def search(self, search_term):
"""
Search the Helm Chart Repositories installed for different Helm charts that can be installed
"""
rt = ResultTable()
cmd = [self.helm, "search", "repo"]
if search_term:
cmd.append(search_term)
process = subprocess.Popen(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = process.communicate()
if stderr:
raise Exception("Exception searching repos: {}".format(stderr))
rows = stdout.split("\n")
result = []
rt.add_column("name", "Name", "STRING")
rt.add_column("chartVersion", "Chart Version", "STRING")
rt.add_column("appVersion", "App Version", "STRING")
rt.add_column("description", "Description", "STRING")
for row in rows[1:]:
record = []
for r in row.split("\t"):
record.append(r.strip())
rt.add_record(record)
return rt
def run(self, progress_callback):
client = DSSClient('http://localhost:%s' % os.environ.get('DKU_BACKEND_PORT'), internal_ticket = os.environ.get('DKU_API_TICKET'))
rt = ResultTable()
rt.set_name("Killed sessions")
rt.add_column("session_id", "Session id", "STRING")
rt.add_column("notebook_project", "Notebook project key", "STRING")
rt.add_column("notebook_project", "Notebook name", "STRING")
simulate = self.config.get('simulate', True)
max_idle = float(self.config.get('maxIdleTimeHours', 0))
max_age = float(self.config.get('maxSessionAgeHours', 0))
dont_kill_busy = self.config.get('dontKillBusyKernels', True)
dont_kill_connected = self.config.get('dontKillConnectedKernels', True)
now = get_epochtime_ms()
logging.info("Listing notebooks max_age_ms=%s max_idle_ms=%s" % (max_age * 1000 * 3600, max_idle * 1000 * 3600))
for nbk in client.list_running_notebooks():
state = nbk.get_state()
for session in state["activeSessions"]:
logging.info("Check kill of %s session_age=%s kernel_idle=%s" % (
session, (now - session["sessionStartTime"]), (now - session["kernelLastActivityTime"])))
kill = False
if max_age > 0 and (now - session["sessionStartTime"]) > max_age * 1000 * 3600:
logging.info( " -> Will kill on max_age")
kill = True
if max_idle > 0 and (now - session["kernelLastActivityTime"]) > max_idle * 1000 * 3600:
logging.info( " -> Will kill on max_idle")
kill = True
if dont_kill_busy and session["kernelExecutionState"] == "busy":
logging.info(" -> Don't kill (busy)")
kill = False
if dont_kill_connected and session["kernelConnections"] > 0:
logging.info(" -> Don't kill (connected)")
kill = False
if kill:
logging.info("Unloading session %s" % session["sessionId"])
rt.add_record([session["sessionId"], session.get("projectKey", "?"), session.get("notebookName", "?")])
if not simulate:
nbk.unload(session["sessionId"])
else:
logging.info("Don't kill %s" % session["sessionId"])
return rt
def run(self, progress_callback):
dip_home = os.environ['DIP_HOME']
saved_models = osp.join(dip_home, 'saved_models')
rt = ResultTable()
rt.set_name("Analysis data used space")
rt.add_column("project", "Project key", "STRING")
rt.add_column("saved_model_id", "Saved model id", "STRING")
rt.add_column("saved_model_name", "Saved model name", "STRING")
rt.add_column("total", "Total space (MB)", "STRING")
rt.add_column("splits", "Splits space (MB)", "STRING")
rt.add_column("versions", "Number of versions", "STRING")
if self.config.get('allProjects', False):
projects = [
project_key for project_key in os.listdir(saved_models)
]
else:
projects = [self.project_key]
for project in projects:
project_sm = osp.join(saved_models, project)
if not osp.isdir(project_sm):
continue
for saved_model in os.listdir(project_sm):
sm_dir = osp.join(project_sm, saved_model)
versions_dir = osp.join(sm_dir, "versions")
if not osp.isdir(versions_dir):
continue
versions = 0
total_splits = 0
total = cleanup.du(sm_dir)
for version in os.listdir(versions_dir):
version_dir = osp.join(versions_dir, version)
split_dir = osp.join(version_dir, "split")
if osp.isdir(split_dir):
total_splits += cleanup.du(split_dir)
versions += 1
record = []
record.append(project)
record.append(saved_model)
record.append(saved_model)
record.append(total / 1024)
record.append(total_splits / 1024)
record.append(versions)
rt.add_record(record)
return rt
def create_resulttable(self):
"""
Transforms the log dataframe into a ResultTable.
"""
result_table = ResultTable()
for column_name in self.log_df.keys():
result_table.add_column(column_name, str.capitalize(column_name), "STRING")
for log_row in self.log_df.itertuples():
result_table.add_record(list(log_row)[1:])
return result_table
def run(self, progress_callback):
to_delete = []
