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Source code for galaxy.tools.evaluation
import json
import logging
import os
import shlex
import string
import tempfile
from datetime import datetime
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Union,
)
from galaxy import model
from galaxy.job_execution.compute_environment import ComputeEnvironment
from galaxy.job_execution.setup import ensure_configs_directory
from galaxy.model.deferred import (
materialize_collection_input,
materializer_factory,
)
from galaxy.model.none_like import NoneDataset
from galaxy.security.object_wrapper import wrap_with_safe_string
from galaxy.structured_app import (
BasicSharedApp,
MinimalToolApp,
)
from galaxy.tool_util.data import TabularToolDataTable
from galaxy.tools.parameters import (
visit_input_values,
wrapped_json,
)
from galaxy.tools.parameters.basic import (
DataCollectionToolParameter,
DataToolParameter,
SelectToolParameter,
)
from galaxy.tools.parameters.grouping import (
Conditional,
Repeat,
Section,
)
from galaxy.tools.wrappers import (
DatasetCollectionWrapper,
DatasetFilenameWrapper,
DatasetListWrapper,
ElementIdentifierMapper,
InputValueWrapper,
RawObjectWrapper,
SelectToolParameterWrapper,
ToolParameterValueWrapper,
)
from galaxy.util import (
find_instance_nested,
listify,
RW_R__R__,
safe_makedirs,
unicodify,
)
from galaxy.util.template import fill_template
from galaxy.work.context import WorkRequestContext
log = logging.getLogger(__name__)
[docs]class ToolErrorLog:
[docs] def add_error(self, file, phase, exception):
self.error_stack.insert(
0, {"file": file, "time": str(datetime.now()), "phase": phase, "error": unicodify(exception)}
)
if len(self.error_stack) > self.max_errors:
self.error_stack.pop()
global_tool_errors = ToolErrorLog()
[docs]def global_tool_logs(func, config_file, action_str):
try:
return func()
except Exception as e:
# capture and log parsing errors
global_tool_errors.add_error(config_file, action_str, e)
raise e
DeferrableObjectsT = Union[
model.DatasetInstance, model.HistoryDatasetCollectionAssociation, model.DatasetCollectionElement
]
[docs]class ToolEvaluator:
"""An abstraction linking together a tool and a job runtime to evaluate
tool inputs in an isolated, testable manner.
"""
app: MinimalToolApp
job: model.Job
materialize_datasets: bool = True
[docs] def __init__(self, app: MinimalToolApp, tool, job, local_working_directory):
self.app = app
self.job = job
self.tool = tool
self.local_working_directory = local_working_directory
self.file_sources_dict: Dict[str, Any] = {}
self.param_dict: Dict[str, Any] = {}
self.extra_filenames: List[str] = []
self.environment_variables: List[Dict[str, str]] = []
self.version_command_line: Optional[str] = None
self.command_line: Optional[str] = None
[docs] def set_compute_environment(self, compute_environment: ComputeEnvironment, get_special: Optional[Callable] = None):
"""
Setup the compute environment and established the outline of the param_dict
for evaluating command and config cheetah templates.
"""
self.compute_environment = compute_environment
job = self.job
incoming = {p.name: p.value for p in job.parameters}
incoming = self.tool.params_from_strings(incoming, self.app)
self.file_sources_dict = compute_environment.get_file_sources_dict()
# Full parameter validation
self._validate_incoming(incoming)
# Restore input / output data lists
inp_data, out_data, out_collections = job.io_dicts()
