Migrate users, workspaces and datasets to Argilla 2.x¶
This guide will help you migrate task to Argilla V2. These do not include the FeedbackDataset
which is just an interim naming convention for the latest extensible dataset. Task-specific datasets are datasets that are used for a specific task, such as text classification, token classification, etc. If you would like to learn about the backstory of SDK this migration, please refer to the SDK migration blog post. Additionally, we will provide guidance on how to maintain your User
's and Workspace
's within the new Argilla V2 format.
Note
Legacy datasets include: DatasetForTextClassification
, DatasetForTokenClassification
, and DatasetForText2Text
.
FeedbackDataset
's do not need to be migrated as they are already in the Argilla V2 format. Anyway, since the 2.x version includes changes to the search index structure, you should reindex the datasets by enabling the docker environment variable REINDEX_DATASET (This step is automatically executed if you're running Argilla in an HF Space). See the server configuration docs section for more details.
To follow this guide, you will need to have the following prerequisites:
- An argilla 1.* server instance running with legacy datasets.
- An argilla >=1.29 server instance running. If you don't have one, you can create one by following this Argilla guide.
- The
argilla
sdk package installed in your environment.
Warning
This guide will recreate all User
's' and Workspace
's' on a new server. Hence, they will be created with new passwords and IDs. If you want to keep the same passwords and IDs, you can can copy the datasets to a temporary v2 instance, then upgrade your current instance to v2.0 and copy the datasets back to your original instance after.
If your current legacy datasets are on a server with Argilla release after 1.29, you could chose to recreate your legacy datasets as new datasets on the same server. You could then upgrade the server to Argilla 2.0 and carry on working their. Your legacy datasets will not be visible on the new server, but they will remain in storage layers if you need to access them.
For migrating the guides you will need to install the new argilla
package. This includes a new v1
module that allows you to connect to the Argilla V1 server.
Migrate Users and Workspaces¶
The guide will take you through two steps:
- Retrieve the old users and workspaces from the Argilla V1 server using the new
argilla
package. - Recreate the users and workspaces on the Argilla V2 server based op
name
as unique identifier.
Step 1: Retrieve the old users and workspaces¶
You can use the v1
module to connect to the Argilla V1 server.
import argilla.v1 as rg_v1
# Initialize the API with an Argilla server less than 2.0
api_url = "<your-url>"
api_key = "<your-api-key>"
rg_v1.init(api_url, api_key)
Next, load the dataset User
and Workspaces
and from the Argilla V1 server:
Step 2: Recreate the users and workspaces¶
To recreate the users and workspaces on the Argilla V2 server, you can use the argilla
package.
First, instantiate the Argilla
class to connect to the Argilla V2 server:
Next, recreate the users and workspaces on the Argilla V2 server:
for user in users_v1:
user = rg.User(
username=user.username,
first_name=user.first_name,
last_name=user.last_name,
role=user.role,
password="<your_chosen_password>" # (1)
).create()
if user.role == "owner":
continue
for workspace_name in user.workspaces:
if workspace_name != user.name:
workspace = client.workspaces(name=workspace_name)
user.add_to_workspace(workspace)
- You need to chose a new password for the user, to do this programmatically you can use the
uuid
package to generate a random password. Take care to keep track of the passwords you chose, since you will not be able to retrieve them later.
Now you have successfully migrated your users and workspaces to Argilla V2 and can continue with the next steps.
Migrate datasets¶
The guide will take you through three steps:
- Retrieve the legacy dataset from the Argilla V1 server using the new
argilla
package. - Define the new dataset in the Argilla V2 format.
- Upload the dataset records to the new Argilla V2 dataset format and attributes.
Step 1: Retrieve the legacy dataset¶
You can use the v1
module to connect to the Argilla V1 server.
import argilla.v1 as rg_v1
# Initialize the API with an Argilla server less than 2.0
api_url = "<your-url>"
api_key = "<your-api-key>"
rg_v1.init(api_url, api_key)
Next, load the dataset settings and records from the Argilla V1 server:
dataset_name = "news-programmatic-labeling"
workspace = "demo"
settings_v1 = rg_v1.load_dataset_settings(dataset_name, workspace)
records_v1 = rg_v1.load(dataset_name, workspace)
hf_dataset = records_v1.to_datasets()
Your legacy dataset is now loaded into the hf_dataset
object.
