ScyllaDB : One of the nodes in a 3-node cluster fails to join the cluster after a restart - scylla

We have a 3 node cluster (single DC) running on Ubuntu 18.04. We had to upgrade all nodes in the cluster (increase the disk space) and hence stopped node-3 first, upgraded the instance and attempted to start it back. It started up after 300 seconds (after failing to connect to the seed nodes), but it has started as a separate cluster. nodetool status on node-3 shows nodes 1 and 2 as down (DN). nodetool status on nodes 1 and 2 show node-3 as down (DN) and 1 and 2 as up (UN).
Why is node-3 not able to connect to the seed nodes? Initially when the cluster was created, seeds list was set to only node-1 in all 3 nodes. Before the node-3 was stopped, the seeds list was updated to node-1,node-2,node3. After restart, node-3 is not able to connect to the seed nodes.
node-3:~$ nodetool status
Datacenter: scylla_data_center
==============================
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns Host ID Rack
DN a.b.c.176 ? 256 ? b1642399-9596-4bc7-8f01-d875f0584e77 scylla_rack
DN a.b.c.177 ? 256 ? e7f6e8f4-c07e-47e7-946d-ba76a272776f scylla_rack
UN a.b.c.198 126.83 GB 256 ? b29ae510-1edc-4ae4-bd4d-fea2de229750 scylla_rack
node-2:~$ nodetool status
Datacenter: scylla_data_center
==============================
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns Host ID Rack
UN a.b.c.176 138.98 GB 256 ? b1642399-9596-4bc7-8f01-d875f0584e77 scylla_rack
UN a.b.c.177 128.26 GB 256 ? e7f6e8f4-c07e-47e7-946d-ba76a272776f scylla_rack
DN a.b.c.198 127.66 GB 256 ? b29ae510-1edc-4ae4-bd4d-fea2de229750 scylla_rack
node-1:~$ nodetool describecluster
Cluster Information:
Name: Scylla_Cluster
Snitch: org.apache.cassandra.locator.GossipingPropertyFileSnitch
DynamicEndPointSnitch: disabled
Partitioner: org.apache.cassandra.dht.Murmur3Partitioner
Schema versions:
224341ff-6870-30a9-b9c2-977007111e00: [a.b.c.177, a.b.c.176]
How can I analyze what is the cause for this? Please help.
Logs from syslog:
Feb 16 05:24:40 e2e-71-39 scylla: [shard 0] gossip - Connect seeds again ... (299 seconds passed)
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] storage_service - Shadow round failed with std::runtime_error (Unable to gossip with any seeds (ShadowRound)), checking remote features with system tables only
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] gossip - Node a.b.c.176 does not contain SUPPORTED_FEATURES in gossip, using features saved in system table, features={COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] gossip - Node a.b.c.177 does not contain SUPPORTED_FEATURES in gossip, using features saved in system table, features={COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] gossip - Feature check passed. Local node a.b.c.198 features = {COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}, Remote common_features = {COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] storage_service - Restarting a node in NORMAL status
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] database - Schema version changed to e0df65b5-0794-39a7-b95f-58df4b065456
Feb 16 05:24:41 e2e-71-39 scylla: [shard 0] storage_service - Starting up server gossip
Feb 16 05:24:41 e2e-71-39 scylla: [shard 4] compaction - Compacting [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1576-big-Data.db:level=0, /var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1564-big-Data.db:level=0, ]
Feb 16 05:24:41 e2e-71-39 scylla: [shard 4] compaction - Compacted 2 sstables to [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1588-big-Data.db:level=0, ]. 18905 bytes to 12286 (~64% of original) in 54ms = 0.22MB/s. ~256 total partitions merged to 1.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] gossip - No gossip backlog; proceeding
