Hello sacred authors! Great work - I'm just starting to embrace sacred and am feeling very excited about the potential.

So far I've been finding it just a bit too magical for my tastes though. I think that the magic has it's place in reducing the boilerplate to almost zero for a quick experiment - but I've been finding it to be somewhat of an activation barrier in getting started for someone not familiar with the library.

The issue is that sacred breaks completely out of the standard python programming paradigm, which makes it difficult to reason about code behavior.

Again, I do think that this has it's place for quick scripting - but for production workflows that prioritize extensibility and maintainability over line count, I think an object-oriented interface would be more appropriate.

My reasoning is that:

• the object hierarchy (which I feel pretty confused about right now) would be explicitly defined in the code, it's clear exactly an experiment is made up of
• easier to integrate with existing code. I know how classes behave, how inheritance works, how to work them into module structure. It's unclear to me how to effectively use the magic interface to scared within a larger project and not as a one-off thing.
• much easier to get less engineer-minded lab members to used sacred when they don't have to learn a whole new paradigm. They just have to fill in a template

Any estimates how challenging this would be? Recommendations on where to start?

In an attempt to structure our discussion I suggest to use this issue to collect a wishlist of how we would like to use Sacred from a birds-eye perspective. I suggest that we edit this issue to reflect the evolving consensus that (hopefully) emerges from the discussion below. To get things started I can think of 3 basic workflows, that I would love for sacred to support. Maybe this is also a good place to think about how to integrate stages and superexperiments.

Interactive (Jupyter Notebook)

Manually control the stages of the experiment / run in an interactive environment. Most suitable for exploration and low complexity experiments. Something like:

# -----------------------------------------------------------
# initialization
ex = Experiment('my_jupyter_experiment')
ex.add_observer(FilestorageObserver('tmp'))
# -----------------------------------------------------------
# Config and Functions
cfg = Configuration()
cfg.learn_rate = 0.01
cfg.hidden_sizes = [100, 100]
cfg.batch_size = 32

@ex.capture
def get_dataset(batch_size):
    ....
# -----------------------------------------------------------
# run experiment
ex.start()   # finalizes config, starts observers
data = get_dataset()  # call functions 
for i in range(1000):
    # do something 
    ex.log_metric('loss', loss)  # log metrics, artifacts, etc.

ex.stop(result=final_loss)
# -----------------------------------------------------------

Scripting

Using a main script that contains most of the experiment and is run from the commandline. This is the current main workflow, most suitable for low to medium complexity experiments.

ex = Experiment('my_experiment_script')

@ex.config
def config(cfg):
    cfg.learn_rate = 0.01
    ...

@ex.capture
def get_dataset(batch_size):
    ....

@ex.automain  # define a main function which automatically starts and stops the experiment
def main():
    ....   # do stuff, log metrics, etc.
    return final_loss

Object Oriented

This is a long-standing feature request #193. Define an experiment as a class to improve modularity (and support frameworks like ray.tune). Should cater to medium to high complexity experiments. Very tentative API sketch:

class MyExperiment(Experiment):
    def __init__(self, config=None):   # context-config to deal with updates and nesting
         super().__init__(config)
         self.learn_rate = 0.001   # using self to store config improves IDE support
         ...

    def get_dataset(self):  # no capturing because self gives access to config anyways
        return ...

    @main   # mark main function / commands 
    def main_function(self):
         ...   # do stuff
         return final_loss

ex = MyExperiment(config=get_commandline_updates())
ex.run()

This is a WIP pullrequest that addresses the error messages as described in #239. I finally got time to prepare a pullrequest for this.

The code is currently very ugly and could more be seen as a proof of concept that those things can work.

There are lists of things that are already done and that need to be done below. Any suggestions for further improvement of the error messages (or the code, since some part of it is not well structured and untested) are welcome.

