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Merge pull request #1387 from iterative/remove_external_local
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docs: use `--external` for `add/run`
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jorgeorpinel authored Jun 22, 2020
2 parents b9d7b16 + 16bb137 commit f76b7d7
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5 changes: 2 additions & 3 deletions content/docs/command-reference/get.md
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Expand Up @@ -96,12 +96,11 @@ model.pkl

Note that the `model.pkl` file doesn't actually exist in the
[root directory](https://github.com/iterative/example-get-started/tree/master/)
of the external Git repo. Instead, it's exported in the
of the source Git repo. Instead, it's exported in the
[`dvc.yaml`](https://github.com/iterative/example-get-started/blob/master/dvc.yaml)
file as an output of the `train` stage (in the `outs` field). DVC then
[pulls](/doc/command-reference/pull) the file from the default
[remote](/doc/command-reference/remote) of the external DVC project (found in
its
[remote](/doc/command-reference/remote) of the source DVC project (found in its
[config file](https://github.com/iterative/example-get-started/blob/master/.dvc/config)).

> A recommended use for downloading binary files from DVC repositories, as done
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3 changes: 3 additions & 0 deletions content/docs/command-reference/run.md
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Expand Up @@ -240,6 +240,9 @@ $ dvc run -n my_stage './my_script.sh $MYENVVAR'
> Note that DVC-files without dependencies are automatically considered
> "always changed", so this option has no effect in those cases.
- `--external` - allow outputs that are outside of the DVC repository. See
[Managing External Data](/doc/user-guide/managing-external-data).

- `-h`, `--help` - prints the usage/help message, and exit.

- `-q`, `--quiet` - do not write anything to standard output. Exit with 0 if no
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15 changes: 10 additions & 5 deletions content/docs/user-guide/managing-external-data.md
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Expand Up @@ -52,8 +52,9 @@ The default local cache location is `.dvc/cache`, so there is no need to specify
it explicitly.

```dvc
$ dvc add /home/shared/mydata
$ dvc add /home/shared/mydata --external
$ dvc run -d data.txt \
--external \
-o /home/shared/data.txt \
cp data.txt /home/shared/data.txt
```
Expand All @@ -68,10 +69,11 @@ $ dvc remote add sshcache ssh://[email protected]:/cache
$ dvc config cache.ssh sshcache
# Add data on SSH directly
$ dvc add ssh://[email protected]:/mydata
$ dvc add ssh://[email protected]:/mydata --external
# Create the stage with external SSH output
$ dvc run -d data.txt \
--external \
-o ssh://[email protected]:/home/shared/data.txt \
scp data.txt [email protected]:/home/shared/data.txt
```
Expand All @@ -86,10 +88,11 @@ $ dvc remote add s3cache s3://mybucket/cache
$ dvc config cache.s3 s3cache
# Add data on S3 directly
$ dvc add s3://mybucket/mydata
$ dvc add s3://mybucket/mydata --external
# Create the stage with external S3 output
$ dvc run -d data.txt \
--external \
-o s3://mybucket/data.txt \
aws s3 cp data.txt s3://mybucket/data.txt
```
Expand All @@ -104,10 +107,11 @@ $ dvc remote add gscache gs://mybucket/cache
$ dvc config cache.gs gscache
# Add data on GS directly
$ dvc add gs://mybucket/mydata
$ dvc add gs://mybucket/mydata --external
# Create the stage with external GS output
$ dvc run -d data.txt \
--external \
-o gs://mybucket/data.txt \
gsutil cp data.txt gs://mybucket/data.txt
```
Expand All @@ -122,10 +126,11 @@ $ dvc remote add hdfscache hdfs://[email protected]/cache
$ dvc config cache.hdfs hdfscache
# Add data on HDFS directly
$ dvc add hdfs://[email protected]/mydata
$ dvc add hdfs://[email protected]/mydata --external
# Create the stage with external HDFS output
$ dvc run -d data.txt \
--external \
-o hdfs://[email protected]/home/shared/data.txt \
hdfs fs -copyFromLocal \
data.txt \
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2 changes: 1 addition & 1 deletion content/docs/user-guide/what-is-dvc/index.md
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Expand Up @@ -7,7 +7,7 @@ between existing tools and data science needs, allowing users to take advantage
of experiment management while reusing existing skills and intuition.

Leveraging an underlying source code management system eliminates the need to
use external services. Data science experiment sharing and collaboration can be
use 3rd-party services. Data science experiment sharing and collaboration can be
done through regular Git features (commit messages, merges, pull requests, etc)
the same way it works for software engineers.

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