# collect deferred datasets and collections.
deferred_objects = self._deferred_objects(inp_data, incoming)
# materialize deferred datasets
materialized_objects = self._materialize_objects(deferred_objects, self.local_working_directory)
# replace materialized objects back into tool input parameters
self._replaced_deferred_objects(inp_data, incoming, materialized_objects)
if get_special:
special = get_special()
if special:
out_data["output_file"] = special
# These can be passed on the command line if wanted as $__user_*__
incoming.update(model.User.user_template_environment(self._user))
# Build params, done before hook so hook can use
self.param_dict = self.build_param_dict(
incoming,
inp_data,
out_data,
output_collections=out_collections,
)
self.execute_tool_hooks(inp_data=inp_data, out_data=out_data, incoming=incoming)
[docs] def execute_tool_hooks(self, inp_data, out_data, incoming):
# Certain tools require tasks to be completed prior to job execution
# ( this used to be performed in the "exec_before_job" hook, but hooks are deprecated ).
self.tool.exec_before_job(self.app, inp_data, out_data, self.param_dict)
# Run the before queue ("exec_before_job") hook
self.tool.call_hook(
"exec_before_job", self.app, inp_data=inp_data, out_data=out_data, tool=self.tool, param_dict=incoming
)
[docs] def build_param_dict(self, incoming, input_datasets, output_datasets, output_collections):
"""
Build the dictionary of parameters for substituting into the command
line. Each value is wrapped in a `InputValueWrapper`, which allows
all the attributes of the value to be used in the template, *but*
when the __str__ method is called it actually calls the
`to_param_dict_string` method of the associated input.
"""
compute_environment = self.compute_environment
job_working_directory = compute_environment.working_directory()
param_dict = self.param_dict
def input():
raise SyntaxError("Unbound variable input.") # Don't let $input hang Python evaluation process.
param_dict["input"] = input
param_dict["__datatypes_config__"] = param_dict["GALAXY_DATATYPES_CONF_FILE"] = os.path.join(
job_working_directory, "registry.xml"
)
if self._history:
param_dict["__history_id__"] = self.app.security.encode_id(self._history.id)
param_dict["__galaxy_url__"] = self.compute_environment.galaxy_url()
param_dict.update(self.tool.template_macro_params)
# All parameters go into the param_dict
param_dict.update(incoming)
self.__populate_wrappers(param_dict, input_datasets, job_working_directory)
self.__populate_input_dataset_wrappers(param_dict, input_datasets)
self.__populate_output_dataset_wrappers(param_dict, output_datasets, job_working_directory)
self.__populate_output_collection_wrappers(param_dict, output_collections, job_working_directory)
self.__populate_unstructured_path_rewrites(param_dict)
# Call param dict sanitizer, before non-job params are added, as we don't want to sanitize filenames.
self.__sanitize_param_dict(param_dict)
# Parameters added after this line are not sanitized
self.__populate_non_job_params(param_dict)
# MinimalJobWrapper.__prepare_upload_paramfile copies the paramfile to the job working directory
# so we should use it (otherwise the upload tool does not work in real user setups)
if self.job.tool_id == "upload1":
param_dict["paramfile"] = os.path.join(job_working_directory, "upload_params.json")
# Return the dictionary of parameters
return param_dict
def _materialize_objects(
self, deferred_objects: Dict[str, DeferrableObjectsT], job_working_directory: str
) -> Dict[str, DeferrableObjectsT]:
if not self.materialize_datasets:
return {}
undeferred_objects: Dict[str, DeferrableObjectsT] = {}
transient_directory = os.path.join(job_working_directory, "inputs")
safe_makedirs(transient_directory)
dataset_materializer = materializer_factory(
False, # unattached to a session.
