Making a Release

Project Release Process Overview

Use this section if you know what you’re doing for a quick release, otherwise first explore the sections below to understand what each make target does.

You can run make help or just make to find out what targets are available and what they do.

If for some reason you can’t use make, remember that it is just a front-end for the normal git and other commands, so you can always make a release without using make.

The essence of the process is simple - bump the version number, build and upload the packages for conda and pypi. All the other steps handle various other things like tagging, testing the code base, testing the installability of the packages, etc.

Note, that the release process uses the master branch and also creates and uses a release-$(version) branch. That release branch remains after the release so that it’s accessible from github as a release branch.

The exact order to be followed is essential.

One Time Preparation

You can skip this step if you have done it once already on the system you’re making the release from.

You need to register (free) with:
After registration, to upload to fastai project, you will need to ask Jeremy to add your username to PyPI and anaconda.

Create file ~/.pypirc with the following content:

 [distutils]
 index-servers =
   pypi
   testpypi

 [testpypi]
 repository: https://test.pypi.org/legacy/
 username: your testpypi username
 password: your testpypi password

 [pypi]
 username: your pypi username
 password: your pypi password

You can also setup your client to have transparent access to anaconda tools, see https://anaconda.org/YOURUSERNAME/settings/access (adjust the url to insert your username).

You don’t really need it, as the anaconda client caches your credentials so you need to login only infrequently.
Install upload clients
```
conda install anaconda-client twine
```

Pre-Release Process

Normally, while testing the code, we only run make test, which completes within a few minutes. But we also have several sets of much heavier and slower, but more complete tests. These should be run and verified to be successful before starting a new release.

Run the test suite, including the slower tests (not much longer than the make test:
```
make test-full
```
Run the notebook tests (0.5-1h):
```
cd docs_src
./run_tests.sh
```

Quick Release Process

No matter which release process you follow, always remember to start with:

git pull

Otherwise it’s very easy to have an outdated checkout and release an outdated version.

If however you’d like to make a release not from the HEAD, but from a specific commit,

git checkout <desired commit>

then do not use the automated process, since it resets to master branch. Use the step-by-step process instead, which is already instrumented for this special case. (But we could change the fully automated release to support this way too if need be).

If you need to make a hotfix to an already released version, follow the Hotfix Release Process instructions.

Here is the “I’m feeling lucky” version, do not attempt unless you understand the build process.

make release

This target will automatically log its stdout and stderr into a log file of date format release-%Y-%m-%d-%H-%M-%S.log.

Ideally, please keep this file around for a few days in case we need to diagnose any problems with the release process at a later time.

make test’s non-deterministic tests may decide to fail right during the release rites. It has now been moved to the head of the process, so if it fails not due to a bug but due to its unreliability, it won’t affect the release process. Just rerun make release again.

Here is the quick version that includes all the steps w/o the explanations. If you’re unfamiliar with this process use the next section instead.

make tools-update
make master-branch-switch && make sanity-check
make test
make bump && make changes-finalize
make release-branch-create && make commit-version
make master-branch-switch
make bump-dev && make changes-dev-cycle
make commit-dev-cycle-push
make prev-branch-switch && make commit-release-push && make tag-version-push
make dist && make upload
make test-install
make backport-check
make master-branch-switch

If the make backport-check target says you need to backport, proceed to the backporting section. This stage can’t be fully automated since it requires you to decide what to backport if anything.

And announce the release and its changes in Developer chat thread.

Step-by-step Release Process

This is a one-step at a time process. If you find any difficulties scroll down to Detailed Release Process, which goes into many more details and explanations.

The starting point of the workflow is a dev version of the master branch. For this process we will use 1.0.6.dev0 starting point as an example.

check that CHANGES.md looks good, remove any empty sections, but don’t modify the line:
```
## 1.0.12.dev0 (Work In Progress)
```
The build process relies on this exact format, it will change the version number and replace Work In Progress with release data automatically. If you change it manually the automated process will fail. So do not.

install the latest tools that will be used during the build

 make tools-update            # update pip/conda build tools

make sure we start with master branch

 make master-branch-switch    # git checkout master

do sanity checks:
- check-dirty - git cleanup/stash/commit so there is nothing in the way
- version number is not messed up
```
 make sanity-check
```
pick a starting point

Normally, git pull to HEAD is fine, but it’s the best to know which ‘stable’ to use as a starting point.
```
 git pull
```
or:
```
 git checkout <commit>
```
validate quality
```
 make test                     # py.test tests
```
Another optional target is test-cpu, which emulates no gpus environment, by running the tests with environment variable CUDA_VISIBLE_DEVICES="":
```
 make test-cpu
```

start release-$(version) branch

 make bump                     # 1.0.6.dev0 => 1.0.6

The following will fix the version and the date in CHANGES.md, you may want to check that it looks tidy.

```
make changes-finalize         # 1.0.6.dev0 (WIP) => 1.0.6 (date)
```

We are ready to make the new release branch:

```
make release-branch-create    # git checkout -b release-1.0.6
make commit-version           # git commit fastai/version.py
```

go back to master and bump it to the next version + .dev0

 make master-branch-switch     # git checkout master
 make bump-dev                 # 1.0.6 => 1.0.7.dev0

