Skip to content
Navigation Menu
{{ message }}
forked from replicate/replicate-python
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtraining.py
More file actions
460 lines (360 loc) · 13.3 KB
/
Copy pathtraining.py
File metadata and controls
460 lines (360 loc) · 13.3 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Literal,
Optional,
Tuple,
TypedDict,
Union,
overload,
)
from typing_extensions import NotRequired, Unpack
from replicate.helpers import async_encode_json, encode_json
from replicate.identifier import ModelVersionIdentifier
from replicate.model import Model
from replicate.pagination import Page
from replicate.resource import Namespace, Resource
from replicate.version import Version
try:
from pydantic import v1 as pydantic # type: ignore
except ImportError:
import pydantic # type: ignore
if TYPE_CHECKING:
from replicate.client import Client
from replicate.file import FileEncodingStrategy
class Training(Resource):
"""
A training made for a model hosted on Replicate.
"""
_client: "Client" = pydantic.PrivateAttr()
id: str
"""The unique ID of the training."""
model: str
"""An identifier for the model used to create the prediction, in the form `owner/name`."""
version: Union[str, Version]
"""The version of the model used to create the training."""
destination: Optional[str]
"""The model destination of the training."""
status: Literal["starting", "processing", "succeeded", "failed", "canceled"]
"""The status of the training."""
input: Optional[Dict[str, Any]]
"""The input to the training."""
output: Optional[Any]
"""The output of the training."""
logs: Optional[str]
"""The logs of the training."""
error: Optional[str]
"""The error encountered during the training, if any."""
created_at: Optional[str]
"""When the training was created."""
started_at: Optional[str]
"""When the training was started."""
completed_at: Optional[str]
"""When the training was completed, if finished."""
urls: Optional[Dict[str, str]]
"""
URLs associated with the training.
The following keys are available:
- `get`: A URL to fetch the training.
- `cancel`: A URL to cancel the training.
"""
def cancel(self) -> None:
"""
Cancel a running training.
"""
canceled = self._client.trainings.cancel(self.id)
for name, value in canceled.dict().items():
setattr(self, name, value)
async def async_cancel(self) -> None:
"""
Cancel a running training asynchronously.
"""
canceled = await self._client.trainings.async_cancel(self.id)
for name, value in canceled.dict().items():
setattr(self, name, value)
def reload(self) -> None:
"""
Load the training from the server.
"""
updated = self._client.trainings.get(self.id)
for name, value in updated.dict().items():
setattr(self, name, value)
async def async_reload(self) -> None:
"""
Load the training from the server asynchronously.
"""
updated = await self._client.trainings.async_get(self.id)
for name, value in updated.dict().items():
setattr(self, name, value)
class Trainings(Namespace):
"""
Namespace for operations related to trainings.
"""
def list(self, cursor: Union[str, "ellipsis", None] = ...) -> Page[Training]: # noqa: F821
"""
List your trainings.
Parameters:
cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.
Returns:
Page[Training]: A page of trainings.
Raises:
ValueError: If `cursor` is `None`.
"""
if cursor is None:
raise ValueError("cursor cannot be None")
resp = self._client._request(
"GET", "/v1/trainings" if cursor is ... else cursor
)
obj = resp.json()
obj["results"] = [
_json_to_training(self._client, result) for result in obj["results"]
]
return Page[Training](**obj)
async def async_list(
self,
cursor: Union[str, "ellipsis", None] = ..., # noqa: F821
) -> Page[Training]:
"""
List your trainings.
Parameters:
cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.
Returns:
Page[Training]: A page of trainings.
Raises:
ValueError: If `cursor` is `None`.
"""
if cursor is None:
raise ValueError("cursor cannot be None")
resp = await self._client._async_request(
"GET", "/v1/trainings" if cursor is ... else cursor
)
obj = resp.json()
obj["results"] = [
_json_to_training(self._client, result) for result in obj["results"]
]
return Page[Training](**obj)
def get(self, id: str) -> Training:
"""
Get a training by ID.
Args:
id: The ID of the training.
Returns:
Training: The training object.
"""
resp = self._client._request(
"GET",
f"/v1/trainings/{id}",
)
return _json_to_training(self._client, resp.json())
async def async_get(self, id: str) -> Training:
"""
Get a training by ID.
Args:
id: The ID of the training.
Returns:
Training: The training object.
"""
resp = await self._client._async_request(
"GET",
f"/v1/trainings/{id}",
)
return _json_to_training(self._client, resp.json())
class CreateTrainingParams(TypedDict):
"""Parameters for creating a training."""
destination: Union[str, Tuple[str, str], "Model"]
"""The destination for the trained model."""
webhook: NotRequired[str]
"""The URL to receive a POST request with training updates."""
webhook_completed: NotRequired[str]
"""The URL to receive a POST request when the training is completed."""
webhook_events_filter: NotRequired[List[str]]
"""List of events to trigger webhooks."""
file_encoding_strategy: NotRequired["FileEncodingStrategy"]
"""The strategy to use for encoding files in the training input."""
@overload
def create( # pylint: disable=too-many-arguments
self,
version: str,
input: Dict[str, Any],
destination: str,
webhook: Optional[str] = None,
webhook_events_filter: Optional[List[str]] = None,
**kwargs,
) -> Training: ...
