3232 },
3333 {
3434 "cell_type" : " markdown" ,
35- "source" : [
36- " ## Batches (*Currently in Public Beta*)"
37- ],
35+ "id" : " Lup2QNWjaxKg" ,
3836 "metadata" : {
3937 "id" : " Lup2QNWjaxKg"
4038 },
41- "id" : " Lup2QNWjaxKg"
39+ "source" : [
40+ " ## Batches (*Currently in Public Beta*)"
41+ ]
4242 },
4343 {
4444 "cell_type" : " markdown" ,
45+ "id" : " KONWmRQkadPf" ,
46+ "metadata" : {
47+ "id" : " KONWmRQkadPf"
48+ },
4549 "source" : [
4650 " * A Batch is collection of datarows picked out of a Data Set.\n " ,
4751 " * A Datarow cannot be part of more than one batch in a project.\n " ,
5054 " * Batches may have Datarows from multiple Datasets.\n " ,
5155 " * Datarows can only be attached to a Project as part of a single Batch.\n " ,
5256 " * You can set priority for each Batch."
53- ],
54- "metadata" : {
55- "id" : " KONWmRQkadPf"
56- },
57- "id" : " KONWmRQkadPf"
57+ ]
5858 },
5959 {
6060 "cell_type" : " code" ,
6161 "execution_count" : null ,
62+ "id" : " HoW5ypnyzpqb" ,
6263 "metadata" : {
6364 "id" : " HoW5ypnyzpqb"
6465 },
6566 "outputs" : [],
6667 "source" : [
6768 " !pip install labelbox[data]"
68- ],
69- "id" : " HoW5ypnyzpqb"
69+ ]
7070 },
7171 {
7272 "cell_type" : " code" ,
7373 "execution_count" : null ,
74+ "id" : " 6-Us9Gj1zpqc" ,
7475 "metadata" : {
7576 "id" : " 6-Us9Gj1zpqc"
7677 },
7778 "outputs" : [],
7879 "source" : [
7980 " from labelbox import DataRow, Client\n " ,
8081 " import random"
81- ],
82- "id" : " 6-Us9Gj1zpqc"
82+ ]
8383 },
8484 {
8585 "cell_type" : " markdown" ,
86+ "id" : " qQiozm-dzpqd" ,
8687 "metadata" : {
8788 "id" : " qQiozm-dzpqd"
8889 },
8990 "source" : [
9091 " Set the following cell with your data to run this notebook:"
91- ],
92- "id" : " qQiozm-dzpqd"
92+ ]
9393 },
9494 {
9595 "cell_type" : " code" ,
9696 "execution_count" : null ,
97+ "id" : " 84Zna5c0zpqd" ,
9798 "metadata" : {
9899 "id" : " 84Zna5c0zpqd"
99100 },
100101 "outputs" : [],
101102 "source" : [
102103 " PROJECT_NAME = \" Batch Queue Demo\" #text project\n " ,
103104 " DATASET_NAME = \" Batch Queue Demo Data\" "
104- ],
105- "id" : " 84Zna5c0zpqd"
105+ ]
106106 },
107107 {
108108 "cell_type" : " markdown" ,
118118 {
119119 "cell_type" : " code" ,
120120 "execution_count" : null ,
121+ "id" : " Ge-dfNh-zpqe" ,
121122 "metadata" : {
122123 "id" : " Ge-dfNh-zpqe"
123124 },
126127 " # Add your api key\n " ,
127128 " API_KEY = None\n " ,
128129 " client = Client(api_key=API_KEY)"
129- ],
130- "id" : " Ge-dfNh-zpqe"
130+ ]
131131 },
132132 {
133133 "cell_type" : " code" ,
134134 "execution_count" : null ,
135+ "id" : " nMVtBYQmzpqe" ,
135136 "metadata" : {
136137 "id" : " nMVtBYQmzpqe"
137138 },
146147 " 'row_data': 'https://picsum.photos/200/300'\n " ,
147148 " })\n " ,
148149 " dataset.create_data_rows(uploads)"
149- ],
150- "id" : " nMVtBYQmzpqe"
150+ ]
151151 },
152152 {
153153 "cell_type" : " markdown" ,
154- "source" : [
155- " # Ensure project is in batch mode:"
156- ],
154+ "id" : " 61CvCD3C7qv6" ,
157155 "metadata" : {
158156 "id" : " 61CvCD3C7qv6"