Insert a new template into `CHANGES.md for the dev cycle with new version number:

 make changes-dev-cycle        # inserts new template + version

 make commit-dev-cycle-push    # git commit fastai/version.py CHANGES.md; git push

now we are no longer concerned with master, all the rest of the work is done on release-$(version) branch (we are using git checkout - here (like in cd -, since we no longer have the previous version)
```
 make prev-branch-switch       # git checkout - (i.e. release-1.0.6 branch)
```
finalize CHANGES.md (remove empty items) - version and date (could be automated)

commit and push CHANGES.md; tag and push version

 make commit-release-push      # git commit CHANGES.md; git push --set-upstream
 make tag-version-push         # git tag; git push

build the packages. Note that this step can take a very long time (15 mins or more). It’s important that before you run it you remove or move away any large files or directories that aren’t part of the release (e.g. data, tmp, models, and checkpoints), and move them back when done.
```
 make dist                     # make dist-pypi; make dist-conda
```
This target is composed of the two individual targets listed above, so if anything goes wrong you can run them separately.
upload packages.
```
 make upload                  # make upload-pypi; make upload-conda
```
This target is composed of the two individual targets listed above, so if anything goes wrong you can run them separately.
test uploads by installing them (telling the installers to install the exact version we uploaded). Following the upload it may take a few minutes for the servers to update their index. This target will wait for each package to become available before it will attempt to install it.
```
 make test-install             # pip install fastai==1.0.6; pip uninstall fastai
                               # conda install -y -c fastai fastai==1.0.6
```
if some problems were detected during the release process, or something was committed by mistake into the release branch, and as a result changes were made to the release branch, merge those back into the master branch. Except for the version change in fastai/version.py.
1. check whether anything needs to be backported
```
 make backport-check
```
If the make backport-check target says you need to backport, proceed to the backporting section. This stage can’t be fully automated since it requires you to decide what to backport if anything.
leave this branch to be indefinitely, and switch back to master, so that you won’t be mistakenly committing to the release branch when you intended master:
```
 make master-branch-switch     # git checkout master
```
announce the release and its changes in Developer chat thread.

Backporting release Branch To master

Discovery Process Quick Version

Check whether there any commits besides fastai/version.py from the point of branching of release-1.0.6 till its HEAD. If there are then probably there are things to backport.

   make backport-check

If the result is “Nothing to backport”, you’re done. Otherwise proceed to the “Performing Backporting” section below.

If by any chance you switched to the master branch already, this target won’t work, since it relies on fastai/version.py from the release branch. So you need to do it manually, by either going back to it, if it was the last one:

   git checkout -

or typing it out:

   git checkout release-1.0.6

Discovery Process Detailed Version

Normally you should have just one commit where fastai/version.py is changed, but if you applied some fixes there will be other commits. So we can’t just merge the whole branch back into the master but need to cherry-pick all but the very first (version.py change commit, which make backport-check will already exclude from its report).

Find what needs to be backported, there are a few ways to approach it:

find the revision at which release-$(version) branched off

  git rev-parse --short $(git merge-base master origin/release-1.0.6)

same, but with the long commit revision

  git merge-base master origin/release-1.0.6

get list of commits between the branching point and the HEAD of the branch

  git log  --oneline $(git merge-base master origin/release-1.0.6)..origin/release-1.0.6

get the diff of commits between the branching point and the HEAD of the branch

  git diff $(git merge-base master origin/release-1.0.6)..origin/release-1.0.6

alternative GUI way: checking what needs to be backported on github

If you want to use github presentation, go to the comparison page for the tag of the release https://github.com/fastai/fastai1/compare/release-1.0.6 or the same in 3 click if you don’t want to manually create it:
1. go to https://github.com/fastai/fastai1
2. select the release branch in the left upper-ish corner
3. click ‘Compare’ in the right upper-ish corner

If you are trying to do this process some time after release since you remembered you didn’t backport something, do the same as above but first sync your git db:

   git fetch --all # update remote info
   git branch -a   # check which branches are visible

Performing Backporting

Now that you looked at any changes that were applied to the release branch since it was branched, besides the version change in fastai/version.py, you can cherry pick the desired changes and merge them into master.

First, switch to master:

   make master-branch-switch

If make backport-check gave you the following output:

   !!! These commits may need to be backported:

   ab345fe conda build fix
   62091ed update release

and you decided you wanted to backport both changes, then you can do that one by one:

   git show 62091ed        # check that this is the right rev
   git cherry-pick 62091ed # merge it into the current checkout

or if there is a contiguous sequence, you can specify the start and the end (end being on top).

   git cherry-pick 62091ed..ab345fe # merge it into the current checkout

When done, complete the backporting

   git commit -m "backporting from release branch to master"
   git push

Detailed Release Process

The following is needed if the combined release instructions are failing or better understanding is needed. So that each step can be done separately.

fastai package is distributed via PyPI and anaconda. Therefore we need to make two different builds and upload them to their respective servers upon a new release.

Test Suite

Before building the packages make sure the test suite runs successfully:

make test

or:

python setup.py test

When building a fastai conda package, it runs a basic import fastai test in a fresh environment. That’s it.

