def parallelize_bounding_box( self,
instance_name,
bounding_box_zyx,
grid,
target_partition_size_voxels ):
"""
Create an RDD for the given data instance (of either grayscale, labelblk, labelarray, or labelmap),
within the given bounding_box (start_zyx, stop_zyx) and split into blocks of the given shape.
The RDD parallelism will be set to include approximately target_partition_size_voxels in total.
"""
block_size_voxels = np.prod(grid.block_shape)
rdd_partition_length = target_partition_size_voxels // block_size_voxels
bricks = generate_bricks_from_volume_source( bounding_box_zyx,
grid,
self.get_volume_accessor(instance_name),
self.sc,
rdd_partition_length )
# If we're working with a tiny volume (e.g. testing),
# make sure we at least parallelize across all cores.
if bricks.getNumPartitions() < cpus_per_worker() * num_worker_nodes():
bricks = bricks.repartition( cpus_per_worker() * num_worker_nodes() )
return bricks
def _sanitize_config(self):
"""
Tidy up some config values, and fill in 'auto' values where needed.
"""
input_config = self.config_data["input"]
output_config = self.config_data["output"]
options = self.config_data["options"]
# Initialize dummy input/output services, just to overwrite 'auto' config values as needed.
VolumeService.create_from_config(input_config, self.config_dir)
# Output bounding-box must match exactly (or left as auto)
input_bb_zyx = self.input_service.bounding_box_zyx
output_bb_zyx = self.output_service.bounding_box_zyx
assert ((output_bb_zyx == input_bb_zyx) | (output_bb_zyx == -1)).all(), \
"Output bounding box must match the input bounding box exactly. (No translation permitted)."
assert output_config["slice-files"]["slice-xy-offset"] == [
0, 0
], "Nonzero xy offset is meaningless for outputs."
if options["slices-per-slab"] == -1:
# Auto-choose a depth that keeps all threads busy with at least one slice
brick_shape_zyx = self.input_service.preferred_message_shape
brick_depth = brick_shape_zyx[0]
assert brick_depth != -1
num_threads = num_worker_nodes() * cpus_per_worker()
threads_per_brick_layer = (
(num_threads + brick_depth - 1) // brick_depth) # round up
options["slices-per-slab"] = brick_depth * threads_per_brick_layer
def _sanitize_config(self):
"""
Tidy up some config values, and fill in 'auto' values where needed.
"""
input_config = self.config_data["input"]
output_config = self.config_data["output"]
options = self.config_data["options"]
# Initialize dummy input/output services, just to overwrite 'auto' config values as needed.
VolumeService.create_from_config( input_config, self.config_dir )
# Output bounding-box must match exactly (or left as auto)
input_bb_zyx = self.input_service.bounding_box_zyx
output_bb_zyx = self.output_service.bounding_box_zyx
assert ((output_bb_zyx == input_bb_zyx) | (output_bb_zyx == -1)).all(), \
"Output bounding box must match the input bounding box exactly. (No translation permitted)."
assert output_config["slice-files"]["slice-xy-offset"] == [0,0], "Nonzero xy offset is meaningless for outputs."
if options["slices-per-slab"] == -1:
# Auto-choose a depth that keeps all threads busy with at least one slice
brick_shape_zyx = self.input_service.preferred_message_shape
brick_depth = brick_shape_zyx[0]
assert brick_depth != -1
num_threads = num_worker_nodes() * cpus_per_worker()
threads_per_brick_layer = ((num_threads + brick_depth-1) // brick_depth) # round up
options["slices-per-slab"] = brick_depth * threads_per_brick_layer
def _execute_labelindices(self, mapping_df):
config = self.config_data
options = config["options"]
resource_manager_client = ResourceManagerClient(
options["resource-server"], options["resource-port"])
last_mutid = options["mutation-id"]
server = config["dvid"]["server"]
uuid = config["dvid"]["uuid"]
instance_name = config["dvid"]["segmentation-name"]
endpoint = f'{server}/api/node/{uuid}/{instance_name}/indices'
processor = StatsBatchProcessor(last_mutid, endpoint)
# Load the h5 file
block_sv_stats = load_stats_h5_to_records(config["block-stats-file"])
# Note: Initializing this generator involves sorting the (very large) stats array
batch_rows = options["batch-row-count"]
batch_generator = generate_stats_batches(block_sv_stats, mapping_df,
batch_rows)
