Tips and tricks =============== This is a collection of various helpful tips that don't fit in elsewhere. .. _`ssh forwarding`: Using SSH forwarding -------------------- As there is no built-in authentication yet, LiberTEM should not listen on a network port where untrusted parties have access. You can use ssh port forwarding from :code:`localhost` instead to access LiberTEM from a different computer. For example with conda: .. code-block:: shell $ ssh -L 9000:localhost:9000 "conda activate libertem; libertem-server" Or, with virtualenv: .. code-block:: shell $ ssh -L 9000:localhost:9000 "/path/to/virtualenv/bin/libertem-server" This makes LiberTEM, which is running on :code:`remote-hostname`, available on your local host via http://localhost:9000/ Alternatively, you can launch and access LiberTEM on remote systems through :ref:`jupyter integration`. Activating ipywidgets in Jupyter -------------------------------- Some examples use :code:`ipywidgets` in notebooks, most notably the fast :class:`~libertem.viz.bqp.BQLive2DPlot`. In some cases the corresponding Jupyter Notebook extension `has to be activated manually `_: .. code-block:: shell $ jupyter nbextension enable --py widgetsnbextension For :class:`~libertem.viz.bqp.BQLive2DPlot` to function correctly in JupyterHub and possibly JupyterLab, the packages :code:`bqplot` and :code:`bqplot-image-gl` have to be installed in both the notebook environment and in the environment for the notebook server. This might require admin privileges. Running in a top-level script ----------------------------- Since LiberTEM uses multiprocessing, the `script entry point may have to be protected `_, most notably on Windows: .. testcode:: if __name__ == '__main__': # Here goes starting a LiberTEM Context etc ... This is not necessary if LiberTEM is used in a Jupyter notebook or IPython. Gatan Digital Micrograph and other embedded interpreters -------------------------------------------------------- If LiberTEM is run from within an embedded interpreter, the following steps should be taken. This is necessary for Python scripting in Gatan Digital Micrograph (GMS), for example. The variable :code:`sys.argv` `may not be set in embedded interpreters `_, but it is expected by the :code:`multiprocessing` module when spawning new processes. This workaround guarantees that :code:`sys.argv` is set `until this is fixed upstream `_: .. testsetup:: import sys .. testcode:: if not hasattr(sys, 'argv'): sys.argv = [] Furthermore, the `correct executable for spawning subprocesses `_ has to be set. .. testsetup:: import multiprocessing import sys import os .. testcode:: multiprocessing.set_executable( os.path.join(sys.exec_prefix, 'pythonw.exe')) # Windows only In GMS the script may have to run in an additional thread since loading SciPy in a GMS background thread doesn't work. See https://www.gatan.com/python-faq for more information. .. testcode:: import threading def main(): # Here goes the actual script ... if __name__ == '__main__': # Start the workload "main()" in a thread and wait for it to finish th = threading.Thread(target=main) th.start() th.join() See `our examples folder `_ for a number of scripts that work in GMS! .. _`show warnings`: Show deprecation warnings ------------------------- Many warning messages via the :code:`warnings` built-in module are suppressed by default, including in interactive shells such as IPython and Jupyter. If you'd like to be informed early about upcoming backwards-incompatible changes, you should activate deprecation warnings. This is recommended since LiberTEM is under active development. .. testcode:: import warnings warnings.filterwarnings("default", category=DeprecationWarning) warnings.filterwarnings("default", category=PendingDeprecationWarning) .. _`profiling tests`: Profiling long-running tests ---------------------------- Since our code base and test coverage is growing continuously, we should make sure that our test suite remains efficient to finish within reasonable time frames. You can find the five slowest tests in the output of Tox, see :ref:`running tests` for details. If you are using :code:`pytest` directly, you can use the :code:`--durations` parameter: .. code-block:: text (libertem) $ pytest --durations=10 tests/ (...) ================= slowest 10 test durations ============================= 31.61s call tests/udf/test_blobfinder.py::test_run_refine_affinematch 17.08s call tests/udf/test_blobfinder.py::test_run_refine_sparse 16.89s call tests/test_analysis_masks.py::test_numerics_fail 12.78s call tests/server/test_job.py::test_run_job_delete_ds 10.90s call tests/server/test_cancel.py::test_cancel_udf_job 8.61s call tests/test_local_cluster.py::test_start_local 8.26s call tests/server/test_job.py::test_run_job_1_sum 6.76s call tests/server/test_job.py::test_run_with_all_zeros_roi 6.50s call tests/test_analysis_masks.py::test_numerics_succeed 5.75s call tests/test_analysis_masks.py::test_avoid_calculating_masks_on_client = 288 passed, 66 skipped, 6 deselected, 2 xfailed, 7 warnings in 260.65 seconds = Please note that tests which involve starting a local cluster have long lead times that are hard to avoid. In order to gain more information on what slows down a particular test, you can install the `pytest-profiling extension `_ and use it to profile individual slow tests that you identified before: .. code-block:: text (libertem) $ pytest --profile tests/udf/test_blobfinder.py::test_run_refine_affinematch (...) 