PyPI Build and Release Details

To build a PyPI package and release it on pypi.org/:

Build the pip packages (source and wheel)
```
 make dist-pypi
```
Publish:
```
 make upload-pypi
```
Note: PyPI won’t allow re-uploading the same package filename, even if it’s a minor fix. If you delete the file from pypi or test.pypi it still won’t let you do it. So either a patch-level version needs to be bumped (A.B.C++) or some post release string added in version.py.
Test that the uploaded package is found and gets installed:

Test the webpage so that the description looks correct: https://pypi.org/project/fastai/

Test installation:
```
 pip install fastai==1.0.10
```
XXX: May be add: --force-reinstall or manually remove preinstalled fastai first from your python installation: e.g. python3.6/site-packages/fastai*, run python -m site to find out the location.

If the install is not working, check the state of the package: https://pypi.org/project/fastai/

Even More Details

Build Source distribution / Source Release

It provides metadata + source files.

It is needed for installing.
```
  python setup.py sdist
```
MANIFEST.in is in charge of what source files are included in the package. Here are some practical usage examples:

To include a sub-directory recursively, e.g. docs (one directory per instruction):
```
  graft docs
```
If you want to include the whole directory tests, but not tests/data for example, adjust MANIFEST.in to have:
```
  graft tests
  prune tests/data
```
To exclude some extensions from everywhere, e.g. all *pyc and *.pyo:
```
  global-exclude *.py[co]
```
For more details, see Creating a Source Distribution
Build Built Distribution

It provides metadata + pre-built files.

It only needs to be moved (usually by pip) to the correct locations on the target system.
```
 python setup.py bdist
```
Build Wheel

This is a Built Distribution.
```
  python setup.py bdist_wheel
```
It’s a ZIP-format archive with .whl extension
```
  {distribution}-{version}(-{build tag})?-{python tag}-{abi tag}-{platform tag}.whl
```
Note: To build all the requirements wheels (not needed for the release):
```
  pip wheel . -w dist
```
setup.py configuration:

Pip Helper Tools

Complete Package Uninstall

Sometimes with too many local installs/uninstalls into the same environment, especially if you nuke folders and files with rm(1), things can get pretty messed up. So this can help diagnose what pip sees:
```
  pip show fastai
  [...]
  Name: fastai
  Version: 1.0.0b1
  Location: /some/path/to/git/clone/of/fastai
```
Yet pip can’t uninstall it:
```
  pip uninstall fastai
  Can't uninstall 'fastai'. No files were found to uninstall.
```
easy-install (pip install -e) can make things very confusing as it may point to git checkouts that are no longer up-to-date. and you can’t uninstall it. It’s db is a plain text file here:
```
  path/to/lib/python3.6/site-packages/easy-install.pth
```
so just removing the relevant path from this file will fix the problem. (or removing the whole file if you need to).

Similarly, this is another place where it can hide:
```
  path/to/lib/python3.6/site-packages/fastai.egg-link
```
Now running:
```
  pip show fastai
```
shows nothing.
To upload to the test server, instead of the live PyPI server, use:
```
  twine upload --repository testpypi dist/*
```
and to install from it:
```
  pip install --index-url https://test.pypi.org/simple/ fastai
```
Doc: https://packaging.python.org/guides/using-testpypi/

Conda Build Details

To build a Conda package and release it on anaconda.org:

Build the fastai conda package:
```
 make dist-conda
```
Upload
```
 make upload-conda
```
Test that the uploaded package is found and gets installed:

Test the webpage so that the description looks correct: https://pypi.org/project/fastai/

Test installation:
```
 conda install -c fastai fastai
```

More Detailed Version

conda-build uses a build recipe conda/meta.yaml.

Note, that conda-build recipe now relies on sdist generated tarball, so you need to run: python setup.py sdist or make dist-pypi-sdist if you plan on using the raw conda-build commands. Otherwise, make dist-conda already does it all for you. Basically it expects the clean tarball with source under ./dist/.

Check that it’s valid:
```
 conda-build --check ./conda/
```
Build the fastai package (include the pytorch channel, for torch/ dependencies, and fastai test channel for torchvision/fastai):
```
 conda-build ./conda/ -c pytorch -c fastai/label/main
```
If conda-build fails with:
```
 conda_build.exceptions.DependencyNeedsBuildingError: Unsatisfiable dependencies for platform linux-64: {'dataclasses', 'jupyter_contrib_nbextensions'}
```
it indicates that these packages are not in the specified via -c and user-pre-configured conda channels. Follow the instructions in the section Dealing with Missing Conda Packages and then come back to the current section and try to build again.

Note, that conda-build recipe now relies on tarball produced by dist-pypi-sdist target (it happens internally if you rely on Makefile, but if you do it without using make, then make sure you built the sdist tarball first, which is done by:
```
 python setup.py sdist
```
which generates dist/fastai-$version.tar.gz, and this is what conda-build recipe needs. It’s important to remember that if you change any files, you must rebuild the tarball, otherwise conda-build will be using the outdated files. If you do make dist-conda then it’ll be taken care of automatically.