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] gossip - Node a.b.c.177 does not contain SUPPORTED_FEATURES in gossip, using features saved in system table, features={COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] gossip - Node a.b.c.176 does not contain SUPPORTED_FEATURES in gossip, using features saved in system table, features={COMPUTED_COLUMNS, CORRECT_COUNTER_ORDER, CORRECT_NON_COMPOUND_RANGE_TOMBSTONES, CORRECT_STATIC_COMPACT_IN_MC, COUNTERS, DIGEST_INSENSITIVE_TO_EXPIRY, DIGEST_MULTIPARTITION_READ, HINTED_HANDOFF_SEPARATE_CONNECTION, INDEXES, LARGE_PARTITIONS, LA_SSTABLE_FORMAT, LWT, MATERIALIZED_VIEWS, MC_SSTABLE_FORMAT, NONFROZEN_UDTS, PER_TABLE_PARTITIONERS, RANGE_TOMBSTONES, ROLES, ROW_LEVEL_REPAIR, SCHEMA_TABLES_V3, STREAM_WITH_RPC_STREAM, TRUNCATION_TABLE, UNBOUNDED_RANGE_TOMBSTONES, VIEW_VIRTUAL_COLUMNS, WRITE_FAILURE_REPLY, XXHASH}
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature COMPUTED_COLUMNS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature CORRECT_COUNTER_ORDER is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature CORRECT_NON_COMPOUND_RANGE_TOMBSTONES is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature CORRECT_STATIC_COMPACT_IN_MC is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature COUNTERS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature DIGEST_INSENSITIVE_TO_EXPIRY is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature DIGEST_MULTIPARTITION_READ is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature HINTED_HANDOFF_SEPARATE_CONNECTION is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature INDEXES is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature LARGE_PARTITIONS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature LWT is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature MATERIALIZED_VIEWS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature MC_SSTABLE_FORMAT is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature NONFROZEN_UDTS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature PER_TABLE_PARTITIONERS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature RANGE_TOMBSTONES is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature ROLES is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature ROW_LEVEL_REPAIR is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature SCHEMA_TABLES_V3 is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature STREAM_WITH_RPC_STREAM is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature TRUNCATION_TABLE is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] system_keyspace - Got cluster agreement on truncation table feature. Removing legacy records.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature UNBOUNDED_RANGE_TOMBSTONES is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature VIEW_VIRTUAL_COLUMNS is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature WRITE_FAILURE_REPLY is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] features - Feature XXHASH is enabled
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] system_keyspace - Legacy records deleted.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting system distributed keyspace
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] storage_service - Using saved tokens {981359583743598234, 914643842153801541, 9010590306704705381, 8978199967542709796, 8823634646786289099, 8796898105082258136, 8735550987922770836, 8716862277181876955, 8409105211318848833, 8377749213602607469, 8368791134507409577, 8279480215642442291, 8226961371913237686, 818381322587006401, 8015510533693741419, 7861744753126011670, 7421129492214354842, 7214971269794117680, 7134961561941729532, 6923641246652266152, 6776104411408891615, 6695671858569773702, 6693992036470326300, 6629009954816350525, 6548856613543647119, 6389196725922261142, 6163371136002122168, 6133043365797828528, 585649641794656355, 5738577221751324225, 5738248321319428204, 5705043832523143106, 5668216508314928077, -4557764141784572195, -1644045961680410408, -7337241265398333233, -843795661146775055, -6302540392497142197, -5833743234028982803, -634705565880395230, -4958499958501506050, -5721899000934394495, -5662226590528258745, 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Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] database - Schema version changed to 224341ff-6870-30a9-b9c2-977007111e00