Done

Use iterate_ingredients for gathering commands and named configs

This causes gather_commands and gather_named_configs to raise a CircularDependencyError instead of a RecursionError, which makes much clearer what is causing the error. In addition, any future gather_something functions that may be implemented can overwrite one method and the error handling is done in iterate_ingredients, and the path filtering for experiments is done there.

Track Ingredients that cause circular dependencies

The CircularDependencyError is caught in iterate_ingredients and the current ingredient is added to a list CircularDependencyError.__ingredients__ to keep track of which ingrediens cuased the circular depenceny.

An example error:

Traceback (most recent call last):
  File "error_messages.py", line 24, in <module>
    @ex.automain
  File ".../sacred/experiment.py", line 141, in automain
    self.run_commandline()
  File ".../sacred/experiment.py", line 248, in run_commandline
    short_usage, usage, internal_usage = self.get_usage()
  File ".../sacred/experiment.py", line 173, in get_usage
    commands = OrderedDict(self.gather_commands())
  File ".../sacred/experiment.py", line 394, in _gather
    for ingredient, _ in self.traverse_ingredients():
  File ".../sacred/ingredient.py", line 370, in traverse_ingredients
    raise e
  File ".../sacred/ingredient.py", line 363, in traverse_ingredients
    for ingred, depth in ingredient.traverse_ingredients():
  File ".../sacred/ingredient.py", line 370, in traverse_ingredients
    raise e
  File ".../sacred/ingredient.py", line 363, in traverse_ingredients
    for ingred, depth in ingredient.traverse_ingredients():
  File ".../sacred/ingredient.py", line 370, in traverse_ingredients
    raise e
  File ".../sacred/ingredient.py", line 363, in traverse_ingredients
    for ingred, depth in ingredient.traverse_ingredients():
  File ".../sacred/ingredient.py", line 357, in traverse_ingredients
    raise CircularDependencyError(ingredients=[self])
sacred.exception.CircularDependencyError: ing->ing2->ing

Track sources of configuration entries

This code is still very ugly, but it allows to track the sources of configuration values. This works up to different resolutions:

• for a ConfigScope, we can find the wrapped function and get the place of definition of this function (file + line of the signature line)
• for a configuration file we can find the file that defines the configuration values. It would be very difficult to get the line of the config value inside of the file.
• for a dict config, we can use inspect.stack to find the line in which the dict configuration value was added.
• for configuration defined in the command line, we can say that it was defined in the command line options

See the InvalidConfigError for examples.

Add a baseclass SacredError for future Excpetions that is pretty printed in experiment.run_commandline

The init definition looks like this:

def __init__(self, *args, print_traceback=True,
                 filter_traceback=None, print_usage=False):
    # ...

It provides the following additional arguments (that are handled in experiment.run_commandline):

• print_traceback: if True, traceback is printed according to filter_traceback. If False, no traceback is printed (except for the Exception itself)
• filter_traceback: If True, the traceback is filtered (WITHOUT sacred internals), if False, it is not filtered and if None, it falls back to the previous behaviour (filter if not raised within sacred)
• print_usage: The short usage is printed when this is set to True.

Add an InvalidConfigError that can be raised in user code

Added an InvalidConfigError that prints the conflicting configuration values.

Example:

ex = Experiment()

@ex.config
def config():
    config1 = 123
    config2 = dict(a=234)

@ex.automain
def main(config1, config2):
  if not type(config1) == type(config2['a']):
    raise InvalidConfigError('Must have same type', conflicting_configs=('config1', 'config2.a'))

$ python error_messages.py with config1=abcde

WARNING - root - Changed type of config entry "config1" from int to str
WARNING - error_messages - No observers have been added to this run
INFO - error_messages - Running command 'main'
INFO - error_messages - Started
ERROR - error_messages - Failed after 0:00:00!
Traceback (most recent calls WITHOUT Sacred internals):
  File ".../wrapt/wrappers.py", line 523, in __call__
    args, kwargs)
  File "error_messages.py", line 27, in main
    raise InvalidConfigError('Must have same type', conflicting_configs=('config1', 'config2.a'))
sacred.exception.InvalidConfigError: Must have same type
Conflicting configuration values:
  config1=abcde
    defined in command line config "config1=abcde"
  config2.a=234
    defined in "error_messages.py:20"

MissingConfigError

Prints missing configuration values. Prints the filtered stack trace by default, so that the function call that is missing values can be found. It also prints the name of the ingredient that captured the function and the file in which the captured function is defined.