batches = self.sc.parallelize(batch_generator,
cpus_per_worker() * num_worker_nodes())
rt.persist_and_execute(batches, "Distributing batches", logger)
def process_batch(item):
stats_batch, total_rows = item
approximate_bytes = 30 * total_rows # this is highly unscientific
with resource_manager_client.access_context(
server, False, 1, approximate_bytes):
processor.process_batch((stats_batch, total_rows))
with Timer("Processing/sending batches", logger):
batches.foreach(process_batch)
def compute_stats(block_shape, concurrent_threads, df):
total_blocks = df['voxel_count'].sum() / np.prod(block_shape)
stats = {
"total-analyzed-requests": len(df),
"num-workers": num_worker_nodes(),
"available-threads": 16 * num_worker_nodes(),
"concurrent-threads": concurrent_threads,
"approx-requests-per-thread": len(df) / (concurrent_threads),
"blocks-per-request": total_blocks / len(df),
"seconds-per-request": df['seconds'].mean(),
"seconds-per-block": df['seconds'].sum() / total_blocks,
"wall-time": (df['timestamp'].iloc[-1] - df['timestamp'].iloc[0]).seconds + df['seconds'].iloc[-1],
}
return stats
def group_by_body(self, segments_and_meshes):
config = self.config_data
# Group according to scheme
grouping_scheme = config["mesh-config"]["storage"]["grouping-scheme"]
n_partitions = num_worker_nodes() * cpus_per_worker()
if grouping_scheme in "hundreds":
def last_six_digits(id_mesh):
body_id, _mesh = id_mesh
group_id = body_id - (body_id % 100)
return group_id
grouped_body_ids_and_meshes = segments_and_meshes.groupBy(
last_six_digits, numPartitions=n_partitions)
elif grouping_scheme == "labelmap":
import pandas as pd
mapping_pairs = self.load_labelmap()
def prepend_mapped_group_id(id_mesh_partition):
df = pd.DataFrame(mapping_pairs,
columns=["body_id", "group_id"])
new_partition = []
for id_mesh in id_mesh_partition:
body_id, mesh = id_mesh
rows = df.loc[df.body_id == body_id]
if len(rows) == 0:
# If missing from labelmap,
# we assume an implicit identity mapping
group_id = body_id
else:
group_id = rows['group_id'].iloc[0]
new_partition.append((group_id, (body_id, mesh)))
return new_partition
# We do this via mapPartitions().groupByKey() instead of a simple groupBy()
# to save time constructing the DataFrame inside the closure above.
# (TODO: Figure out why the dataframe isn't pickling properly...)
skip_groups = set(config["mesh-config"]["storage"]["skip-groups"])
grouped_body_ids_and_meshes = segments_and_meshes.mapPartitions( prepend_mapped_group_id ) \
.filter(lambda item: item[0] not in skip_groups) \
.groupByKey(numPartitions=n_partitions)
elif grouping_scheme in ("singletons", "no-groups"):
# Create 'groups' of one item each, re-using the body ID as the group id.
# (The difference between 'singletons', and 'no-groups' is in how the mesh is stored, below.)
grouped_body_ids_and_meshes = segments_and_meshes.map(
lambda id_mesh: (id_mesh[0], [(id_mesh[0], id_mesh[1])]))
persist_and_execute(
grouped_body_ids_and_meshes,
f"Grouping meshes with scheme: '{grouping_scheme}'", logger)
return grouped_body_ids_and_meshes
def from_accessor_func(cls, bounding_box, grid, volume_accessor_func=None, sc=None, target_partition_size_voxels=None, sparse_boxes=None, lazy=False):
"""
Convenience constructor, taking an arbitrary volume_accessor_func.
Args:
bounding_box:
(start, stop)
grid:
Grid (see brick.py)
volume_accessor_func:
Callable with signature: f(box) -> ndarray
Note: The callable will be unpickled only once per partition, so initialization
costs after unpickling are only incurred once per partition.
sc:
SparkContext. If provided, an RDD is returned. Otherwise, returns an ordinary Python iterable.
target_partition_size_voxels:
Optional. If provided, the RDD partition lengths (i.e. the number of bricks per RDD partition)
will be chosen to have (approximately) this many total voxels in each partition.
sparse_boxes:
A list of (physical) sparse boxes indicating which bricks should actually be present in the BrickWall.
If not provided, all bricks within the bounding_box will be present.
lazy:
If True, the bricks' data will not be created until their 'volume' member is first accessed.
"""
if target_partition_size_voxels is None:
if sc:
num_threads = num_worker_nodes() * cpus_per_worker()
else:
# See RDDtools -- for now, non-spark pseudo-RDDs are just a single partition.
num_threads = 1
if sparse_boxes is None:
total_voxels = np.prod(bounding_box[1] - bounding_box[0])
else:
if not hasattr(sparse_boxes, '__len__'):
sparse_boxes = list(sparse_boxes)
total_voxels = sum( map(lambda physbox: np.prod(physbox[1] - physbox[0]), sparse_boxes ) )
voxels_per_thread = total_voxels / num_threads
target_partition_size_voxels = (voxels_per_thread // 2) # Arbitrarily aim for 2 partitions per thread
block_size_voxels = np.prod(grid.block_shape)
rdd_partition_length = target_partition_size_voxels // block_size_voxels
bricks = generate_bricks_from_volume_source(bounding_box, grid, volume_accessor_func, sc, rdd_partition_length, sparse_boxes, lazy)
return BrickWall( bounding_box, grid, bricks )
def compute_stats(block_shape, concurrent_threads, df):
total_blocks = df['voxel_count'].sum() / np.prod(block_shape)
stats = {
"total-analyzed-requests":
len(df),
"num-workers":
num_worker_nodes(),
"available-threads":
16 * num_worker_nodes(),
"concurrent-threads":
concurrent_threads,
"approx-requests-per-thread":
len(df) / (concurrent_threads),
"blocks-per-request":
total_blocks / len(df),
"seconds-per-request":
df['seconds'].mean(),
"seconds-per-block":
df['seconds'].sum() / total_blocks,
"wall-time":
(df['timestamp'].iloc[-1] - df['timestamp'].iloc[0]).seconds +
df['seconds'].iloc[-1],
}
return stats
def group_by_body(self, segments_and_meshes):
config = self.config_data
# Group according to scheme
grouping_scheme = config["mesh-config"]["storage"]["grouping-scheme"]
n_partitions = num_worker_nodes() * cpus_per_worker()
if grouping_scheme in "hundreds":
def last_six_digits( id_mesh ):
body_id, _mesh = id_mesh
group_id = body_id - (body_id % 100)
return group_id
grouped_body_ids_and_meshes = segments_and_meshes.groupBy(last_six_digits, numPartitions=n_partitions)
elif grouping_scheme == "labelmap":
import pandas as pd
mapping_pairs = self.load_labelmap()
def prepend_mapped_group_id( id_mesh_partition ):
df = pd.DataFrame( mapping_pairs, columns=["body_id", "group_id"] )
new_partition = []
for id_mesh in id_mesh_partition:
body_id, mesh = id_mesh
rows = df.loc[df.body_id == body_id]
if len(rows) == 0:
# If missing from labelmap,
# we assume an implicit identity mapping
group_id = body_id
else:
group_id = rows['group_id'].iloc[0]
new_partition.append( (group_id, (body_id, mesh)) )
return new_partition
# We do this via mapPartitions().groupByKey() instead of a simple groupBy()
# to save time constructing the DataFrame inside the closure above.
# (TODO: Figure out why the dataframe isn't pickling properly...)
skip_groups = set(config["mesh-config"]["storage"]["skip-groups"])
grouped_body_ids_and_meshes = segments_and_meshes.mapPartitions( prepend_mapped_group_id ) \
.filter(lambda item: item[0] not in skip_groups) \
.groupByKey(numPartitions=n_partitions)
elif grouping_scheme in ("singletons", "no-groups"):
# Create 'groups' of one item each, re-using the body ID as the group id.
# (The difference between 'singletons', and 'no-groups' is in how the mesh is stored, below.)
grouped_body_ids_and_meshes = segments_and_meshes.map( lambda id_mesh: (id_mesh[0], [(id_mesh[0], id_mesh[1])]) )
persist_and_execute(grouped_body_ids_and_meshes, f"Grouping meshes with scheme: '{grouping_scheme}'", logger)
return grouped_body_ids_and_meshes
def _process_slab(self, scale, slab_fullres_box_zyx, slab_index, num_slabs, upscale_slab_wall):
num_threads = num_worker_nodes() * cpus_per_worker()
slab_voxels = np.prod(slab_fullres_box_zyx[1] - slab_fullres_box_zyx[0]) // (2**scale)**3
voxels_per_thread = slab_voxels // num_threads
options = self.config_data["options"]
pyramid_source = options["pyramid-source"]
if pyramid_source == "copy" or scale == 0:
# Copy from input source
bricked_slab_wall = BrickWall.from_volume_service(self.input_service, scale, slab_fullres_box_zyx, self.sc, voxels_per_thread // 2)
bricked_slab_wall.persist_and_execute(f"Slab {slab_index}: Downloading scale {scale}", logger)
else:
# Downsample from previous scale
bricked_slab_wall = upscale_slab_wall.downsample( (2,2,2), 'grayscale' )
bricked_slab_wall.persist_and_execute(f"Slab {slab_index}: Downsampling to scale {scale}", logger)
upscale_slab_wall.unpersist()
del upscale_slab_wall
if scale == 0:
bricked_slab_wall = self.adjust_contrast(bricked_slab_wall, slab_index)
# Remap to output bricks
output_grid = Grid(self.output_service.preferred_message_shape)
output_slab_wall = bricked_slab_wall.realign_to_new_grid( output_grid )
# Pad from previously-existing pyramid data until
# we have full storage blocks, e.g. (64,64,64),
# but not necessarily full bricks, e.g. (64,64,6400)
output_accessor_func = partial(self.output_service.get_subvolume, scale=scale)
# But don't bother fetching real data for scale 0
# the input slabs are already block-aligned, and the edges of each slice will be zeros anyway.
if scale == 0:
output_accessor_func = lambda _box: 0
padding_grid = Grid( 3*(self.output_service.block_width,), output_grid.offset )
padded_slab_wall = output_slab_wall.fill_missing(output_accessor_func, padding_grid)
padded_slab_wall.persist_and_execute(f"Slab {slab_index}: Assembling scale {scale} bricks", logger)
# Discard original bricks
bricked_slab_wall.unpersist()
del bricked_slab_wall
logger.info(f"Slab {slab_index}: Writing scale {scale}", extra={"status": f"Writing {slab_index}/{num_slabs}"})
rt.foreach( partial(write_brick, self.output_service, scale), padded_slab_wall.bricks )
return padded_slab_wall
def run_on_each_worker(self, func):
"""
Run the given function once per worker node.