Feb 16 05:24:53 e2e-71-39 scylla: message repeated 2 times: [ [shard 0] database - Schema version changed to 224341ff-6870-30a9-b9c2-977007111e00]
Feb 16 05:24:53 e2e-71-39 scylla: [shard 4] compaction - Compacting [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1600-big-Data.db:level=0, /var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1588-big-Data.db:level=0, ]
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] storage_service - Node a.b.c.198 state jump to normal
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] storage_service - Remove node a.b.c.198 from pending replacing endpoint
Feb 16 05:24:53 e2e-71-39 scylla: [shard 4] compaction - Compacted 2 sstables to [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1624-big-Data.db:level=0, ]. 18365 bytes to 12119 (~65% of original) in 43ms = 0.27MB/s. ~256 total partitions merged to 1.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 4] compaction - Compacting [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1612-big-Data.db:level=0, /var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1624-big-Data.db:level=0, ]
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] storage_service - NORMAL: node is now in normal status
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] cdc - No generation seen during startup.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting tracing
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] storage_service - SSTable data integrity checker is disabled.
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting batchlog manager
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting load meter
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting cf cache hit rate calculator
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - starting view update backlog broker
Feb 16 05:24:53 e2e-71-39 scylla: [shard 0] init - Waiting for gossip to settle before accepting client requests...
Feb 16 05:24:53 e2e-71-39 scylla: [shard 4] compaction - Compacted 2 sstables to [/var/lib/scylla/data/system/local-7ad54392bcdd35a684174e047860b377/mc-1636-big-Data.db:level=0, ]. 18198 bytes to 12115 (~66% of original) in 37ms = 0.31MB/s. ~256 total partitions merged to 1.
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] gossip - No gossip backlog; proceeding
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - allow replaying hints
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - Launching generate_mv_updates for non system tables
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - starting the view builder
Feb 16 05:25:05 e2e-71-39 scylla: [shard 1] compaction - Compacting [/var/lib/scylla/data/system/truncated-38c19fd0fb863310a4b70d0cc66628aa/mc-229-big-Data.db:level=0, /var/lib/scylla/data/system/truncated-38c19fd0fb863310a4b70d0cc66628aa/mc-217-big-Data.db:level=0, ]
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - starting native transport
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] storage_service - Starting listening for CQL clients on a.b.c.198:9042 (unencrypted)
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] storage_service - Thrift server listening on a.b.c.198:9160 ...
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - serving
Feb 16 05:25:05 e2e-71-39 scylla: [shard 0] init - Scylla version 4.1.7-0.20200918.2251a1c577 initialization completed.
Feb 16 05:25:05 e2e-71-39 scylla: [shard 1] compaction - Compacted 2 sstables to [/var/lib/scylla/data/system/truncated-38c19fd0fb863310a4b70d0cc66628aa/mc-241-big-Data.db:level=0, ]. 10782 bytes to 5549 (~51% of original) in 50ms = 0.11MB/s. ~256 total partitions merged to 1.