transient_directory=transient_directory,
file_sources=self.app.file_sources,
)
for key, value in deferred_objects.items():
if isinstance(value, model.DatasetInstance):
if value.state != model.Dataset.states.DEFERRED:
continue
assert isinstance(value, (model.HistoryDatasetAssociation, model.LibraryDatasetDatasetAssociation))
undeferred = dataset_materializer.ensure_materialized(value)
undeferred_objects[key] = undeferred
else:
undeferred_collection = materialize_collection_input(value, dataset_materializer)
undeferred_objects[key] = undeferred_collection
return undeferred_objects
def _replaced_deferred_objects(
self,
inp_data: Dict[str, Optional[model.DatasetInstance]],
incoming: dict,
materalized_objects: Dict[str, DeferrableObjectsT],
):
for key, value in materalized_objects.items():
if isinstance(value, model.DatasetInstance):
inp_data[key] = value
def replace_deferred(input, value, context, prefixed_name=None, **kwargs):
if prefixed_name in materalized_objects:
return materalized_objects[prefixed_name]
visit_input_values(self.tool.inputs, incoming, replace_deferred)
def _validate_incoming(self, incoming: dict):
request_context = WorkRequestContext(app=self.app, user=self._user, history=self._history)
def validate_inputs(input, value, context, **kwargs):
value = input.from_json(value, request_context, context)
input.validate(value, request_context)
visit_input_values(self.tool.inputs, incoming, validate_inputs)
def _deferred_objects(
self,
input_datasets: Dict[str, Optional[model.DatasetInstance]],
incoming: dict,
) -> Dict[str, DeferrableObjectsT]:
"""Collect deferred objects required for execution.
Walk input datasets and collections and find inputs that need to be materialized.
"""
deferred_objects: Dict[str, DeferrableObjectsT] = {}
for key, value in input_datasets.items():
if value is not None and value.state == model.Dataset.states.DEFERRED:
deferred_objects[key] = value
def find_deferred_collections(input, value, context, prefixed_name=None, **kwargs):
if (
isinstance(value, (model.HistoryDatasetCollectionAssociation, model.DatasetCollectionElement))
and value.has_deferred_data
):
deferred_objects[prefixed_name] = value
visit_input_values(self.tool.inputs, incoming, find_deferred_collections)
return deferred_objects
def __walk_inputs(self, inputs, input_values, func):
def do_walk(inputs, input_values):
"""
Wraps parameters as neccesary.
"""
for input in inputs.values():
if isinstance(input, Repeat):
for d in input_values[input.name]:
do_walk(input.inputs, d)
elif isinstance(input, Conditional):
values = input_values[input.name]
current = values["__current_case__"]
func(values, input.test_param)
do_walk(input.cases[current].inputs, values)
elif isinstance(input, Section):
values = input_values[input.name]
do_walk(input.inputs, values)
else:
func(input_values, input)
do_walk(inputs, input_values)
def __populate_wrappers(self, param_dict, input_datasets, job_working_directory):
def wrap_input(input_values, input):
value = input_values[input.name]
if isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = DatasetListWrapper(
job_working_directory,
dataset_instances,
compute_environment=self.compute_environment,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name,
formats=input.formats,
)
elif isinstance(input, DataToolParameter):
dataset = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name,
compute_environment=self.compute_environment,
)
element_identifier = element_identifier_mapper.identifier(dataset, param_dict)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = DatasetFilenameWrapper(dataset, **wrapper_kwds)
elif isinstance(input, DataCollectionToolParameter):
dataset_collection = value
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
tool=self.tool,
name=input.name,
)
wrapper = DatasetCollectionWrapper(job_working_directory, dataset_collection, **wrapper_kwds)
input_values[input.name] = wrapper
elif isinstance(input, SelectToolParameter):
if input.multiple:
value = listify(value)
input_values[input.name] = SelectToolParameterWrapper(
input, value, other_values=param_dict, compute_environment=self.compute_environment
)
else:
input_values[input.name] = InputValueWrapper(
input, value, param_dict, profile=self.tool and self.tool.profile
)
# HACK: only wrap if check_values is not false, this deals with external
# tools where the inputs don't even get passed through. These
# tools (e.g. UCSC) should really be handled in a special way.
if self.tool.check_values:
element_identifier_mapper = ElementIdentifierMapper(input_datasets)
self.__walk_inputs(self.tool.inputs, param_dict, wrap_input)
def __populate_input_dataset_wrappers(self, param_dict, input_datasets):