"""
status_filepath = '/tmp/' + self._execution_uuid + '-' + str(self._worker_task_id)
self._worker_task_id += 1
@self.collect_log(lambda i: socket.gethostname() + '[' + func.__name__ + ']')
def task_f(i):
with FileLock(status_filepath):
if os.path.exists(status_filepath):
return None
# create empty file to indicate the task was executed
open(status_filepath, 'w')
result = func()
return (socket.gethostname(), result)
num_workers = num_worker_nodes()
# It would be nice if we only had to schedule N tasks for N workers,
# but we couldn't ensure that tasks are hashed 1-to-1 onto workers.
# Instead, we'll schedule **LOTS** of extra tasks, but the logic in
# task_f() will skip the unnecessary work.
num_tasks = num_workers * 1000
# Execute the tasks. Returns [(hostname, result), None, None, (hostname, result), ...],
# with 'None' interspersed for hosts that were hit multiple times.
# (Each host only returns a single non-None result)
host_results = self.sc.parallelize(list(range(num_tasks)), num_tasks)\
.repartition(num_tasks).map(task_f).collect()
host_results = [_f for _f in host_results if _f] # Drop Nones
host_results = dict(host_results)
assert len(host_results) == num_workers, \
"Task '{}' was not executed all workers ({}), or some tasks failed! Nodes processed: \n{}"\
.format(func.__name__, num_workers, host_results)
logger.info("Ran {} on {} nodes: {}".format(func.__name__, len(host_results), host_results))
return host_results
def _execute_mesh_generation(self, large_id_box_mask_factor_err):
config = self.config_data
@self.collect_log(lambda _: '_MESH_GENERATION_ERRORS')
def logged_generate_mesh(arg):
return generate_mesh_in_subprocess(config, arg)
# --> (body_id, mesh_bytes, error_msg)
body_ids_and_meshes_with_err = large_id_box_mask_factor_err.map(
logged_generate_mesh)
persist_and_execute(body_ids_and_meshes_with_err, "Computing meshes",
logger)
# Errors were already written to a separate file, but let's duplicate them in the master log.
errors = body_ids_and_meshes_with_err.map(
lambda id_mesh_err: id_mesh_err[-1]).filter(bool).collect()
for error in errors:
logger.error(error)
# Filter out error cases
body_ids_and_meshes = body_ids_and_meshes_with_err.filter(lambda id_mesh_err: id_mesh_err[-1] is None) \
.map( lambda id_mesh_err: id_mesh_err[:2] )
# Group according to scheme
grouping_scheme = config["mesh-config"]["storage"]["grouping-scheme"]
n_partitions = num_worker_nodes() * cpus_per_worker()
if grouping_scheme in "hundreds":
def last_six_digits(id_mesh):
body_id, _mesh = id_mesh
group_id = body_id - (body_id % 100)
return group_id
grouped_body_ids_and_meshes = body_ids_and_meshes.groupBy(
last_six_digits, numPartitions=n_partitions)
elif grouping_scheme == "labelmap":
import pandas as pd
mapping_pairs = load_labelmap(
config["mesh-config"]["storage"]["labelmap"], self.config_dir)
def prepend_mapped_group_id(id_mesh_partition):
df = pd.DataFrame(mapping_pairs,
columns=["body_id", "group_id"])
new_partition = []
for id_mesh in id_mesh_partition:
body_id, mesh = id_mesh
rows = df.loc[df.body_id == body_id]
if len(rows) == 0:
# If missing from labelmap,
# we assume an implicit identity mapping
group_id = body_id
else:
group_id = rows['group_id'].iloc[0]
new_partition.append((group_id, (body_id, mesh)))
return new_partition
# We do this via mapPartitions().groupByKey() instead of a simple groupBy()
# to save time constructing the DataFrame inside the closure above.
# (TODO: Figure out why the dataframe isn't pickling properly...)
skip_groups = set(config["mesh-config"]["storage"]["skip-groups"])
grouped_body_ids_and_meshes = body_ids_and_meshes.mapPartitions( prepend_mapped_group_id ) \
.filter(lambda item: item[0] not in skip_groups) \
.groupByKey(numPartitions=n_partitions)
elif grouping_scheme in ("singletons", "no-groups"):
