def requested_figure_object_traces_streamer(
run: Run, requested_traces: List[TraceBase], rec_range, rec_num: int = 50
) -> List[dict]:
for requested_trace in requested_traces:
trace_name = requested_trace.name
context = Context(requested_trace.context)
trace = run.get_figure_sequence(name=trace_name, context=context)
if not trace:
continue
record_range_missing = rec_range.start is None or rec_range.stop is None
if record_range_missing:
rec_range = IndexRange(trace.first_step(), trace.last_step() + 1)
steps = []
values = []
steps_vals = trace.values.items_in_range(
rec_range.start, rec_range.stop, rec_num
)
for step, val in steps_vals:
steps.append(step)
values.append(preparer(val, trace, step, decode=True))
trace_dict = {
'name': trace.name,
'context': trace.context.to_dict(),
'values': values,
'iters': steps,
'record_range': (trace.first_step(), trace.last_step() + 1),
}
encoded_tree = encode_tree(trace_dict)
yield collect_run_streamable_data(encoded_tree)
def requested_image_traces_streamer(run: Run,
requested_traces: List[TraceBase],
rec_range, idx_range,
rec_num: int = 50, idx_num: int = 5) -> List[dict]:
for requested_trace in requested_traces:
trace_name = requested_trace.name
context = Context(requested_trace.context)
trace = run.get_image_sequence(name=trace_name, context=context)
if not trace:
continue
record_range_missing = rec_range.start is None or rec_range.stop is None
if record_range_missing:
rec_range = IndexRange(trace.first_step(), trace.last_step() + 1)
index_range_missing = idx_range.start is None or idx_range.stop is None
if index_range_missing:
idx_range = IndexRange(0, trace.record_length() or 1)
rec_length = trace.record_length() or 1
idx_step = rec_length // idx_num or 1
idx_slice = slice(idx_range.start, idx_range.stop, idx_step)
steps_vals = trace.values.items_in_range(rec_range.start, rec_range.stop, rec_num)
steps = []
values = []
for step, val in steps_vals:
steps.append(step)
if isinstance(val, list):
values.append(
img_collection_record_to_encodable(sliced_custom_object_record(val, idx_slice), trace, step)
)
elif idx_slice.start == 0:
values.append(img_record_to_encodable(val, trace, step))
else:
values.append([])
trace_dict = {
'record_range': (trace.first_step(), trace.last_step() + 1),
'index_range': (0, rec_length),
'name': trace.name,
'context': trace.context.to_dict(),
'values': values,
'iters': steps,
}
encoded_tree = encode_tree(trace_dict)
yield collect_run_streamable_data(encoded_tree)
def _pack_run_data(run_: Run, traces_: list):
_rec_range = (
trcs_rec_range if record_range_missing or calc_total_ranges else rec_range
)
run_dict = {
run_.hash: {
'ranges': {
'record_range': [_rec_range.start, _rec_range.stop],
'record_slice': [rec_slice.start, rec_slice.stop, rec_slice.step],
},
'params': run_.get(...),
'traces': traces_,
'props': get_run_props(run_),
}
}
encoded_tree = encode_tree(run_dict)
return collect_run_streamable_data(encoded_tree)
def audios_batch_result_streamer(uri_batch: List[str], repo: 'Repo'):
uri_service = URIService(repo=repo)
batch_iterator = uri_service.request_batch(uri_batch=uri_batch)
for it in batch_iterator:
yield collect_run_streamable_data(encode_tree(it))