# As `docker images` sorts images by creation date, we only have to keep the most recent one built for DSS.
# Sample cmd: $ docker images 'dku-exec-base-notattributed' --format '{{.Repository}}\t{{.Tag}}\t{{.ID}}\t{{.CreatedAt}}'
if self.config['rm_dss_images']:
cmd = self._get_docker_cmd('images', self.config['base_image_name'], '--format', '{{.Repository}}\t{{.Tag}}\t{{.ID}}\t{{.CreatedAt}}')
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
is_most_recent = True
for line in iter(p.stdout.readline, ''):
elements = line.split('\t')
if len(elements) != 4:
continue
if is_most_recent:
is_most_recent = False
else:
to_delete.append({'repo': elements[0], 'tag': elements[1], 'id': elements[2], 'createdAt': elements[3]})
# Dangling images, that could be wiped with `docker image prune` (but would need the docker daemon to be up-to-date)
# Sample cmd: $ docker images -f 'dangling=true' --format '{{.Repository}}\t{{.Tag}}\t{{.ID}}\t{{.CreatedAt}}'
if self.config['rm_none_images']:
cmd = self._get_docker_cmd('images', '-f', 'dangling=true', '--format', '{{.Repository}}\t{{.Tag}}\t{{.ID}}\t{{.CreatedAt}}')
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
for line in iter(p.stdout.readline, ''):
elements = line.split('\t')
if len(elements) != 4:
continue
to_delete.append({'repo': elements[0], 'tag': elements[1], 'id': elements[2], 'createdAt': elements[3]})
if self.config['perform_deletion']:
rmi_args = [elt['id'] for elt in to_delete]
print('Will delete these images: ' + str(rmi_args))
if self.config['force_rm']:
rmi_args.insert(0, '--force')
cmd = self._get_docker_cmd('rmi', *rmi_args)
subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
rt = ResultTable()
rt.set_name("Removed containers")
rt.add_column("repo", "Repository", "STRING")
rt.add_column("tag", "Tag", "STRING")
rt.add_column("id", "Identifier", "STRING")
rt.add_column("createdAt", "Created at", "STRING")
for elt in to_delete:
rt.add_record([elt['repo'], elt['tag'], elt['id'], elt['createdAt']])
return rt
def run(self, progress_callback):
dip_home = os.environ['DIP_HOME']
exports_folder = os.path.join(dip_home, 'exports')
simulate = bool(self.config.get("simulate", False))
maximum_age = int(self.config.get("age", 15))
maximum_timestamp = int(
time.mktime((datetime.datetime.now() -
datetime.timedelta(days=maximum_age)).timetuple()))
to_delete = []
for export_id in os.listdir(exports_folder):
if os.stat(os.path.join(exports_folder,
export_id)).st_mtime < maximum_timestamp:
to_delete.append(export_id)
def folder_size(folder):
total_size = 0
for dirpath, dirnames, filenames in os.walk(folder):
for f in filenames:
fp = os.path.join(dirpath, f)
total_size += os.path.getsize(fp)
return total_size
rt = ResultTable()
rt.set_name("Removed exports")
rt.add_column("id", "Export identifier", "STRING")
rt.add_column("age", "Age (days)", "STRING")
rt.add_column("size", "Size (KB)", "STRING")
for export_id in to_delete:
export_folder = os.path.join(exports_folder, export_id)
size = folder_size(export_folder)
mtime = os.stat(export_folder).st_mtime
age = (time.mktime(datetime.datetime.now().timetuple()) -
mtime) / 86400
if not simulate:
shutil.rmtree(export_folder)
rt.add_record([export_id, int(age), size / 1024])
return rt
def run(self, progress_callback):
included = self.config.get('includedTags', '')
excluded = self.config.get('excludedTags', '')
included_or = self.config.get('includedTagsCombine', 'OR') == 'OR'
excluded_or = self.config.get('excludedTagsCombine', 'OR') == 'OR'
included_set = set(included.split(','))
excluded_set = set(excluded.split(','))
project = self.client.get_project(self.project_key)
to_delete = []
for dataset in project.list_datasets():
tags = dataset.get('tags', [])
included = apply(tags, included_set, included_or)
excluded = apply(tags, excluded_set, excluded_or)
if included and not excluded:
to_delete.append(dataset)
rt = ResultTable()
rt.set_name("Delete datasets by tag")
simulate = self.config.get('simulate', True)
rt.add_column("dataset", simulate and "Dataset to delete" or "Deleted dataset", "LOCAL_DATASET_WITH_TYPE")
if not simulate:
rt.add_column("result", "Result", "STRING")
if not simulate:
for dataset in to_delete:
try:
project.get_dataset(dataset.get('name')).delete(drop_data=self.config.get("drop_data", True))
rt.add_record(["%s:%s" % (dataset.get("type"), dataset.get("name")), "SUCCESS"])
except Exception as e:
rt.add_record(["%s:%s" % (dataset.get("type"), dataset.get("name")), "FAILED: %s" % str(e)])
return rt
else:
rows = []
for dataset in to_delete:
rt.add_record(["%s:%s" % (dataset.get("type"), dataset.get("name"))])
return rt
def run(self, progress_callback):
maximum_age = int(self.config.get("age", 15))
maximum_timestamp = int(time.mktime((datetime.datetime.now() - datetime.timedelta(days=maximum_age)).timetuple()))
lines = int(self.config.get("lines", 5))
orphans_only = bool(self.config.get("orphansOnly", False))
do_it = bool(self.config.get("performDeletion", False))
