* add support for tracking remote dataset
This adds support for virtually tracking a
dataset such as dvcx dataset, dataset from remote dvc/git
registries, and cloud-versioned remotes.
This PR introduces two different commands under `ds` namespace:
`add` and `update`.
`dvc ds add` command adds the dataset to `dvc.yaml` and tracks
the sources in `dvc.lock` file. Similarly, `dvc ds update`
updates the sources in `dvc.lock` file.
Example Usage:
```console
dvc ds add --name dogs --url dvcx://dogs # tracking dvcx dataset
# tracking dvc/git registries
dvc ds add --name example --url dvc://[email protected]/iterative/example.git
# cloud-versioned-remotes
dvc ds add --name versioning --url s3://cloud-versioning-demo
```
To update, specify the name.
```console
dvc ds update <name>
```
`dvc ds add` freezes (in the traditional sense of `dvc import/import-url`).
It keeps a "specification" of the dataset in `dvc.yaml` and also freezes
information about the dataset in the `dvc.lock` file. They are kept
inside `datasets` section in both `dvc.yaml` and `dvc.lock` files.
This metadata is used in the pipelines. You can add a dependency to your
stage using `ds://` scheme, followed by the name of the dataset in
`dvc.yaml` file. As it is used in pipelines, the `name` of the dataset
has to be unique in the repository. Different dataset of same names
are not allowed. On `dvc repro`, `dvc` copies the frozen information
about the particular dataset into the `deps` field for the stage in
`dvc.lock`. On successive invocation, `dvc` will compare the information
from the deps field of the lock with the frozen information in the
`datasets` section and decides whether to rerun or not.
As the dataset is frozen, `dvc repro` won't rerun until the dataset is
updated via `dvc update`.
Here are some examples for how `dvc.yaml` looks like:
```yaml
datasets:
- name: dogs
url: dvcx://dogs
type: dvcx
- name: example-get-started
url: [email protected]:iterative/example-get-started.git
type: dvc
path: path
- name: dogs2
url: dvcx://dogs@v2
type: dvcx
- name: cloud-versioning-demo
url: s3://cloud-versioning-demo
type: url
```
```yaml
schema: '2.0'
datasets:
- name: dogs
url: dvcx://dogs
type: dvcx
version: 3
created_at: '2023-12-11T10:32:05.942708+00:00'
- name: example-get-started
url: [email protected]:iterative/example-get-started.git
type: dvc
path: path
rev_lock: df75c16ef61f0772d6e4bb27ba4617b06b4b5398
- name: cloud-versioning-demo
url: s3://cloud-versioning-demo
type: url
meta:
isdir: true
size: 323919
nfiles: 33
files:
- relpath: myproject/model.pt
meta:
size: 106433
version_id: 5qrtnhnQ4fBzV73kqqK6pMGhTOzd_IPr
etag: 3bc0028677ce6fb65bec8090c248b002
# truncated
```
The pipeline stages keep them in `dataset` section of individual deps.
```console
dvc stage add -n train -d ds://dogs python train.py
cat dvc.yaml
```
```yaml
# truncated
stages:
train:
cmd: python train.py
deps:
- ds://dogs
```
When it is reproduced, the `dvc.lock` will look something like follows:
```yaml
# truncated
stages:
train:
cmd: python train.py
deps:
- path: ds://dogs
dataset:
name: dogs
url: dvcx://dogs
type: dvcx
version: 3
created_at: '2023-12-11T10:32:05.942708+00:00'
```
* remodeling
* remove non-cloud-versioned remotes
* improve deserializing; handle invalidation
* dvc.api for dataset
* expose the API in the top-level
* add support for tracking remote dataset
This adds support for virtually tracking a
dataset such as dvcx dataset, dataset from remote dvc/git
registries, and cloud-versioned remotes.
This PR introduces two different commands under `ds` namespace:
`add` and `update`.
`dvc ds add` command adds the dataset to `dvc.yaml` and tracks
the sources in `dvc.lock` file. Similarly, `dvc ds update`
updates the sources in `dvc.lock` file.
Example Usage:
```console
dvc ds add --name dogs --url dvcx://dogs # tracking dvcx dataset
# tracking dvc/git registries
dvc ds add --name example --url dvc://[email protected]/iterative/example.git
# cloud-versioned-remotes
dvc ds add --name versioning --url s3://cloud-versioning-demo
```
To update, specify the name.
```console
dvc ds update <name>
```
`dvc ds add` freezes (in the traditional sense of `dvc import/import-url`).