Dealing with Missing Conda Packages

Packages that are missing from conda, but available on pypi, need to built one at a time and uploaded to the fastai channel. For example, let’s do it for the fastprogress package:

conda skeleton pypi fastprogress
conda-build fastprogress
# the output from the above command will tell the path to the built package
anaconda upload -u fastai ~/anaconda3/conda-bld/path/to/fastprogress-0.1.4-py36_0.tar.bz2

and then rerun conda-build and see if some packages are still missing. Repeat until all missing conda packages have been built and uploaded.

Note, that it’s possible that a build of a certain package will fail as it’ll depend on yet other packages that aren’t on conda. So the (recursive) process will need to be repeated for those as well.

Once the extra packages have been built you can install them from the build directory locally:

conda install --use-local fastprogress

Or upload them first and then install normally via conda install.

See fastai/builds/custom-conda-builds for recipes we created already.

The Problem Of Supporting Different Architectures

Every package we release on conda needs to be either noarch or we need to build a whole slew of packages for each platform we choose to support, linux-64, win-64, etc.

At this moment fastai is released as a generic noarch (pure python), and we don’t even make separate py36 and py37 releases. That means we can’t use preprocess-selectors, since they will all evaluate to True, no matter the platform or python version, according to this. As such we can’t instruct conda to install a certain dependency only for a specific python version. For example, this doesn’t do anything:

  run:
    - dataclasses # [py36]

That is, dataclasses will be installed on any python platform regardless of its version. For the above to work, i.e. install dataclasses dependency only on py36 platforms, requires that we make separate py36 and py37 fastai releases.

As shown in the previous section we also have to deal with several dependencies which are not on conda. If they are noarch, it should be easy to release conda packages for dependencies every so often. If they are platform-specific we will have to remove them from conda dependencies and ask users to install those via pip. An easy way to check whether a package for a specific platform is available is to:

conda search -i --platform win-64

Uploading and Testing

Upload to the main channel:

anaconda upload /path/to/fastai-xxx.tar.bz2 -u fastai

To test, see that you can find it:

conda search fastai

and then validate that the installation works correctly:

conda install -c fastai fastai

Testing Release

If this is just a test release that shouldn’t be visible to all, add the --label test option, like so:

anaconda upload /path/to/fastai-xxx.tar.bz2 -u fastai --label test

And then only those who use -c fastai/label/test in conda install command will see this package:

conda install -c fastai/label/test fastai

Any label name can be used. If none was specified, the implicit label main is assigned to the package.

The label can be changed either on anaconda.org, or via it’s client:

anaconda label --copy test main

this will copy all of the test package(s) back to the main label. Use this one with care.

You can move individual packages from one label to another (anaconda v1.7+):

anaconda move --from-label OLD --to-label NEW SPEC

Replace OLD with the old label, NEW with the new label, and SPEC with the package to move. SPEC can be either user/package/version/file, or user/package/version in which case it moves all files in that version. For example to move any released packages that match fastai-1.0.5-*.tar.bz2 from the test label to main and thus making it visible to all:

anaconda move --from-label test --to-label main fastai/fastai/1.0.5

Re-uploading

Note, that anaconda client won’t let you re-upload a file with the same name, as previously uploaded one, i.e. fastai-1.0.0-py_1.tar.bz2, so to release an update with the same package version you either (1) use anaconda upload --force or (2) manually delete it from anaconda.org, or (3) create a release file with a new name, by bumping the value of number in meta.yaml.

build:
  number: 1

Now you need to rebuild the package, and if you changed the number to 2, the package will now become 'fastai-1.0.0-py_2.tar.bz2.

Conda Helper Tools

conda-build useful options

Sometimes it helps to see what conda-build copied into its work folder, so there is a currently not working ` –keep-old-work option that is supposed to do that. Until it's fixed there –dirty which is somewhat similar, but you have clear out /path/to/anaconda3/envs/your-env/conda-bld/` manually before using it 2nd time - if you don’t it will not sync the changes in the source tree.
To render the final meta.yaml:
```
  conda-render ./conda/
```
This is very useful when you do any jinja2 template processing inside meta.yaml and you want to see what the final outcome is.
Once the package is built, it can be validated:
```
  conda-verify path/to/package.tar.bz2
```
To validate the meta.yaml recipe (similar to using conda-build --check):
```
  conda-verify ./conda/
```

Documentation

To figure out the nuances of the meta.yaml recipe writing see this tutorial
meta.yaml is written using jinja2 python templating language. API docs

Support

conda dev chat channel

Version Bumping

You can either edit fastai/version.py and change the version number by hand.

Or run one of these make targets:

Target	Function
bump-major	bump major level; remove .devX if any
bump-minor	bump minor level; remove .devX if any
bump-patch	bump patch level unless has .devX, then don’t bump, but remove .devX
bump	alias to bump-patch (as it’s used often)
bump-post-release	add .post1 or bump post-release level .post2, .post3, …
bump-major-dev	bump major level and add .dev0
bump-minor-dev	bump minor level and add .dev0
bump-patch-dev	bump patch level and add .dev0
bump-dev	alias to bump-patch-dev (as it’s used often)

e.g.:

make bump

We use the semver version convention w/ python adjustment to .devX, instead of -devX:

release: major.minor.patch, 0.1.10
dev or rc: major.minor.patch.devX, 0.1.10.dev0

Remember that master should always have .dev0 in its version number, e.g. 0.1.10.dev0. Only the release branch will turn it into 0.1.10. So when a release is made, master should immediately be switched to 0.1.11.dev0.