159157 },
160- "id" : " 61CvCD3C7qv6"
158+ "source" : [
159+ " # Ensure project is in batch mode:"
160+ ]
161161 },
162162 {
163163 "cell_type" : " code" ,
164- "source" : [
165- " project = client.create_project(name=PROJECT_NAME)\n " ,
166- " project.update(queue_mode=project.QueueMode.Batch)"
167- ],
164+ "execution_count" : null ,
165+ "id" : " tqtT4q31787T" ,
168166 "metadata" : {
169167 "id" : " tqtT4q31787T"
170168 },
171- "id" : " tqtT4q31787T" ,
172- "execution_count" : null ,
173- "outputs" : []
169+ "outputs" : [],
170+ "source" : [
171+ " project = client.create_project(name=PROJECT_NAME)\n " ,
172+ " project.update(queue_mode=project.QueueMode.Batch)"
173+ ]
174174 },
175175 {
176176 "cell_type" : " markdown" ,
177- "source" : [
178- " # Collect Datarow id's:"
179- ],
177+ "id" : " Xti9AoZWELrq" ,
180178 "metadata" : {
181179 "id" : " Xti9AoZWELrq"
182180 },
183- "id" : " Xti9AoZWELrq"
181+ "source" : [
182+ " # Collect Datarow id's:"
183+ ]
184184 },
185185 {
186186 "cell_type" : " markdown" ,
187- "source" : [
188- " ### Select All Data Rows from dataset."
189- ],
187+ "id" : " 9JVLsXdevywS" ,
190188 "metadata" : {
191189 "id" : " 9JVLsXdevywS"
192190 },
193- "id" : " 9JVLsXdevywS"
191+ "source" : [
192+ " ### Select All Data Rows from dataset."
193+ ]
194194 },
195195 {
196196 "cell_type" : " code" ,
197- "source" : [
198- " data_rows = [dr.uid for dr in list(dataset.export_data_rows())]"
199- ],
197+ "execution_count" : null ,
198+ "id" : " U4C1ZyJ2EgTS" ,
200199 "metadata" : {
201200 "id" : " U4C1ZyJ2EgTS"
202201 },
203- "id" : " U4C1ZyJ2EgTS" ,
204- "execution_count" : null ,
205- "outputs" : []
202+ "outputs" : [],
203+ "source" : [
204+ " data_row_ids = [dr.uid for dr in dataset.export_data_rows()]"
205+ ]
206206 },
207207 {
208208 "cell_type" : " markdown" ,
209+ "id" : " 6699941a" ,
210+ "metadata" : {},
211+ "source" : []
212+ },
213+ {
214+ "cell_type" : " markdown" ,
215+ "id" : " B0UqO_O1V8ei" ,
216+ "metadata" : {
217+ "id" : " B0UqO_O1V8ei"
218+ },
209219 "source" : [
210220 " ### Randomly sample\n " ,
211221 " \n " ,
212222 " Rather than selecting all of the data we sample 5 data rows at random"
213- ],
214- "metadata" : {
215- "id" : " B0UqO_O1V8ei"
216- },
217- "id" : " B0UqO_O1V8ei"
223+ ]
218224 },
219225 {
220226 "cell_type" : " code" ,
221- "source" : [
222- " sample = random.sample(data_rows, 5)"
223- ],
227+ "execution_count" : null ,
228+ "id" : " WJAXBf1bV-td" ,
224229 "metadata" : {
225230 "id" : " WJAXBf1bV-td"
226231 },
227- "id" : " WJAXBf1bV-td" ,
228- "execution_count" : null ,
229- "outputs" : []
232+ "outputs" : [],
233+ "source" : [
234+ " sample = random.sample(data_rows, 5)"
235+ ]
230236 },
231237 {
232238 "cell_type" : " markdown" ,
233- "source" : [
234- " # Batch Manipulation"
235- ],
239+ "id" : " UPdaTqkgYyvt" ,
236240 "metadata" : {
237241 "id" : " UPdaTqkgYyvt"
238242 },
239- "id" : " UPdaTqkgYyvt"
243+ "source" : [
244+ " # Batch Manipulation"
245+ ]
240246 },
241247 {
242248 "cell_type" : " markdown" ,