# Create 'groups' of one item each, re-using the body ID as the group id.
# (The difference between 'singletons', and 'no-groups' is in how the mesh is stored, below.)
grouped_body_ids_and_meshes = body_ids_and_meshes.map(
lambda id_mesh: (id_mesh[0], [(id_mesh[0], id_mesh[1])]))
persist_and_execute(
grouped_body_ids_and_meshes,
f"Grouping meshes with scheme: '{grouping_scheme}'", logger)
unpersist(body_ids_and_meshes)
del body_ids_and_meshes
with Timer() as timer:
grouped_body_ids_and_meshes.foreachPartition(
partial(post_meshes_to_dvid, config))
logger.info(f"Writing meshes to DVID took {timer.seconds}")
def execute(self):
self._init_services()
self._sanitize_config()
options = self.config_data["options"]
output_service = self.output_service
logger.info(f"Output bounding box: {output_service.bounding_box_zyx[:,::-1]}")
# Data is processed in Z-slabs
slab_depth = options["slices-per-slab"]
input_bb_zyx = self.input_service.bounding_box_zyx
_, slice_start_y, slice_start_x = input_bb_zyx[0]
slab_shape_zyx = input_bb_zyx[1] - input_bb_zyx[0]
slab_shape_zyx[0] = slab_depth
slice_shape_zyx = slab_shape_zyx.copy()
slice_shape_zyx[0] = 1
# This grid outlines the slabs -- each grid box is a full slab
slab_grid = Grid(slab_shape_zyx, (0, slice_start_y, slice_start_x))
slab_boxes = list(clipped_boxes_from_grid(input_bb_zyx, slab_grid))
for slab_index, slab_box_zyx in enumerate(slab_boxes):
# Contruct BrickWall from input bricks
num_threads = num_worker_nodes() * cpus_per_worker()
slab_voxels = np.prod(slab_box_zyx[1] - slab_box_zyx[0])
voxels_per_thread = slab_voxels / num_threads
bricked_slab_wall = BrickWall.from_volume_service(self.input_service, 0, slab_box_zyx, self.sc, voxels_per_thread / 2)
# Force download
bricked_slab_wall.persist_and_execute(f"Downloading slab {slab_index}/{len(slab_boxes)}: {slab_box_zyx[:,::-1]}", logger)
# Remap to slice-sized "bricks"
sliced_grid = Grid(slice_shape_zyx, offset=slab_box_zyx[0])
sliced_slab_wall = bricked_slab_wall.realign_to_new_grid( sliced_grid )
sliced_slab_wall.persist_and_execute(f"Assembling slab {slab_index}/{len(slab_boxes)} slices", logger)
# Discard original bricks
bricked_slab_wall.unpersist()
del bricked_slab_wall
def write_slice(brick):
assert (brick.physical_box == brick.logical_box).all()
output_service.write_subvolume(brick.volume, brick.physical_box[0])
# Export to PNG or TIFF, etc. (automatic via slice path extension)
with Timer() as timer:
logger.info(f"Exporting slab {slab_index}/{len(slab_boxes)}", extra={"status": f"Exporting {slab_index}/{len(slab_boxes)}"})
rt.foreach( write_slice, sliced_slab_wall.bricks )
logger.info(f"Exporting slab {slab_index}/{len(slab_boxes)} took {timer.timedelta}",
extra={"status": f"Done: {slab_index}/{len(slab_boxes)}"})
# Discard slice data
sliced_slab_wall.unpersist()
del sliced_slab_wall
logger.info(f"DONE exporting {len(slab_boxes)} slabs.", extra={'status': "DONE"})
def generate_bricks_from_volume_source( bounding_box, grid, volume_accessor_func, sc=None, rdd_partition_length=None, sparse_boxes=None, lazy=False ):
"""
Generate an RDD or iterable of Bricks for the given bounding box and grid.
Args:
bounding_box:
(start, stop)
grid:
Grid (see above)
volume_accessor_func:
Callable with signature: f(box) -> ndarray
Note: The callable will be unpickled only once per partition, so initialization
costs after unpickling are only incurred once per partition.
sc:
SparkContext. If provided, an RDD is returned. Otherwise, returns an ordinary Python iterable.
rdd_partition_length:
Optional. If provided, the RDD will have (approximately) this many bricks per partition.
sparse_boxes:
Optional.
A pre-calculated list of boxes to use instead of instead of calculating
the complete (dense) list of grid boxes within the bounding box.
If provided, should be a list of physical boxes, and no two should occupy
the same logical box, as defined by their midpoints.
Note: They will still be clipped to the overall bounding_box.
halo: An integer or shape indicating how much halo to add to each Brick's physical_box.
The halo is applied in both 'dense' and 'sparse' cases.
