mtg
/
maest
MAEST is a family of Transformer models based on PASST and focused on music analysis applications. The MAEST models are also available for inference in the Essentia library and for inference and training in the official repository.
Prediction
mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19ModelID2aofxxrcexjvrf76kyr2u5xmgqStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- url
- https://www.youtube.com/watch?v=LclGelDVnac
- top_n
- 10
- output_format
- Visualization
{ "url": "https://www.youtube.com/watch?v=LclGelDVnac", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", { input: { url: "https://www.youtube.com/watch?v=LclGelDVnac", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", input={ "url": "https://www.youtube.com/watch?v=LclGelDVnac", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", "input": { "url": "https://www.youtube.com/watch?v=LclGelDVnac", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T15:30:39.591906Z", "created_at": "2023-11-04T15:28:33.588401Z", "data_removed": false, "error": null, "id": "2aofxxrcexjvrf76kyr2u5xmgq", "input": { "url": "https://www.youtube.com/watch?v=LclGelDVnac", "top_n": 10, "output_format": "Visualization" }, "logs": "[youtube] LclGelDVnac: Downloading webpage\n[youtube] LclGelDVnac: Downloading player 9d15588c\n[dashsegments] Total fragments: 1\n[download] Destination: /tmp/tmpt564hijc/audio.webm\n[download] 0.0% of ~3.15MiB at 15.11KiB/s ETA 03:33\n[download] 0.1% of ~3.15MiB at 45.22KiB/s ETA 01:11\n[download] 0.2% of ~3.15MiB at 104.79KiB/s ETA 00:30\n[download] 0.5% of ~3.15MiB at 223.18KiB/s ETA 00:14\n[download] 1.0% of ~3.15MiB at 458.29KiB/s ETA 00:06\n[download] 2.0% of ~3.15MiB at 915.05KiB/s ETA 00:03\n[download] 3.9% of ~3.15MiB at 1.76MiB/s ETA 00:01\n[download] 7.9% of ~3.15MiB at 3.44MiB/s ETA 00:00\n[download] 15.8% of ~3.15MiB at 6.72MiB/s ETA 00:00\n[download] 31.7% of ~3.15MiB at 13.07MiB/s ETA 00:00\n[download] 63.5% of ~3.15MiB at 25.12MiB/s ETA 00:00\n[download] 100.0% of ~3.15MiB at 11.44MiB/s ETA 00:00\n[download] 100.0% of ~3.15MiB at 11.41MiB/s ETA 00:00\n[download] 100% of 3.15MiB in 00:00\n[ffmpeg] Destination: /tmp/tmpt564hijc/audio.wav\nDeleting original file /tmp/tmpt564hijc/audio.webm (pass -k to keep)\nloading audio...\nrunning the model...\nplotting...\n2023-11-04 15:30:04.017105: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2199995000 Hz\n2023-11-04 15:30:09.659050: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327\n2023-11-04 15:30:17.716207: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 15:30:18.018761: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 15:30:18.320289: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 15:30:18.605200: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 15:30:18.891654: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 15:30:38.754365: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 15:30:38.756447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 15:30:38.756486: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n2023-11-04 15:30:38.756508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 15:30:38.756517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 15:30:38.756545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 44.50581, "total_time": 126.003505 }, "output": "https://replicate.delivery/pbxt/ndSUKssBlFZmPhsnDAs7Byb9HnYDjqaTY8PPPtVYpC2nVQdE/out.png", "started_at": "2023-11-04T15:29:55.086096Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2aofxxrcexjvrf76kyr2u5xmgq", "cancel": "https://api.replicate.com/v1/predictions/2aofxxrcexjvrf76kyr2u5xmgq/cancel" }, "version": "42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19" }Generated in[youtube] LclGelDVnac: Downloading webpage [youtube] LclGelDVnac: Downloading player 9d15588c [dashsegments] Total fragments: 1 [download] Destination: /tmp/tmpt564hijc/audio.webm [download] 0.0% of ~3.15MiB at 15.11KiB/s ETA 03:33 [download] 0.1% of ~3.15MiB at 45.22KiB/s ETA 01:11 [download] 0.2% of ~3.15MiB at 104.79KiB/s ETA 00:30 [download] 0.5% of ~3.15MiB at 223.18KiB/s ETA 00:14 [download] 1.0% of ~3.15MiB at 458.29KiB/s ETA 00:06 [download] 2.0% of ~3.15MiB at 915.05KiB/s ETA 00:03 [download] 3.9% of ~3.15MiB at 1.76MiB/s ETA 00:01 [download] 7.9% of ~3.15MiB at 3.44MiB/s ETA 00:00 [download] 15.8% of ~3.15MiB at 6.72MiB/s ETA 00:00 [download] 31.7% of ~3.15MiB at 13.07MiB/s ETA 00:00 [download] 63.5% of ~3.15MiB at 25.12MiB/s ETA 00:00 [download] 100.0% of ~3.15MiB at 11.44MiB/s ETA 00:00 [download] 100.0% of ~3.15MiB at 11.41MiB/s ETA 00:00 [download] 100% of 3.15MiB in 00:00 [ffmpeg] Destination: /tmp/tmpt564hijc/audio.wav Deleting original file /tmp/tmpt564hijc/audio.webm (pass -k to keep) loading audio... running the model... plotting... 2023-11-04 15:30:04.017105: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2199995000 Hz 2023-11-04 15:30:09.659050: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327 2023-11-04 15:30:17.716207: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 15:30:18.018761: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 15:30:18.320289: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 15:30:18.605200: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 15:30:18.891654: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 15:30:38.754365: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 15:30:38.756447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 15:30:38.756486: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2023-11-04 15:30:38.756508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 15:30:38.756517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 15:30:38.756545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!Prediction
mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19ModelIDw7h6bqjcm5vgz5oiy7s357hjjaStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- audio
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%00:00:000Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- top_n
- 10
- output_format
- Visualization
{ "audio": "https://replicate.delivery/pbxt/Jor1FUDHxgZjyIrY9ctGfw3BxRKQN4chzUNL62jRdlOEChmT/03%20Marigold.mp3", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", { input: { audio: "https://replicate.delivery/pbxt/Jor1FUDHxgZjyIrY9ctGfw3BxRKQN4chzUNL62jRdlOEChmT/03%20Marigold.mp3", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", input={ "audio": "https://replicate.delivery/pbxt/Jor1FUDHxgZjyIrY9ctGfw3BxRKQN4chzUNL62jRdlOEChmT/03%20Marigold.mp3", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19", "input": { "audio": "https://replicate.delivery/pbxt/Jor1FUDHxgZjyIrY9ctGfw3BxRKQN4chzUNL62jRdlOEChmT/03%20Marigold.mp3", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T16:41:01.275189Z", "created_at": "2023-11-04T16:38:42.451341Z", "data_removed": false, "error": null, "id": "w7h6bqjcm5vgz5oiy7s357hjja", "input": { "audio": "https://replicate.delivery/pbxt/Jor1FUDHxgZjyIrY9ctGfw3BxRKQN4chzUNL62jRdlOEChmT/03%20Marigold.mp3", "top_n": 10, "output_format": "Visualization" }, "logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 16:39:52.257383: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2199995000 Hz\n2023-11-04 16:39:58.432012: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_193/Assert/AssertGuard/branch_executed/_1407\n2023-11-04 16:40:05.902349: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 16:40:06.189085: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 16:40:06.482689: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 16:40:06.767606: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 16:40:07.055907: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory.\n2023-11-04 16:41:00.108269: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:41:00.110228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 16:41:00.110262: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\nSkipping registering GPU devices...\n2023-11-04 16:41:00.110280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 16:41:00.110289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 16:41:00.110297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 71.559825, "total_time": 138.823848 }, "output": "https://replicate.delivery/pbxt/JCe2aLfsY8s8AUheVDcqGzisETdxCn1ERI1wfcSKfE1kDToOC/out.png", "started_at": "2023-11-04T16:39:49.715364Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w7h6bqjcm5vgz5oiy7s357hjja", "cancel": "https://api.replicate.com/v1/predictions/w7h6bqjcm5vgz5oiy7s357hjja/cancel" }, "version": "42a8990333282beb949122de38f3a91a7f910ecc2ad4f0a881c9ce8397fbcd19" }Generated inloading audio... running the model... plotting... 2023-11-04 16:39:52.257383: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2199995000 Hz 2023-11-04 16:39:58.432012: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_193/Assert/AssertGuard/branch_executed/_1407 2023-11-04 16:40:05.902349: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 16:40:06.189085: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 16:40:06.482689: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 16:40:06.767606: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 16:40:07.055907: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 136282800 exceeds 10% of free system memory. 2023-11-04 16:41:00.108269: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:41:00.110228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 16:41:00.110262: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2023-11-04 16:41:00.110280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 16:41:00.110289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 16:41:00.110297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /usr/local/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!Prediction
mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01ModelIDxovtf4rbbgwydhjlkuf33g2dn4StatusSucceededSourceWebHardwareT4Total durationCreatedInput
- audio
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
No compatible source was found for this media.Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- top_n
- 10
- output_format
- Visualization
{ "audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", { input: { audio: "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", input={ "audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", "input": { "audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T16:51:22.493432Z", "created_at": "2023-11-04T16:48:24.038840Z", "data_removed": false, "error": null, "id": "xovtf4rbbgwydhjlkuf33g2dn4", "input": { "audio": "https://replicate.delivery/pbxt/JorAR7cuGY5dPMrI3t6eKXMexGuRmjsJPXE8PG0IrZ3EawEV/Mapi%20Quintana%20%26%20Eli%CC%81as%20Garci%CC%81a%20-%20Severina%20-%2003%20Afke%20y%20Gu%CC%88elita.flac", "top_n": 10, "output_format": "Visualization" }, "logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 16:50:54.787371: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz\n2023-11-04 16:51:00.648102: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327\n2023-11-04 16:51:09.785253: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n2023-11-04 16:51:11.289404: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101\n2023-11-04 16:51:16.034519: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n2023-11-04 