import hashlib from copy import deepcopy from multiprocessing.pool import ThreadPool from tempfile import mkdtemp from threading import Thread, Event import numpy as np from pathlib2 import Path from ..debugging.log import LoggerRoot try: import pandas as pd except ImportError: pd = None class Artifacts(object): _flush_frequency_sec = 300. # notice these two should match _save_format = '.csv.gz' _compression = 'gzip' # hashing constants _hash_block_size = 65536 class _ProxyDictWrite(dict): """ Dictionary wrapper that updates an arguments instance on any item set in the dictionary """ def __init__(self, artifacts_manager, *args, **kwargs): super(Artifacts._ProxyDictWrite, self).__init__(*args, **kwargs) self._artifacts_manager = artifacts_manager # list of artifacts we should not upload (by name & weak-reference) self.artifact_metadata = {} def __setitem__(self, key, value): # check that value is of type pandas if isinstance(value, np.ndarray) or (pd and isinstance(value, pd.DataFrame)): super(Artifacts._ProxyDictWrite, self).__setitem__(key, value) if self._artifacts_manager: self._artifacts_manager.flush() else: raise ValueError('Artifacts currently support pandas.DataFrame objects only') def unregister_artifact(self, name): self.artifact_metadata.pop(name, None) self.pop(name, None) def add_metadata(self, name, metadata): self.artifact_metadata[name] = deepcopy(metadata) def get_metadata(self, name): return self.artifact_metadata.get(name) @property def artifacts(self): return self._artifacts_dict @property def summary(self): return self._summary def __init__(self, task): self._task = task # notice the double link, this important since the Artifact # dictionary needs to signal the Artifacts base on changes self._artifacts_dict = self._ProxyDictWrite(self) self._last_artifacts_upload = {} self._unregister_request = set() self._thread = None self._flush_event = Event() self._exit_flag = False self._thread_pool = ThreadPool() self._summary = '' self._temp_folder = [] def register_artifact(self, name, artifact, metadata=None): # currently we support pandas.DataFrame (which we will upload as csv.gz) if name in self._artifacts_dict: LoggerRoot.get_base_logger().info('Register artifact, overwriting existing artifact \"{}\"'.format(name)) self._artifacts_dict[name] = artifact if metadata: self._artifacts_dict.add_metadata(name, metadata) def unregister_artifact(self, name): # Remove artifact from the watch list self._unregister_request.add(name) self.flush() def flush(self): # start the thread if it hasn't already: self._start() # flush the current state of all artifacts self._flush_event.set() def stop(self, wait=True): # stop the daemon thread and quit # wait until thread exists self._exit_flag = True self._flush_event.set() if wait: if self._thread: self._thread.join() # remove all temp folders for f in self._temp_folder: try: Path(f).rmdir() except Exception: pass def _start(self): if not self._thread: # start the daemon thread self._flush_event.clear() self._thread = Thread(target=self._daemon) self._thread.daemon = True self._thread.start() def _daemon(self): while not self._exit_flag: self._flush_event.wait(self._flush_frequency_sec) self._flush_event.clear() try: artifact_keys = list(self._artifacts_dict.keys()) for name in artifact_keys: self._upload_artifacts(name) except Exception as e: LoggerRoot.get_base_logger().warning(str(e)) # create summary self._summary = self._get_statistics() def _upload_artifacts(self, name): logger = self._task.get_logger() artifact = self._artifacts_dict.get(name) # remove from artifacts watch list if name in self._unregister_request: try: self._unregister_request.remove(name) except KeyError: pass self._artifacts_dict.unregister_artifact(name) if artifact is None: return local_csv = (Path(self._get_temp_folder()) / (name + self._save_format)).absolute() if local_csv.exists(): # we are still uploading... get another temp folder local_csv = (Path(self._get_temp_folder(force_new=True)) / (name + self._save_format)).absolute() artifact.to_csv(local_csv.as_posix(), index=False, compression=self._compression) current_sha2 = self.sha256sum(local_csv.as_posix(), skip_header=32) if name in self._last_artifacts_upload: previous_sha2 = self._last_artifacts_upload[name] if previous_sha2 == current_sha2: # nothing to do, we can skip the upload local_csv.unlink() return self._last_artifacts_upload[name] = current_sha2 # now upload and delete at the end. logger.report_image_and_upload(title='artifacts', series=name, path=local_csv.as_posix(), delete_after_upload=True, iteration=self._task.get_last_iteration(), max_image_history=2) def _get_statistics(self): summary = '' thread_pool = ThreadPool() try: # build hash row sets artifacts_summary = [] for a_name, a_df in self._artifacts_dict.items(): if not pd or not isinstance(a_df, pd.DataFrame): continue a_unique_hash = set() def hash_row(r): a_unique_hash.add(hash(bytes(r))) a_shape = a_df.shape # parallelize thread_pool.map(hash_row, a_df.values) # add result artifacts_summary.append((a_name, a_shape, a_unique_hash,)) # build intersection summary for i, (name, shape, unique_hash) in enumerate(artifacts_summary): summary += '[{name}]: shape={shape}, {unique} unique rows, {percentage:.1f}% uniqueness\n'.format( name=name, shape=shape, unique=len(unique_hash), percentage=100*len(unique_hash)/float(shape[0])) for name2, shape2, unique_hash2 in artifacts_summary[i+1:]: intersection = len(unique_hash & unique_hash2) summary += '\tIntersection with [{name2}] {intersection} rows: {percentage:.1f}%\n'.format( name2=name2, intersection=intersection, percentage=100*intersection/float(len(unique_hash2))) except Exception as e: LoggerRoot.get_base_logger().warning(str(e)) finally: thread_pool.close() thread_pool.terminate() return summary def _get_temp_folder(self, force_new=False): if force_new or not self._temp_folder: new_temp = mkdtemp(prefix='artifacts_') self._temp_folder.append(new_temp) return new_temp return self._temp_folder[0] @staticmethod def sha256sum(filename, skip_header=0): # create sha2 of the file, notice we skip the header of the file (32 bytes) # because sometimes that is the only change h = hashlib.sha256() b = bytearray(Artifacts._hash_block_size) mv = memoryview(b) with open(filename, 'rb', buffering=0) as f: # skip header f.read(skip_header) for n in iter(lambda: f.readinto(mv), 0): h.update(mv[:n]) return h.hexdigest()