Binary files /dev/null and b/images/ferris.png differ diff --git a/images/iron-oxide.jpg b/images/iron-oxide.jpg new file mode 100644 index 0000000..4287dec Binary files /dev/null and b/images/iron-oxide.jpg differ diff --git a/images/python-logo.png b/images/python-logo.png new file mode 100644 index 0000000..20f36f4 Binary files /dev/null and b/images/python-logo.png differ diff --git a/pyproject.toml b/pyproject.toml index 28a0196..c1501da 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,3 +24,8 @@ build-backend = "maturin" [tool.maturin] features = ["pyo3/extension-module"] + +[dependency-groups] +dev = [ + "pytest>=8.3.5", +] diff --git a/requirements-linux.txt b/requirements-linux.txt deleted file mode 100644 index 4104379..0000000 --- a/requirements-linux.txt +++ /dev/null @@ -1,122 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements-linux.txt -filelock==3.13.4 - # via - # torch - # triton -fsspec==2024.3.1 - # via torch -jinja2==3.1.3 - # via torch -markupsafe==2.1.5 - # via jinja2 -maturin==1.5.1 -mpmath==1.3.0 - # via sympy -networkx==3.3 - # via torch -numpy==1.26.4 -nvidia-cublas-cu12==12.1.3.1 - # via - # nvidia-cudnn-cu12 - # nvidia-cusolver-cu12 - # torch -nvidia-cuda-cupti-cu12==12.1.105 - # via torch -nvidia-cuda-nvrtc-cu12==12.1.105 - # via torch -nvidia-cuda-runtime-cu12==12.1.105 - # via torch -nvidia-cudnn-cu12==8.9.2.26 - # via torch -nvidia-cufft-cu12==11.0.2.54 - # via torch -nvidia-curand-cu12==10.3.2.106 - # via torch -nvidia-cusolver-cu12==11.4.5.107 - # via torch -nvidia-cusparse-cu12==12.1.0.106 - # via - # nvidia-cusolver-cu12 - # torch -nvidia-nccl-cu12==2.19.3 - # via torch -nvidia-nvjitlink-cu12==12.4.127 - # via - # nvidia-cusolver-cu12 - # nvidia-cusparse-cu12 -nvidia-nvtx-cu12==12.1.105 - # via torch -patchelf==0.17.2.1 -pip==24.0 -setuptools==69.2.0 -sympy==1.12 - # via torch -tomli==2.0.1 - # via maturin -torch==2.2.0 -triton==2.2.0 - # via torch -typing-extensions==4.11.0 - # via torch -# This file was autogenerated by uv via the following command: -# uv pip compile requirements.in -o requirements.txt -filelock==3.13.4 - # via - # torch - # triton -fsspec==2024.3.1 - # via torch -jinja2==3.1.3 - # via torch -markupsafe==2.1.5 - # via jinja2 -maturin==1.5.1 -mpmath==1.3.0 - # via sympy -networkx==3.3 - # via torch -numpy==1.26.4 -nvidia-cublas-cu12==12.1.3.1 - # via - # nvidia-cudnn-cu12 - # nvidia-cusolver-cu12 - # torch -nvidia-cuda-cupti-cu12==12.1.105 - # via torch -nvidia-cuda-nvrtc-cu12==12.1.105 - # via torch -nvidia-cuda-runtime-cu12==12.1.105 - # via torch -nvidia-cudnn-cu12==8.9.2.26 - # via torch -nvidia-cufft-cu12==11.0.2.54 - # via torch -nvidia-curand-cu12==10.3.2.106 - # via torch -nvidia-cusolver-cu12==11.4.5.107 - # via torch -nvidia-cusparse-cu12==12.1.0.106 - # via - # nvidia-cusolver-cu12 - # torch -nvidia-nccl-cu12==2.19.3 - # via torch -nvidia-nvjitlink-cu12==12.4.127 - # via - # nvidia-cusolver-cu12 - # nvidia-cusparse-cu12 -nvidia-nvtx-cu12==12.1.105 - # via torch -patchelf==0.17.2.1 -pip==24.0 -setuptools==69.2.0 -sympy==1.12 - # via torch -tomli==2.0.1 - # via maturin -torch==2.2.0 -triton==2.2.0 - # via torch -typing-extensions==4.11.0 - # via torch \ No newline at end of file diff --git a/requirements-macos.txt b/requirements-macos.txt deleted file mode 100644 index d99b1d3..0000000 --- a/requirements-macos.txt +++ /dev/null @@ -1,26 +0,0 @@ -# This file was autogenerated by uv via the following command: -# uv pip compile pyproject.toml -o requirements-macos.txt -filelock==3.13.4 - # via torch -fsspec==2024.3.1 - # via torch -jinja2==3.1.3 - # via torch -markupsafe==2.1.5 - # via jinja2 -maturin==1.5.1 -mpmath==1.3.0 - # via sympy -networkx==3.2.1 - # via torch -numpy==1.26.4 -patchelf==0.17.2.1 -pip==24.0 -setuptools==69.2.0 -sympy==1.12 - # via torch -tomli==2.0.1 - # via maturin -torch==2.2.0 -typing-extensions==4.11.0 - # via torch diff --git a/talk.html b/talk.html new file mode 100644 index 0000000..ca13e41 --- /dev/null +++ b/talk.html @@ -0,0 +1,115 @@ +
+