Other Makefile Targets

make clean removes any intermediary build artifacts.

make will show all possible targets with a short description of what they do.

Tagging

Tagging targets:

List tags

all tags:

  git tag

tags matching pattern:

  git tag -l "1.8.5*"

by date:

  git log --tags --simplify-by-decoration --pretty="format:%ci %d"

last tag:

  git describe --abbrev=0 --tags

Creating tags

To tag current checkout with tag “1.0.5” with current date:

  git tag -a test-1.0.5 -m "test-1.0.5"
  git push --tags origin master

To tag commit 9fceb02a with tag “1.0.5” with current date:

  git checkout 9fceb02a
  git tag -a v1.0.5 -m "1.0.5"
  git push --tags origin master
  git checkout master

To tag commit 9fceb02a with tag “1.0.5” with the date of that commit:

  git checkout 9fceb02a
  GIT_COMMITTER_DATE="$(git show --format=%aD | head -1)" git tag -a 1.0.5 -m "1.0.5"
  git push --tags origin master
  git checkout master

or the same without needing to git checkout and with typing the variables only once:

  tag="0.1.3" commit="9fceb02a" bash -c 'GIT_COMMITTER_DATE="$(git show --format=%aD $commit)" git tag -a $tag -m $tag $commit'
  git push --tags origin master

To find out the hash of the last commit in a branch, to use in back-tagging:

  git log -n 1 origin/release-1.0.25

Delete remote tag:

An unambiguous way:
```
  git push origin :refs/tags/v1.0.5
```
An ambiguous way (may delete a branch if it’s named the same as the tag)
```
  git push --delete origin v1.0.5
```
Delete multiple tags:
```
  git push --delete origin tag1 tag2
```
Delete local tag:
```
  git tag --delete v0.1.5
  git push --tags origin master
```
This is important since if the remote tag is deleted, but the local is not, then on the next git push --tags origin master it will get restored in remote.

Useful scripts:

git-backtag

Rollback Release Commit And Tag

In case something is discovered wrong after release commit was made, here is how to rollback.

git reset --hard HEAD~1                        # rollback the commit
git tag -d `git describe --tags --abbrev=0`    # delete the tag

Careful with this as it’ll reset any modified files, probably git stash first just in case.

Once, things were fixed, git push, etc…

Hotfix Release Process

If something found to be wrong in the last release, yet the HEAD is unstable to make a new release, instead, apply the fixes to the branch of the desired release and make a new hotfix release of that branch. Follow these step-by-step instructions to accomplish that, which involved two parts - backporting (manual) and releasing (automated).

Part 1: Backporting fixes and preparing for hotfix-release

Start with the desired branch.

For example if the last release was 1.0.36
```
git checkout release-1.0.36
```
Apply the fix.

a. Apply the desired fixes, e.g. applying some specific fix commit:
```
git cherry-pick 34499e1b8
git push
```
b. Document the fixes in CHANGES.md (the reason for this hotfix)

c. commit/push all changes to the branch.
```
git commit CHANGES.md whatever-files-were-fixed
git push
```

Part 2. Making the hotfix release

All of the following steps can be done in one command:

   make release-hotfix

If it fails, then pick up where it failed and continue with the step-by-step process as explained below.

Check that everything is committed and good to go.
```
make sanity-check-hotfix
```
Test.
```
make test
```
Adjust version.

According to PEP-0440 add .post1 to the version, or if it already was a .postX, increment its version:
```
make bump-post-release
```
Commit and push all the changes to the branch.
```
make commit-hotfix-push
```
Make a new tag with the new version.
```
make tag-version-push
```
Make updated release.
```
make dist
make upload
```
or if only conda release is needed (e.g. only a dependencies fix):
```
make dist-conda
make upload-conda
```
or if only pypi release is needed (e.g. only a dependencies fix):
```
make dist-pypi
make upload-pypi
```
Test release.

If you made a release on both platforms:
```
make test-install
```
If the hotfix was made only for pypi:
```
make test-install-pypi
```
or for conda:
```
make test-install-conda
```
Don’t forget to switch back to the master branch for continued development.
```
make master-branch-switch
```

When finished, make sure that the fix is in the master branch too, in case it was fixed in the release branch first.