@overload
def create(
self,
model: Union[str, Tuple[str, str], "Model"],
version: Union[str, Version],
input: Optional[Dict[str, Any]] = None,
**params: Unpack["Trainings.CreateTrainingParams"],
) -> Training: ...
def create( # type: ignore
self,
*args,
model: Optional[Union[str, Tuple[str, str], "Model"]] = None,
version: Optional[Union[str, Version]] = None,
input: Optional[Dict[str, Any]] = None,
**params: Unpack["Trainings.CreateTrainingParams"],
) -> Training:
"""
Create a new training using the specified model version as a base.
"""
url = None
# Support positional arguments for backwards compatibility
if args:
if shorthand := args[0] if len(args) > 0 else None:
url = _create_training_url_from_shorthand(shorthand)
input = args[1] if len(args) > 1 else input
if len(args) > 2:
params["destination"] = args[2]
if len(args) > 3:
params["webhook"] = args[3]
if len(args) > 4:
params["webhook_completed"] = args[4]
if len(args) > 5:
params["webhook_events_filter"] = args[5]
elif model and version:
url = _create_training_url_from_model_and_version(model, version)
elif model is None and isinstance(version, str):
url = _create_training_url_from_shorthand(version)
if not url:
raise ValueError("model and version or shorthand version must be specified")
file_encoding_strategy = params.pop("file_encoding_strategy", None)
if input is not None:
input = encode_json(
input,
client=self._client,
file_encoding_strategy=file_encoding_strategy,
)
body = _create_training_body(input, **params)
resp = self._client._request(
"POST",
url,
json=body,
)
return _json_to_training(self._client, resp.json())
async def async_create(
self,
model: Union[str, Tuple[str, str], "Model"],
version: Union[str, Version],
input: Dict[str, Any],
**params: Unpack["Trainings.CreateTrainingParams"],
) -> Training:
"""
Create a new training using the specified model version as a base.
Args:
version: The ID of the base model version that you're using to train a new model version.
input: The input to the training.
destination: The desired model to push to in the format `{owner}/{model_name}`. This should be an existing model owned by the user or organization making the API request.
webhook: The URL to send a POST request to when the training is completed. Defaults to None.
webhook_completed: The URL to receive a POST request when the prediction is completed.
webhook_events_filter: The events to send to the webhook. Defaults to None.
Returns:
The training object.
"""
url = _create_training_url_from_model_and_version(model, version)
file_encoding_strategy = params.pop("file_encoding_strategy", None)
if input is not None:
input = await async_encode_json(
input,
client=self._client,
file_encoding_strategy=file_encoding_strategy,
)
body = _create_training_body(input, **params)
resp = await self._client._async_request(
"POST",
url,
json=body,
)
return _json_to_training(self._client, resp.json())
def cancel(self, id: str) -> Training:
"""
Cancel a training.
Args:
id: The ID of the training to cancel.
Returns:
Training: The canceled training object.
"""
resp = self._client._request(
"POST",
f"/v1/trainings/{id}/cancel",
)
return _json_to_training(self._client, resp.json())
async def async_cancel(self, id: str) -> Training:
"""
Cancel a training.
Args:
id: The ID of the training to cancel.
Returns:
Training: The canceled training object.
"""
resp = await self._client._async_request(
"POST",
f"/v1/trainings/{id}/cancel",
)
return _json_to_training(self._client, resp.json())
def _create_training_body(
input: Optional[Dict[str, Any]] = None,
*,
destination: Optional[Union[str, Tuple[str, str], "Model"]] = None,
webhook: Optional[str] = None,
webhook_completed: Optional[str] = None,
webhook_events_filter: Optional[List[str]] = None,
**_kwargs,
) -> Dict[str, Any]:
body = {}
if input is not None:
body["input"] = input
if destination is None:
raise ValueError(
"A destination must be provided as a positional or keyword argument."
)
if isinstance(destination, Model):
destination = f"{destination.owner}/{destination.name}"
elif isinstance(destination, tuple):
destination = f"{destination[0]}/{destination[1]}"
body["destination"] = destination
if webhook is not None:
body["webhook"] = webhook
if webhook_completed is not None:
body["webhook_completed"] = webhook_completed
if webhook_events_filter is not None:
body["webhook_events_filter"] = webhook_events_filter
return body
def _create_training_url_from_shorthand(ref: str) -> str:
owner, name, version_id = ModelVersionIdentifier.parse(ref)
return f"/v1/models/{owner}/{name}/versions/{version_id}/trainings"
def _create_training_url_from_model_and_version(
model: Union[str, Tuple[str, str], "Model"],
version: Union[str, "Version"],
) -> str:
if isinstance(model, Model):
owner, name = model.owner, model.name
elif isinstance(model, tuple):
owner, name = model[0], model[1]
elif isinstance(model, str):
owner, name, _ = ModelVersionIdentifier.parse(model)
else:
raise ValueError(
"model must be a Model, a tuple of (owner, name), or a string in the format 'owner/name'"
)
if isinstance(version, Version):
version_id = version.id
else:
version_id = version
return f"/v1/models/{owner}/{name}/versions/{version_id}/trainings"
def _json_to_training(client: "Client", json: Dict[str, Any]) -> Training:
training = Training(**json)
training._client = client
# FIXME: This should be populated by the API
if (
training.output
and isinstance(training.output, dict)
and "version" in training.output
):
id = ModelVersionIdentifier.parse(training.output["version"])
training.destination = f"{id.owner}/{id.name}"
return training
You can’t perform that action at this time.