dip_home = os.environ['DIP_HOME']
config_home = os.path.join(dip_home, "config")
analysis_data_folder = osp.join(dip_home, "analysis-data")
def truncate_file(path, rows):
yourfile = pd.read_csv(path, nrows = rows)
yourfile.to_csv(path, index = False)
rt = ResultTable()
rt.set_name("Saved models cleanup")
rt.add_column("project", "Project key", "STRING")
rt.add_column("analysis", "Analysis", "STRING")
rt.add_column("dataset", "Dataset", "STRING")
rt.add_column("model", "Model", "STRING")
rt.add_column("total_size_before", "Total space before", "STRING")
rt.add_column("total_size_after", "Total space after", "STRING")
rt.add_column("kept_splits", "Kept splits", "STRING")
rt.add_column("truncated_splits", "Truncated splits", "STRING")
rt.add_column("reclaimed_size", "Reclaimed size", "STRING")
grand_total_used = 0
grand_total_reclaimed = 0
grand_total_kept = 0
grand_total_deleted = 0
for project_key in cleanup.get_projects_to_consider(self.project_key, self.config):
analysis_data_project_folder = osp.join(analysis_data_folder, project_key)
if not osp.isdir(analysis_data_project_folder):
continue
project_folder = os.path.join(config_home, "projects", project_key)
for analysis_id in os.listdir(analysis_data_project_folder):
analysis_data_analysis_folder = osp.join(analysis_data_project_folder,analysis_id)
if not osp.isdir(analysis_data_analysis_folder):
continue
analysis_folder = os.path.join(project_folder, "analysis", analysis_id)
total_used = 0
total_reclaimed = 0
total_kept = 0
total_deleted = 0
model_records = []
dataset_name = None
analysis_name = None
try:
core_params_file = os.path.join(analysis_folder, "core_params.json")
if os.path.isfile(core_params_file):
with open(core_params_file, 'r') as f:
core_params = json.load(f)
dataset_name = core_params.get('inputDatasetSmartName', None)
analysis_name = core_params.get('name', None)
except Exception:
pass
model_ids = []
for model_id in os.listdir(analysis_data_analysis_folder):
analysis_data_model_folder = os.path.join(analysis_data_analysis_folder, model_id)
if not osp.isdir(analysis_data_model_folder):
continue
model_folder = os.path.join(analysis_folder, "ml", model_id)
used = 0
reclaimed = 0
kept = 0
deleted = 0
try:
used = cleanup.du(analysis_data_model_folder,size_unit="b")
except Exception:
pass
model_name = None
try:
model_params_file = os.path.join(model_folder, "params.json")
if os.path.isfile(model_params_file):
with open(model_params_file, 'r') as f:
model_params = json.load(f)
model_name = model_params.get('name', None)
except:
pass
splits_dates = {}
# Scan session to find out split usage
sessions_folder = os.path.join(analysis_data_model_folder,"sessions")
if osp.isdir(sessions_folder):
for session in os.listdir(sessions_folder):
split_ref_file = osp.join(sessions_folder, session, "split_ref.json")
if not osp.isfile(split_ref_file):
continue
session_timestamp = os.stat(osp.join(sessions_folder, session)).st_mtime
split_ref = None
with open(split_ref_file, 'r') as f:
split_ref = json.load(f).get("splitInstanceId",None)
if split_ref is not None and splits_dates.get(split_ref,0) < session_timestamp:
splits_dates[split_ref] = session_timestamp
# Check it agaisnt actual splits
splits_folder = os.path.join(analysis_data_model_folder,"splits")
if osp.isdir(splits_folder):
for split in glob.glob(osp.join(splits_folder,"*.json")):
split_name, _ = osp.splitext(split)
split_short_name = osp.basename(split_name)
split_date = splits_dates.get(split_short_name, None)
if split_date is None or (split_date < maximum_timestamp and not orphans_only):
deleted += 1
split_data = {}
with open(split, 'r') as f:
split_data = json.load(f)
for split_data_filename in [split_data.get("testPath",None), split_data.get("trainPath",None)]:
if split_data_filename is None:
continue
split_data_file = osp.join(splits_folder,split_data_filename)
_, split_data_extension = osp.splitext(split_data_filename)
if osp.isfile(split_data_file):
if do_it:
if split_date is None:
reclaimed = os.stat(split_data_file).st_size
os.unlink(split_data_file)
else:
size_before = os.stat(split_data_file).st_size
try:
data_file = pd.read_csv(split_data_file, nrows = lines)
data_file.to_csv(split_data_file, index = False, compression="gzip" if split_data_extension == ".gz" else None)
except Exception as e:
logging.getLogger().error("{}: {}".format(split_data_file,str(e)))
reclaimed = size_before - os.stat(split_data_file).st_size
pass
else:
reclaimed = os.stat(split_data_file).st_size
if do_it and split_date is None:
os.unlink(split)
pass
else:
kept += 1
total_reclaimed += reclaimed
total_used += used
total_kept += kept
total_deleted += deleted
model_records.append([
project_key,
analysis_name,
dataset_name,
model_name,
cleanup.format_size(used),
cleanup.format_size(used-reclaimed),
kept,
deleted,
cleanup.format_size(reclaimed)
])
rt.add_record([
project_key,
analysis_name,
dataset_name,
"Total all models",
cleanup.format_size(total_used),