Step 2: Define the new dataset¶
Define the new dataset in the Argilla V2 format. The new dataset format is defined in the argilla
package. You can create a new dataset with the Settings
and Dataset
classes:
First, instantiate the Argilla
class to connect to the Argilla V2 server:
Next, define the new dataset settings:
settings = rg.Settings(
fields=[
rg.TextField(name="text"), # (1)
],
questions=[
rg.LabelQuestion(name="label", labels=settings_v1.label_schema),
],
metadata=[
rg.TermsMetadataProperty(name="split"), # (2)
],
vectors=[
rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384), # (3)
],
)
- The default field in
DatasetForTextClassification
istext
, but make sure you provide all fields included inrecord.inputs
. - Make sure you provide all relevant metadata fields available in the dataset.
- Make sure you provide all relevant vectors available in the dataset.
settings = rg.Settings(
fields=[
rg.TextField(name="text"), # (1)
],
questions=[
rg.MultiLabelQuestion(name="labels", labels=settings_v1.label_schema),
],
metadata=[
rg.TermsMetadataProperty(name="split"), # (2)
],
vectors=[
rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384), # (3)
],
)
- The default field in
DatasetForTextClassification
istext
, but we should provide all fields included inrecord.inputs
. - Make sure you provide all relevant metadata fields available in the dataset.
- Make sure you provide all relevant vectors available in the dataset.
settings = rg.Settings(
fields=[
rg.TextField(name="text"),
],
questions=[
rg.SpanQuestion(name="spans", labels=settings_v1.label_schema),
],
metadata=[
rg.TermsMetadataProperty(name="split"), # (1)
],
vectors=[
rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384), # (2)
],
)
- Make sure you provide all relevant metadata fields available in the dataset.
- Make sure you provide all relevant vectors available in the dataset.
settings = rg.Settings(
fields=[
rg.TextField(name="text"),
],
questions=[
rg.TextQuestion(name="text_generation"),
],
metadata=[
rg.TermsMetadataProperty(name="split"), # (1)
],
vectors=[
rg.VectorField(name='mini-lm-sentence-transformers', dimensions=384), # (2)
],
)
- We should provide all relevant metadata fields available in the dataset.
- We should provide all relevant vectors available in the dataset.
Finally, create the new dataset on the Argilla V2 server:
Note
If a dataset with the same name already exists, the create
method will raise an exception. You can check if the dataset exists and delete it before creating a new one.
Step 3: Upload the dataset records¶
To upload the records to the new server, we will need to convert the records from the Argilla V1 format to the Argilla V2 format. The new argilla
sdk package uses a generic Record
class, but legacy datasets have specific record classes. We will need to convert the records to the generic Record
class.
Here are a set of example functions to convert the records for single-label and multi-label classification. You can modify these functions to suit your dataset.
def map_to_record_for_single_label(data: dict, users_by_name: dict, current_user: rg.User) -> rg.Record:
""" This function maps a text classification record dictionary to the new Argilla record."""
suggestions = []
responses = []
if prediction := data.get("prediction"):
label, score = prediction[0].values()
agent = data["prediction_agent"]
suggestions.append(
rg.Suggestion(
question_name="label", # (1)
value=label,
score=score,
agent=agent
)
)
if annotation := data.get("annotation"):
user_id = users_by_name.get(data["annotation_agent"], current_user).id
responses.append(
rg.Response(
question_name="label", # (2)
value=annotation,
user_id=user_id
)
)
return rg.Record(
id=data["id"],
fields=data["inputs"],
# The inputs field should be a dictionary with the same keys as the `fields` in the settings
metadata=data["metadata"],
# The metadata field should be a dictionary with the same keys as the `metadata` in the settings
vectors=data.get("vectors") or {},
suggestions=suggestions,
responses=responses,
)
-
Make sure the
question_name
matches the name of the question in question settings. -
Make sure the
question_name
matches the name of the question in question settings.