Install The Locally Build Packages

If you want to install the package directly from your filesystem, e.g. to test before uploading, run:

make dist-conda
make install-conda-local

Speeding Up Build Time

When experimenting with different builds (in particular custom conda builds) the following are useful:

use all or several CPU cores:
```
 MAKEFLAGS="-j" conda-build ...
```
skip the testing stage:
```
 conda-build ...  --no-test
```
This could speed up the build time x5 times! But of course, the final build to be uploaded, shouldn’t skip this stage.
if just needing to check that the build is successful (e.g. for packages requiring compiling code:
```
 conda-build ... --build-only
```

Run Install Tests In A Fresh Environment

While CI builds now do exactly this, it might be still useful to be able to do it manually, since CI builds are very slow to tweak and experiment with. So here is a quick copy-n-paste recipe to build one and clean it up.

conda create -y python=3.7 --name fastai-py3.7
conda activate fastai-py3.7
conda install -y conda
conda install -y pip setuptools
conda install -y -c fastai -c pytorch fastai
conda uninstall -y fastai
pip install -e ".[dev]"
conda deactivate
conda env remove -y --name fastai-py3.7

Installed Packages

When debugging issues it helps to know what packages have been installed. The following will dump the installed versions list in identical format for conda and pypi (package-name==version):

Conda:

 conda list | egrep -v '^#' | perl -ne 's/_/-/g; @x=split /\s+/, lc $_; print "$x[0]==$x[1]\n"' | sort | uniq > packages-conda.txt

PyPi:

 pip list | egrep -v '^(Package|-----)' | perl -ne 's/_/-/g; @x=split /\s+/, lc $_; print "$x[0]==$x[1]\n"' | sort | uniq > packages-pip.txt

Comparing the output of both environments:

 diff -u0 --suppress-common-lines packages-conda.txt packages-pip.txt | grep -v "@@"

The comparison is useful for identifying differences in these two package environment (for example when CI build fails with pypi but not with conda).

If you want an easier to read output use conda-env-compare.pl from conda-tools.

Package Dependencies

We need to make sure that setup.py sets identical dependencies to conda/meta.yml. It’s not always possible but it should be attempted.

To find the dependencies of a given package (including the pinned versions), using spacy as an example:

Conda:
```
 conda search --info spacy==2.0.16
```
Pypi:

Currently it can’t be done without first installing the package. And you need to install pipdeptree that shows the complete requirements and not just the installed versions.
```
 pip install pipdeptree
 pip install spacy==2.0.16
 pipdeptree --packages spacy
```

The following sections go into pip/conda-specific tools and methods for figuring out and resolving dependencies.

Conda Dependencies

Here is how you can find out currently installed packages and conda dependencies:

To find out the currently installed version of a package:
```
  conda list spacy
```
Same, but do not show pip-only installed packages.
```
  conda list --no-pip spacy
```
To find out the dependencies of a package:
```
  conda search --info spacy==2.0.16
```
Narrow down to a specific platform build:
```
  conda search --info spacy==2.0.16=py37h962f231_0
```
Also can use a wildcard:
```
  conda search --info spacy==2.0.16=py37*
```
It supports -c channel, for packages not in a main channel
```
  conda search --info -c fastai fastai=1.0.6
```
If version is not specified it’ll show that information on all the versions it has:
```
  conda search --info -c fastai fastai
```
Another hacky way to find out what the exact dependencies for a given conda package are:
```
  conda create --dry-run --json -n dummy fastai -c fastai
```
Add -c fastai/label/test to make it check our test package.

Here is the full Conda packages version specification table:

Constraint type	Specification	Result
Fuzzy	numpy=1.11	1.11.0, 1.11.1, 1.11.2, 1.11.18 etc.
Exact	numpy==1.11	1.11.0
Greater than or equal to	“numpy>=1.11”	1.11.0 or higher
OR	“numpy=1.11.1	1.11.3”	1.11.1, 1.11.3
AND	“numpy>=1.8,<2”	1.8, 1.9, not 2.0

Other conda search tricks:

conda search outputs results as following:

  conda search -c pytorch "pytorch"
  Loading channels: done
  # Name                  Version           Build                   Channel
  pytorch 0.5.0.dev20180914 py3.5_cpu_0                     pytorch
  pytorch 0.5.0.dev20180914 py3.5_cuda8.0.61_cudnn7.1.2_0   pytorch
  pytorch 0.5.0.dev20180914 py3.5_cuda9.0.176_cudnn7.1.2_0  pytorch
  pytorch 0.5.0.dev20180914 py3.5_cuda9.2.148_cudnn7.1.4_0  pytorch
  [...]

To narrow the results, e.g. show only python3 cpu builds:

  conda search -c pytorch "pytorch[build=py3*_cpu_0]"

and then feed it to conda install with specific ==version=build after the package name, e.g. pytorch==1.0.0.dev20180916=py3.6_cpu_0

To search for packages for a given system (by default, packages for your current platform are shown):

  conda search -c pytorch "pytorch[subdir=osx-64]"

Some of the possible platforms include linux-32, linux-64, win-64, osx-64.

And these can be combined:

  conda search -c pytorch "pytorch[subdir=osx-64, build=py3.7*]"

To search all packages released by user fastai:

  conda search -c fastai --override

To search all packages released by user fastai for a specific platform, e.g. linux-64:

  conda search -c fastai --override --platform linux-64

To find out why a particular package is installed (i.e. which package requires it):
```
  conda create -n conda-4.3 conda=4.3
  conda activate conda-4.3
  python -m conda search --reverse-dependency --full-name pillow
```
Note, that conda==4.4 removed this functionality, that’s why we need a special downgraded to conda==4.3 environment to make this work as a workaround.