Example error:

Traceback (most recent calls WITHOUT Sacred internals):
  File .../wrapt/wrappers.py", line 523, in __call__
    args, kwargs)
sacred.exception.MissingConfigError: main is missing value(s) for ['config3']
Function that caused the exception: <function main at 0x0F7A0780> captured by the experiment "error_messages" at "error_messages.py:24"

NamedConfigNotFoundError

Raise a NamedConfigNotFoundError instead of KeyError, and don't print traceback.

TODO

• print list of available named configs
• give suggestion based on levenshtein distance

ConfigAddedError

Raise a ConfigAddedError when a config value is added that is not used anywhere. This is a sublcass of ConfigError and prints the source where the new configuration value is defined:

Traceback (most recent call last):
sacred.utils.ConfigAddedError: Added new config entry "unused" that is not used anywhere
Conflicting configuration values:
  unused=3
    defined in command line config "unused=3"
Did you mean "config1" instead of "unused"

TODO

• print suggestions based on levenshtein distance

TODO

• print suggestions for ConfigAddedError
• (colored exception output?)
• make source tracking optional in SETTINGS
• improve resolution of source tracking (line of config file, line in a config scope maybe using inspect.stack)
• CommandNotFoundError (?)
• Error when parameter is not present for config scope
• tests

This is an issue that has been on my mind for a long time. The configuration process in sacred is very powerful, but it also has gotten rather complex. I would like to rewrite it from the ground up and fix a few long-standing issues along the way. Warning: this is going to be a lengthy post with many thoughts and half-baked ideas. But I would really appreciate your input for this.

Current state

Configuration is divided into (by priority)

1. config updates (highest)
2. named configurations
3. default configuration

Usually you would evaluate the lowest priority first, and overwrite values with the higher priorities. But in Sacred, we support the fancy config scopes which allow the default config to interact with the higher priority values. E.g.:

@ex.config
def config():
    a = 10
    b = 'low' if a < 100 else 'high'

To support this we evaluate the highest priority first and consider them fixed during the evaluation of the lower priorities. In the example above you could pass with a=200 from the commandline and get b='high' in the final config.

I really enjoy this flexibility and would like to keep it. But the implementation has become rather messy, and hard to maintain. It also still lacks several useful features.

Limitations

No support for non-str keys

The keys of dictionaries must be valid python-identifiers, otherwise they cannot be set from the commandline. This has come up, for example, with sklearn which uses int keys to specify class_weights for classifiers.

Setting list elements

Currently only dictionaries can be modified from the commandline. This can be worked around by storing everything as dictionaries in the config and converting them to lists in the experiment code. But this is clearly ugly.

Meta information

Sacred keeps track of meta information for config entries such as documentation, if the type of entry has changed, and if an entry was added. This process is currently very convoluted and error prone. This is an important feature that can help tremendously with catching errors in configuration of an experiment so it should be rock-solid.

Ingredients

The Ingredient system meant to support modularization of experiments. The idea was to separate out reusable parts such as dataset loading, that have their own configuration. That way they could be used from multiple experiments. But in practice the current system has fallen short. The main problem is that currently ingredients have to be completely self-contained, and cannot be configured from the within the experiment that uses them. Often what is needed, is a way to pass parameters to an ingredient from the experiment. They are also implicitly added to the configuration, which is confusing, and does not allow using an ingredient multiple times from the same experiment.