cleanup.format_size(total_used-total_reclaimed),
total_kept,
total_deleted,
cleanup.format_size(total_reclaimed)
])
for record in model_records:
rt.add_record(record)
grand_total_reclaimed += total_reclaimed
grand_total_used += total_used
grand_total_kept += total_kept
grand_total_deleted += total_deleted
rt.add_record([
"Total used",
"-",
"-",
"-",
cleanup.format_size(grand_total_used),
cleanup.format_size(grand_total_used-grand_total_reclaimed),
grand_total_kept,
grand_total_deleted,
cleanup.format_size(grand_total_reclaimed)
])
return rt
def run(self, progress_callback):
def update_percent(percent, last_update_time):
new_time = time.time()
if (new_time - last_update_time) > 3:
progress_callback(percent)
return new_time
else:
return last_update_time
# Get project and folder containing the Excel files
client = dataiku.api_client()
project = client.get_project(self.project_key)
folder_id = self.config.get("model_folder_id")
overwrite = self.config.get("overwrite", False)
folder = dataiku.Folder(folder_id, project_key=self.project_key)
folder_path = folder.get_path()
macro_creates_dataset = False # A boolean used to provide an informative message to the user when the macro creates a dataset
# List files in folder and get path
files_list = os.listdir(folder_path)
# List the datasets in the project
datasets_in_project = []
for i in range(len(project.list_datasets())):
datasets_in_project.append(project.list_datasets()[i]['name'])
# Actions performed
actions_performed = dict()
num_files = len(files_list)
update_time = time.time()
for file_index, my_file in enumerate(files_list):
## Get file path
file_path = os.path.join(folder_path, my_file)
## Get Excel file and load in a pandas dataframe
sheets_names = pd.ExcelFile(file_path).sheet_names
for sheet in sheets_names:
### Rename sheets by "file_sheet"
ss=openpyxl.load_workbook(file_path)
ss_sheet = ss.get_sheet_by_name(sheet)
title = ss_sheet.title
if not my_file.split(".")[0] in title:
title = '_'.join((my_file.split(".")[0] + "_" + sheet).split())
title = '_'.join(title.split())
title = title.replace(')','')
title = title.replace('(','')
create_dataset = True
if title in datasets_in_project:
if overwrite:
project.get_dataset(title).delete()
actions_performed[title] = "replaced"
else:
create_dataset = False
actions_performed[title] = "skipped (already exists)"
else:
actions_performed[title] = "created"
macro_creates_dataset = True
if create_dataset:
dataset = project.create_dataset(title
,'FilesInFolder'
, params={'folderSmartId': folder_id,
'filesSelectionRules': {'mode': 'EXPLICIT_SELECT_FILES',
'explicitFiles': [my_file]}}
, formatType='excel'
, formatParams={"xlsx":True, "sheets":"*"+ss_sheet.title,'parseHeaderRow': True})
df = pd.read_excel(file_path, sheet_name=ss_sheet.title, nrows=1000)
dataset.set_schema({'columns': [{'name': column, 'type': 'string'} for column, column_type in df.dtypes.items()]})
percent = 100*float(file_index+1)/num_files
update_time = update_percent(percent, update_time)
# Output table
rt = ResultTable()
rt.add_column("actions", "Actions", "STRING")
# Actions : "dataset" has been created or replaced
for i in range(len(actions_performed)):
record = []
record.append(list(actions_performed.keys())[i] + " has been " + list(actions_performed.values())[i])
rt.add_record(record)
if macro_creates_dataset:
rt.add_record(["Please refresh this page to see new datasets."])
return rt
def run(self, progress_callback):
dip_home = os.environ["DIP_HOME"]
config_home = os.path.join(dip_home, "config")
analysis_data_folder = os.path.join(dip_home, "analysis-data")
rt = ResultTable()
rt.set_name("Analysis data")
rt.add_column("projectKey", "Project", "STRING")
rt.add_column("dataset", "Dataset", "STRING")
rt.add_column("analysis", "Analysis", "STRING")
rt.add_column("model", "Model", "STRING")
rt.add_column("used", "Disk space used", "STRING")
rt.add_column("path", "Path", "STRING")
for project_key in os.listdir(analysis_data_folder):
analysis_data_project_folder = os.path.join(analysis_data_folder, project_key)
if not os.path.isdir(analysis_data_project_folder):
continue
project_folder = os.path.join(config_home, "projects", project_key)
orphaned_project = not os.path.isdir(project_folder)
for analysis_id in os.listdir(analysis_data_project_folder):
analysis_data_analysis_folder = os.path.join(analysis_data_project_folder, analysis_id)
if not os.path.isdir(analysis_data_analysis_folder):
continue
analysis_folder = os.path.join(project_folder, "analysis", analysis_id)
orphaned_analysis = not os.path.isdir(analysis_folder) if not orphaned_project else None
total_used = 0
model_records = []
for model_id in os.listdir(analysis_data_analysis_folder):
analysis_data_model_folder = os.path.join(analysis_data_analysis_folder, model_id)
if not os.path.isdir(analysis_data_model_folder):
continue
model_folder = os.path.join(analysis_folder, "ml", model_id)
