Dataerai Python SDK

Python client for the Dataerai transfer daemon.
Supports authenticated uploads, downloads, and metadata operations. The daemon is started automatically if it is not already running.

Requirements

Python ≥ 3.10
The dataerai binary must be installed and on PATH (or pass binary_path explicitly)
The user must be logged in via dataerai auth login before calling auth_status() / upload() / download()

Installation

pip install --pre "dataerai-sdk>=0.2.0b1,<0.3"
# or, from source:
pip install -e sdk/python/

Optional ML adapters are installed separately so the base SDK stays small:

pip install --pre "dataerai-sdk[ml]>=0.2.0b1,<0.3"          # PyTorch Zarr datasets
pip install --pre "dataerai-sdk[ml-keras]>=0.2.0b1,<0.3"    # Keras batches
pip install --pre "dataerai-sdk[ml-hls4ml]>=0.2.0b1,<0.3"   # HLS4ML conversion

Quick start

from dataerai import DataeraiClient

with DataeraiClient(binary_path="/usr/local/bin/dataerai") as client:
    # Check auth
    status = client.auth_status()
    print(f"Logged in as {status.user_email} ({status.user_id}), token expires {status.expires_at}")
    if status.user_id is None:
        raise RuntimeError("Upgrade the dataerai CLI to use user-owned uploads")

    # Upload a user-owned file. owner_id is a UUID, not an email address.
    result = client.upload(
        "/path/to/data.csv",
        title="My dataset",
        owner_type="user",
        owner_id=status.user_id,
        on_progress=lambda p: print(f"  {p.percent:.0f}%  {p.rate_mbps:.1f} MB/s"),
    )
    print(f"Uploaded  asset_id={result.asset_id}  content_id={result.content_id}")

    # Download it back
    dl = client.download(result.asset_id, dest_dir="/tmp/downloads")
    for f in dl.files:
        print(f"  {f.local_path}  ({f.size:,} bytes)")

    # Read / update metadata
    meta = client.get_metadata(result.asset_id)
    updated = client.set_metadata(result.asset_id, title="My dataset v2", tags=["csv", "demo"])

    # Link a derived result to the source asset that produced it
    relationship = client.create_relationship(
        result.asset_id,
        "source-asset-id",
        relationship_type="derived_from",
        analysis_mode="non_destructive",
        qualifiers={"tool": "pycroscopy"},
    )
    related_id = relationship.related_asset["id"] if relationship.related_asset else "source-asset-id"
    print(f"Linked via {relationship.type} to {related_id}")

API reference

`DataeraiClient(*, socket_path, binary_path, auto_start, start_timeout_s, request_timeout_s)`

Parameter	Default	Description
`socket_path`	`$DATAERAI_SOCKET` or `/run/user/<uid>/dataerai-transfer.sock`	Unix socket path
`binary_path`	`None`	Path to the `dataerai` binary (required for `auto_start`)
`auto_start`	`True`	Spawn the daemon if the socket does not exist
`start_timeout_s`	`10.0`	Seconds to wait for the daemon socket to appear
`request_timeout_s`	`30.0`	Per-request timeout in seconds

Use as a context manager (with DataeraiClient(...) as client:) for automatic cleanup, or call client.connect() / client.close() manually.

Methods

Method	Returns	Description
`auth_status()`	`AuthStatus`	Logged-in user ID, email, and token expiry; `user_id` is `None` with older daemons
`create_project(name, *, description="")`	`Project`	Create a project (+ root collection, owner membership, default allocation); requires write scope
`upload(local_path, *, title, owner_type, owner_id, ...)`	`UploadResult`	Upload a file; blocks until complete
`download(asset_id, dest_dir, ...)`	`DownloadResult`	Download latest asset content; blocks until complete
`get_metadata(asset_id)`	`AssetMetadata`	Retrieve asset metadata
`set_metadata(asset_id, **fields)`	`AssetMetadata`	Update metadata fields
`create_relationship(from_asset_id, to_asset_id, rel_type=None, *, relationship_type=None, ...)`	`Relationship`	Create a directed provenance link between two assets