Internal values

Sacred automatically adds a seed to the configuration that can be accessed and used. But that process again is rather intransparent and confusing. We should have a well-defined behavior for such internal config values. That would also be good for adding the _id or a default temp-dir for storing artifacts.

Wishlist

More explicit config scopes

This would remove some of the magic and make the process more explicit:

@ex.config
def config(cfg):
    cfg.a = 10  # explicitly use a cfg object to define entries
    cfg.b = 'low' if cfg.a < 100 else 'high'

Dotted access internally

For convenience, the final configuration should allow dotted access

@ex.capture
def foo(network):
    print(network.layers[2].size)

Commandline updates

Updates should mostly follow the python syntax and support the general form <VAR>=<VALUE>, with dotted access for nesting if the keys are python-identifiers: foo='bar' optimizer.learn_rate=0.1 network.layer_sizes=[100,200,100]

Square brackets can be used to access any key that is a valid and hashable python literal: foo[True]=17 layers[2].size=50 people['Jane Doe'].height=176

Advanced nice-to-have features could include:

• slicing support: sizes[:3]=[1, 2, 3]
• recursive descent: network...act_func='tanh' i.e. all size entries that are decendants of network.
• wildcard: parent.*=7 matches all children of parent
• regex support: net[/.*_layer/].size=100

These might be very tricky to get right, so I would postpone them. But I want to keep these usecases in mind for later.

Ingredients

Ideally Ingredients would be explicitly instantiated in the config. That way they could take parameters, be used multiple times, and even be included conditionally:

@ex.config
def config(cfg):
    cfg.normalize=True
    cfg.test_on_other_dataset=False
    cfg.train_dataset = MyDatasetIngredient(normalize=cfg.normalize)
    if cfg.test_on_other_dataset:
        cfg.test_dataset = MyOtherDatasetIngredient()
    else:
        cfg.test_dataset = MyDatasetIngredient()

Well defined priorities / stages

The internal resolution of priorities should be very clear, transparent and extendable. To keep the support for config scopes, the entries still need to be collected starting from the highest priority. But having a general system for evaluating partial configurations and updating them incrementally would go a long way in simplifying this mess.

Further Steps

I have several ideas on how to implement this and a few prototypes already. But this post is already too long, and getting your feedback on the problems and goals would be valuable before moving on anyways. So what do you think? Do you agree with the raised points? Anything else that comes to mind?

Added implementation of notification threw a notificator.

Ingredient now have a notificator attribute and his added with the add_notificator method. (documented)

This allow to push a notification (like a Slack message, a Facebook messenger message or else) when the script is failling or when the job is done.

I've worked on implementation of notificator with my notif package, but was not sure if adding dependancies was the appropriated approach. So one can simply use the notif package or any other class which implement send_notification_error and send_notification method.

The motivation of this PR is to have notification when a script is failling or when the script is done.

Since I was not sure about your commenting approach, i've place comment where code have been added. Feel free to remove it since I think code is pretty clear without the comment.

Also, I added a code example on how to use the notificator. But since to fully work you need a valid webhook url, the example is not working (one cann't execute the script). Also, I was not sure where (and if) it would be appropriated to mention it in the documentation. Any ideas ?

Testing

I have added two test to verify if the notificator are called when needed and present. Since the notificator are throwing message, i've mocked the notificator.

TensorFlow 1.14.0 was released couple of days ago and it isn't unfortunately compatible with sacred. Tensorflow package in 1.14.0 has __spec__ property set to None, which results in exception in pkgutil.find_loader when one imprts sacred.

I'm not sure whether this is a bug/feature on tensorflow, however the fact that it breaks sacred is unfortunate.