It keeps a "specification" of the dataset in `dvc.yaml` and also freezes
information about the dataset in the `dvc.lock` file. They are kept
inside `datasets` section in both `dvc.yaml` and `dvc.lock` files.
This metadata is used in the pipelines. You can add a dependency to your
stage using `ds://` scheme, followed by the name of the dataset in
`dvc.yaml` file. As it is used in pipelines, the `name` of the dataset
has to be unique in the repository. Different dataset of same names
are not allowed. On `dvc repro`, `dvc` copies the frozen information
about the particular dataset into the `deps` field for the stage in
`dvc.lock`. On successive invocation, `dvc` will compare the information
from the deps field of the lock with the frozen information in the
`datasets` section and decides whether to rerun or not.
As the dataset is frozen, `dvc repro` won't rerun until the dataset is
updated via `dvc update`.
Here are some examples for how `dvc.yaml` looks like:
```yaml
datasets:
- name: dogs
url: dvcx://dogs
type: dvcx
- name: example-get-started
url: [email protected]:iterative/example-get-started.git
type: dvc
path: path
- name: dogs2
url: dvcx://dogs@v2
type: dvcx
- name: cloud-versioning-demo
url: s3://cloud-versioning-demo
type: url
```
```yaml
schema: '2.0'
datasets:
- name: dogs
url: dvcx://dogs
type: dvcx
version: 3
created_at: '2023-12-11T10:32:05.942708+00:00'
- name: example-get-started
url: [email protected]:iterative/example-get-started.git
type: dvc
path: path
rev_lock: df75c16ef61f0772d6e4bb27ba4617b06b4b5398
- name: cloud-versioning-demo
url: s3://cloud-versioning-demo
type: url
meta:
isdir: true
size: 323919
nfiles: 33
files:
- relpath: myproject/model.pt
meta:
size: 106433
version_id: 5qrtnhnQ4fBzV73kqqK6pMGhTOzd_IPr
etag: 3bc0028677ce6fb65bec8090c248b002
# truncated
```
The pipeline stages keep them in `dataset` section of individual deps.
```console
dvc stage add -n train -d ds://dogs python train.py
cat dvc.yaml
```
```yaml
# truncated
stages:
train:
cmd: python train.py
deps:
- ds://dogs
```
When it is reproduced, the `dvc.lock` will look something like follows:
```yaml
# truncated
stages:
train:
cmd: python train.py
deps:
- path: ds://dogs
dataset:
name: dogs
url: dvcx://dogs
type: dvcx
version: 3
created_at: '2023-12-11T10:32:05.942708+00:00'
```
* remodeling
* remove non-cloud-versioned remotes
* improve deserializing; handle invalidation
This was introduced in Python 3.9. The change makes
dict merges 'obvious' and looks cleaner.
I haven't changed `.update()` to `|=` (update operator),
as I find the former to be more readable and "obvious".
The code are wrapped due to us using line-length=79 before, but now some
of the code can easily fit in 88 chars.
The formatter did not change this automatically when we changed line-length,
because of magic trailing commas that gets added when they turn into multiple lines.
Black/ruff wrapping them in parens and splitting them into multiple lines
was making it difficult to read.
* drop Python 3.8
Note that this PR only makes typing related changes.
Other changes can be done once this gets merged, they
are relatively few in numbers.
* bump build python version
* add typing_extensions to lint group
* experimental: support connection strings for databases using connectorx
This adds an experimental support for connector strings using connectorx.
User has to provide config for the connection strings with:
Example:
```console
dvc config --local db_connection.pg postgres://user:pass@localhost:5432/database
```
And then, they can import using:
```console
dvc import-db --sql 'query' --conn pg
```
Check supported databases and how to configure in https://sfu-db.github.io/connector-x/databases.html.
This `db_connection` config is subject to change.
connectorx still has the same problem with dbt as before: no support for streaming,
but it'll be a bit more simpler to configure with the connection strings.
* add pandas to deps
* sqlalchemy
* dbt streaming sql
* fix test
* remove connectorx
* remove check in set
* use mode='wb' for serialization
We should not register a pytest plugin automaticallly, as we may
have fixtures that might override user's fixtures. Also, it might
raise import errors as in https://github.com/iterative/dvc/issues/10118.
We can still use this as a plugin ourselves by setting the following
in `conftest.py` file.
```python
pytest_plugins = ['dvc.testing.plugin']
```
* remove pytest-virtualenv
It does not look like it's being actively maintained.