PyPI Dependencies

Tools for finding out currently installed packages and pip dependencies (direct and reversed).

pipdeptree: (pip install pipdeptree)

For a specific package:
```
  pipdeptree --packages  pillow
```
or with more details:
```
  pip show pillow
```
Print the whole tree of the installed base:
```
  pipdeptree -fl
```
To find out why a particular package is installed (i.e. which package requires it):
```
  pipdeptree --reverse --packages  pillow
```
johnnydep: pip install johnnydep (the tool is very slow!):

Pretty-print a dependency tree for a Python distribution
```
  johnnydep spacy
```
Resolve the dependency tree:
```
  johnnydep spacy --output-format pinned
```

Creating requirements.txt File By Analyzing The Code Base

We will use 2 tools, each not finding all packages, but together they get it mostly right. So we run both and combine their results.

Install them with:

pip install pipreqs pigar

conda install pipreqs pigar -c conda-forge

And then to the mashup:

cd fastai/fastai/
pipreqs --savepath req1.txt .
pigar -p req2.txt
perl -pi -e 's| ||g' req2.txt
cat req1.txt req2.txt | grep "##" | sort | uniq > req.txt

So this gives us requirements.txt-like file which can be used for pip. But we will get pip to sort things out from setup.py, by putting . inside fastai/requirements.txt.

Now make a list for setup.py’s install_requires:

perl -nle '$q # chr(39); m/^(.*?)#/ && push @l, $1; END{ print join ", ", map {qq[$q$_$q]} @l}' req.txt

and use the output to update setup.py.

When merging make sure to not overwrite minimal version requirements, e.g. pytorch>#0.5. Also, you should manually clean these up since some will be deps only for doc authors or fastai library contributors; these don’t need to be in the main requirements list.

Cleanup:

rm req1.txt req2.txt req.txt

The same can be repeated for getting test requirements, just repeat the same process inside tests directory.

Copying packages from other channels

Currently we want to use the version of spacy and some of its deps from the conda-forge channel, instead of the main anaconda channel. To do this, we copy the desired packages and their dependencies in to our channel with:

anaconda copy conda-forge/spacy/2.0.18 --to-owner fastai
anaconda copy conda-forge/regex/2018.01.10 --to-owner fastai
anaconda copy conda-forge/thinc/6.12.1 --to-owner fastai

This copies all available architectures, and not just your current architecture.

To copy from a specific label, e.g. gcc7, add --from-label gcc7 to the commands above.

Note that you can’t re-copy. If for example the source has changed, or added an architecture. Currently, you have to delete the copy from the fastai channel and re-copy. Try to do that as fast as possible not to impact users.

Conditional Dependencies

Here is how to specify conditional dependencies, e.g. depending on python version:

Conda (do not use this!, see below)

In meta.yaml:
```
   run:
     - dataclasses # [py36]
     - fastprogress >=0.1.18
     [...]
```
Here # [py36] tells conda-build that this requirement is only for python3.6, it’s not a comment.

Except this doesn’t work unless we start making py36 and py37 conda builds, which we don’t. And if the above is used it’ll break the dependency if it’s built on py37. The problem is that conda can only handle conditional dependencies at build time, unlike pip that does it at install time!
Pypi

In setup.py:
```
 requirements = ["dataclasses ; python_version<'3.7'", "fastprogress>=0.1.18", ...]
```
Here ; python_version<'3.7' instructs the wheel to use a dependency on dataclasses only for python versions lesser than 3.7.

Unlike conda, pip checks conditional dependencies during install time, so the above actually works and doesn’t require multiple wheel builds.

This recent syntax requires setuptools>=36.2 on the build system. For more info see.

CI/CD

Azure DevOps CI (CPU-only)

Usage

All the good stuff is here: Builds

It uses fastai/azure-pipelines.yml script to do the testing. See notes inside the script for more details on how to modify it.

By default it runs the fastai installation and a few basic tests when either master gets a non-document-only push event, or PR is submitted. More details on this topic can be found in the following sections.

[...] options in the right upper corner, next to Queue hides a bunch of useful functions:

‘Pause builds’ which may be important…
Status Badge MD code for the README.md project page

To see various stats/graphs based on tests outcome, go to [Test Plans] => [Runs].

Under Project Settings, important things are:

[Notifications]

CI Builds

CI Builds are triggered on every git push to master (except when it’s an obvious document only change commit, like a change to an .md file).

PR Builds

PR Builds get triggered (1) when a new PR is submitted and (2) each time a new commit is added to that PR. It will also get triggered (3) if a closed PR gets re-opened.

If you want to manually trigger a PR Build re-run, you can click on the build status which will take you to the build page at Azure Devops and there under the “…” there is an option to Rebuild.

Note, that neither green or red status of the PR guarantees that it’s so. Since the check is done at the point of the PR opening (or if new commits were added to it), it’s not redone if master has changed since then. So the only way to know for sure is to force a manual rebuild for a given PR.

Currently we don’t have the following enforcement enabled (PR won’t be merge-able at github if the PR’s build status is failed.)

Path Filters

By default CI runs on any push made, regardless of whether it’s a code or a document changed, which is a waste, so it helps to add Include/Exclude path filters.

