conda install

In this document we will explore what happens in Conda from the moment a user types their installation command until the process is finished successfully. For the sake of completeness, we will consider the following situation:

• The user is running commands on a Linux x64 machine with a working installation of Miniconda.

• This means we have a base environment with conda, python, and their dependencies.

• The base environment is already preactivated for Bash. For more details on activation, check conda init and conda activate.

Ok, so… what happens when you run conda install numpy? Roughly, these steps:

1. Command line interface

• argparse parsers

• Environment variables

• Configuration files

• Context initialization

2. Fetching the index

• Retrieving all the channels and platforms

• A note on channel priorities

3. Solving the install request

• Requested packages + prefix state = list of specs

• Index reduction (sometimes)

• Running the solver

• Post-processing the list of packages

4. Generating the transaction and the corresponding actions

6. Integrity verification

Command line interface

First, a quick note on an implementation detail that might be not obvious.

When you type conda install numpy in your terminal, Bash takes those three words and looks for a conda command to pass a list of arguments ['conda', 'install', 'numpy']. Before finding the conda executable located at CONDA_HOME/condabin, it probably finds the shell function defined here. This shell function runs the activation/deactivation logic on the shell if requested, or delegates over to the actual Python entry-points otherwise. This part of the logic can be found in conda.shell.

Once we are running the Python entry-point, we are in the conda.cli realm. The function called by the entry point is conda.cli.main:main(). Here, another check is done for shell.* subcommands, which generate the shell initializers you see in ~/.bashrc and others. If you are curious where this happens, it’s conda.activate.

Since our command is conda install ..., we still need to arrive somewhere else. You will notice that the rest of the logic is delegated to conda.cli.main:_main(), which will invoke the parser generators, initialize the context and loggers, and, eventually, pass the argument list over to the corresponding command function. These four steps are implemented in four functions/classes:

1. conda.cli.conda_argparse:generate_parser(): This uses argparse to generate the CLI. Each subcommand is initialized in separate functions. Note that the command line options are not generated dynamically from the Context object, but annotated manually. If this is needed (e.g. --repodata-fn is exposed in Context.repodata_fn), the dest variable of each CLI option should match the target attribute in the context object.

2. conda.base.context.Context: This object stores the configuration options in conda and will be initialized taking into account, among other things, the arguments parsed in the step above. This is covered in more detail in a separate deep dive: conda config and context.

3. conda.gateways.logging:initialize_logging(): Not too exciting and easy to follow. This part of the code base is more or less self-explanatory.

4. conda.cli.conda_argparse:do_call(): The argument parsing will populate a func value that contains the import path to the function responsible for that subcommand. For example, conda install is taken care of by conda.cli.main_install. By design, all the modules reported by func must contain an execute() function that implements the command logic. execute() takes the parsed arguments and the parser itself as arguments. For example, in the case of conda install, execute() only redirects to a certain mode in conda.cli.install:install().

Let’s go take a look at that module now. conda.cli.install:install() implements the logic behind conda create, conda install, conda update and conda remove. In essence, they all deal with the same task: changing which packages are present in an environment. If you go and read that function, you will see there are several lines of code handling diverse situations (new environments, clones, etc.) before we arrive to the next section. We will not discuss them here, but feel free to explore that section. It’s mostly ensuring that the destination prefix exists, whether we are creating a new environment and massaging some command line flags that would allow us to skip the solver (e.g. --clone).

Check the concepts for Conda environments.

Fetching the index

At this point, we are ready to start doing some work! All of the previous code was telling us what to do, and now we know. We want conda to install numpy on our base environment. The first thing we need to know is where we can find packages with the name numpy. The answer is… the channels!

Users download packages from conda channels. These are normally hosted at anaconda.org. A channel is essentially a directory structure with these elements:

<channel>
├── channeldata.json
├── index.html
├── <platform> (e.g. linux-64)
│   ├── current_repodata.json
│   ├── current_repodata.json.bz2
│   ├── index.html
│   ├── repodata.json
│   ├── repodata.json.bz2
│   ├── repodata_from_packages.json
│   └── repodata_from_packages.json.bz2
└── noarch
├── current_repodata.json
├── current_repodata.json.bz2
├── index.html
├── repodata.json
├── repodata.json.bz2
├── repodata_from_packages.json
└── repodata_from_packages.json.bz2


You can find some more user-oriented notes on Channels at What is a "conda channel"? and Repository structure and index. If you are interested in more technical details, check the corresponding documentation pages at conda-build.