# FIXME: when self.check_values==True, input datasets are being wrapped
# twice (above and below, creating 2 separate
# DatasetFilenameWrapper objects - first is overwritten by
# second), is this necessary? - if we get rid of this way to
# access children, can we stop this redundancy, or is there
# another reason for this?
# - Only necessary when self.check_values is False (==external dataset
# tool?: can this be abstracted out as part of being a datasouce tool?)
# For now we try to not wrap unnecessarily, but this should be untangled at some point.
matches = None
for name, data in input_datasets.items():
param_dict_value = param_dict.get(name, None)
if data and param_dict_value is None:
# We may have a nested parameter that is not fully prefixed.
# We try recovering from param_dict, but tool authors should really use fully-qualified
# variables
if matches is None:
matches = find_instance_nested(param_dict, instances=(DatasetFilenameWrapper, DatasetListWrapper))
wrapper = matches.get(name)
if wrapper:
param_dict[name] = wrapper
continue
if not isinstance(param_dict_value, (DatasetFilenameWrapper, DatasetListWrapper)):
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=name,
compute_environment=self.compute_environment,
)
param_dict[name] = DatasetFilenameWrapper(data, **wrapper_kwds)
def __populate_output_collection_wrappers(self, param_dict, output_collections, job_working_directory):
tool = self.tool
for name, out_collection in output_collections.items():
if name not in tool.output_collections:
continue
# message_template = "Name [%s] not found in tool.output_collections %s"
# message = message_template % ( name, tool.output_collections )
# raise AssertionError( message )
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
io_type="output",
tool=tool,
name=name,
)
wrapper = DatasetCollectionWrapper(job_working_directory, out_collection, **wrapper_kwds)
param_dict[name] = wrapper
# TODO: Handle nested collections...
for element_identifier, output_def in tool.output_collections[name].outputs.items():
if not output_def.implicit:
dataset_wrapper = wrapper[element_identifier]
param_dict[output_def.name] = dataset_wrapper
log.info(f"Updating param_dict for {output_def.name} with {dataset_wrapper}")
def __populate_output_dataset_wrappers(self, param_dict, output_datasets, job_working_directory):
for name, hda in output_datasets.items():
# Write outputs to the working directory (for security purposes)
# if desired.
param_dict[name] = DatasetFilenameWrapper(
hda, compute_environment=self.compute_environment, io_type="output"
)
if "|__part__|" in name:
unqualified_name = name.split("|__part__|")[-1]
if unqualified_name not in param_dict:
param_dict[unqualified_name] = param_dict[name]
output_path = str(param_dict[name])
# Conditionally create empty output:
# - may already exist (e.g. symlink output)
# - parent directory might not exist (e.g. Pulsar)
# TODO: put into JobIO, needed for fetch_data tasks
if not os.path.exists(output_path) and os.path.exists(os.path.dirname(output_path)):
open(output_path, "w").close()
for out_name, output in self.tool.outputs.items():
if out_name not in param_dict and output.filters:
# Assume the reason we lack this output is because a filter
# failed to pass; for tool writing convienence, provide a
# NoneDataset
ext = getattr(output, "format", None) # populate only for output datasets (not collections)
param_dict[out_name] = NoneDataset(datatypes_registry=self.app.datatypes_registry, ext=ext)
def __populate_non_job_params(self, param_dict):
# -- Add useful attributes/functions for use in creating command line.
# Function for querying a data table.
def get_data_table_entry(table_name, query_attr, query_val, return_attr):
"""
Queries and returns an entry in a data table.
