Add more exp info (#33)

* added info on dependencies

* added newline

* fixed flake8

* fixed typos

* printing dependencies now

* updated numpy version

tests/end2end_pytorch/run.sh

@@ -7,16 +7,18 @@ cd ${DIR}
 cookiecutter ../.. --no-input --output-dir=./
 cd wonderful_project
 pip install -e . --quiet
+pip install flake8 pytest --quiet
 
 # necessary cause tf dependencies are sometimes not updated
 pip install -U setuptools numpy six --quiet
 
+# print all dependencies
+pip freeze
+
 # run flake8 test first
-pip install flake8 --quiet
 sh config/hooks/pre-commit
 
 # run tests
-pip install pytest --quiet
 pytest .
 
 # run the example

tests/end2end_tensorflow/run.sh

@@ -7,15 +7,17 @@ cd ${DIR}
 python -c "from cookiecutter.main import cookiecutter; cookiecutter('../..', no_input=True, extra_context={'dl_framework': 'tensorflow_cpu'}, output_dir='./')"
 cd wonderful_project
 pip install -e . --quiet
+pip install flake8 pytest --quiet
 # necessary cause tf dependencies are sometimes not updated
 pip install -U setuptools numpy six --quiet
 
+# print all dependencies
+pip freeze
+
 # run flake8 test first
-pip install flake8 --quiet
 sh config/hooks/pre-commit
 
 # run tests
-pip install pytest --quiet
 pytest .
 
 # run the examples

{{cookiecutter.project_slug}}/README.md

@@ -109,7 +109,7 @@ First, bring you project on the Mila cluster (assuming you didn't create your
 project directly there). To do so, simply login on the Mila cluster and git
 clone your project:
 
-    [email protected]:{{ cookiecutter.github_username }}/{{ cookiecutter.project_slug }}.git
+    git clone git@github.com:{{ cookiecutter.github_username }}/{{ cookiecutter.project_slug }}.git
 
 Then activate your virtual env, and install the dependencies:
 
@@ -153,7 +153,7 @@ To do so, simply run `sh run.sh` N times.
 When Orion has completed the trials, you will find the orion db file and the
 mlruns folder (i.e., the folder containing the mlflow results).
 
-You will also find the output of your experiments in `orion_workinf_dir`, which
+You will also find the output of your experiments in `orion_working_dir`, which
 will contain a folder for every trial.
 Inside these folders, you can find the models (the best one and the last one), the config file with
 the hyper-parameters for this trial, and the log file.

{{cookiecutter.project_slug}}/setup.py

@@ -11,7 +11,7 @@
         'flake8-docstrings',
         'gitpython',
         'tqdm',
-        'mlflow',
+        'mlflow==1.10.0',
         'orion>=0.1.8',
         'pyyaml>=5.3',
         'pytest>=4.6',
@@ -34,7 +34,7 @@
         'scipy==1.4.1',
         'setuptools>=41.0.0',
         'six>=1.12.0',
-        'numpy==1.19'],
+        'numpy==1.19.1'],
         {%- endif %}
     entry_points={
         'console_scripts': [

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/utils/logging_utils.py

@@ -3,6 +3,7 @@
 import os
 import socket
 
+from pip._internal.operations import freeze
 from git import InvalidGitRepositoryError, Repo
 from mlflow.utils.mlflow_tags import MLFLOW_RUN_NOTE
 
@@ -63,7 +64,10 @@ def log_exp_details(script_location, args):  # pragma: no cover
     """
     git_hash = get_git_hash(script_location)
     hostname = socket.gethostname()
-    message = "\nhostname: {}\ngit code hash: {}\ndata folder: {}\ndata folder (abs): {}".format(
-        hostname, git_hash, args.data, os.path.abspath(args.data))
-    logger.info('Experiment info:' + message + '\n')
-    mlflow.set_tag(key=MLFLOW_RUN_NOTE, value=message)
+    dependencies = freeze.freeze()
+    details = "\nhostname: {}\ngit code hash: {}\ndata folder: {}\ndata folder (abs): {}\n\n" \
+              "dependencies:\n{}".format(
+                  hostname, git_hash, args.data, os.path.abspath(args.data),
+                  '\n'.join(dependencies))
+    logger.info('Experiment info:' + details + '\n')
+    mlflow.set_tag(key=MLFLOW_RUN_NOTE, value=details)