16:51:18.479632: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n2023-11-04 16:51:21.498935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.500252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 16:51:21.500957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.502372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.503392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n2023-11-04 16:51:21.503432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 16:51:21.503441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 16:51:21.503449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n2023-11-04 16:51:21.503608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.504818: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 16:51:21.505847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 29.198043, "total_time": 178.454592 }, "output": "https://replicate.delivery/pbxt/wXZQcGdcqypFG9aeyQ0XZTSruzMVX0h4yMt9YYed6RmKiC1RA/out.png", "started_at": "2023-11-04T16:50:53.295389Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xovtf4rbbgwydhjlkuf33g2dn4", "cancel": "https://api.replicate.com/v1/predictions/xovtf4rbbgwydhjlkuf33g2dn4/cancel" }, "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01" }Generated inloading audio... running the model... plotting... 2023-11-04 16:50:54.787371: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz 2023-11-04 16:51:00.648102: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327 2023-11-04 16:51:09.785253: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8 2023-11-04 16:51:11.289404: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101 2023-11-04 16:51:16.034519: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11 2023-11-04 16:51:18.479632: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11 2023-11-04 16:51:21.498935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:51:21.500252: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 16:51:21.500957: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:51:21.502372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:51:21.503392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2023-11-04 16:51:21.503432: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 16:51:21.503441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 16:51:21.503449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2023-11-04 16:51:21.503608: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:51:21.504818: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 16:51:21.505847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5) /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!Prediction
mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01ModelIDjhdflvjbztbuwlrg2toq6fsapqStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- audio
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
No compatible source was found for this media.Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- top_n
- 10
- output_format
- Visualization
{ "audio": "https://replicate.delivery/pbxt/JoraRTFLHWkG2eHxJW7hNje34NPZtJW6cGG29pKCCMLIv2aj/02%20Archangel.flac", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", { input: { audio: "https://replicate.delivery/pbxt/JoraRTFLHWkG2eHxJW7hNje34NPZtJW6cGG29pKCCMLIv2aj/02%20Archangel.flac", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", input={ "audio": "https://replicate.delivery/pbxt/JoraRTFLHWkG2eHxJW7hNje34NPZtJW6cGG29pKCCMLIv2aj/02%20Archangel.flac", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", "input": { "audio": "https://replicate.delivery/pbxt/JoraRTFLHWkG2eHxJW7hNje34NPZtJW6cGG29pKCCMLIv2aj/02%20Archangel.flac", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T17:18:45.935621Z", "created_at": "2023-11-04T17:15:51.209227Z", "data_removed": false, "error": null, "id": "jhdflvjbztbuwlrg2toq6fsapq", "input": { "audio": "https://replicate.delivery/pbxt/JoraRTFLHWkG2eHxJW7hNje34NPZtJW6cGG29pKCCMLIv2aj/02%20Archangel.flac", "top_n": 10, "output_format": "Visualization" }, "logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 17:18:23.253246: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz\n2023-11-04 17:18:29.127149: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327\n2023-11-04 17:18:37.873539: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n2023-11-04 17:18:39.684176: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101\n2023-11-04 17:18:41.569480: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n2023-11-04 17:18:42.507158: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n2023-11-04 17:18:44.619122: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 17:18:44.620573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 17:18:44.622334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 17:18:44.623828: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 17:18:44.624933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n2023-11-04 17:18:44.624987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 17:18:44.625014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 17:18:44.625027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n2023-11-04 17:18:44.625386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 17:18:44.626710: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 17:18:44.627801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 24.059153, "total_time": 174.726394 }, "output": "https://replicate.delivery/pbxt/CkpEaYvAD2beWiEbYCOl0BIX7EF9letwDxnWYLfJH48r3FqjA/out.png", "started_at": "2023-11-04T17:18:21.876468Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jhdflvjbztbuwlrg2toq6fsapq", "cancel": "https://api.replicate.com/v1/predictions/jhdflvjbztbuwlrg2toq6fsapq/cancel" }, "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01" }Generated inloading audio... running the model... plotting... 