"""
if table_name in self.app.tool_data_tables:
table = self.app.tool_data_tables[table_name]
if not isinstance(table, TabularToolDataTable):
raise Exception(f"Expected a TabularToolDataTable but got a {type(table)}: {table}.")
return table.get_entry(query_attr, query_val, return_attr)
param_dict["__tool_directory__"] = self.compute_environment.tool_directory()
param_dict["__get_data_table_entry__"] = get_data_table_entry
param_dict["__local_working_directory__"] = self.local_working_directory
# We add access to app here, this allows access to app.config, etc
param_dict["__app__"] = RawObjectWrapper(self.app)
# More convienent access to app.config.new_file_path; we don't need to
# wrap a string, but this method of generating additional datasets
# should be considered DEPRECATED
param_dict["__new_file_path__"] = self.compute_environment.new_file_path()
# The following points to location (xxx.loc) files which are pointers
# to locally cached data
param_dict["__tool_data_path__"] = param_dict["GALAXY_DATA_INDEX_DIR"] = self.app.config.tool_data_path
# For the upload tool, we need to know the root directory and the
# datatypes conf path, so we can load the datatypes registry
param_dict["__root_dir__"] = param_dict["GALAXY_ROOT_DIR"] = os.path.abspath(self.app.config.root)
param_dict["__admin_users__"] = self.app.config.admin_users
param_dict["__user__"] = RawObjectWrapper(param_dict.get("__user__", None))
def __populate_unstructured_path_rewrites(self, param_dict):
def rewrite_unstructured_paths(input_values, input):
if isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(
input,
input_values[input.name],
other_values=param_dict,
compute_environment=self.compute_environment,
)
if not self.tool.check_values and self.compute_environment:
# The tools weren't "wrapped" yet, but need to be in order to get
# the paths rewritten.
self.__walk_inputs(self.tool.inputs, param_dict, rewrite_unstructured_paths)
[docs] def populate_interactivetools(self):
"""
Populate InteractiveTools templated values.
"""
it = []
for ep in getattr(self.tool, "ports", []):
ep_dict = {}
for key in "port", "name", "url", "requires_domain":
val = ep.get(key, None)
if val is not None and not isinstance(val, bool):
val = fill_template(
val, context=self.param_dict, python_template_version=self.tool.python_template_version
)
clean_val = []
for line in val.split("\n"):
clean_val.append(line.strip())
val = "\n".join(clean_val)
val = val.replace("\n", " ").replace("\r", " ").strip()
ep_dict[key] = val
it.append(ep_dict)
return it
def __sanitize_param_dict(self, param_dict):
"""
Sanitize all values that will be substituted on the command line, with the exception of ToolParameterValueWrappers,
which already have their own specific sanitization rules and also exclude special-cased named values.
We will only examine the first level for values to skip; the wrapping function will recurse as necessary.
Note: this method follows the style of the similar populate calls, in that param_dict is modified in-place.
"""
# chromInfo is a filename, do not sanitize it.
skip = ["chromInfo"] + list(self.tool.template_macro_params.keys())
if not self.tool or not self.tool.options or self.tool.options.sanitize:
for key, value in list(param_dict.items()):
if key not in skip:
# Remove key so that new wrapped object will occupy key slot
del param_dict[key]
# And replace with new wrapped key
param_dict[
wrap_with_safe_string(key, no_wrap_classes=ToolParameterValueWrapper)
] = wrap_with_safe_string(value, no_wrap_classes=ToolParameterValueWrapper)
[docs] def build(self):
"""
Build runtime description of job to execute, evaluate command and
config templates corresponding to this tool with these inputs on this
compute environment.
"""
config_file = self.tool.config_file
global_tool_logs(self._build_config_files, config_file, "Building Config Files")
global_tool_logs(self._build_param_file, config_file, "Building Param File")
global_tool_logs(self._build_command_line, config_file, "Building Command Line")
global_tool_logs(self._build_version_command, config_file, "Building Version Command Line")
global_tool_logs(self._build_environment_variables, config_file, "Building Environment Variables")
return self.command_line, self.version_command_line, self.extra_filenames, self.environment_variables
def _build_command_line(self):
"""
Build command line to invoke this tool given a populated param_dict
"""
command = self.tool.command or ""
param_dict = self.param_dict
interpreter = self.tool.interpreter
command_line = None
if not command:
return
try:
# Substituting parameters into the command
command_line = fill_template(
command, context=param_dict, python_template_version=self.tool.python_template_version
)
cleaned_command_line = []