Please ignore, for some reason, the firewall service in the node3 was not allowing any incoming traffic on Scylla port. Once the firewall service was restarted, node3 was able to connect to nodes 1 and 2 and join the cluster successfully.

Related

Meshroom in Google Colab but no output file

I tried using meshroom to make a 3d model using google colab. Everything worked fine but there was no output file. I tried mounting mega instead of google drive, but no luck. Instead, I got the following text(some of output cut due to word limit). This is the colab notebook that I used: https://colab.research.google.com/drive/10T2pDZGRUd5r1UiAvQUwJZTqE_tLydcu
[12:26:49.520867][info] Bundle Adjustment Statistics:
- local strategy enabled: no
- adjustment duration: 0.00275099 s
- poses:
- # refined: 2
- # constant: 0
- # ignored: 0
- landmarks:
- # refined: 88
- # constant: 0
- # ignored: 0
- intrinsics:
- # refined: 0
- # constant: 1
- # ignored: 0
- # residual blocks: 176
- # successful iterations: 12
- # unsuccessful iterations: 0
- initial RMSE: 0.386763
- final RMSE: 0.36501
[12:26:49.520935][info] Remove outliers:
- # outliers residual error: 0
- # outliers angular error: 0
[12:26:49.520954][info] Bundle adjustment iteration: 0 took 3 msec.
[12:26:49.520966][info] Bundle adjustment with 1 iterations took 3 msec.
[12:26:49.521135][info] Initial pair is: 738871193, 833403405
[12:26:49.521201][info] Begin Incremental Reconstruction:
- mode: SfM augmentation
- # images in input: 236
- # images in resection: 234
- # landmarks in input: 44
- # cameras already calibrated: 2
[12:26:49.521225][info] Incremental Reconstruction start iteration 0:
- # number of resection groups: 0
- # number of poses: 2
- # number of landmarks: 44
- # remaining images: 234
[12:26:49.522265][info] Update Reconstruction:
- resection id: 0
- # images in the resection group: 1
- # images remaining: 234
[12:26:49.522355][info] [3/236] Robust Resection of view: 18451152
[12:26:49.529856][info] Robust Resection information:
- resection status: true
- threshold (error max): 2.89881
- # points used for resection: 32
- # points validated by robust resection: 31
[12:26:49.532694][info] Bundle adjustment start.
[12:26:49.532746][info] Start bundle adjustment iteration: 0
block_sparse_matrix.cc:81 Allocating values array with 15600 bytes.
detect_structure.cc:95 Dynamic f block size because the block size changed from 6 to 4
detect_structure.cc:113 Schur complement static structure <2,3,-1>.
detect_structure.cc:95 Dynamic f block size because the block size changed from 6 to 4
detect_structure.cc:113 Schur complement static structure <2,3,-1>.
[12:26:49.552254][info] Bundle Adjustment Statistics:
- local strategy enabled: no
- adjustment duration: 0.0190014 s
- poses:
- # refined: 3
- # constant: 0
- # ignored: 0
- landmarks:
- # refined: 75
- # constant: 0
- # ignored: 0
- intrinsics:
- # refined: 1
- # constant: 0
- # ignored: 0
- # residual blocks: 150
- # successful iterations: 51
- # unsuccessful iterations: 0
- initial RMSE: 0.476511
- final RMSE: 0.313505
[12:26:49.552361][info] Remove outliers:
- # outliers residual error: 0
- # outliers angular error: 0
[12:26:49.552432][info] Bundle adjustment iteration: 0 took 19 msec.
[12:26:49.552454][info] Bundle adjustment with 1 iterations took 19 msec.
[12:26:49.625419][info] Incremental Reconstruction start iteration 1:
- # number of resection groups: 1
- # number of poses: 0
- # number of landmarks: 0
- # remaining images: 233
[12:26:49.625465][info] Incremental Reconstruction completed with 2 iterations:
- # number of resection groups: 1
- # number of poses: 0
- # number of landmarks: 0
- # remaining images: 233
[12:26:49.625540][info] Structure from Motion statistics:
- # input images: 236
- # cameras calibrated: 0
- # poses: 0
- # landmarks: 0
- elapsed time: 0.104
- residual RMSE: -nan
[12:26:49.625566][info] Histogram of residuals:
0 | 0
0.1 | 0
0.2 | 0
0.3 | 0
0.4 | 0
0.5 | 0
0.6 | 0
0.7 | 0
0.8 | 0
0.9 | 0
1
[12:26:49.625587][info] Histogram of observations length:
0 | 0
0.1 | 0
0.2 | 0
0.3 | 0
0.4 | 0
0.5 | 0
0.6 | 0
0.7 | 0
0.8 | 0
0.9 | 0
1
[12:26:49.625605][info] Histogram of nb landmarks per view:
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
0 | 0
1
Have you tried this part of the code?
# Choose format (tar.gz or zip)
!tar -czvf out.tar.gz ./out
from google.colab import files
files.download('out.tar.gz')
!zip -r out.zip ./out
files.download('out.zip')
For more information you can check this source:
https://colab.research.google.com/gist/natowi/3044484ad0c98877692c399297e3ab7e/meshroomcolab.ipynb#scrollTo=VQ8F_rxPw4dK