To do that choose a build setup, go to Edit => Triggers, “Continuous Integration”, check the “Override” on the right, and enable “Path filters”. Important rules - paths start with / and if you include an Exclude filter you must also include an Include filter!!! So for example to exclude anything under /docs from triggering a build, add 2 rules:

Type	Path specification
Include	/
Exclude	/docs

Now repeat the same for “Pull request validation”.

Choose ‘Save’, under “Save & Queue”.

Skipping Jobs

To skip a specific job from running, edit the spec to include:

- job: Foo
  condition: False

Manual Jobs

To trigger a manual build of go to Builds, choose Queue, choose the branch (master) and in the Commit field either nothing or enter the desired commit hash. This is the way to get occasional CI builds against non-master branches, which is useful when testing a new pipeline.

Scheduled Jobs

If you want to run a build as a cron-job, rather than it getting triggered by a PR or a push, add the pipeline script as normal, and then go to that build’s [Edit], and then [Triggers], disable CI and PR entries and configure a scheduled entry.

Also, most likely you don’t want the outcome of the scheduled job to be attached to the most recent commit on github (as it will most likely be misleading if it’s a failure). So to fix that under [Edit], and then [YAML], followed by [Get Sources], and uncheck “Report Build Status” on the right side.

Modifying `azure-pipelines.yml`

We now have CI builds running and therefore we shouldn’t break those when need to tweak azure-pipelines.yml, which often involves a lot of trial and error and meanwhile all CI builds and PRs will be broken. Not good.

Therefore Do not modify azure-pipelines.yml directly in master. Do that only in a branch, then use manual build from that branch: go to Builds, choose Queue, choose the branch and the commit hash (most likely of the latest commit) and run. Only once the pipeline is working merge it into master.

And remember to sync the branch with the master changes so that you’re testing the equivalent of branch.

Configuration

to enable azure CI
pipelines cookbook
azure installed automatically via github’s project webhooks these push events:
- triggers CI build on every push (except when it’s only doc change)!:
```
  https://dev.azure.com/fastdotai/_apis/public/hooks/externalEvents (push)
```
- triggers CI build on every PR:
```
  https://dev.azure.com/fastdotai/_apis/public/hooks/externalEvents (pull_request)
```

Multiple Pipelines In The Same Repo

Currently [New] will not let you choose an alternative pipeline. So until this is fixed, let it use the default azure-pipelines.yml, Save and then go and Edit it and replace with a different file from the repository (and perhaps switching to a different branch if needed), using […].

Debug

Download the logs from the build report page, unzip the file, and then cleanup the timestamps:

mkdir logs
mv logs_2005.zip logs
cd logs
unzip logs_2005.zip
find . -type f -exec perl -pi -e 's|^\S+ ||' {} \;
find . -type f -exec perl -0777 -pi -e 's|\n\n|\n|g' {} \;

Debugging segfaults

Here is how to get segfault backtrace directly or via the core dump in a non-interactive way:

MacOS

 # allow large core files
 ulimit -c unlimited
 # test core dump files can be written by this user
 touch /cores/test && rm /cores/test
 # any cores prior to the run?
 ls -l /cores/
 # run the program that segfaults
 py.test tests/test_vision_data_block.py
 # any cores after the run?
 ls -l /cores/
 # get the backtrace of the first core file
 echo bt | lldb -c /cores/core.*

Linux

 # allow large core files
 ulimit -c unlimited
 export SEGFAULT_SIGNALS="all"
 # catch the segfault and get the backtrace
 catchsegv py.test tests/test_vision_data_block.py

Support

Package Download Statistics

How many times fastai was downloaded?

from PyPI
from Conda

The numbers are probably higher due to caches, CDNs, etc.

Deprecated: This is v1 of fastai, which is not supported.

Making a Release

Project Release Process Overview

One Time Preparation

Pre-Release Process

Quick Release Process

Step-by-step Release Process

Backporting release Branch To master

Discovery Process Quick Version

Discovery Process Detailed Version

Performing Backporting

Detailed Release Process

Test Suite

PyPI Build and Release Details

Even More Details

Pip Helper Tools

Conda Build Details

More Detailed Version

Dealing with Missing Conda Packages

The Problem Of Supporting Different Architectures

Uploading and Testing

Testing Release

Re-uploading

Conda Helper Tools

Documentation

Support

Version Bumping

Other Makefile Targets

Tagging

Rollback Release Commit And Tag

Hotfix Release Process

Release Making Related Topics

Install The Locally Build Packages

Speeding Up Build Time

Run Install Tests In A Fresh Environment

Installed Packages

Package Dependencies

Conda Dependencies

PyPI Dependencies

Creating requirements.txt File By Analyzing The Code Base

Copying packages from other channels

Conditional Dependencies

CI/CD

Azure DevOps CI (CPU-only)

Usage

CI Builds

PR Builds

Path Filters

Skipping Jobs

Manual Jobs

Scheduled Jobs

Modifying azure-pipelines.yml

Configuration

Multiple Pipelines In The Same Repo

Debug

Debugging segfaults

Support

Package Download Statistics

Modifying `azure-pipelines.yml`