"""
if sparse_boxes is None:
# Generate boxes from densely populated grid
logical_boxes = boxes_from_grid(bounding_box, grid, include_halos=False)
physical_boxes = clipped_boxes_from_grid(bounding_box, grid)
logical_and_physical_boxes = zip( logical_boxes, physical_boxes )
else:
# User provided list of physical boxes.
# Clip them to the bounding box and calculate the logical boxes.
if not hasattr(sparse_boxes, '__len__'):
sparse_boxes = list( sparse_boxes )
physical_boxes = np.asarray( sparse_boxes )
assert physical_boxes.ndim == 3 and physical_boxes.shape[1:3] == (2,3)
def logical_and_clipped( box ):
midpoint = (box[0] + box[1]) // 2
logical_box = grid.compute_logical_box( midpoint )
box += (-grid.halo_shape, grid.halo_shape)
# Note: Non-intersecting boxes will have non-positive shape after clipping
clipped_box = box_intersection(box, bounding_box)
return ( logical_box, clipped_box )
logical_and_physical_boxes = map(logical_and_clipped, physical_boxes)
# Drop any boxes that fall completely outside the bounding box
# Check that physical box doesn't completely fall outside its logical_box
def is_valid(logical_and_physical):
logical_box, physical_box = logical_and_physical
return (physical_box[1] > logical_box[0]).all() and (physical_box[0] < logical_box[1]).all()
logical_and_physical_boxes = filter(is_valid, logical_and_physical_boxes )
if sc:
if not hasattr(logical_and_physical_boxes, '__len__'):
logical_and_physical_boxes = list(logical_and_physical_boxes) # need len()
num_rdd_partitions = None
if rdd_partition_length is not None:
rdd_partition_length = max(1, rdd_partition_length)
num_rdd_partitions = int( np.ceil( len(logical_and_physical_boxes) / rdd_partition_length ) )
# If we're working with a tiny volume (e.g. testing),
# make sure we at least parallelize across all cores.
if num_rdd_partitions is not None and (num_rdd_partitions < cpus_per_worker() * num_worker_nodes()):
num_rdd_partitions = cpus_per_worker() * num_worker_nodes()
def brick_size(log_phys):
_logical, physical = log_phys
return np.uint64(np.prod(physical[1] - physical[0]))
total_volume = sum(map(brick_size, logical_and_physical_boxes))
logger.info(f"Initializing RDD of {len(logical_and_physical_boxes)} Bricks "
f"(over {num_rdd_partitions} partitions) with total volume {total_volume/1e9:.1f} Gvox")
# Enumerate and repartition to get perfect partition sizes,
# rather than relying on spark's default hash
class _enumerated_value(tuple):
# Return a hash based on the key alone.
def __hash__(self):
return self[0]
enumerated_logical_and_physical_boxes = sc.parallelize( enumerate(logical_and_physical_boxes), num_rdd_partitions )
enumerated_logical_and_physical_boxes = enumerated_logical_and_physical_boxes.map(_enumerated_value)
enumerated_logical_and_physical_boxes = enumerated_logical_and_physical_boxes.partitionBy(num_rdd_partitions, lambda x: x)
logical_and_physical_boxes = enumerated_logical_and_physical_boxes.values()
def make_bricks( logical_and_physical_box ):
logical_box, physical_box = logical_and_physical_box
if lazy:
return Brick(logical_box, physical_box, lazy_creation_fn=volume_accessor_func)
else:
volume = volume_accessor_func(physical_box)
return Brick(logical_box, physical_box, volume)
bricks = rt.map( make_bricks, logical_and_physical_boxes )
return bricks
def execute(self):
self._init_services()
self._sanitize_config()
options = self.config_data["options"]
output_service = self.output_service
logger.info(
f"Output bounding box: {output_service.bounding_box_zyx[:,::-1]}")
# Data is processed in Z-slabs
slab_depth = options["slices-per-slab"]
input_bb_zyx = self.input_service.bounding_box_zyx
_, slice_start_y, slice_start_x = input_bb_zyx[0]
slab_shape_zyx = input_bb_zyx[1] - input_bb_zyx[0]
slab_shape_zyx[0] = slab_depth
slice_shape_zyx = slab_shape_zyx.copy()
slice_shape_zyx[0] = 1
# This grid outlines the slabs -- each grid box is a full slab
slab_grid = Grid(slab_shape_zyx, (0, slice_start_y, slice_start_x))
slab_boxes = list(clipped_boxes_from_grid(input_bb_zyx, slab_grid))
for slab_index, slab_box_zyx in enumerate(slab_boxes):
# Contruct BrickWall from input bricks
num_threads = num_worker_nodes() * cpus_per_worker()
slab_voxels = np.prod(slab_box_zyx[1] - slab_box_zyx[0])
voxels_per_thread = slab_voxels / num_threads
bricked_slab_wall = BrickWall.from_volume_service(
self.input_service, 0, slab_box_zyx, self.sc,
voxels_per_thread / 2)
# Force download
bricked_slab_wall.persist_and_execute(
f"Downloading slab {slab_index}/{len(slab_boxes)}: {slab_box_zyx[:,::-1]}",
logger)
# Remap to slice-sized "bricks"
sliced_grid = Grid(slice_shape_zyx, offset=slab_box_zyx[0])
sliced_slab_wall = bricked_slab_wall.realign_to_new_grid(
sliced_grid)
sliced_slab_wall.persist_and_execute(
f"Assembling slab {slab_index}/{len(slab_boxes)} slices",
logger)
# Discard original bricks
bricked_slab_wall.unpersist()
del bricked_slab_wall
def write_slice(brick):
assert (brick.physical_box == brick.logical_box).all()
output_service.write_subvolume(brick.volume,
brick.physical_box[0])
# Export to PNG or TIFF, etc. (automatic via slice path extension)
with Timer() as timer:
logger.info(f"Exporting slab {slab_index}/{len(slab_boxes)}",
extra={
"status":
f"Exporting {slab_index}/{len(slab_boxes)}"
})
rt.foreach(write_slice, sliced_slab_wall.bricks)
logger.info(
f"Exporting slab {slab_index}/{len(slab_boxes)} took {timer.timedelta}",
extra={"status": f"Done: {slab_index}/{len(slab_boxes)}"})
# Discard slice data
sliced_slab_wall.unpersist()
del sliced_slab_wall
logger.info(f"DONE exporting {len(slab_boxes)} slabs.",
extra={'status': "DONE"})
def execute(self):
from pyspark import StorageLevel
self._sanitize_config()
config = self.config_data
options = config["options"]
resource_mgr_client = ResourceManagerClient(options["resource-server"], options["resource-port"])
total_cpus = 16 * num_worker_nodes()
concurrent_threads = total_cpus
if options["resource-server"]:
concurrent_threads = options["resource-server-config"]["read_reqs"]
if concurrent_threads > total_cpus:
msg = "You're attempting to use the resource manager to constrain concurrency, but you "\
"aren't running with a large enough cluster to saturate the resource manager settings"
raise RuntimeError(msg)