Trying to understand shuffle within mini-batch in tensorflow Dataset

From here I understand what shuffle, batch and repeat do. I'm working on Medical image data where each mini-batch has slices from one patient record. I'm looking for a way to shuffle within the minibatch while training. I cannot increase the buffer size because I don't want slices from different records to get mixed up. Could someone please explain how this can be done?
dataset = tf.data.Dataset.from_tensor_slices(tf.range(1, 20))
data = dataset.batch(5).shuffle(5).repeat(1)
for element in data.as_numpy_iterator():
print(element)
Current Output :
[ 6 7 8 9 10]
[1 2 3 4 5]
[11 12 13 14 15]
[16 17 18 19]
Expected Output :
[ 6 8 9 7 10]
[3 4 1 5 2]
[15 12 11 14 13]
[16 17 19 20 17]
I just realized, there is no need to shuffle within the mini-batch as shuffling within the minibatch doesn't contribute to improving training in any way. Appretiate if anyone has other views on this.

Having created the structure of the multiindex columns, I'm unable to fit this template to the dataframe; how to do this?

I have the right multi-index structure for the dataset, however I'm unable to fit this template to the dataset.
Importing the dataset:
data = pd.read_excel('IRCC_M_TRStudy_0001_E.xls')
code for multiindex columns:
years = (2015,2016,2017,2018,2019)
months = [
("Jan", "Feb", "Mar"),
("Apr", "May", "Jun"),
("Jul", "Aug", "Sep"),
("Oct", "Nov", "Dec"),
]
tuples = [(year, f"Q{i + 1}", month) for year in years for i in range(4) for month in months[i]]
multi_index = pd.MultiIndex.from_tuples(tuples)
My attempt to fit this template to the dataset:
df = pd.DataFrame(data, index = data['Country of Citizenship'], columns = multi_index)
the result:
consists of an index of 'countries of citizenship' and multi-index columns consisting of 3 levels - years(2015 - 2019), 4 Quarters for each year and 3 months per quarter (as expected). However, all the data is missing - all columns and rows show 'nan' values.
The expected results should look like this:
2015
Q1 Q2 Q3 Q4
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Country
USA 34 33 23 12 34 23 23 12 34 56 67 57
India 33 12 29 16 35 27 25 15 33 57 63 51
The above table repeats for years 2016,2017,2018,2019 from left to right. The data above is only for the purpose of representation, I want to fit the multi-index template to the dataset that consists of similar data.
Also, how can I position the index 'country' a row below the row containing months as shown in the expected results?

DataFrame creation from dict & index order?

I am using a recent download of Anaconda with Python 3.7.1 & pandas 0.23.4
The pandas doc says:
When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order
I instantiate a pandas DataFrame from a dict with no index passed:
newspapers = {'Jim':{'Mon':15,'Tue':17,'Wed':21,'Thu':16,'Fri':19},\
'Tony':{'Mon':8,'Tue':15,'Wed':11,'Thu':16,'Fri':13}, \
'Colin':{'Mon':13,'Tue':17,'Wed':19,'Thu':17,'Fri':20} \
}
newspapers_df = pd.DataFrame(newspapers)
Why does this not show in insertion order, Mon, Tue, Wed, Thu, Fri?:
print(newspapers_df)
outputs:
Jim Tony Colin
Fri 19 13 20
Mon 15 8 13
Thu 16 16 17
Tue 17 15 17
Wed 21 11 19
It seems bug, for me working in python 3.5, pandas 0.24.2 create Series in dictionary comprehension and pass to DataFrame constructor:
newspapers_df = pd.DataFrame({k:pd.Series(v) for k, v in newspapers.items()})
print (newspapers_df)
Jim Tony Colin
Mon 15 8 13
Tue 17 15 17
Wed 21 11 19
Thu 16 16 17
Fri 19 13 20
Possible solutions with your data - DataFrame.reindex or ordered CategoricalIndex:
newspapers_df = pd.DataFrame(newspapers)
L = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
newspapers_df = newspapers_df.reindex(L)
Or:
newspapers_df.index = pd.CategoricalIndex(newspapers_df.index, ordered=True, categories=L)
newspapers_df = newspapers_df.sort_index()
print (newspapers_df)
Jim Tony Colin
Mon 15 8 13
Tue 17 15 17
Wed 21 11 19
Thu 16 16 17
Fri 19 13 20
The same sorting could be done using numpy.argsort():
days_dict = {'Mon':0, 'Tue':1,'Wed':2,'Thu':3,'Fri':4,'Sat':5,'Sun':6}
df = pd.DataFrame(newspapers).reset_index()
df.iloc[np.argsort(df['index'].map(days_dict)),:]
Looks the pd.Series and pd.DataFrame has different way to handle the case.
I has similar python environment and the result could be replicated in my computer. Also, the Jezrel's pd.Series case could be replicated in my computer.
I check the manual and found both result are folllow the doc.
For series, the key is index
When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23.
For dataframe: the key is column.
If axis labels are not passed, they will be constructed from the input data based on common sense rules.
Note When the data is a dict, and columns is not specified, the DataFrame columns will be ordered by the dict’s insertion order, if you are using Python version >= 3.6 and Pandas >= 0.23.
That means, if you want to make sure both columns and index sort, you might need pass index or sort them later.
Hope it answer you question.