# Remove leading and trailing whitespace from each line for readability.
for line in command_line.split("\n"):
cleaned_command_line.append(line.strip())
command_line = "\n".join(cleaned_command_line)
# Remove newlines from command line, and any leading/trailing white space
command_line = command_line.replace("\n", " ").replace("\r", " ").strip()
except Exception:
# Modify exception message to be more clear
# e.args = ( 'Error substituting into command line. Params: %r, Command: %s' % ( param_dict, self.command ), )
raise
if interpreter:
# TODO: path munging for cluster/dataset server relocatability
executable = command_line.split()[0]
tool_dir = os.path.abspath(self.tool.tool_dir)
abs_executable = os.path.join(tool_dir, executable)
command_line = command_line.replace(executable, f"{interpreter} {shlex.quote(abs_executable)}", 1)
self.command_line = command_line
def _build_version_command(self):
version_string_cmd_raw = self.tool.version_string_cmd
if version_string_cmd_raw:
version_command_template = string.Template(version_string_cmd_raw)
version_command = version_command_template.safe_substitute(
{"__tool_directory__": self.compute_environment.tool_directory()}
)
self.version_command_line = f"{version_command} > {self.compute_environment.version_path()} 2>&1;\n"
def _build_config_files(self):
"""
Build temporary file for file based parameter transfer if needed
"""
param_dict = self.param_dict
config_filenames = []
for name, filename, content in self.tool.config_files:
config_text, is_template = self.__build_config_file_text(content)
# If a particular filename was forced by the config use it
directory = ensure_configs_directory(self.local_working_directory)
with tempfile.NamedTemporaryFile(dir=directory, delete=False) as temp:
config_filename = temp.name
if filename is not None:
# Explicit filename was requested, this is implemented as symbolic link
# to the actual config file that is placed in tool working directory
directory = os.path.join(self.local_working_directory, "working")
os.link(config_filename, os.path.join(directory, filename))
self.__write_workdir_file(config_filename, config_text, param_dict, is_template=is_template)
self.__register_extra_file(name, config_filename)
config_filenames.append(config_filename)
return config_filenames
def _build_environment_variables(self):
param_dict = self.param_dict
environment_variables = self.environment_variables
for environment_variable_def in self.tool.environment_variables:
directory = self.local_working_directory
environment_variable = environment_variable_def.copy()
environment_variable_template = environment_variable_def["template"]
inject = environment_variable_def.get("inject")
if inject == "api_key":
if self._user and isinstance(self.app, BasicSharedApp):
from galaxy.managers import api_keys
environment_variable_template = api_keys.ApiKeyManager(self.app).get_or_create_api_key(self._user)
else:
environment_variable_template = ""
is_template = False
else:
is_template = True
with tempfile.NamedTemporaryFile(dir=directory, prefix="tool_env_", delete=False) as temp:
config_filename = temp.name
self.__write_workdir_file(
config_filename,
environment_variable_template,
param_dict,
is_template=is_template,
strip=environment_variable_def.get("strip", False),
)
config_file_basename = os.path.basename(config_filename)
# environment setup in job file template happens before `cd $working_directory`
environment_variable[
"value"
] = f'`cat "{self.compute_environment.env_config_directory()}/{config_file_basename}"`'
environment_variable["raw"] = True
environment_variable["job_directory_path"] = config_filename
environment_variables.append(environment_variable)
home_dir = self.compute_environment.home_directory()
tmp_dir = self.compute_environment.tmp_directory()
if home_dir:
environment_variable = dict(name="HOME", value=f'"{home_dir}"', raw=True)
environment_variables.append(environment_variable)
if tmp_dir:
for tmp_directory_var in self.tool.tmp_directory_vars:
environment_variable = dict(name=tmp_directory_var, value=f'"{tmp_dir}"', raw=True)