The important bits are:

• A channel contains one or more platform-specific directories (linux-64, osx-64, etc.), plus a platform-agnostic directory called noarch. In conda jargon, these are also referred to as channel subdirs. Officially, the noarch subdirectory is enough to make it a conda channel; e.g. no platform subdirectory is necessary.

• Each subdir contains at least a repodata.json file: a gigantic dictionary with all the metadata for each package available on that platform.

• In most cases, the same subdirs also contain the *.tar.bz2 files for each of the published packages. This is what conda downloads and extracts once solving is complete. The anatomy of these files is well defined, both in content and naming structure. See What is a conda package?, Package metadata and/or Package naming conventions for more details.

Additionally, the channel’s main directory might contain a channeldata.json file, with channel-wide metadata (this is not specific per platform). Not all channels include this, and in general it is not currently something that is commonly utilized.

Since conda’s philosophy is to keep all packages ever published around for reproducibility, repodata.json is always growing, which presents a problem both for the download itself and the solver engine. To reduce download times and bandwidth usage, repodata.json is also served as a BZIP2 compressed file, repodata.json.bz2. This is what most conda clients end up downloading.

Note on ‘current_repodata.json’

More repodatas variations can be found in some channels, but they are always reduced versions of the main one for the sake of performance. For example, current_repodata.json only contains the most recent version of each package, plus their dependencies. The rationale behind this optimization trick can be found here.

So, in essence, fetching the channel information means it can be expressed in pseudo-code like this:

platform = {}
noarch = {}
for channel in reversed(context.channels):
channel.full_url / context.subdir / "repodata.json.bz2"
)
platform.update(platform_repodata)
channel.full_url / "noarch" / "repodata.json.bz2"
)
noarch.update(noarch_repodata)


Note that these dictionaries are keyed by filename, so higher priority channels will overwrite entries with the exact same filename (e.g. numpy-1.19-py36h87ha43_0.tar.bz2). If they don’t have the same filename (e.g., same version and build number but different hash), this ambiguity will be resolved later in the solver, taking into account the channel priority mode.

In this example, context.channels has been populated through different, cascading mechanisms:

• The default settings as found in ~/.condarc or equivalent.

• The CONDA_CHANNELS environment variable (rare usage).

• The command-line flags, such as -c <channel>, --use-local or --override-channels.

• The channels present in a command-line spec. Remember that users can say channel::numpy instead of simply numpy to require that numpy comes from that specific channel. That means that the repodata for such channel needs to be fetched, too!

The items in context.channels are supposed to be conda.models.channels.Channel objects, but the Solver API also allows strings that refer to their name, alias or full URL. In that case, you can use Channel objects to parse and retrieve the full URL for each subdir using the Channel.urls() method. Several helper functions can be found in conda.core.index, if needed.

Sadly, fetch_extract_and_read() does not exist as such, but as a combination of objects. The main driving function is actually get_index(), which passes the channel URLs to fetch_index, a wrapper that delegates directly to conda.core.subdir_data.SubdirData objects. This object implements caching, authentication, proxies and other things that complicate the simple idea of “just download the file, please”. Most of the logic is in SubdirData._load(), which ends up calling conda.core.subdir_data.fetch_repodata_remote_request() to process the request. Finally, SubdirData._process_raw_repodata_str() does the parsing and loading.

Internally, the SubdirData stores all the package metadata as a list of PackageRecord objects. Its main usage is via .query() (one result at a time) or .query_all() (all possible matches). These .query* methods accept spec strings (e.g. numpy =1.14), MatchSpec and PackageRecord instances. Alternatively, if you want all records with no queries, use SubdirData.iter_records().

Tricks to reduce the size of the index

conda supports the notion of trying with different versions of the index in an effort to minimize the solution space. A smaller index means a faster search, after all! The default logic starts with current_repodata.json files in the channel, which contain only the latest versions of each package plus their dependencies. If that fails, then the full repodata.json is used. This happens before the Solver is even invoked.

The second trick is done within the solver logic: an informed index reduction. In essence, the index (whether it’s current_repodata.json or full repodata.json) is pruned by the solver, trying to keep only the parts that it anticipates will be needed. More details can be found on the get_reduced_index function. Interestingly, this optimization step also takes longer the bigger the index gets.