$ docker run -it tensorflow/tensorflow:1.14.0-py3 bash
root@d70a18c72a0b:/# pip install sacred
Successfully installed colorama-0.4.1 docopt-0.6.2 jsonpickle-0.9.6 munch-2.3.2 packaging-19.0 py-cpuinfo-5.0.0 pyparsing-2.4.0 sacred-0.7.5
root@d70a18c72a0b:/# python3
Python 3.6.8 (default, Jan 14 2019, 11:02:34) 
[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.14.0'
>>> import sacred
Traceback (most recent call last):
  File "/usr/lib/python3.6/pkgutil.py", line 490, in find_loader
    spec = importlib.util.find_spec(fullname)
  File "/usr/lib/python3.6/importlib/util.py", line 102, in find_spec
    raise ValueError('{}.__spec__ is None'.format(name))
ValueError: tensorflow.__spec__ is None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/sacred/__init__.py", line 13, in <module>
    from sacred.experiment import Experiment
  File "/usr/local/lib/python3.6/dist-packages/sacred/experiment.py", line 13, in <module>
    from sacred.arg_parser import format_usage, get_config_updates
  File "/usr/local/lib/python3.6/dist-packages/sacred/arg_parser.py", line 16, in <module>
    from sacred.serializer import restore
  File "/usr/local/lib/python3.6/dist-packages/sacred/serializer.py", line 8, in <module>
    from sacred import optional as opt
  File "/usr/local/lib/python3.6/dist-packages/sacred/optional.py", line 40, in <module>
    has_tensorflow = modules_exist("tensorflow")
  File "/usr/local/lib/python3.6/dist-packages/sacred/utils.py", line 656, in modules_exist
    return all(module_exists(m) for m in modnames)
  File "/usr/local/lib/python3.6/dist-packages/sacred/utils.py", line 656, in <genexpr>
    return all(module_exists(m) for m in modnames)
  File "/usr/local/lib/python3.6/dist-packages/sacred/utils.py", line 652, in module_exists
    return pkgutil.find_loader(modname) is not None
  File "/usr/lib/python3.6/pkgutil.py", line 496, in find_loader
    raise ImportError(msg.format(fullname, type(ex), ex)) from ex
ImportError: Error while finding loader for 'tensorflow' (<class 'ValueError'>: tensorflow.__spec__ is None)

Closes #499.

Initially I tried to add pickle support using __getstate__ and __setstate. However, pickle, seems to special-case built-in container types: my __setstate__ method never even got called in ReadOnlyDict. Accordingly I decided to switch to a proxy style, where we are wrapping a dict/list object. This is similar to the MappingProxyType, which we could probably replace ReadOnlyDict with once Python 2.7 support is dropped (it's only available in Python 3.3+).

Hi, In my current project I'm importing modules through the function importlib.import_module. Unfortunately Sacred does not keep track of the imported modules in this way. Is there a way in which I can add those files to sacred?

Example:

def instantiate_model(path):
    """Instantiates a model given its fullname as a python.package.name"""

    return getattr(
        importlib.import_module("project.models." + ".".join(path.split(".")[:-1])),
        path.split(".")[-1],
    )()

This is command description: root@350ab4117e66:/code# python main.py print_config with eval_end="2018-11-25"
WARNING - root - Changed type of config entry "eval_end" from str to int
INFO - percolata_experiment - Running command 'print_config'
INFO - percolata_experiment - Started
Configuration (modified, added, typechanged, doc):
algo_config_file = None
algo_type = 'prophet'
baseline_expid = None
cus_name = None
data_type = 'fill'
desc = None
emails = '[email protected]'
eval_end = 1982
eval_start = '2018-11-23'
executor = None
exp_identity = 'percolata_experiment'
forecast_type = 'expt'
location_id = 1249
mode = 'pred'
pred_end = '2019-02-26'
pred_start = '2019-01-22'
seed = 191843317
time_res = 'slot'
train_start = None
weight = 'weight.tar.pth'

I don't understand why Sacred will always throw " WARNING - root - Changed type of config entry "eval_end" from str to int" message.

Allows the usage of type hints in config scopes. Closes #818.