From TensorFlow to PyTorch

+

With some help from Rust

+
+

Gavrie Philipson
+Rusty Bits Software Ltd.
+June 2025

+
+
+

About Me

+

Gavrie Philipson

+
    +
  • Rust, Python, Cloud, Backend, DevOps, and more.
    • +
  • Bootstrapping software development teams: Training, mentoring, and hiring
    • +
  • Consulting to startup companies on software development and architecture
    • +
+
+

Rusty Bits Software Ltd.
+https://rustybits.io
+gavrie@rustybits.io

+
+
+

About You

+
+
+

Using Rust to improve Python

+
+
+
+
+
+
+
+

Astral
+PyO3

+
+
+

The Mission

+
    +
  • Port ML model from TensorFlow to PyTorch
    • +
  • Lots of training data in TFRecord format
    • +
+
+
+

The TFRecord format

+
    +
  • A sequence of HashMap<String, Vec<T>>
    • +
  • where T: u8 | f32 | i64
    • +
  • Serialized with protobuf
    • +
+
+
+

TFRecord Example

+
[
+  {
+    "label": "cat",
+    "image/shape": [320, 200, 3],
+    "image/encoded": [0x12, 0x34, 0x56, ...],
+  },
+  {
+    "label": "dog",
+    "image/shape": [320, 200, 3],
+    "image/encoded": [0x78, 0x9a, 0xbc, ...],
+  },
+]
+
+
+
+

The Constraints

+
    +
  • Dependencies (look at venv size)
    • +
  • Performance: Keep GPUs busy
    • +
  • Ease of use for Python devs
    • +
+
+
+

Challenge: Getting Test Data

+
    +
  • No access to the original data
    • +
  • Vibe code some Python to generate test data!
    • +
+
+
+

