You can simply install datago with [uv] pip install datago

Use the package from Python

Please note that in all the of the following cases, you can directly get an IterableDataset (torch compatible) with the following code snippet

from dataset import DatagoIterDataset
client_config = {} # See below for examples
datago_dataset = DatagoIterDataset(client_config, return_python_types=True)

return_python_types enforces that images will be of the PIL.Image sort for instance, being an external binary module should be transparent.

from datago import DatagoClient, initialize_logging
import os
import json

# Respects RUST_LOG=INFO env var for setting log level
# If omitted the logger will be initialized when the client starts.
initialize_logging()

config = {
    "source_config": {
        "sources": os.environ.get("DATAROOM_TEST_SOURCE", ""),
        "page_size": 500,
        "rank": 0,
        "world_size": 1,
    },
    "limit": 200,
    "samples_buffer_size": 32,
}

client = DatagoClient(json.dumps(config))

for _ in range(10):
    sample = client.get_sample()

from datago import DatagoClient, initialize_logging
import os
import json

# Can also set the log level directly instead of using RUST_LOG env var
initialize_logging(log_level="warn")

config = {
    "source_type": "file",
    "source_config": {
        "root_path": "myPath",
        "random_sampling": False, # True if used directly for training
        "rank": 0, # Optional, distributed workloads are possible
        "world_size": 1,
    },
    "limit": 200,
    "samples_buffer_size": 32,
}

client = DatagoClient(json.dumps(config))

for _ in range(10):
    sample = client.get_sample()

from datago import DatagoClient, initialize_logging
import os
import json

# Can also set the log level directly instead of using RUST_LOG env var
initialize_logging(log_level="warn")

# URL of the test bucket
bucket = "https://storage.googleapis.com/webdataset/fake-imagenet"
dataset = "/imagenet-train-{000000..001281}.tar"
url = bucket + dataset

client_config = {
    "source_type": "webdataset",
    "source_config": {
        "url": url,
        "random_sampling": False,
        "concurrent_downloads": 8, # The number of TarballSamples which should be handled concurrently
        "rank": 0,
        "world_size": 1,
    },
    "prefetch_buffer_size": 128,
    "samples_buffer_size": 64,
    "limit": 1_000_000, # Dummy example, max number of samples you would like to serve
}

client = DatagoClient(json.dumps(client_config))

for _ in range(10):
    sample = client.get_sample()

Process images on the fly

Datago can also process images on the fly, for instance to align different image payloads. This is done by adding an image_config to the configuration. The following example shows how to align different image payloads.

Processing can be very CPU heavy, but it will be distributed over all CPU cores wihout requiring multiple python processes. I.e., you can keep a single python process using get_sample() on the client and still saturate all CPU cores.

There are three main processing topics that you can choose from:

• crop the images to within an aspect ratio bucket (which is very handy for all Transformer / patch based architectures)
• resize the images (setting here will be related to the square aspect ratio bucket, other buckets will differ of course)
• pre-encode the images to a specific format (jpg, png, ...)

   config = {
    "source_type": "file",
    "source_config": {
        "root_path": "myPath",
        "random_sampling": False, # True if used directly for training
    },
    # Optional pre-processing of the images, placing them in an aspect ratio bucket to preserve as much as possible of the original content
    "image_config": {
        "crop_and_resize": True, # False to turn it off, or just omit this part of the config
        "default_image_size": 1024,
        "downsampling_ratio": 32,
        "min_aspect_ratio": 0.5,
        "max_aspect_ratio": 2.0,
        "pre_encode_images": False,
    },
    "limit": 200,
    "samples_buffer_size": 32,
}

Match the raw exported buffers with typical python types

See helper functions provided in raw_types.py, should be self explanatory. Check python benchmarks for examples. As mentioned above, we also provide a wrapper so that you get a dataset directly.

Logging

We are using the log crate with env_logger. You can set the log level using the RUST_LOG environment variable. E.g. RUST_LOG=INFO.

When using the library from Python, env_logger will be initialized automatically when creating a DatagoClient. There is also a initialize_logging function in the datago module, which if called before using a client, allows to customize the log level. This only works if RUST_LOG is not set.

Env variables

There are a couple of env variables which will change the behavior of the library, for settings which felt too low level to be exposed in the config.

• DATAGO_MAX_TASKS: refers to the number of threads which will be used to load the samples. Defaults to a multiple of the CPU cores.
• RUST_LOG: see above, will change the level of logging for the whole library, could be useful for debugging or to report an issue here.
• DATAGO_MAX_RETRIES: number of retries for a failed sample load, defaults to 3.

Preamble

Just install the rust toolchain via rustup

[Apple Silicon MacOS only]

If you are using an Apple Silicon Mac OS machine, create a .cargo/config file and paste the following:

[target.x86_64-apple-darwin]
rustflags = [
  "-C", "link-arg=-undefined",
  "-C", "link-arg=dynamic_lookup",
]

[target.aarch64-apple-darwin]
rustflags = [
  "-C", "link-arg=-undefined",
  "-C", "link-arg=dynamic_lookup",
]

Build a benchmark CLI

Cargo run --release -- -h to get all the information, should be fairly straightforward

Run the rust test suite

From the datago folder

cargo test

Generate the python package binaries manually

Build a wheel useable locally

maturin build -i python3.11 --release --target "x86_64-unknown-linux-gnu"

Build a wheel which can be uploaded to pypi or related

• either use a manylinux docker image

• or cross compile using zip

maturin build -i python3.11 --release --target "x86_64-unknown-linux-gnu" --manylinux 2014 --zig

then you can pip install from target/wheels

Update the pypi release (maintainers)

Create a new tag and a new release in this repo, a new package will be pushed automatically.

In general, Datago will be impactful if you want to load a lot of images very fast, but if you consume them as you go at a more leisury pace then it's not really needed. The more CPU work there is with the images and the higher quality they are, the more Datago will shine.

From disk: ImageNet

The following benchmarks are using ImageNet 1k, which is very low resolution and thus kind of a worst case scenario. Data is served from cache (i.e. the OS cache) and the images are not pre-processed. In this case the receiving python process is typically the bottleneck, and caps at around 3000 images per second.

AMD Zen3 laptop - IN1k - disk - no processing

AMD EPYC 9454 - IN1k - disk - no processing

Webdataset: FakeIN

This benchmark is using low resolution images. It's accessed through the webdataset front end, datago is compared with the popular python webdataset library. Note that datago will start streaming the images faster here (almost instantly !), which emphasizes throughput differences depending on how long you test it for.

Of note is also that this can be bottlenecked by your external bandwidth to the remote storage where WDS is hosted, in which case both solution would yield comparable numbers.

AMD Zen3 laptop - webdataset - no processing

Webdataset: PD12M

This benchmark is using high resolution images. It's accessed through the webdataset front end, datago is compared with the popular python webdataset library. Note that datago will start streaming the images faster here (almost instantly !), which emphasizes throughput differences depending on how long you test it for.

Of note is also that this can be bottlenecked by your external bandwidth to the remote storage where WDS is hosted, in which case both solution would yield comparable numbers.

AMD Zen3 laptop - webdataset - no processing

AMD EPYC 9454 - pd12m - webdataset - no processing

AMD Zen3 laptop - webdataset - processing

Adding image processing (crop and resize to Transformer compatible size buckets) to the equation changes the picture, as the work spread becomes more important. If you're training a diffusion model or an image encoder from a diverse set of images, this is likely to be the most realistic micro-benchmark.