Notes:

• A new test is added for this behaviour.
• Instead of fixing the regexp, it uses ast and tokenize modules. With ast we find the first line with code in the function body. As comments are removed in the AST representation, we then generate the tokens of the function until that line is reached, in order to keep the previous lines that only have comments and whitespace.

This change is backward compatible (given that a user didn't use the undocumented __init__ method of observers)

I would propose that the recommended way of calling an observer would be by calling the constructor __init__(), not the create() method. We can be backward compatible by forwarding the arguments of the create method to the __init__().

I understand from the codebase that __init__ was used internally, to recreate observers from existing metrics and other existing data (in the middle of the experiment). I propose that we move this functionality to a new method called create_from().

Calling the class constructor will feel natural and avoid documentation lookups. It would be better that the most convenient method (the constructor) is used externally than internally.

We can drop a warning in the create function and keep it around indefinitely.

If all lights are greens from the maintainers I can do the change and update the docs.

Setting interactive=True doesn't work when the notebook as a script through ipython.

$ ipython notebook.ipynb

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
Cell In[1], line 1
----> 1 ex = Experiment("image_classification", interactive=True)
      2 ex.observers.append(NeptuneObserver(run=neptune_run))

File ~\miniconda3\envs\py38\lib\site-packages\sacred\experiment.py:119, in Experiment.__init__(self, name, ingredients, interactive, base_dir, additional_host_info, additional_cli_options, save_git_info)
    117     elif name.endswith(".pyc"):
    118         name = name[:-4]
--> 119 super().__init__(
    120     path=name,
    121     ingredients=ingredients,
    122     interactive=interactive,
    123     base_dir=base_dir,
    124     _caller_globals=caller_globals,
    125     save_git_info=save_git_info,
    126 )
    127 self.default_command = None
    128 self.command(print_config, unobserved=True)

File ~\miniconda3\envs\py38\lib\site-packages\sacred\ingredient.py:75, in Ingredient.__init__(self, path, ingredients, interactive, _caller_globals, base_dir, save_git_info)
     69 self.save_git_info = save_git_info
     70 self.doc = _caller_globals.get("__doc__", "")
     71 (
     72     self.mainfile,
     73     self.sources,
     74     self.dependencies,
---> 75 ) = gather_sources_and_dependencies(
     76     _caller_globals, save_git_info, self.base_dir
     77 )
     78 if self.mainfile is None and not interactive:
     79     raise RuntimeError(
     80         "Defining an experiment in interactive mode! "
     81         "The sourcecode cannot be stored and the "
     82         "experiment won't be reproducible. If you still"
     83         " want to run it pass interactive=True"
     84     )

File ~\miniconda3\envs\py38\lib\site-packages\sacred\dependencies.py:725, in gather_sources_and_dependencies(globs, save_git_info, base_dir)
    723 def gather_sources_and_dependencies(globs, save_git_info, base_dir=None):
    724     """Scan the given globals for modules and return them as dependencies."""
--> 725     experiment_path, main = get_main_file(globs, save_git_info)
    727     base_dir = base_dir or experiment_path
    729     gather_sources = source_discovery_strategies[SETTINGS["DISCOVER_SOURCES"]]

File ~\miniconda3\envs\py38\lib\site-packages\sacred\dependencies.py:596, in get_main_file(globs, save_git_info)
    594     main = None
    595 else:
--> 596     main = Source.create(globs.get("__file__"), save_git_info)
    461 return Source(main_file, get_digest(main_file), repo, commit, is_dirty)

File ~\miniconda3\envs\py38\lib\site-packages\sacred\dependencies.py:382, in get_py_file_if_possible(pyc_name)
    380 if pyc_name.endswith((".py", ".so", ".pyd")):
    381     return pyc_name
--> 382 assert pyc_name.endswith(".pyc")
    383 non_compiled_file = pyc_name[:-1]
    384 if os.path.exists(non_compiled_file):

Environment details:

• Windows11
• python3.8.15
• sacred==0.8.2

https://github.com/IDSIA/sacred/blob/f6191d173ab9e3db2021ae32e5ae9cbd882de75c/sacred/observers/mongo.py#L445

Looks like md5 hash doesn't work as earlier in gridfs, and all files will be added without checking md5 (file in file = self.fs.find_one({"filename": abs_path, "md5": md5}) always None), I fixed it like _id = self.fs.put(f, filename=abs_path, md5=md5) but not sure this is the correct one

Adds a github actions workflow that triggers automatically on a version change and uploads the new version to pypi. (based on https://github.com/marketplace/actions/pypi-github-auto-release )

Not sure how to best test this.