orphaned_model = not os.path.isdir(model_folder) if not orphaned_project and not orphaned_analysis else None
try:
used = self.disk_space_used(analysis_data_model_folder)
total_used += used
except:
used = None
try:
core_params_file = os.path.join(analysis_folder, "core_params.json")
if os.path.isfile(core_params_file):
with open(core_params_file, 'r') as f:
core_params = json.load(f)
dataset_name = core_params.get('inputDatasetSmartName', None)
analysis_name = core_params.get('name', None)
else:
dataset_name = None
analysis_name = None
except:
dataset_name = None
analysis_name = None
try:
model_params_file = os.path.join(model_folder, "params.json")
if os.path.isfile(model_params_file):
with open(model_params_file, 'r') as f:
model_params = json.load(f)
model_name = model_params.get('name', None)
else:
model_name = None
except:
model_name = None
record = []
# 0
if orphaned_project:
record.append('(orphaned)')
else:
record.append(project_key)
# 1
record.append(dataset_name)
# 2
if orphaned_analysis:
record.append('(orphaned)')
elif analysis_name is not None:
record.append(analysis_name)
else:
record.append(analysis_id)
# 3
if orphaned_model:
record.append('(orphaned)')
elif model_name is not None:
record.append(model_name)
else:
record.append(model_id)
# 4
if used is None:
record.append('N/A')
elif used < 1024:
record.append('%s b' % used)
elif used < 1024 * 1024:
record.append('%s Kb' % int(used/1024))
elif used < 1024 * 1024 * 1024:
record.append('%s Mb' % int(used/(1024*1024)))
else:
record.append('%s Gb' % int(used/(1024*1024*1024)))
# 5
record.append(analysis_data_model_folder)
model_records.append(record)
record = []
# 0
if orphaned_project:
record.append('(orphaned)')
else:
record.append(project_key)
# 1
record.append(dataset_name)
# 2
if orphaned_analysis:
record.append('(orphaned)')
elif analysis_name is not None:
record.append(analysis_name)
else:
record.append(analysis_id)
# 3
record.append(None)
# 4
if total_used is None:
record.append('N/A')
elif total_used < 1024:
record.append('%s b' % used)
elif total_used < 1024 * 1024:
record.append('%s Kb' % int(total_used/1024))
elif total_used < 1024 * 1024 * 1024:
record.append('%s Mb' % int(total_used/(1024*1024)))
else:
record.append('%s Gb' % int(total_used/(1024*1024*1024)))
# 5
record.append(analysis_data_analysis_folder)
rt.add_record(record)
for model_record in model_records:
rt.add_record(model_record)
table_rows = []
idx = 0
for record in rt.records:
analysis_row = record[3] is None
row_cells = []
for i in range(0, 6):
if analysis_row and i == 3:
continue
value = record[i]
if value is not None:
if i == 5:
show_path_var = "showPath%s" % idx
row_cells.append('<td class="mx-textellipsis" title="%s"><a class="mx-link-nodecoration" href="" ng-click="%s = !%s"><i class="icon-eye"></i></a></td>' % (value, show_path_var, show_path_var))
else:
row_cells.append('<td class="mx-textellipsis" title="%s" %s>%s</td>' % (value, (' colspan="2"' if analysis_row and i == 2 else ''), value))
else:
row_cells.append('<td></td>')
if analysis_row:
# analysis row
table_rows.append('<tr style="font-weight: bold;">%s</tr>' % (''.join(row_cells)))
else:
# model row
table_rows.append('<tr>%s</tr>' % (''.join(row_cells)))
path_cell_style = 'white-space: nowrap; padding-left: 20px; font-family: monospace; font-size: 11px;'
if analysis_row:
path_cell_style = path_cell_style + '; font-weight: bold'
table_rows.append('<tr ng-if="%s"><td colspan="6" title="%s" style="%s">%s</td></tr>' % (show_path_var, record[5], path_cell_style, record[5]))
idx += 1
html = '<div>'
table_header = '<th>%s</th>' % ('</th><th>'.join(['Project', 'Dataset', 'Analysis', 'Model', 'Disk usage', 'Path']))
html += '<table class="table table-striped" style="table-layout: fixed;">%s%s</table>' % (table_header, ''.join(table_rows))
html += '</div>'
return html
# return rt
def run(self, progress_callback):
dip_home = os.environ['DIP_HOME']
analysis_data = osp.join(dip_home, 'analysis-data')
projects_sessions = {}
projects_splits = {}
analyses_sessions = {}
analyses_splits = {}
projects_analyses = {}
if self.config.get('allProjects', False):
projects = [
project_key for project_key in os.listdir(analysis_data)
]
else:
projects = [self.project_key]
for project in projects:
project_analysis_data = osp.join(analysis_data, project)
project_sessions = 0
project_splits = 0
projects_analyses[project] = []
if not osp.isdir(project_analysis_data):
projects_sessions[project] = 0
projects_splits[project] = 0
continue
for analysis in os.listdir(project_analysis_data):
analysis_dir = osp.join(project_analysis_data, analysis)
analysis_sessions = 0
analysis_splits = 0
projects_analyses[project].append(analysis)
for mltask in os.listdir(analysis_dir):
mltask_dir = osp.join(analysis_dir, mltask)
sessions_dir = osp.join(mltask_dir, "sessions")
splits_dir = osp.join(mltask_dir, "splits")
if osp.isdir(sessions_dir):
analysis_sessions += cleanup.du(sessions_dir)
if osp.isdir(splits_dir):
analysis_splits += cleanup.du(splits_dir)