def map_to_record_for_multi_label(data: dict, users_by_name: dict, current_user: rg.User) -> rg.Record:
""" This function maps a text classification record dictionary to the new Argilla record."""
suggestions = []
responses = []
if prediction := data.get("prediction"):
labels, scores = zip(*[(pred["label"], pred["score"]) for pred in prediction])
agent = data["prediction_agent"]
suggestions.append(
rg.Suggestion(
question_name="labels", # (1)
value=labels,
score=scores,
agent=agent
)
)
if annotation := data.get("annotation"):
user_id = users_by_name.get(data["annotation_agent"], current_user).id
responses.append(
rg.Response(
question_name="labels", # (2)
value=annotation,
user_id=user_id
)
)
return rg.Record(
id=data["id"],
fields=data["inputs"],
# The inputs field should be a dictionary with the same keys as the `fields` in the settings
metadata=data["metadata"],
# The metadata field should be a dictionary with the same keys as the `metadata` in the settings
vectors=data.get("vectors") or {},
suggestions=suggestions,
responses=responses,
)
-
Make sure the
question_name
matches the name of the question in question settings. -
Make sure the
question_name
matches the name of the question in question settings.
def map_to_record_for_span(data: dict, users_by_name: dict, current_user: rg.User) -> rg.Record:
""" This function maps a token classification record dictionary to the new Argilla record."""
suggestions = []
responses = []
if prediction := data.get("prediction"):
scores = [span["score"] for span in prediction]
agent = data["prediction_agent"]
suggestions.append(
rg.Suggestion(
question_name="spans", # (1)
value=prediction,
score=scores,
agent=agent
)
)
if annotation := data.get("annotation"):
user_id = users_by_name.get(data["annotation_agent"], current_user).id
responses.append(
rg.Response(
question_name="spans", # (2)
value=annotation,
user_id=user_id
)
)
return rg.Record(
id=data["id"],
fields={"text": data["text"]},
# The inputs field should be a dictionary with the same keys as the `fields` in the settings
metadata=data["metadata"],
# The metadata field should be a dictionary with the same keys as the `metadata` in the settings
vectors=data.get("vectors") or {},
# The vectors field should be a dictionary with the same keys as the `vectors` in the settings
suggestions=suggestions,
responses=responses,
)
-
Make sure the
question_name
matches the name of the question in question settings. -
Make sure the
question_name
matches the name of the question in question settings.
def map_to_record_for_text_generation(data: dict, users_by_name: dict, current_user: rg.User) -> rg.Record:
""" This function maps a text2text record dictionary to the new Argilla record."""
suggestions = []
responses = []
if prediction := data.get("prediction"):
first = prediction[0]
agent = data["prediction_agent"]
suggestions.append(
rg.Suggestion(
question_name="text_generation", # (1)
value=first["text"],
score=first["score"],
agent=agent
)
)
if annotation := data.get("annotation"):
# From data[annotation]
user_id = users_by_name.get(data["annotation_agent"], current_user).id
responses.append(
rg.Response(
question_name="text_generation", # (2)
value=annotation,
user_id=user_id
)
)
return rg.Record(
id=data["id"],
fields={"text": data["text"]},
# The inputs field should be a dictionary with the same keys as the `fields` in the settings
metadata=data["metadata"],
# The metadata field should be a dictionary with the same keys as the `metadata` in the settings
vectors=data.get("vectors") or {},
# The vectors field should be a dictionary with the same keys as the `vectors` in the settings
suggestions=suggestions,
responses=responses,
)
-
Make sure the
question_name
matches the name of the question in question settings. -
Make sure the
question_name
matches the name of the question in question settings.
The functions above depend on the users_by_name
dictionary and the current_user
object to assign responses to users, we need to load the existing users. You can retrieve the users from the Argilla V2 server and the current user as follows:
Finally, upload the records to the new dataset using the log
method and map functions.
records = []
for data in hf_records:
records.append(map_to_record_for_single_label(data, users_by_name, current_user))
# Upload the records to the new dataset
dataset.records.log(records)
You have now successfully migrated your legacy dataset to Argilla V2. For more guides on how to use the Argilla SDK, please refer to the How to guides.