I tried to re-run some code with the newest version of sacred that used to work with a sacred version from late 2019 and now get the following error. The problem already seems to occur when trying to import the FileStorageObserver. What would be the best fix for this issue?

I am using python version 3.6 on macOS Mojave 10.14.6.

Traceback (most recent call last): File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/importlib_metadata-4.12.0-py3.6.egg/importlib_metadata/_compat.py", line 9, in from typing import Protocol ImportError: cannot import name 'Protocol'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "run_code.py", line 5, in from sacred.observers import FileStorageObserver File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/sacred/init.py", line 11, in from sacred.experiment import Experiment File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/sacred/experiment.py", line 12, in from sacred.arg_parser import format_usage, get_config_updates File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/sacred/arg_parser.py", line 14, in from sacred.serializer import restore File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/sacred/serializer.py", line 1, in import jsonpickle File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/jsonpickle-2.2.0-py3.6.egg/jsonpickle/init.py", line 81, in from .version import version # noqa: F401 File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/jsonpickle-2.2.0-py3.6.egg/jsonpickle/version.py", line 5, in import importlib_metadata as metadata File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/importlib_metadata-4.12.0-py3.6.egg/importlib_metadata/init.py", line 17, in from . import _adapters, _meta File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/importlib_metadata-4.12.0-py3.6.egg/importlib_metadata/_meta.py", line 1, in from ._compat import Protocol File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/importlib_metadata-4.12.0-py3.6.egg/importlib_metadata/_compat.py", line 12, in from typing_extensions import Protocol # type: ignore File "/Users/k47h4/anaconda3/envs/topdown_plasticity/lib/python3.6/site-packages/typing_extensions-4.3.0-py3.6.egg/typing_extensions.py", line 160, in class _FinalForm(typing._SpecialForm, _root=True): AttributeError: module 'typing' has no attribute '_SpecialForm'

The entire code can be found in this repository: https://github.com/k47h4/interneuron_circuits_plasticity Here is a code snippet from run_code.py:

from sacred.observers import FileStorageObserver

def run_in_thread(values):
    from Spiking_model import ex
    ex.observers.append(FileStorageObserver.create('Spiking_model'))
    ex.run('run_network')

values1 = np.array([0])
n_threads = len(values1)   
pool = multiprocessing.Pool(n_threads)
pool.map(run_in_thread, values1)

Thank you!

Adds a units field to linearize_metrics output per discussion in #880.

I took a slightly different approach. Instead of adding units to ScalarMetricLogEntry, I added "units" to the linearized output and filled it in based on whether or not value is of type pint.Quantity. Not sure if it would be better to add units=None to log_scalar_metric and do the pint support in the background, or to do it as I've done. On the one hand, it would remove the hard dependency on pint and would be a little easier for users. On the other hand, users wouldn't have full access to pints features, so they couldn't define their own units.

I went ahead and added in the unit conversion as pint makes it quite easy to do. Units will be converted to the unit of the first log entry. If the units cannot be converted, a custom exception is raised. If you submit some entries with units and some without, it assumes that the entries without units use the same units as the entries with units. Might be better to throw an exception there as a user really shouldn't be doing that.

While working on this feature, I added some type hints where they were missing. Not my finest type hints, but it's better than nothing!

I have an update adding metric support (with units) to SqlObserver ready, but it relies on this PR.