project_sessions += analysis_sessions
project_splits += analysis_splits
analyses_splits[(project, analysis)] = analysis_splits
analyses_sessions[(project, analysis)] = analysis_sessions
projects_sessions[project] = project_sessions
projects_splits[project] = project_splits
rt = ResultTable()
rt.set_name("Analysis data used space")
if self.config["granularity"] == "project":
rt.add_column("project", "Project key", "STRING")
rt.add_column("total", "Total space (MB)", "STRING")
rt.add_column("sessions", "Sessions space (MB)", "STRING")
rt.add_column("splits", "Splits space (MB)", "STRING")
for project in projects:
total = (projects_sessions[project] + projects_splits[project])
if len(projects) > 0 and total == 0:
continue
record = []
record.append(project)
record.append(total / 1024)
record.append(projects_sessions[project] / 1024)
record.append(projects_splits[project] / 1024)
rt.add_record(record)
else:
rt.add_column("project", "Project key", "STRING")
rt.add_column("analysis", "Analysis id", "STRING")
rt.add_column("total", "Total space (MB)", "STRING")
rt.add_column("sessions", "Sessions space (MB)", "STRING")
rt.add_column("splits", "Splits space (MB)", "STRING")
for project in projects:
for analysis in projects_analyses[project]:
record = []
record.append(project)
record.append(analysis)
record.append(
(analyses_sessions[(project, analysis)] +
analyses_splits[(project, analysis)]) / 1024)
record.append(analyses_sessions[(project, analysis)] /
1024)
record.append(analyses_splits[(project, analysis)] / 1024)
rt.add_record(record)
return rt
def run(self, progress_callback):
# Retrieve macro parameters:
is_dry_run = self.config.get("is_dry_run")
keep_partitioned = self.config.get("keep_partitioned")
keep_shared = self.config.get("keep_shared")
logging.info("DRY RUN is set to {}".format(str(is_dry_run)))
logging.info("KEEP PARTITIONED is set to {}".format(
str(keep_partitioned)))
logging.info("KEEP SHARED is set to {}".format(str(keep_shared)))
# Initialize macro result table:
result_table = ResultTable()
result_table.add_column("dataset", "Dataset", "STRING")
result_table.add_column("type", "Type", "STRING")
result_table.add_column("action", "Action", "STRING")
result_table.add_column("action_status", "Action Status", "STRING")
action_status = "Not done (Dry run)" if is_dry_run else "Done"
client = dataiku.api_client()
if self.config.get("project_key", None):
project = client.get_project(self.config.get("project_key"))
else:
project = client.get_project(self.project_key)
manually_selected_datasets = self.config.get("datasets_to_exclude")
all_datasets = project.list_datasets()
all_recipes = project.list_recipes()
# Build deduplicated lists of input/output datasets:
input_datasets = []
output_datasets = []
for recipe in all_recipes:
recipe_inputs_dict = recipe["inputs"]
recipe_outputs_dict = recipe["outputs"]
# CASE: no input dataset
if recipe_inputs_dict:
append_datasets_to_list(recipe_inputs_dict, input_datasets)
append_datasets_to_list(recipe_outputs_dict, output_datasets)
# Identify Flow input/outputs:
flow_inputs = [
dataset for dataset in input_datasets
if dataset not in output_datasets
]
flow_outputs = [
dataset for dataset in output_datasets
if dataset not in input_datasets
]
logging.info("Found {} FLOW INPUT datasets: {}".format(
str(len(flow_inputs)), str(flow_inputs)))
logging.info("Found {} FLOW OUTPUT datasets: {}".format(
str(len(flow_outputs)), str(flow_outputs)))
# Identify standalone, intermediate, and partitioned datasets
excluded_datasets = []
standalone_datasets = []
intermediate_datasets = []
partitioned_datasets = []
for dataset in all_datasets:
if dataset["name"] in manually_selected_datasets:
excluded_datasets.append(dataset["name"])
if dataset["name"] not in input_datasets + output_datasets:
standalone_datasets.append(dataset["name"])
if dataset[
"name"] not in flow_inputs + flow_outputs + standalone_datasets:
intermediate_datasets.append(dataset["name"])
is_partitioned = lambda dataset: len(dataset["partitioning"][
"dimensions"]) > 0
if is_partitioned(dataset):
partitioned_datasets.append(dataset["name"])
logging.info("Found {} EXCLUDED datasets: {}".format(
str(len(excluded_datasets)), str(excluded_datasets)))
logging.info("Found {} STANDALONE datasets: {}".format(
str(len(standalone_datasets)), str(standalone_datasets)))
logging.info("Found {} INTERMEDIATE datasets: {}".format(
str(len(intermediate_datasets)), str(intermediate_datasets)))
logging.info("Found {} PARTITIONED datasets: {}".format(
str(len(partitioned_datasets)), str(partitioned_datasets)))
# Identify shared datasets:
shared_objects = project.get_settings(
).settings["exposedObjects"]["objects"]
shared_datasets = [
object["localName"] for object in shared_objects
if object["type"] == "DATASET"
]
logging.info("Found {} SHARED datasets: {}".format(
str(len(shared_datasets)), str(shared_datasets)))
# Add dataset types to results list
results = []
datasets = {
