def upload(self,
results,
ds: Dataset,
token: dict,
progressbar: bool = True):
"""Batchified upload of results
For each tensor batchify based on its chunk and upload
If tensor is dynamic then still upload element by element
For dynamic tensors, it disable dynamicness and then enables it back
Parameters
----------
dataset: hub.Dataset
Dataset object that should be written to
results:
Output of transform function
progressbar: bool
Returns
----------
ds: hub.Dataset
Uploaded dataset
"""
for key, value in results.items():
length = ds[key].chunksize[0]
value = str_to_int(value, ds.dataset.tokenizer)
if length == 0:
length = 1
batched_values = batchify(value, length)
def upload_chunk(i_batch):
i, batch = i_batch
batch_length = len(batch)
if batch_length != 1:
ds[key, i * length:i * length + batch_length] = batch
else:
ds[key, i * length] = batch[0]
index_batched_values = list(
zip(list(range(len(batched_values))), batched_values))
# Disable dynamic arrays
ds.dataset._tensors[f"/{key}"].disable_dynamicness()
list(self.map(upload_chunk, index_batched_values))
# Enable and rewrite shapes
if ds.dataset._tensors[f"/{key}"].is_dynamic:
ds.dataset._tensors[f"/{key}"].enable_dynamicness()
[
ds.dataset._tensors[f"/{key}"].set_shape([i + ds.offset],
v)
for i, v in enumerate(value)
]
ds.commit()
return ds
def upload(self,
results,
ds: Dataset,
token: dict,
progressbar: bool = True):
"""Batchified upload of results.
For each tensor batchify based on its chunk and upload.
If tensor is dynamic then still upload element by element.
For dynamic tensors, it disable dynamicness and then enables it back.
Parameters
----------
dataset: hub.Dataset
Dataset object that should be written to
results:
Output of transform function
progressbar: bool
Returns
----------
ds: hub.Dataset
Uploaded dataset
"""
for key, value in results.items():
chunk = ds[key].chunksize[0]
chunk = 1 if chunk == 0 else chunk
value = get_value(value)
value = str_to_int(value, ds.dataset.tokenizer)
num_chunks = math.ceil(len(value) / (chunk * self.workers))
length = num_chunks * chunk if self.workers != 1 else len(value)
batched_values = batchify(value, length)
def upload_chunk(i_batch):
i, batch = i_batch
length = len(batch)
slice_ = slice(i * length, (i + 1) * length)
ds[key, slice_] = batch
index_batched_values = list(
zip(list(range(len(batched_values))), batched_values))
# Disable dynamic arrays
ds.dataset._tensors[f"/{key}"].disable_dynamicness()
list(self.map(upload_chunk, index_batched_values))
offset = ds.indexes[
0] # here ds.indexes will always be a contiguous list as obtained after slicing
# Enable and rewrite shapes
if ds.dataset._tensors[f"/{key}"].is_dynamic:
ds.dataset._tensors[f"/{key}"].enable_dynamicness()
ds.dataset._tensors[f"/{key}"].set_shape(
[slice(offset, offset + len(value))], value)
ds.commit()
return ds
def upload(self, results, url: str, token: dict, progressbar: bool = True):
"""Batchified upload of results
For each tensor batchify based on its chunk and upload
If tensor is dynamic then still upload element by element
Parameters
----------
dataset: hub.Dataset
Dataset object that should be written to
results:
Output of transform function
progressbar: bool
Returns
----------
ds: hub.Dataset
Uploaded dataset
"""
shape = (len(list(results.values())[0]), )
ds = Dataset(
url,
mode="w",
shape=shape, # unkownn
schema=self.schema,
token=token,
cache=False,
)
tasks = []
for key, value in results.items():
length = ds[key].chunksize[0]
batched_values = batchify(value, length)
chunk_id = list(range(len(batched_values)))
index_batched_values = list(zip(chunk_id, batched_values))
results = [
self.upload_chunk.remote(el, key=key, ds=ds)
for el in index_batched_values
]
tasks.extend(results)
ray.get(tasks)
ds.commit()
return ds
def upload(
self,
results,
url: str,
token: dict,
progressbar: bool = True,
public: bool = True,
):
"""Batchified upload of results.
For each tensor batchify based on its chunk and upload.
If tensor is dynamic then still upload element by element.
Parameters
----------
dataset: hub.Dataset
Dataset object that should be written to
results:
Output of transform function
progressbar: bool
public: bool, optional
only applicable if using hub storage, ignored otherwise
setting this to False allows only the user who created it to access the dataset and
the dataset won't be visible in the visualizer to the public
Returns
----------
ds: hub.Dataset
Uploaded dataset
"""
if len(list(results.values())) == 0:
shape = (0, )
else:
shape = (len(list(results.values())[0]), )
ds = Dataset(
url,
mode="w",
shape=shape,
schema=self.schema,
token=token,
cache=False,
public=public,
)
tasks = []
for key, value in results.items():
length = ds[key].chunksize[0]
value = get_value(value)
value = str_to_int(value, ds.tokenizer)
batched_values = batchify(value, length)
chunk_id = list(range(len(batched_values)))
index_batched_values = list(zip(chunk_id, batched_values))
ds._tensors[f"/{key}"].disable_dynamicness()
results = [
self.upload_chunk.remote(el, key=key, ds=ds)
for el in index_batched_values
]
tasks.extend(results)
results = ray.get(tasks)
self.set_dynamic_shapes(results, ds)
ds.commit()
return ds