environment_variables.append(environment_variable)
def _build_param_file(self):
"""
Build temporary file for file based parameter transfer if needed
"""
param_dict = self.param_dict
directory = self.local_working_directory
command = self.tool.command
if self.tool.profile < 16.04 and command and "$param_file" in command:
with tempfile.NamedTemporaryFile(mode="w", dir=directory, delete=False) as param:
for key, value in param_dict.items():
# parameters can be strings or lists of strings, coerce to list
if not isinstance(value, list):
value = [value]
for elem in value:
param.write(f"{key}={elem}\n")
self.__register_extra_file("param_file", param.name)
return param.name
else:
return None
def __build_config_file_text(self, content):
if isinstance(content, str):
return content, True
config_type = content.get("type", "inputs")
if config_type == "inputs":
content_format = content["format"]
handle_files = content["handle_files"]
if content_format != "json":
template = "Galaxy can only currently convert inputs to json, format [%s] is unhandled"
message = template % content_format
raise Exception(message)
elif config_type == "files":
file_sources_dict = self.file_sources_dict
rval = json.dumps(file_sources_dict)
return rval, False
else:
raise Exception(f"Unknown config file type {config_type}")
return (
json.dumps(
wrapped_json.json_wrap(self.tool.inputs, self.param_dict, self.tool.profile, handle_files=handle_files)
),
False,
)
def __write_workdir_file(self, config_filename, content, context, is_template=True, strip=False):
parent_dir = os.path.dirname(config_filename)
if not os.path.exists(parent_dir):
safe_makedirs(parent_dir)
if is_template:
value = fill_template(content, context=context, python_template_version=self.tool.python_template_version)
else:
value = unicodify(content)
if strip:
value = value.strip()
with open(config_filename, "w", encoding="utf-8") as f:
f.write(value)
# For running jobs as the actual user, ensure the config file is globally readable
os.chmod(config_filename, RW_R__R__)
def __register_extra_file(self, name, local_config_path):
"""
Takes in the local path to a config file and registers the (potentially
remote) ultimate path of the config file with the parameter dict.
"""
self.extra_filenames.append(local_config_path)
config_basename = os.path.basename(local_config_path)
compute_config_path = self.__join_for_compute(self.compute_environment.config_directory(), config_basename)
self.param_dict[name] = compute_config_path
def __join_for_compute(self, *args):
"""
os.path.join but with compute_environment.sep for cross-platform
compat.
"""
return self.compute_environment.sep().join(args)
@property
def _history(self):
return self.job.history
@property
def _user(self):
history = self._history
if history:
return history.user
else:
return self.job.user
[docs]class PartialToolEvaluator(ToolEvaluator):
"""
ToolEvaluator that only builds Environment Variables.
"""
materialize_datasets = False
[docs] def build(self):
config_file = self.tool.config_file
global_tool_logs(self._build_environment_variables, config_file, "Building Environment Variables")
return self.command_line, self.version_command_line, self.extra_filenames, self.environment_variables
[docs]class RemoteToolEvaluator(ToolEvaluator):
"""ToolEvaluator that skips unnecessary steps already executed during job setup."""
materialize_datasets = True
[docs] def execute_tool_hooks(self, inp_data, out_data, incoming):
# These have already run while preparing the job
pass
[docs] def build(self):
config_file = self.tool.config_file
global_tool_logs(self._build_config_files, config_file, "Building Config Files")
global_tool_logs(self._build_param_file, config_file, "Building Param File")
global_tool_logs(self._build_command_line, config_file, "Building Command Line")
global_tool_logs(self._build_version_command, config_file, "Building Version Command Line")
return self.command_line, self.version_command_line, self.extra_filenames, self.environment_variables