ML adapters

The dataerai.ml package reads Dataerai-hosted Zarr image stores directly from the short-lived /zarr-access/ bundle. PyTorch users can keep using ZarrAssetDataset / ZarrOriginalsDataset; Keras users get matching batch datasets:

from dataerai.ml import access
from dataerai.ml.keras_dataset import ZarrOriginalsSequence

creds = access.load_credentials()
train = ZarrOriginalsSequence(
    creds,
    asset_id="dataset-asset-id",
    size=64,
    batch_size=32,
    shuffle=True,
    label_mode="categorical",
)

model.fit(train, epochs=5)

For HLS4ML conversion, keep inputs in Keras' channels-last layout:

from dataerai.ml.hls4ml import convert_keras_model

hls_model = convert_keras_model(
    model,
    output_dir="hls-project",
    project_name="dataerai_model",
    data=train,              # writes input/output .npy testbench arrays
    testbench_batches=2,
)

`create_relationship()` arguments

Authors a directed provenance edge from_asset_id → to_asset_id. You need write access to the source and read access to the target. rel_type is a free-form verb describing the source's role, e.g. "analysis_of" or "acquired_with". Notebook integrations can pass the same value with the keyword-only alias relationship_type.

Argument	Type	Description
`from_asset_id`	`str`	Source (dependent) asset — the edge starts here (required)
`to_asset_id`	`str`	Target (origin) asset — the edge points here (required)
`rel_type`	`str \| None`	Free-form relationship type, ≤255 chars (required unless `relationship_type` is provided)
`relationship_type`	`str \| None`	Keyword-only alias for `rel_type`
`analysis_mode`	`str \| None`	`non_destructive`, `altering`, `destructive`, `in_situ`, `ex_situ`, `invasive`, `non_invasive`
`qualifier_note`	`str \| None`	Free-text note on the relationship
`qualifier_time`	`str \| None`	ISO-8601 timestamp
`qualifiers`	`dict \| None`	JSON-serializable extra qualifiers

# Link a processed result back to the raw data it came from.
client.create_relationship(analysis.asset_id, raw.asset_id, "analysis_of",
                           analysis_mode="non_destructive")

`upload()` keyword arguments

Argument	Type	Description
`title`	`str`	Asset title (required)
`owner_type`	`str`	`"project"` or `"user"` (required)
`owner_id`	`str`	Owner entity ID (required)
`description`	`str \| None`	Free-text description
`alias`	`str \| None`	Short identifier
`tags`	`list[str] \| None`	Tag list
`metadata`	`dict \| None`	Arbitrary key-value metadata
`collection_id`	`str \| None`	Collection to add the asset to
`chunk_size_mb`	`int \| None`	Override default 64 MiB chunk size
`on_progress`	`Callable[[ProgressEvent], None] \| None`	Progress callback
`transfer_timeout_s`	`float`	Max seconds to wait for completion (default 3600)

Progress events

@dataclass
class ProgressEvent:
    transfer_id: str
    bytes_done: int
    bytes_total: int
    chunk_index: int
    chunk_count: int
    rate_bps: float
    file_index: int
    file_name: str

    @property
    def percent(self) -> float: ...   # 0–100

    @property
    def rate_mbps(self) -> float: ...

Error types

Exception	When raised
`DaemonError(code, message)`	Daemon returned a coded error (see `code` attribute)
`DaemonTimeoutError`	Request or transfer exceeded the configured timeout
`ConnectionError`	Daemon disconnected unexpectedly

dev bambu ml-io ml croissant ml-gpu-cu12 ml-gpu ml-keras ml-hls4ml ml-stream ml-stream-video ml-stream-train runner metaextract metaextract-converters metaextract-viz metaextract-checksums metaextract-utils metaextract-containers metaextract-microscopy metaextract-medical metaextract-bio metaextract-spectroscopy metaextract-all

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