"EXCLUDED": excluded_datasets,
"STANDALONE": standalone_datasets,
"INPUT": flow_inputs,
"OUTPUT": flow_outputs,
"INTERMEDIATE": intermediate_datasets,
"SHARED": shared_datasets,
"PARTITIONED": partitioned_datasets
}
for dataset_type, dataset_type_list in datasets.items():
for dataset in dataset_type_list:
results.append([dataset, dataset_type])
# Identify which datasets should be kept
to_keep = excluded_datasets + standalone_datasets + flow_inputs + flow_outputs
if keep_partitioned:
to_keep += partitioned_datasets
if keep_shared:
to_keep += shared_datasets
logging.info("Total of {} datasets to KEEP: {}".format(
str(len(to_keep)), str(to_keep)))
# Create df with all results
results_df = pd.DataFrame(results, columns=["Dataset", "Type"])
results_grouped = results_df.groupby(
["Dataset"])['Type'].apply(lambda x: ', '.join(x)).reset_index()
results_grouped["Action"] = results_grouped["Dataset"].apply(
lambda x: "KEEP" if x in to_keep else "CLEAR")
results_grouped["Status"] = action_status
results_grouped = results_grouped.sort_values(by=['Action', 'Type'])
# Perform cleanup
to_clear = list(
results_grouped["Dataset"][results_grouped['Action'] == "CLEAR"])
logging.info("Total of {} datasets to CLEAR: {}".format(
str(len(to_clear)), str(to_clear)))
if not is_dry_run:
for ds in to_clear:
dataset = project.get_dataset(ds)
logging.info("Clearing {}...".format(ds))
dataset.clear()
logging.info("Clearing {} datasets: done.".format(
str(len(to_clear))))
# Pass results to result table
for index, row in results_grouped.iterrows():
result_table.add_record(list(row))
return result_table
def run(self, progress_callback):
maximum_age = int(self.config.get("age", 15))
maximum_timestamp = int(
time.mktime((datetime.datetime.now() -
datetime.timedelta(days=maximum_age)).timetuple()))
lines = int(self.config.get("lines", 5))
do_it = bool(self.config.get("performDeletion", False))
dip_home = os.environ['DIP_HOME']
saved_models = osp.join(dip_home, 'saved_models')
def truncate_file(path, rows):
yourfile = pd.read_csv(path, nrows=rows)
yourfile.to_csv(path, index=False)
rt = ResultTable()
rt.set_name("Saved models cleanup")
rt.add_column("project", "Project key", "STRING")
rt.add_column("saved_model_id", "Saved model id", "STRING")
rt.add_column("saved_model_name", "Saved model name", "STRING")
rt.add_column("total_size_before", "Total space before (MB)", "STRING")
rt.add_column("total_size_after", "Total space after (MB)", "STRING")
rt.add_column("kept_splits", "Kept splits", "STRING")
rt.add_column("truncated_splits", "Truncated splits", "STRING")
rt.add_column("reclaimed_size", "Reclaimed size", "STRING")
grand_total_used = 0
grand_total_reclaimed = 0
grand_total_kept = 0
grand_total_deleted = 0
for project in cleanup.get_projects_to_consider(
self.project_key, self.config):
project_sm = osp.join(saved_models, project)
if not osp.isdir(project_sm):
continue
for saved_model in os.listdir(project_sm):
sm_dir = osp.join(project_sm, saved_model)
versions_dir = osp.join(sm_dir, "versions")
if not osp.isdir(versions_dir):
continue
kept_versions = 0
deleted_versions = 0
size_reclaimed = 0
total_size_before = cleanup.du(sm_dir, size_unit="b")
for version in os.listdir(versions_dir):
version_dir = osp.join(versions_dir, version)
if os.stat(version_dir).st_mtime < maximum_timestamp:
# Need to clean this version
deleted_versions += 1
split_dir = osp.join(version_dir, "split")
if osp.isdir(split_dir):
for name in os.listdir(split_dir):
path = osp.join(split_dir, name)
ext = osp.splitext(path)[-1].lower()
if ext == ".csv":
if do_it:
try:
initial = os.stat(path).st_size
truncate_file(path, lines)
size_reclaimed += initial - os.stat(
path).st_size
except Exception as e:
logging.getLogger().error(
"{}: {}".format(path, str(e)))
else:
size_reclaimed += os.stat(path).st_size
else:
kept_versions += 1
total_size_after = cleanup.du(sm_dir, size_unit="b")
record = []
record.append(project)
record.append(saved_model)
record.append(saved_model)
record.append(cleanup.format_size(total_size_before))
record.append(cleanup.format_size(total_size_after))
record.append(kept_versions)
record.append(deleted_versions)
record.append(cleanup.format_size(size_reclaimed))
rt.add_record(record)
grand_total_reclaimed += size_reclaimed
grand_total_used += total_size_before
grand_total_kept += kept_versions
grand_total_deleted += deleted_versions
rt.add_record([
"Total", "-", "-",
cleanup.format_size(grand_total_used),
cleanup.format_size(grand_total_used - grand_total_reclaimed),
grand_total_kept, grand_total_deleted,
cleanup.format_size(grand_total_reclaimed)
])
return rt
def run(self, progress_callback):
# Retrieve macro parameters:
is_dry_run = self.config.get("is_dry_run")
keep_partitioned = self.config.get("keep_partitioned")
keep_shared = self.config.get("keep_shared")
logging.info("DRY RUN is set to {}".format(str(is_dry_run)))
logging.info("KEEP PARTITIONED is set to {}".format(
str(keep_partitioned)))