243- "source" : [
244- " ### Create a Batch:"
245- ],
249+ "id" : " Al-K1lBBEjtb" ,
246250 "metadata" : {
247251 "id" : " Al-K1lBBEjtb"
248252 },
249- "id" : " Al-K1lBBEjtb"
253+ "source" : [
254+ " ### Create a Batch:"
255+ ]
250256 },
251257 {
252258 "cell_type" : " code" ,
259+ "execution_count" : null ,
260+ "id" : " resH3xqeErVv" ,
261+ "metadata" : {
262+ "id" : " resH3xqeErVv"
263+ },
264+ "outputs" : [],
253265 "source" : [
254266 " batch = project.create_batch(\n " ,
255267 " \" first batch\" , # Each batch in a project must have a unique name\n " ,
256268 " sample, # A list of data rows or data row ids\n " ,
257269 " 5 # priority between 1(Highest) - 5(lowest)\n " ,
258270 " )"
259- ],
260- "metadata" : {
261- "id" : " resH3xqeErVv"
262- },
263- "id" : " resH3xqeErVv" ,
264- "execution_count" : null ,
265- "outputs" : []
271+ ]
266272 },
267273 {
268274 "cell_type" : " code" ,
269- "source" : [
270- " # number of data rows in the batch\n " ,
271- " batch.size"
272- ],
275+ "execution_count" : null ,
276+ "id" : " gFio7ONOWYdJ" ,
273277 "metadata" : {
274278 "id" : " gFio7ONOWYdJ"
275279 },
276- "id" : " gFio7ONOWYdJ" ,
277- "execution_count" : null ,
278- "outputs" : []
280+ "outputs" : [],
281+ "source" : [
282+ " # number of data rows in the batch\n " ,
283+ " batch.size"
284+ ]
279285 },
280286 {
281287 "cell_type" : " markdown" ,
288+ "id" : " 8Cj64Isxzpqe" ,
282289 "metadata" : {
283290 "id" : " 8Cj64Isxzpqe"
284291 },
285292 "source" : [
286- " ### List DataRows in a Batch (Not supported yet)\n " ,
287- " Note: You can view your batch through in the Data Row table of the project"
288- ],
289- "id" : " 8Cj64Isxzpqe"
293+ " ### List DataRows in a Batch\n " ,
294+ " Note: You can view your batch through in the *Data Row tab* of the project"
295+ ]
296+ },
297+ {
298+ "cell_type" : " code" ,
299+ "execution_count" : null ,
300+ "id" : " 0a7d1d3e" ,
301+ "metadata" : {},
302+ "outputs" : [],
303+ "source" : [
304+ " data_rows = [dr for dr in batch.export_data_rows()]"
305+ ]
290306 },
291307 {
292308 "cell_type" : " markdown" ,
309+ "id" : " rU7iddSQzpqg" ,
293310 "metadata" : {
294311 "id" : " rU7iddSQzpqg"
295312 },
296313 "source" : [
297314 " ### Remove queued data rows by batch (Not supported yet)\n " ,
298315 " Note: You can do this through the batch management pane on the data rows tab of the project"
299- ],
300- "id" : " rU7iddSQzpqg"
316+ ]
301317 }
302318 ],
303319 "metadata" : {
320+ "colab" : {
321+ "collapsed_sections" : [],
322+ "name" : " Batches.ipynb" ,
323+ "provenance" : []
324+ },
304325 "kernelspec" : {
305326 "display_name" : " Python 3" ,
306327 "language" : " python" ,
317338 "nbconvert_exporter" : " python" ,
318339 "pygments_lexer" : " ipython3" ,
319340 "version" : " 3.8.5"
320- },
321- "colab" : {
322- "name" : " Batches.ipynb" ,
323- "provenance" : [],
324- "collapsed_sections" : []
325341 }
326342 },
327343 "nbformat" : 4 ,
328344 "nbformat_minor" : 5
329- }
345+ }
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