* force virtualenv
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
__name__ looks something like `dvc.commands.diff`, but if we are already instantiating
root logger with `dvc`, it adds up to create something like `dvc.dvc.commands.diff`.
We should be using a root logger here instead.
* use logger.getChild from `dvc.log` instead of `logging.getLogger`
I got tired adding `# type: ignore[attr-defined]` while using `logger.trace`.
So, now you need to import logger from `from dvc.log import logger` and
create a child logger using `logger.getChild(__name__)`. `logger.getChild`
will return a logger type-hinted as a logger having a `trace` method.
So, from now on, instead of:
```python
import logging
logger = logging.getogger(__name__)
```
you'll have to do following:
```python
from dvc.log import logger
logger = logger.getChild(__name__)
```
* remove all dvc imports from dvc.logger
* use Tqdm back
See https://docs.python.org/3/library/sys.html#sys.__stdin__.
> [!Note]
> Under some conditions stdin, stdout and stderr as well as the original values __stdin__, __stdout__ and __stderr__ can be None. It is usually the case for Windows GUI apps that aren’t connected to a console and Python apps started with pythonw.
This is a best-effort solution. External libraries (eg: `tqdm`) and future code that is unaware of this stuff may still fail.
As a console application, `stdout`/`stderr` being `None` is not really something that we should support in general.
Regarding the failure of the daemon, it has been fixed so that it'll always have a stream attached (if not, it falls back to
/dev/null). Today, this is a problem in daemon only if the main process is run with no streams attached, which is not
something that we support or go on length to support.
It is tricky to write tests for this case, and I could not come up with one. Also decided not to test daemon as it should no longer be a problem.
* use popen to run updater/analytics in detached mode
subprocess does not provide a way to avoid a zombie process. Not to confuse with orphaned processes, which will be `wait`-ed by `pid 1`.
It might not work for long-running processes, but I don't think zombie processes are going to be a problem for us, as the parent process immediately exits after spawning a child process.
Even in case of `queue` based experiment runner, parent will exit before than the child process. So I don't think it's a requirement here. But it does feel wrong to leak the processes, so I have to sleep on that.
---
This PR:
1. makes `daemonized` process inherit the logging verbosity, so if the parent process used `-vv`, the daemonized process will run the same way.
2. Add `DVC_DAEMON_LOGFILE` envvar, that the daemon will log to the file. By default, the verbosity is the same as running parent processes. Without `DVC_DAEMON_LOGFILE`, the output will be redirected to `/dev/null`.
3. Adds debug/trace for updater/analytics.
I hope by doing this, we can make our life easier on debugging and make daemon more transparent/observable.
* add tests
* fixes
* use kwargs
* pass all os.environ with some modified
`dvc commit --help` was also completely broken, which is also fixed.
This adds unit tests for completion generation for both zsh/bash, and
tests that --help works for all commands.
* repro: introduce --keep-going/--ignore to continue on error
`-k`/`--keep-going` will skip all dependents of a stage if it fails.
It'll still try to reproduce the other dependencies of the pending
targets, before it exits with a non-zero status.
`--ignore-errors` will treat any error during stage execution as a
success, and will continue executing other dependencies as if it
succeeded. Exits with 0 even on failure.
Also fixes:
* --force-downstream used to force everything even if one upstream nodes changed.
This used to work when we supported a single targets, but there could be independent
pipelines, so we should not force everything if any node changes.
Now we'll force reproduce the stage only if one of its dependency changed upstream
or it's upstream node was also forced.
* Makes `--downstream` more correct, in relation to `--allow-missing` and `--pull`.
Now we use an active graph for downstream/upstream logic instead of a subgraph.
Note that this does not affect the downstream logic though, as dvc always goes
downstream, so subgraph was no different than active graph.
The subgraph only did not have the upstream nodes.
* add tests
* add spacing in the message
The logic is now moved to experiments, where it tracks all the stage related files
itself. The `_get_stage_files()` has been modified to also return lockfile path,
so that it can be staged, and has been moved to `stage/utils.py`, and out of `reproduce.py`.
Fixes#9688.
* repro: make stages run in order of the graph.
In case of --single-item, this means it'll run in the order of arguments passed.
For --downstream, this fixes running of stage out of order when multiple arguments are passed.
For normal case, this enforces the same logic, so that they are never run out-of-order.
* add tests
* tests: repro: add test for --single-item with multiple targets
* graph: support when no nodes are provided
skshetry