Playing on Rust's strengths

+ +
+
+

The End Result

+ +
+
+

Getting the Code

+

https://pypi.org/project/rustfrecord/
+https://github.com/gavrie/rustfrecord

+
+
\ No newline at end of file diff --git a/talk.md b/talk.md new file mode 100644 index 0000000..68910f5 --- /dev/null +++ b/talk.md @@ -0,0 +1,123 @@ +--- +marp: true +--- + +# From TensorFlow to PyTorch +## With some help from Rust + +
+ +Gavrie Philipson +Rusty Bits Software Ltd. +June 2025 + +--- + +# About Me + +## Gavrie Philipson + +- Rust, Python, Cloud, Backend, DevOps, and more. +- Bootstrapping software development teams: Training, mentoring, and hiring +- Consulting to startup companies on software development and architecture + +
+ +Rusty Bits Software Ltd. +https://rustybits.io +gavrie@rustybits.io + +--- + +# About You + +--- + +# Using Rust to improve Python + +
+
+
+
+
+ +![bg 40%](images/python-logo.png) +![bg 60%](images/ferris.png) +![bg 50%](images/iron-oxide.jpg) + +
+
+ +[Astral](https://astral.sh) +[PyO3](https://github.com/PyO3) + +--- + +# The Mission + +- Port ML model from TensorFlow to PyTorch +- Lots of training data in `TFRecord` format + +--- + +# The `TFRecord` format + +- A sequence of `HashMap>` +- `where T: u8 | f32 | i64` +- Serialized with `protobuf` + +--- + +# `TFRecord` Example + +```python +[ + { + "label": "cat", + "image/shape": [320, 200, 3], + "image/encoded": [0x12, 0x34, 0x56, ...], + }, + { + "label": "dog", + "image/shape": [320, 200, 3], + "image/encoded": [0x78, 0x9a, 0xbc, ...], + }, +] +``` + +--- + +# The Constraints + +- Dependencies (look at venv size) +- Performance: Keep GPUs busy +- Ease of use for Python devs + +--- + +# Challenge: Getting Test Data + +- No access to the original data +- Vibe code [some Python](tf_example/main.py) to generate test data! + +--- + +# Playing on Rust's strengths + +- Designing with types +- Dive into the [Rust implementation](tfrecord_reader/src/lib.rs) and [`tests.rs`](tfrecord_reader/src/tests.rs) + +--- + +# The End Result + +- `pip install rustfrecord` +- [`test_rustfrecord.py`](test_rustfrecord.py) +- [`src/lib.rs`](src/lib.rs) + +--- + +# Getting the Code + +https://pypi.org/project/rustfrecord/ +https://github.com/gavrie/rustfrecord \ No newline at end of file diff --git a/talk.pdf b/talk.pdf new file mode 100644 index 0000000..dc554c3 Binary files /dev/null and b/talk.pdf differ diff --git a/test_rustfrecord.py b/test_rustfrecord.py index 0416622..ae06ccb 100644 --- a/test_rustfrecord.py +++ b/test_rustfrecord.py @@ -5,6 +5,8 @@ # torch.set_num_threads(1) # torch.set_num_interop_threads(1) +filename = "tf_example/sample_images.tfrecord" + class TFRecordDataset(torch.utils.data.IterableDataset): def __init__(self, filename: str, compressed: bool = True, features: list = None): @@ -23,7 +25,6 @@ def __iter__(self): def test_loader(): - filename = "data/002scattered.training_examples.tfrecord" ds = TFRecordDataset( filename, compressed=filename.endswith(".gz"), @@ -52,9 +53,7 @@ def test_loader(): def test_dataset(): - filename = "data/002scattered.training_examples.tfrecord" - - for _ in range(10): + for _ in range(1): ds = TFRecordDataset( filename, compressed=filename.endswith(".gz"), @@ -68,17 +67,14 @@ def test_dataset(): print() for i, features in enumerate(ds): - label: Tensor = features["label"] + label: Tensor = features["label"].tobytes().decode("utf-8") shape = torch.Size(tuple(features["image/shape"])) - image: Tensor = features["image/encoded"][0].reshape(shape) + image: Tensor = features["image/encoded"].reshape(shape) - if i % 1000 == 0: - print(i, label, image.shape) + print(i, label, image.shape) def test_reader(): - filename = "data/002scattered.training_examples.tfrecord" - r = Reader( filename, compressed=filename.endswith(".gz"), @@ -94,7 +90,7 @@ def test_reader(): for i, features in enumerate(r): label: Tensor = features["label"] shape = torch.Size(tuple(features["image/shape"])) - image: Tensor = features["image/encoded"][0].reshape(shape) + image: Tensor = features["image/encoded"].reshape(shape) if i % 1000 == 0: print(i, label, image.shape) diff --git a/tf_example/.gitignore b/tf_example/.gitignore new file mode 100644 index 0000000..1172266 --- /dev/null +++ b/tf_example/.gitignore @@ -0,0 +1,2 @@ +sample_images.tfrecord +generated_images/ diff --git a/tf_example/README.md b/tf_example/README.md new file mode 100644 index 0000000..e69de29 diff --git a/tf_example/main.py b/tf_example/main.py new file mode 100644 index 0000000..d97c8b7 --- /dev/null +++ b/tf_example/main.py @@ -0,0 +1,172 @@ +import tensorflow as tf +import numpy as np +from PIL import Image +import io +import os +import openai +import requests +from typing import Tuple + +def _bytes_feature(value): + """Returns a bytes_list from a string / byte.""" + if isinstance(value, type(tf.constant(0))): + value = value.numpy() + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) + +def _int64_list_feature(value): + """Returns an int64_list from a list of int / bool.""" + return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) + +def _bytes_list_feature(value): + """Returns a bytes_list from a list of string / byte.""" + return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) + +def generate_dalle_image(animal_name: str, width: int, height: int) -> Tuple[bytes, np.ndarray]: + """Generate an image using DALL-E API based on animal name.""" + try: + # Get OpenAI API key from environment + client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY')) + + # Create prompt based on animal name - make it more descriptive and safe + animal_desc = animal_name.replace('_', ' ') + prompt = f"A cute cartoon illustration of a {animal_desc} in a whimsical art style, colorful and friendly" + + # Generate image using DALL-E + response = client.images.generate( + model="dall-e-3", + prompt=prompt, + size="1024x1024", + quality="standard", + n=1, + ) + + # Download the generated image + image_url = response.data[0].url + image_response = requests.get(image_url) + image_response.raise_for_status() + + # Open image (no need to resize since it's already 1024x1024) + image = Image.open(io.BytesIO(image_response.content)) + image = image.convert('RGB') + + # Convert to numpy array + image_array = np.array(image, dtype=np.uint8) + + return image_array.tobytes(), image_array + + except Exception as e: + print(f"DALL-E generation failed for {animal_name}: {e}") + print(f"Retrying with simpler prompt...") + try: + # Retry with a simpler, safer prompt + simple_prompt = f"A {animal_name.split('_')[-1]} animal cartoon" + response = client.images.generate( + model="dall-e-3", + prompt=simple_prompt, + size="1024x1024", + quality="standard", + n=1, + ) + + image_url = response.data[0].url + image_response = requests.get(image_url) + image_response.raise_for_status() + + image = Image.open(io.BytesIO(image_response.content)) + image = image.convert('RGB') + image_array = np.array(image, dtype=np.uint8) + + return image_array.tobytes(), image_array + + except Exception as e2: + print(f"Retry also failed: {e2}") + print("Falling back to random image generation...") + return generate_fallback_image(width, height) + +def generate_funny_animal_names(count: int) -> list: + """Generate funny animal names using OpenAI API.""" + try: + client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY')) + + prompt = f"""Generate {count} funny, creative animal names in the style of Ubuntu version names (adjective_animal format). Make them humorous and imaginative but family-friendly. Examples: "giggling_giraffe", "dancing_dolphin", "sneezing_sloth". Return only the names, one per line, with underscores between words, no numbering or extra text.""" + + response = client.chat.completions.create( + model="gpt-4.1", + messages=[{"role": "user", "content": prompt}], + max_tokens=200, + temperature=0.8 + ) + + names = [name.strip() for name in response.choices[0].message.content.strip().split('\n') if name.strip()] + return names[:count] # Ensure we don't get more than requested + + except Exception as e: + print(f"Failed to generate funny names: {e}") + print("Using fallback names...") + return [ + "giggling_giraffe", "dancing_dolphin", "sneezing_sloth", "bouncing_bear", "singing_seal", + "tickled_tiger", "laughing_llama", "jumping_jaguar", "winking_whale", "chuckling_cheetah" + ][:count] + +def generate_fallback_image(width, height): + """Generate a random RGB image as fallback.""" + image_array = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8) + return image_array.tobytes(), image_array + +def save_image_as_jpeg(image_array, filename): + """Save numpy image array as JPEG file.""" + image = Image.fromarray(image_array, 'RGB') + image.save(filename, format='JPEG', quality=95) + +def create_tf_example(label, image_bytes, width, height): + """Create a tf.train.Example from image data.""" + feature = { + 'label': _bytes_feature(label.encode('utf-8')), + 'image/encoded': _bytes_feature(image_bytes), + 'image/shape': _int64_list_feature([width, height, 3]), # RGB image has 3 channels + } + + return tf.train.Example(features=tf.train.Features(feature=feature)) + +def main(): + output_file = "sample_images.tfrecord" + width, height = 1024, 1024 + num_samples = 5 + + # Generate funny animal names using OpenAI + print("Generating funny animal names...") + names = generate_funny_animal_names(num_samples) + + # Find the longest name to determine padding + max_length = max(len(name) for name in names) + + # Create output directory for images + os.makedirs("generated_images", exist_ok=True) + + # Create TFRecord writer + with tf.io.TFRecordWriter(output_file) as writer: + for i in range(num_samples): + # Generate DALL-E image + print(f"Generating image {i+1}/{num_samples} for {names[i]}...") + image_bytes, image_array = generate_dalle_image(names[i], width, height) + # Pad label to consistent length + label = names[i].ljust(max_length) + + # Save image as JPEG + image_filename = f"generated_images/{names[i]}.jpg" + save_image_as_jpeg(image_array, image_filename) + + # Create tf.train.Example + tf_example = create_tf_example(label, image_bytes, width, height) + + # Write to TFRecord + writer.write(tf_example.SerializeToString()) + + print(f"Created example {i+1}/{num_samples}: '{label}' -> {image_filename}") + + print(f"\nTFRecord file '{output_file}' created successfully with {num_samples} examples.") + print(f"File size: {os.path.getsize(output_file) / 1024:.2f} KB") + print(f"Label length: {max_length} characters") + +if __name__ == "__main__": + main() 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b/tfrecord_reader/src/lib.rs @@ -7,8 +7,8 @@ use std::{ use anyhow::{Context, Result}; use flate2::read::GzDecoder; -use tfrecord::{Example, ExampleIter, FeatureKind, RecordReaderConfig}; pub use tfrecord::Error; +use tfrecord::{Example, ExampleIter, FeatureKind, RecordReaderConfig}; #[cfg(test)] mod tests; @@ -18,6 +18,7 @@ pub enum Compression { Gzip, } +#[derive(Debug)] pub enum Array { Bytes(Vec>), F32(Vec), diff --git a/tfrecord_reader/src/tests.rs b/tfrecord_reader/src/tests.rs index 4054d65..0927d47 100644 --- a/tfrecord_reader/src/tests.rs +++ b/tfrecord_reader/src/tests.rs @@ -1,18 +1,57 @@ +use anyhow::{anyhow, Result}; + use super::*; #[test] -fn test_read() { +fn test_read() -> Result<()> { let reader = Reader::new( - // "data/002scattered.training_examples.tfrecord", - "/data/Runs/training_example/002scattered.training_examples.tfrecord", + "../tf_example/sample_images.tfrecord", Compression::None, &["label", "image/encoded", "image/shape"], - ); - assert!(reader.is_ok()); 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