Graphing time series in ggplot2 with CDC weeks ordered sensibly

I have a data frame ('Example') like this.
n CDCWeek Year Week
25.512324 2011-39 2011 39
26.363035 2011-4 2011 4
25.510500 2011-40 2011 40
25.810663 2011-41 2011 41
25.875451 2011-42 2011 42
25.860873 2011-43 2011 43
25.374876 2011-44 2011 44
25.292944 2011-45 2011 45
24.810807 2011-46 2011 46
24.793090 2011-47 2011 47
22.285000 2011-48 2011 48
23.015480 2011-49 2011 49
26.296376 2011-5 2011 5
22.074581 2011-50 2011 50
22.209183 2011-51 2011 51
22.270705 2011-52 2011 52
25.391377 2011-6 2011 6
25.225481 2011-7 2011 7
24.678918 2011-8 2011 8
24.382214 2011-9 2011 9
I want to plot this as a time series with 'CDCWeek' as the X-axis and 'n' as the Y using this code.
ggplot(Example, aes(CDCWeek, n, group=1)) + geom_line()
The problem I am running into is that it is not graphing CDCWeek in the right order. CDCWeek is the year followed by the week number (1 to 52 or 53 depending on the year). It is being graphed in the order shown in the data frame, with 2011-39 followed by 2011-4, etc. I understand why this is happening but is there anyway to force ggplot2 to use the proper order of weeks?
EDIT: I can't just use the 'week' variable because the actual dataset covers many years.
Thank you
aweek::get_date allows you to get weekly dates only using the year and epiweek.
Here I created a reprex with a sequence of dates (link), extract the epiweek with lubridate::epiweek, defined sunday as start of a week with aweek::set_week_start, summarized weekly values, created a new date vector with aweek::get_date, and plot them.
library(tidyverse)
library(lubridate)
library(aweek)
data_ts <- tibble(date=seq(ymd('2012-04-07'),
ymd('2014-03-22'),
by = '1 day')) %>%
mutate(value = rnorm(n(),mean = 5),
#using aweek
epidate=date2week(date,week_start = 7),
#using lubridate
epiweek=epiweek(date),
dayw=wday(date,label = T,abbr = F),
month=month(date,label = F,abbr = F),
year=year(date)) %>%
print()
#> # A tibble: 715 x 7
#> date value epidate epiweek dayw month year
#> <date> <dbl> <aweek> <dbl> <ord> <dbl> <dbl>
#> 1 2012-04-07 3.54 2012-W14-7 14 sábado 4 2012
#> 2 2012-04-08 5.79 2012-W15-1 15 domingo 4 2012
#> 3 2012-04-09 4.50 2012-W15-2 15 lunes 4 2012
#> 4 2012-04-10 5.44 2012-W15-3 15 martes 4 2012
#> 5 2012-04-11 5.13 2012-W15-4 15 miércoles 4 2012
#> 6 2012-04-12 4.87 2012-W15-5 15 jueves 4 2012
#> 7 2012-04-13 3.28 2012-W15-6 15 viernes 4 2012
#> 8 2012-04-14 5.72 2012-W15-7 15 sábado 4 2012
#> 9 2012-04-15 6.91 2012-W16-1 16 domingo 4 2012
#> 10 2012-04-16 4.58 2012-W16-2 16 lunes 4 2012
#> # ... with 705 more rows
#CORE: Here you set the start of the week!
set_week_start(7) #sunday
get_week_start()
#> [1] 7
data_ts_w <- data_ts %>%