logging.info("KEEP SHARED is set to {}".format(str(keep_shared)))
# Initialize macro result table:
result_table = ResultTable()
result_table.add_column("dataset", "dataset", "STRING")
result_table.add_column("status", "status", "STRING")
client = dataiku.api_client()
if self.config.get("project_key", None):
project = client.get_project(self.config.get("project_key"))
else:
project = client.get_project(self.project_key)
all_datasets = project.list_datasets()
all_recipes = project.list_recipes()
# Build deduplicated lists of input/output datasets:
input_datasets = []
output_datasets = []
for rcp in all_recipes:
rcp_inputs_dict = rcp["inputs"]
rcp_outputs_dict = rcp["outputs"]
# CASE: no input dataset
if rcp_inputs_dict:
input_key = list(rcp_inputs_dict.keys())[0]
rcp_inputs_list = [
x["ref"] for x in rcp_inputs_dict[input_key]["items"]
]
input_datasets += rcp_inputs_list
output_key = list(rcp_outputs_dict.keys())[0]
rcp_outputs_list = [
x["ref"] for x in rcp_outputs_dict[output_key]["items"]
]
# Append them to the overall input list:
output_datasets += rcp_outputs_list
# Deduplicate input/output lists:
input_datasets = list(set(input_datasets))
output_datasets = list(set(output_datasets))
# Identify Flow input/outputs & add them to result table:
flow_inputs = [x for x in input_datasets if x not in output_datasets]
for obj in flow_inputs:
result_table.add_record([obj, "KEEP(INPUT)"])
flow_outputs = [x for x in output_datasets if x not in input_datasets]
for obj in flow_outputs:
result_table.add_record([obj, "KEEP(OUTPUT)"])
logging.info("Found {} FLOW INPUT datasets: {}".format(
str(len(flow_inputs)), str(flow_inputs)))
logging.info("Found {} FLOW OUTPUT datasets: {}".format(
str(len(flow_outputs)), str(flow_outputs)))
# Identify shared datasets:
shared_objs = project.get_settings(
).settings["exposedObjects"]["objects"]
shared_datasets = [
x["localName"] for x in shared_objs if x["type"] == "DATASET"
]
logging.info("Found {} SHARED datasets: {}".format(
str(len(shared_datasets)), str(shared_datasets)))
# Identify partitioned (partd) datasets:
is_partd = lambda x: len(x["partitioning"]["dimensions"]) > 0
partd_datasets = [x["name"] for x in all_datasets if is_partd(x)]
logging.info("Found {} PARTITIONED datasets: {}".format(
str(len(partd_datasets)), str(partd_datasets)))
# List all datasets to keep, potentially including shared & partd ones:
to_keep = flow_inputs + flow_outputs
if keep_partitioned:
to_keep += partd_datasets
# Add them to result table:
for obj in partd_datasets:
result_table.add_record([obj, "KEEP(PARTITIONED)"])
if keep_shared:
to_keep += shared_datasets
# Add them to result table:
for obj in shared_datasets:
result_table.add_record([obj, "KEEP(SHARED)"])
logging.info("Total of {} datasets to KEEP: {}".format(
str(len(to_keep)), str(to_keep)))
# Perform cleanup or simulate it (dry run):
if not is_dry_run:
for ds in all_datasets:
ds_name = ds["name"]
if ds_name not in to_keep:
dataset = project.get_dataset(ds_name)
logging.info("Clearing {}...".format(ds_name))
dataset.clear()
return result_table
def run(self, progress_callback):
# Get project and folder containing the Excel files
client = dataiku.api_client()
project = client.get_project(self.project_key)
folder_id = self.config.get("model_folder_id")
folder = dataiku.Folder(folder_id, project_key=self.project_key)
folder_path = folder.get_path()
# List files in folder and get path
files_list = os.listdir(folder_path)
# List the datasets in the project
datasets_in_project = []
for i in range(len(project.list_datasets())):
datasets_in_project.append(project.list_datasets()[i]['name'])
# Actions performed
actions_performed = dict()
for my_file in files_list:
## Get file path
file_path = os.path.join(folder_path, my_file)
## Get Excel file and load in a pandas dataframe
sheets_names = pd.ExcelFile(file_path).sheet_names
for sheet in sheets_names:
### Rename sheets by "file_sheet"
ss = openpyxl.load_workbook(file_path)
ss_sheet = ss.get_sheet_by_name(sheet)
if not my_file.split(".")[0] in ss_sheet.title:
ss_sheet.title = my_file.split(".")[0] + "_" + sheet
ss.save(file_path)
## If the dataset already exists, delete and replace it
actions_performed[ss_sheet.title] = "created"
if ss_sheet.title in datasets_in_project:
project.get_dataset(ss_sheet.title).delete()
actions_performed[ss_sheet.title] = "replaced"
### Create dataset from Excel sheet
project.create_dataset(ss_sheet.title,
'FilesInFolder',
params={
'folderSmartId': folder_id,
'path': my_file
},
formatType='excel',
formatParams={
"xlsx": True,
"sheets": "*" + ss_sheet.title,
'parseHeaderRow': True
})
# Output table
from dataiku.runnables import Runnable, ResultTable
rt = ResultTable()
rt.add_column("actions", "Actions", "STRING")
# Actions : "dataset" has been created or replaced
for i in range(len(actions_performed)):
record = []
record.append(actions_performed.keys()[i] + " has been " +
actions_performed.values()[i])
rt.add_record(record)
return rt