2023-11-04 17:18:23.253246: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz 2023-11-04 17:18:29.127149: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327 2023-11-04 17:18:37.873539: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8 2023-11-04 17:18:39.684176: I tensorflow/stream_executor/cuda/cuda_dnn.cc:359] Loaded cuDNN version 8101 2023-11-04 17:18:41.569480: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11 2023-11-04 17:18:42.507158: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11 2023-11-04 17:18:44.619122: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 17:18:44.620573: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 17:18:44.622334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 17:18:44.623828: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 17:18:44.624933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2023-11-04 17:18:44.624987: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 17:18:44.625014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 17:18:44.625027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2023-11-04 17:18:44.625386: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 17:18:44.626710: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 17:18:44.627801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5) /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!Prediction
mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01ModelIDfn2yrujbnqrudkjmv5sigwmylaStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- audio
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%00:00:000Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- top_n
- 10
- output_format
- Visualization
{ "audio": "https://replicate.delivery/pbxt/JosNxmt74AOVcCi1uwCHKFJR1jvzi7U2ffKcUPUFUz9J1z1b/07%20-%20The%20Enemy.mp3", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", { input: { audio: "https://replicate.delivery/pbxt/JosNxmt74AOVcCi1uwCHKFJR1jvzi7U2ffKcUPUFUz9J1z1b/07%20-%20The%20Enemy.mp3", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", input={ "audio": "https://replicate.delivery/pbxt/JosNxmt74AOVcCi1uwCHKFJR1jvzi7U2ffKcUPUFUz9J1z1b/07%20-%20The%20Enemy.mp3", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", "input": { "audio": "https://replicate.delivery/pbxt/JosNxmt74AOVcCi1uwCHKFJR1jvzi7U2ffKcUPUFUz9J1z1b/07%20-%20The%20Enemy.mp3", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T18:08:22.028391Z", "created_at": "2023-11-04T18:08:05.361577Z", "data_removed": false, "error": null, "id": "fn2yrujbnqrudkjmv5sigwmyla", "input": { "audio": "https://replicate.delivery/pbxt/JosNxmt74AOVcCi1uwCHKFJR1jvzi7U2ffKcUPUFUz9J1z1b/07%20-%20The%20Enemy.mp3", "top_n": 10, "output_format": "Visualization" }, "logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 18:08:12.037336: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_193/Assert/AssertGuard/branch_executed/_1407\n2023-11-04 18:08:20.984002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:08:20.985428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 18:08:20.986119: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:08:20.987442: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:08:20.988540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n2023-11-04 18:08:20.988584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 18:08:20.988594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 18:08:20.988602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n2023-11-04 18:08:20.988783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:08:20.990431: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:08:20.991548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 16.69758, "total_time": 16.666814 }, "output": "https://replicate.delivery/pbxt/jBekyAv1ExXUVac0vcvVaJdaZoJ6K6IRWpcKSPyFoczK1h6IA/out.png", "started_at": "2023-11-04T18:08:05.330811Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fn2yrujbnqrudkjmv5sigwmyla", "cancel": "https://api.replicate.com/v1/predictions/fn2yrujbnqrudkjmv5sigwmyla/cancel" }, "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01" }Generated inloading audio... running the model... plotting... 2023-11-04 18:08:12.037336: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_193/Assert/AssertGuard/branch_executed/_1407 2023-11-04 18:08:20.984002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:08:20.985428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 18:08:20.986119: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:08:20.987442: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:08:20.988540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2023-11-04 18:08:20.988584: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 18:08:20.988594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 18:08:20.988602: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2023-11-04 18:08:20.988783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:08:20.990431: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:08:20.991548: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5) /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!Prediction
mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01ModelIDa4ttmnjbhre3l4jgbhceqoqtraStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- audio
- Video Player is loading.Current Time 00:00:000/Duration 00:00:000Loaded: 0%Stream Type LIVERemaining Time -00:00:0001x
- Chapters
- descriptions off, selected
- captions settings, opens captions settings dialog
- captions off, selected
This is a modal window.