group_by(year,epiweek) %>%
summarise(sum_week_value=sum(value)) %>%
ungroup() %>%
#using aweek
mutate(epi_date=get_date(week = epiweek,year = year),
wik_date=date2week(epi_date)
) %>%
print()
#> # A tibble: 104 x 5
#> year epiweek sum_week_value epi_date wik_date
#> <dbl> <dbl> <dbl> <date> <aweek>
#> 1 2012 1 11.0 2012-01-01 2012-W01-1
#> 2 2012 14 3.54 2012-04-01 2012-W14-1
#> 3 2012 15 34.7 2012-04-08 2012-W15-1
#> 4 2012 16 35.1 2012-04-15 2012-W16-1
#> 5 2012 17 34.5 2012-04-22 2012-W17-1
#> 6 2012 18 34.7 2012-04-29 2012-W18-1
#> 7 2012 19 36.5 2012-05-06 2012-W19-1
#> 8 2012 20 32.1 2012-05-13 2012-W20-1
#> 9 2012 21 35.4 2012-05-20 2012-W21-1
#> 10 2012 22 37.5 2012-05-27 2012-W22-1
#> # ... with 94 more rows
#you can use get_date output with ggplot
data_ts_w %>%
slice(-(1:3)) %>%
ggplot(aes(epi_date, sum_week_value)) +
geom_line() +
scale_x_date(date_breaks="5 week", date_labels = "%Y-%U") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Weekly time serie",
x="Time (Year - CDC epidemiological week)",
y="Sum of weekly values")
ggsave("figure/000-timeserie-week.png",height = 3,width = 10)
Created on 2019-08-12 by the reprex package (v0.3.0)
Convert the Year and Week into a date with dplyr:
df <- df %>%
mutate(date=paste(Year, Week, 1, sep="-") %>%
as.Date(., "%Y-%U-%u"))
ggplot(df, aes(date, n, group=1)) +
geom_line() +
scale_x_date(date_breaks="8 week", date_labels = "%Y-%U")
One option would be to use the Year and Week variables you already have but facet by Year. I changed the Year variable in your data a bit to make my case.
Example$Year = rep(2011:2014, each = 5)
ggplot(Example, aes(x = Week, y = n)) +
geom_line() +
facet_grid(Year~., scales = "free_x")
#facet_grid(.~Year, scales = "free_x")
This has the added advantage of being able to compare across years. If you switch the final line to the option I've commented out then the facets will be horizontal.
Yet another option would be to group by Year as a factor level and include them all on the same figure.
ggplot(Example, aes(x = Week, y = n)) +
geom_line(aes(group = Year, color = factor(Year)))
It turns out I just had to order Example$CDCWeek properly and then ggplot would graph it properly.
1) Put the database in the proper order.
Example <- Example[order(Example$Year, Example$Week), ]
2) Reset the rownames.
row.names(Example) <- NULL
3) Create a new variable with the observation number from the rownames
Example$Obs <- as.numeric(rownames(Example))
4) Order the CDCWeeks variable as a factor according to the observation number
Example$CDCWeek <- factor(Example$CDCWeek, levels=Example$CDCWeek[order(Example$Obs)], ordered=TRUE)
5) Graph it
ggplot(Example, aes(CDCWeek, n, group=1)) + geom_line()
Thanks a lot for the help, everyone!