# We instantiate a VolumeService as an easy way to plug in missing config values as necessary.
# (We won't actually use it.)
volume_service = VolumeService.create_from_config(config["input"], self.config_dir)
server = volume_service.server
uuid = volume_service.uuid
instance = volume_service.instance_name
block_shape = 3*(volume_service.block_width,)
def timed_fetch_blocks_from_box(box):
"""
Fetch the blocks for a given box and return the time it took to fetch them.
Do not bother decompressing the blocks or combining them into a single volume.
"""
assert not (box % block_shape).any(), "For this test, all requests must be block-aligned"
block_boxes = list( boxes_from_grid(box, Grid(block_shape)) )
block_coords_xyz = np.array(block_boxes)[:,0,::-1] // block_shape
block_coords_str = ','.join(map(str, block_coords_xyz.flat))
voxel_count = np.prod(box[1] - box[0])
session = default_dvid_session()
url = f'{server}/api/node/{uuid}/{instance}/specificblocks?blocks={block_coords_str}'
with resource_mgr_client.access_context(server, True, 1, voxel_count):
timestamp = datetime.now()
with Timer() as timer:
r = session.get(url)
r.raise_for_status()
return timestamp, voxel_count, len(r.content), timer.seconds
# This hash-related hackery is to ensure uniform partition lengths, which Spark is bad at by default.
boxes = list(clipped_boxes_from_grid( volume_service.bounding_box_zyx, Grid(volume_service.preferred_message_shape) ))
indexed_boxes = list(map(rt.tuple_with_hash, (enumerate(boxes))))
for i_box in indexed_boxes:
i_box.set_hash(i_box[0])
rdd_boxes = self.sc.parallelize(indexed_boxes).values()
timestamps_voxels_sizes_times = rdd_boxes.map(timed_fetch_blocks_from_box)
# The only reason I'm persisting this is to see the partition distribution in the log.
rt.persist_and_execute(timestamps_voxels_sizes_times, "Fetching blocks", logger, StorageLevel.MEMORY_ONLY) #@UndefinedVariable
# Execute the workload
timestamps, voxels, sizes, times = zip( *timestamps_voxels_sizes_times.collect() )
# Process the results
self.dump_stats(timestamps, voxels, sizes, times, block_shape, concurrent_threads)
def _execute_mesh_generation(self, large_id_box_mask_factor_err):
config = self.config_data
@self.collect_log(lambda _: '_MESH_GENERATION_ERRORS')
def logged_generate_mesh(arg):
return generate_mesh_in_subprocess(config, arg)
# --> (body_id, mesh_bytes, error_msg)
body_ids_and_meshes_with_err = large_id_box_mask_factor_err.map( logged_generate_mesh )
persist_and_execute(body_ids_and_meshes_with_err, "Computing meshes", logger)