Channel priorities

context.channels returns an IndexedSet of Channel objects; essentially a list of unique items. The different channels in this list can have overlapping or even conflicting information for the same package name. For example, defaults and conda-forge will for sure contain packages that fullfil the conda install numpy request. Which one is chosen by conda in this case? It depends on the context.channel_priority setting: From the help message:

Accepts values of ‘strict’, ‘flexible’, and ‘disabled’. The default value is ‘flexible’. With strict channel priority, packages in lower priority channels are not considered if a package with the same name appears in a higher priority channel. With flexible channel priority, the solver may reach into lower priority channels to fulfill dependencies, rather than raising an unsatisfiable error. With channel priority disabled, package version takes precedence, and the configured priority of channels is used only to break ties.

In practice, channel_priority=strict is often the recommended setting for most users. It’s faster to solve and causes fewer problems down the line. Check more details here.

Solving the install request

At this point, we can start asking the solver things. After all, we have loaded the channels into our index, building the catalog of available packages and versions we can install. We also have the command line instructions and configurations needed to customize the solver request. So, let’s just do it: “Solver, please install numpy on this prefix using these channels as package sources”.

The details are complicated, but in essence, the Solver will:

1. Express the requested packages, command line options and prefix state as MatchSpec objects

2. Query the index for the best possible match that satisfy those constraints

3. Return a list of PackageRecord objects

The full details are covered in Solvers if you are curious. Just keep in mind that point (1) is conda-specific, while (2) can be tackled, in principle, by any SAT solver.

Generating the transaction and the corresponding actions

The Solver API defines three public methods:

• .solve_final_state(): this is the core function, described in the section above. Given some input state, it returns an IndexedSet of PackageRecord objects that reflect what the final state of the environment should look like. This is the largest method, and its details are fully covered here.

• .solve_for_diff(): this method takes the final state and diffs it with the current state of the environment, discovering which old records need to be removed, and which ones need to be added.

• .solve_for_transaction(): this method takes the diff and creates a Transaction object for this operation. This is what the main CLI logic expects back from the solver.

So what is a Transaction object and why is it needed? Transactional actions were introduced in conda 4.3. They seem to be the last iteration of a set of changes designed to check whether conda would be able to download and link the needed packages (e.g. check that there is enough space on disk, whether the user has enough permissions for the target paths, etc.). For more info, refer to PRs #3571, #3301, and #3034.

The transaction is essentially a set of action objects. Each action is allowed to run some checks to determine whether it can be executed successfully. If that’s not the case, the failed checks will signal the parent transaction that the whole operation needs to be aborted and rolled back to leave things in the state they were before running that conda command. It is also responsible for some of the messages you will see in the CLI output, like the reports of what will be installed, updated or removed.

Transactions and parallelism

Since the transaction object knows about all the actions that need to happen, it also enables parallelism for verifying, downloading and (un)linking tasks. The level of parallelism can be changed through the following context settings:

• default_threads

• verify_threads

• execute_threads

• repodata_threads

• fetch_threads

There’s only one class of transaction in conda: LinkUnlinkTransaction. It only accepts one input parameter: a list of PrefixSetup objects, which are just namedtuple objects with the followiing fields. These are populated by Solver.solve_for_transaction after running Solver.solve_for_diff:

• target_prefix: the environment path the command is running on.

• unlink_precs: PackageRecord objects that need to be unlinked (removed).

• link_precs: PackageRecord objects that need to be linked (added).

• remove_specs: MatchSpec objects that need to be marked as removed in the history (the user asked for these packages to be uninstalled).

• update_specs: MatchSpec objects that need to be marked as added in the history (the user asked for these packages to be installed or updated).

• neutered_specs: MatchSpec objects that were already in history but had to be relaxed in order to avoid solving conflicts.

Whatever happens after instantiation depends on the content of these PrefixSetup objects. Sometimes, the transaction results in no actions (see the nothing_to_do property) because the request asked by the user is already fulfilled by the current state of the environment.

However, most of the time the transaction will involve a number of actions. This is done via two public methods:

• download_and_extract(): essentially a forwarder to instantiate and call ProgressiveFetchExtract, responsible for deciding which PackageRecords need to be downloaded and extracted to the packages cache.