No compatible source was found for this media.Beginning of dialog window. Escape will cancel and close the window.
End of dialog window.
- top_n
- 10
- output_format
- Visualization
{ "audio": "https://replicate.delivery/pbxt/JosPJFWnvdX97WMVGLH5N6xNL8cU9TWMnwpHShwm5P35W3Rw/02%20-%20Exprimelimones%20%28Buleri%CC%81as%29.flac", "top_n": 10, "output_format": "Visualization" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", { input: { audio: "https://replicate.delivery/pbxt/JosPJFWnvdX97WMVGLH5N6xNL8cU9TWMnwpHShwm5P35W3Rw/02%20-%20Exprimelimones%20%28Buleri%CC%81as%29.flac", top_n: 10, output_format: "Visualization" } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mtg/maest:170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", input={ "audio": "https://replicate.delivery/pbxt/JosPJFWnvdX97WMVGLH5N6xNL8cU9TWMnwpHShwm5P35W3Rw/02%20-%20Exprimelimones%20%28Buleri%CC%81as%29.flac", "top_n": 10, "output_format": "Visualization" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run mtg/maest using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01", "input": { "audio": "https://replicate.delivery/pbxt/JosPJFWnvdX97WMVGLH5N6xNL8cU9TWMnwpHShwm5P35W3Rw/02%20-%20Exprimelimones%20%28Buleri%CC%81as%29.flac", "top_n": 10, "output_format": "Visualization" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-04T18:09:51.391832Z", "created_at": "2023-11-04T18:09:32.457168Z", "data_removed": false, "error": null, "id": "a4ttmnjbhre3l4jgbhceqoqtra", "input": { "audio": "https://replicate.delivery/pbxt/JosPJFWnvdX97WMVGLH5N6xNL8cU9TWMnwpHShwm5P35W3Rw/02%20-%20Exprimelimones%20%28Buleri%CC%81as%29.flac", "top_n": 10, "output_format": "Visualization" }, "logs": "loading audio...\nrunning the model...\nplotting...\n2023-11-04 18:09:40.150219: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327\n2023-11-04 18:09:50.274523: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:09:50.275911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:\npciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5\ncoreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s\n2023-11-04 18:09:50.276494: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:09:50.277725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:09:50.278807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n2023-11-04 18:09:50.278857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n2023-11-04 18:09:50.278870: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0\n2023-11-04 18:09:50.278877: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N\n2023-11-04 18:09:50.279035: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:09:50.280553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2023-11-04 18:09:50.281653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5)\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\n/root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\nif pd.api.types.is_categorical_dtype(vector):\ndone!", "metrics": { "predict_time": 19.043153, "total_time": 18.934664 }, "output": "https://replicate.delivery/pbxt/WFoiHs8Bc3ptFhT6uB41KVniA8mDCKgJQyCjJreq9wl31h6IA/out.png", "started_at": "2023-11-04T18:09:32.348679Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a4ttmnjbhre3l4jgbhceqoqtra", "cancel": "https://api.replicate.com/v1/predictions/a4ttmnjbhre3l4jgbhceqoqtra/cancel" }, "version": "170cfd0b56fb97ed732c815431e52546efac47b8a2b0096d58dd40b099191e01" }Generated inloading audio... running the model... plotting... 2023-11-04 18:09:40.150219: W tensorflow/core/grappler/optimizers/loop_optimizer.cc:906] Skipping loop optimization for Merge node with control input: StatefulPartitionedCall/StatefulPartitionedCall/assert_equal_177/Assert/AssertGuard/branch_executed/_1327 2023-11-04 18:09:50.274523: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:09:50.275911: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: pciBusID: 0000:00:05.0 name: Tesla T4 computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.58GiB deviceMemoryBandwidth: 298.08GiB/s 2023-11-04 18:09:50.276494: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:09:50.277725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:09:50.278807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0 2023-11-04 18:09:50.278857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix: 2023-11-04 18:09:50.278870: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 2023-11-04 18:09:50.278877: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N 2023-11-04 18:09:50.279035: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:09:50.280553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2023-11-04 18:09:50.281653: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13421 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5) /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): /root/.pyenv/versions/3.10.13/lib/python3.10/site-packages/seaborn/_core.py:1225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead if pd.api.types.is_categorical_dtype(vector): done!
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