# Errors were already written to a separate file, but let's duplicate them in the master log.
errors = body_ids_and_meshes_with_err.map(lambda id_mesh_err: id_mesh_err[-1]).filter(bool).collect()
for error in errors:
logger.error(error)
# Filter out error cases
body_ids_and_meshes = body_ids_and_meshes_with_err.filter(lambda id_mesh_err: id_mesh_err[-1] is None) \
.map( lambda id_mesh_err: id_mesh_err[:2] )
# Group according to scheme
grouping_scheme = config["mesh-config"]["storage"]["grouping-scheme"]
n_partitions = num_worker_nodes() * cpus_per_worker()
if grouping_scheme in "hundreds":
def last_six_digits( id_mesh ):
body_id, _mesh = id_mesh
group_id = body_id - (body_id % 100)
return group_id
grouped_body_ids_and_meshes = body_ids_and_meshes.groupBy(last_six_digits, numPartitions=n_partitions)
elif grouping_scheme == "labelmap":
import pandas as pd
mapping_pairs = load_labelmap( config["mesh-config"]["storage"]["labelmap"], self.config_dir )
def prepend_mapped_group_id( id_mesh_partition ):
df = pd.DataFrame( mapping_pairs, columns=["body_id", "group_id"] )
new_partition = []
for id_mesh in id_mesh_partition:
body_id, mesh = id_mesh
rows = df.loc[df.body_id == body_id]
if len(rows) == 0:
# If missing from labelmap,
# we assume an implicit identity mapping
group_id = body_id
else:
group_id = rows['group_id'].iloc[0]
new_partition.append( (group_id, (body_id, mesh)) )
return new_partition
# We do this via mapPartitions().groupByKey() instead of a simple groupBy()
# to save time constructing the DataFrame inside the closure above.
# (TODO: Figure out why the dataframe isn't pickling properly...)
skip_groups = set(config["mesh-config"]["storage"]["skip-groups"])
grouped_body_ids_and_meshes = body_ids_and_meshes.mapPartitions( prepend_mapped_group_id ) \
.filter(lambda item: item[0] not in skip_groups) \
.groupByKey(numPartitions=n_partitions)
elif grouping_scheme in ("singletons", "no-groups"):
# Create 'groups' of one item each, re-using the body ID as the group id.
# (The difference between 'singletons', and 'no-groups' is in how the mesh is stored, below.)
grouped_body_ids_and_meshes = body_ids_and_meshes.map( lambda id_mesh: (id_mesh[0], [(id_mesh[0], id_mesh[1])]) )
persist_and_execute(grouped_body_ids_and_meshes, f"Grouping meshes with scheme: '{grouping_scheme}'", logger)
unpersist(body_ids_and_meshes)
del body_ids_and_meshes
with Timer() as timer:
grouped_body_ids_and_meshes.foreachPartition( partial(post_meshes_to_dvid, config) )
logger.info(f"Writing meshes to DVID took {timer.seconds}")
def execute(self):
from pyspark import StorageLevel
self._sanitize_config()
config = self.config_data
options = config["options"]
resource_mgr_client = ResourceManagerClient(options["resource-server"],
options["resource-port"])
total_cpus = 16 * num_worker_nodes()
concurrent_threads = total_cpus
if options["resource-server"]:
concurrent_threads = options["resource-server-config"]["read_reqs"]
if concurrent_threads > total_cpus:
msg = "You're attempting to use the resource manager to constrain concurrency, but you "\
"aren't running with a large enough cluster to saturate the resource manager settings"
raise RuntimeError(msg)
# We instantiate a VolumeService as an easy way to plug in missing config values as necessary.
# (We won't actually use it.)
volume_service = VolumeService.create_from_config(
config["input"], self.config_dir)
server = volume_service.server
uuid = volume_service.uuid
instance = volume_service.instance_name
block_shape = 3 * (volume_service.block_width, )
def timed_fetch_blocks_from_box(box):
"""
Fetch the blocks for a given box and return the time it took to fetch them.
Do not bother decompressing the blocks or combining them into a single volume.
"""
assert not (box % block_shape).any(
), "For this test, all requests must be block-aligned"
block_boxes = list(boxes_from_grid(box, Grid(block_shape)))
block_coords_xyz = np.array(block_boxes)[:, 0, ::-1] // block_shape
block_coords_str = ','.join(map(str, block_coords_xyz.flat))
voxel_count = np.prod(box[1] - box[0])
session = default_dvid_session()
url = f'{server}/api/node/{uuid}/{instance}/specificblocks?blocks={block_coords_str}'
with resource_mgr_client.access_context(server, True, 1,
voxel_count):
timestamp = datetime.now()
with Timer() as timer:
r = session.get(url)
r.raise_for_status()
return timestamp, voxel_count, len(r.content), timer.seconds
# This hash-related hackery is to ensure uniform partition lengths, which Spark is bad at by default.
boxes = list(
clipped_boxes_from_grid(
volume_service.bounding_box_zyx,
Grid(volume_service.preferred_message_shape)))
indexed_boxes = list(map(rt.tuple_with_hash, (enumerate(boxes))))
for i_box in indexed_boxes:
i_box.set_hash(i_box[0])
rdd_boxes = self.sc.parallelize(indexed_boxes).values()
timestamps_voxels_sizes_times = rdd_boxes.map(
timed_fetch_blocks_from_box)
# The only reason I'm persisting this is to see the partition distribution in the log.
rt.persist_and_execute(timestamps_voxels_sizes_times,
"Fetching blocks", logger,
StorageLevel.MEMORY_ONLY) #@UndefinedVariable
# Execute the workload
timestamps, voxels, sizes, times = zip(
*timestamps_voxels_sizes_times.collect())
# Process the results
self.dump_stats(timestamps, voxels, sizes, times, block_shape,
concurrent_threads)