• execute(): the core logic is layed out here. It involves preparing, verifying and performing the rest of the actions. Among others:

• Unlinking packages (removing a package from the environment)

• Compiling bytecode (generating the pyc counterpart for each py module)

• Adding entry points (generate command line executables for the configured functions)

• Adding the JSON records (for each package, a JSON file is added to conda-meta/)

• Make menu items (create shortcuts for packages featuring a JSON file under Menu/)

• Remove menu items (remove the shortcuts created by that package)

It’s important to notice that download and extraction happen separately from all the other actions. This separation is important and core to the idea of what a conda environment is. Essentially, when you create a new conda environment, you are not necessarily copying files over to the target prefix location. Instead, conda maintains a cache of every package ever downloaded to disk (both the tarball and the extracted contents). To save space and speed up environment creation and deletion, files are not copied over, but instead they are linked (usually via a hardlink). That’s why these two tasks are separated in the transaction logic: you don’t need to download and extract packages that are already in the cache; you only need to link them!

Transactions also drive reports

The type and number of actions can also be calculated by _make_legacy_action_groups(), which returns a list of action groups (one per PrefixSetup). Each action group is a just a dictionary following this specification:

{
"FETCH": Iterable[PackageRecord],  # estimated by ProgressiveFetchExtract
"PREFIX": str,
}


These simpler action groups are only used for reporting, either via a processed text report (via print_transaction_summary) or just the raw JSON (via stdout_json_success). As you can see, they do not know anything about other types of tasks.

conda maintains a cache of downloaded tarballs and their extracted contents to save disk space and improve the performance of environment modifications. This requires some code to check whether a given PackageRecord is already present in the cache, and, if it’s not, how to download the tarball and extract its contents in a performant way. This is all handled by the ProgressiveFetchExtract class, which can instantiate up to two Action objects for each passed PackageRecord:

• CacheUrlAction: downloads (if remote) or copies (if local) a tarball to the cache location.

• ExtractPackageAction: extracts the contents of the tarball.

These two actions only take place if the package is not in cache yet and if it has already been extracted, respectively. They can also revert the changes if the transaction is aborted (either due to an error or because the user pressed Ctrl+C).

Populating the prefix

When all the necessary packages have been downloaded and extracted to the cache, it is time to start populating the prefix with the needed files. This means we need to:

1. For each package that needs to be unlinked, run the pre-unlink logic (deactivate and pre-unlink scripts, as well as shortcut removal, if needed) and then unlink the package files.

2. For each package that needs to be linked, create the links and run the post-link logic (post-link and activate scripts, as well as creating the shortcuts, if needed).

Note that when you are updating a package version, you are actually removing the installed version entirely and then adding the new one. In other words, an update is just unlink+link.

How is this implemented? For each PrefixSetup object passed to UnlinkLinkTransaction, a number of ActionGroup namedtuples (one per task category) will be instantiated and grouped together in a PrefixActionGroup namedtuple. These are then passed to .verify(). This method will take each action, run its checks and, if all of them passed, will allow us to perform the actual execution in .execute(). If one of them fails, the transaction can be aborted and rolled back.

For all this to work, each action object follows the PathAction API contract:

class PathAction:
_verified = False

def verify(self):
"Run checks to assess if the action can proceed"

def execute(self):
"Perform the action"

def reverse(self):
"Undo execute"

def cleanup(self):
"Remove artifacts from verification, execution or reversal"

@property
def verified(self):
"True if verification was run and successful"


Additional PathAction subclasses will add more methods and properties, but this is what the transaction execution logic expects. To support all the different actions involved in populating the prefix, the PathAction class tree holds quite the graph:

PathAction
PrefixPathAction
CreateInPrefixPathAction
CreatePythonEntryPointAction
CreatePrefixRecordAction
UpdateHistoryAction
RemoveFromPrefixPathAction
RegisterEnvironmentLocationAction
UnregisterEnvironmentLocationAction
CacheUrlAction
ExtractPackageAction

MultiPathAction
CompileMultiPycAction
AggregateCompileMultiPycAction



You are welcome to read on the docstring for each of those classes to understand which each one is doing; all of them are listed under conda.core.path_actions. In the following sections, we will only comment on the most important ones.

Linking the files in the environment

When conda links a file from the cache location to the prefix location, it can actually mean three different actions:

3. Copying the file

The difference between soft links and hard links is subtle, but important. You can find more info on the differences elsewhere (e.g. here), but for our purposes it means that:

• Hard links are cheaper to resolve, behave like a real file, but can only link files in the same mount point.

