import os import weakref import numpy as np import hashlib from tempfile import mkstemp, mkdtemp from threading import Thread, Event from multiprocessing.pool import ThreadPool 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.local_artifacts = {} 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 supports pandas.DataFrame objects only') def disable_upload(self, name): if name in self.keys(): self.local_artifacts[name] = weakref.ref(self.get(name)) def do_upload(self, name): # return True is this artifact should be uploaded return name not in self.local_artifacts or self.local_artifacts[name] != self.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._thread = None self._flush_event = Event() self._exit_flag = False self._thread_pool = ThreadPool() self._summary = '' self._temp_folder = [] def add_artifact(self, name, artifact, upload=True): # currently we support pandas.DataFrame (which we will upload as csv.gz) # or numpy array, which we will upload as npz self._artifacts_dict[name] = artifact if not upload: self._artifacts_dict.disable_upload(name) 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: self._upload_artifacts() except Exception as e: LoggerRoot.get_base_logger().warning(str(e)) # create summary self._summary = self._get_statistics() def _upload_artifacts(self): logger = self._task.get_logger() for name, artifact in self._artifacts_dict.items(): if not self._artifacts_dict.do_upload(name): # only register artifacts, and leave, TBD continue 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() continue 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()