749921 function calls (713493 primitive calls) in 5.346 seconds Ordered by: cumulative time List reduced from 1031 to 20 due to restriction <20> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 5.346 5.346 runner.py:76(pytest_runtest_protocol) 44/11 0.000 0.000 5.344 0.486 hooks.py:270(__call__) 44/11 0.000 0.000 5.344 0.486 manager.py:65(_hookexec) 44/11 0.000 0.000 5.344 0.486 manager.py:59() 44/11 0.001 0.000 5.344 0.486 callers.py:157(_multicall) 1 0.000 0.000 5.331 5.331 runner.py:83(runtestprotocol) 3 0.000 0.000 5.331 1.777 runner.py:172(call_and_report) 3 0.000 0.000 5.330 1.777 runner.py:191(call_runtest_hook) 3 0.000 0.000 5.329 1.776 runner.py:219(from_call) 3 0.000 0.000 5.329 1.776 runner.py:198() 1 0.000 0.000 5.138 5.138 runner.py:119(pytest_runtest_call) 1 0.000 0.000 5.138 5.138 python.py:1355(runtest) 1 0.000 0.000 5.138 5.138 python.py:155(pytest_pyfunc_call) 1 0.004 0.004 5.137 5.137 test_blobfinder.py:149(test_run_refine_affinematch) 5 0.159 0.032 3.150 0.630 generate.py:6(cbed_frame) 245 0.001 0.000 2.989 0.012 masks.py:98(circular) 245 0.046 0.000 2.988 0.012 masks.py:8(_make_circular_mask) 245 0.490 0.002 2.941 0.012 masks.py:280(radial_bins) 245 0.152 0.001 2.229 0.009 masks.py:212(polar_map) 25 0.001 0.000 1.968 0.079 blobfinder.py:741(run_refine) =============================== 1 passed, 1 warnings in 7.81 seconds ============================ .. _`os mismatch`: Platform-dependent code and remote executor ------------------------------------------- Platform-dependent code in a lambda function or nested function can lead to incompatibilities when run on an executor with remote workers, such as the :class:`~libertem.executor.dask.DaskJobExecutor`. Instead, the function should be defined as part of a module, for example as a stand-alone function or as a method of a class. That way, the correct remote implementation for platform-dependent code is used on the remote worker since only a reference to the function and not the implementation itself is sent over. Benchmark Numba compilation time -------------------------------- One has to capture the very first execution of a jitted function and compare it with subsequent executions to measure its compilation time. By default, pytest-benchmark performs calibration runs and possibly warmup rounds that don't report the very first run. The only way to completely disable this is to use the `pedantic mode `_ specifying no warmup rounds and two rounds with one iteration each: .. code-block:: python @numba.njit def hello(): return "world" @pytest.mark.compilation @pytest.mark.benchmark( group="compilation" ) def test_numba_compilation(benchmark): benchmark.extra_info["mark"] = "compilation" benchmark.pedantic(hello, warmup_rounds=0, rounds=2, iterations=1) That way the maximum is the first run with compilation, and the minimum is the second one without compilation. Tests are marked as compilation tests in the extra info as well to aid later data evaluation. Note that the compilation tests will have poor statistics since it only runs once. If you have an idea on how to collect better statistics, please `let us know `_! Simulating slow systems with control groups ------------------------------------------- Under Linux, it is possible to simulate a slow system using control groups: .. code-block:: shell sudo cgcreate -g cpu:/slow sudo cgset -r cpu.cfs_period_us=1000000 slow sudo cgset -r cpu.cfs_quota_us=200000 slow sudo chown root: /sys/fs/cgroup/cpu,cpuacct/slow sudo chmod 664 /sys/fs/cgroup/cpu,cpuacct/slow Then, as a user, you can use :code:`cgexec` to run a command in that control group: .. code-block:: shell cgexec -g cpu:slow pytest tests/ This is useful, for example, to debug test failures that only seem to happen in CI or under heavy load. Note that tools like :code:`cgcreate` only work with cgroups v1, with newer distributions using cgroups v2 you may have to adapt these instructions. .. _`jupyter install`: Jupyter ------- To use the Python API from within a Jupyter notebook, you can install Jupyter into your LiberTEM virtual environment. .. code-block:: shell (libertem) $ python -m pip install jupyter You can then run a local notebook from within the LiberTEM environment, which should open a browser window with Jupyter that uses your LiberTEM environment. .. code-block:: shell (libertem) $ jupyter notebook .. _`jupyterhub install`: JupyterHub ---------- If you'd like to use the Python API from a LiberTEM virtual environment on a system that manages logins with JupyterHub, you can easily `install a custom kernel definition `_ for your LiberTEM environment. First, you can launch a terminal on JupyterHub from the "New" drop-down menu in the file browser. Alternatively you can execute shell commands by prefixing them with "!" in a Python notebook. In the terminal you can create and activate virtual environments and perform the LiberTEM installation as described above. Within the activated LiberTEM environment you additionally install ipykernel: .. code-block:: shell (libertem) $ python -m pip install ipykernel Now you can create a custom ipython kernel definition for your environment: .. code-block:: shell (libertem) $ python -m ipykernel install --user --name libertem --display-name "Python (libertem)" After reloading the file browser window, a new Notebook option "Python (libertem)" should be available in the "New" drop-down menu. You can test it by creating a new notebook and running .. code-block:: python In [1]: import libertem See also :ref:`jupyter integration` for launching the web GUI from JupyterHub or JupyterLab.