• Soft links can link files across mount points, but they don’t behave exactly like files (more like forwarders), so it’s possible that they break assumptions made in certain pieces of code.

Most of the time, conda will try to hard link files and, if that fails, it will copy them over. Copying a file is an expensive disk operation, both in terms of time and space, so it should be the last option. However, sometimes it’s the only way. Especially, when the file needs to be modified to be used in the target prefix.

Ummm… what? Why would conda modify a file to install it? This has to do with relocatability. When a conda package is created, conda-build creates up to three temporary environments:

• Build environment: where compilers and other build tools are installed, separate from the host environment to support cross-compilation.

• Host environment: where build-time dependencies are installed, together with the package you are building.

• Test environment: where run-time dependencies are installed, together with the package you just built. It simulates what will happen when a user installs the package so you can run arbitrary checks on your package.

When you are building a package, references to the build-time paths can leak into the content of some files, both text and binary. This is not a problem for users who build their own packages from source, since they can choose this path and leave the files there. However, this is almost never true for conda packages. They are created in one machine and installed in another. To avoid “path not found” issues and other problems, conda-build marks those packages that hold references to the build-time paths by replacing them with placeholders. At install-time, conda will replace those placeholders with the target prefix and everything works!

But there’s a problem: we can’t modify the files on the cache location because they might be used across environments (with obviously different paths). In these cases, files are not linked, but copied; the path replacement only happens on the target copy, of course!

How does conda know how to link a given package or, more precisely, its extracted files? All of this is determined in the preparation routines contained in UnlinkLinkTransaction._prepare() (more specifically, through determine_link_type()), as well as LinkPathAction.create_file_link_actions().

Action groups and actions, in detail

Once the old packages have been removed and the new ones have been linked through the appropriate means, we are done, right? Not yet! There’s one step left: the post-linking logic.

It turns out that there’s a number of smaller tasks that need to happen to make conda as convenient as it is. You can find all of them listed a few paragraphs above, but we’ll cover them here, too. The execution order is determined in UnlinLinkTransaction._execute. All the possible groups are listed under PrefixActionGroup. Their order is roughly how they happen in practice:

1. remove_menu_action_groups, composed of RemoveMenuAction actions.

2. unlink_action_groups, includes UnlinkPathAction, RemoveLinkedPackageRecordAction, as well as the logic to run the pre- and post-unlink scripts.

3. unregister_action_groups, basically a single UnregisterEnvironmentLocationAction action.

4. link_action_groups, includes LinkPathAction, PrefixReplaceLinkAction, as well as the logic to run pre- and post-link scripts.

5. entry_point_action_groups, a collection of CreatePythonEntryPointAction actions.

6. register_action_groups, a single RegisterEnvironmentLocationAction action.

7. compile_action_groups, several CompileMultiPycAction that end up aggregated as a AggregateCompileMultiPycAction for performance.

8. make_menu_action_groups, composed of MakeMenuAction actions.

9. prefix_record_groups, records installed packages in the environment via CreatePrefixRecordAction actions.

Let’s discuss these actions groups for the command we are describing in this guide: conda install numpy. The solution given by the solver says we need to:

This is what would happen:

1. No menu items are removed because Python 3.9.6 didn’t create any.

2. Pre-unlink scripts for Python 3.9.6 would run, but in this case there are none.

3. Python 3.9.6 files are removed from the environment. This can be parallelized.

4. Post-unlink scripts are run, if any.

5. Pre-link scripts are run for Python 3.9.9 and numpy 1.19, if any.

6. Files in the Python 3.9.9 and numpy 1.19 packages are linked and/or copied to the prefix. This can be parallelized.

7. Entry points are created for the new packages, if any.

9. pyc files are generated for the new packages.

10. The new packages are registered under conda-meta/.

11. The menu shortcuts are created for the new packages, if any.

Any of these steps can fail with a given exception. If that’s the case, the first of those exceptions is printed to STDOUT. Additionally, if rollback_enabled is properly configured in the context, the transaction will be rolled back by calling the .reverse() method in each action, from last to first.

If no exceptions are reported, then the actions can run their cleanup routines.

And that’s it! If this command had resulted in a new environment being created, you would get a message telling you how to activate the newly created environment.

Conclusion

This is what happens when you type conda install. It might be a bit more involved than you initially thought, but it all boils down to only some steps. TL;DR:

1. Parse arguments and initialize the context